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Review

Recent Advances in Bidirectional Converters and Regenerative Braking Systems in Electric Vehicles

Department of Electrical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Author to whom correspondence should be addressed.
Actuators 2025, 14(7), 347; https://doi.org/10.3390/act14070347 (registering DOI)
Submission received: 7 June 2025 / Revised: 1 July 2025 / Accepted: 11 July 2025 / Published: 14 July 2025
(This article belongs to the Special Issue Feature Papers in Actuators for Surface Vehicles)

Abstract

As electric vehicles (EVs) continue to advance toward widespread adoption, innovations in power electronics are playing a pivotal role in improving efficiency, performance, and sustainability. This review presents recent progress in bidirectional converters and regenerative braking systems (RBSs), highlighting their contributions to energy recovery, battery longevity, and vehicle-to-grid integration. Bidirectional converters support two-way energy flow, enabling efficient regenerative braking and advanced charging capabilities. The integration of wide-bandgap semiconductors, such as silicon carbide and gallium nitride, further enhances power density and thermal performance. The paper evaluates various converter topologies, including single-stage and multi-stage architectures, and assesses their suitability for high-voltage EV platforms. Intelligent control strategies, including fuzzy logic, neural networks, and sliding mode control, are discussed for optimizing braking force and maximizing energy recuperation. In addition, the paper explores the influence of regenerative braking on battery degradation and presents hybrid energy storage systems and AI-based methods as mitigation strategies. Special emphasis is placed on the integration of RBSs in advanced electric vehicle platforms, including autonomous systems. The review concludes by identifying current challenges, emerging trends, and key design considerations to inform future research and practical implementation in electric vehicle energy systems.

1. Introduction

The global transportation sector faces significant sustainability challenges due to its reliance on fossil fuels, which account for a substantial share of greenhouse gas (GHG) emissions. To address these environmental concerns, electric vehicles (EVs) have emerged as a key solution. The Paris Agreement, signed by 193 countries and the European Union, underscores a global commitment to limiting temperature increases to 1.5 °C above pre-industrial levels [1]. Countries such as the U.S. and Germany are leading decarbonization efforts, targeting 100% clean electricity by 2035 and GHG neutrality by 2050 [2]. Supporting this shift, the UK plans to ban new petrol and diesel vehicle sales by 2030, while the EU targets 2035 [3].
Although EVs currently constitute a small fraction of the global vehicle fleet, their accelerating market penetration signals a transformative shift in the automotive landscape. Figure 1 presents data from the Global EV Data Explorer, illustrating recent growth in battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), alongside future sales projections.
In this transition, power electronics serve as the backbone of energy management in EVs. They enable the conversion, distribution, and regulation of electrical energy across subsystems with diverse voltage and power requirements [4,5]. Among these technologies, bidirectional converters are of particular importance, as they allow two-way energy transfer between the battery, electric motor, and external grid [6]. This bidirectional flow supports regenerative braking and vehicle-to-grid (V2G) applications, which are crucial for improving energy efficiency and reducing fossil fuel dependency.
Figure 1. EV sales trends and projections [7].
Figure 1. EV sales trends and projections [7].
Actuators 14 00347 g001
The bidirectional converters are essential for power flow control in EVs, allowing energy transfer from the battery to the motor and back, particularly during regenerative braking [8]. Modern EV powertrains typically operate between 250V and 450V DC [9], but recent advancements have extended this range to 800V DC [10], significantly reducing charging times and alleviating range anxiety. Furthermore, emerging 1500V DC systems mark a key advancement toward ultra-fast charging infrastructure, improving the practicality of long-distance EV travel [11].
Wide-bandgap semiconductors, such as silicon carbide (SiC) and gallium nitride (GaN), have further enhanced converter efficiency, offering superior switching performance and thermal stability compared to conventional silicon devices despite challenges related to cost and packaging [12,13]. A broad range of converter topologies optimized for SiC and GaN has been reviewed, categorized by switch type and application domain [14]. The integration of regenerative braking systems (RBSs) with bidirectional converters has significantly improved energy recovery and extended EV range [15]. RBS converts kinetic energy into electrical energy by operating the motor as a generator during deceleration, which improves battery longevity and vehicle efficiency [16]. These systems are typically integrated with safety features such as anti-lock braking systems (ABSs) and wheel slip control [17], ensuring safe and stable operation.
To further optimize performance, advanced control algorithms and artificial intelligence (AI) further enhance energy recovery by optimizing braking force distribution [18,19]. In addition, integrating hybrid energy storage systems, which combine lithium-ion batteries with supercapacitors, helps address high-power demands during regenerative events [20,21]. This hybrid approach improves battery durability and system responsiveness. Various braking strategies have been proposed to maximize energy capture under diverse driving conditions [22].
This review paper presents a comprehensive analysis of recent developments in bidirectional converters and RBSs for EVs. It covers non-isolated and isolated converter topologies, including advanced configurations such as interleaved, multilevel, and switched-capacitor designs. The paper examines the use of wide-bandgap semiconductor materials, SiC and GaN, to enhance power conversion efficiency and achieve higher power density. It also explores intelligent control strategies including fuzzy logic, neural networks, model predictive control, and sliding mode control, which are used to optimize braking performance and energy recuperation. Additionally, the review evaluates the effect of regenerative braking on battery longetivity, highlights the benefits of hybrid energy storage systems (HESSs) in mitigating battery degradation, and discusses emerging trends such as AI-based control, high-efficiency motor technologies, and the application of regenerative braking in autonomous vehicles.

2. Bidirectional DC-DC Converter Topologies

The bidirectional DC-DC converters (BDCs) are essential building blocks in modern power conversion systems utilized in EVs, renewable energy, aerospace, and other high-efficiency applications. Depending on whether galvanic isolation is implemented, BDCs are typically categorized into non-isolated and isolated topologies. The classification of commonly used bidirectional converter topologies is illustrated in Figure 2, which provides a structured overview of the configurations and their design basis.

2.1. Non-Isolated Topologies

The non-isolated BDCs facilitate bidirectional power flow without the use of galvanic isolation. These topologies are typically adapted from conventional unidirectional converters by incorporating bidirectional switching elements. This often involves adding a controlled switch parallel to existing diodes or replacing unidirectional switches with additional active components to enable reverse current flow. Several architectures originate from classical buck, boost, and buck-boost designs. More advanced versions have evolved from voltage-optimized configurations, such as switched-capacitor networks, interleaved structures, and multilevel stages.

2.1.1. Buck, Boost, and Buck-Boost Derived Converters

The bidirectional versions of classical buck, boost, and buck–boost converters are implemented by replacing unidirectional switches with actively controlled bidirectional ones. These configurations support efficient power flow in both directions, making them ideal for battery charging and discharging. Figure 3 shows typical buck- or/and boost-derived topologies. The buck–boost-derived converter (Figure 3a) operates in boost mode when charging (from V s to V o ) and as a buck converter during discharging, enabling bidirectional energy transfer. The buck–boost-derived topology (Figure 3b) allows both voltage step-up and step-down with polarity inversion, offering flexibility for systems with wide input–output voltage ranges.
Ongoing innovations have introduced enhanced buck–boost configurations that have improved the conversion ratio, efficiency, and control. One design integrates a modified boost and Zeta topology to deliver continuous input–output currents, semi-quadratic voltage gain, and low output ripple, making it suitable for renewable and industrial applications [23]. Another semi-quadratic buck–boost converter, optimized for photovoltaic (PV) systems, ensures continuous input current, reduced EMI and ripple, and high efficiency in both buck and boost modes, with experimental results showing up to 91.8% efficiency in boost operation [24]. Deep learning has also been applied to buck–boost control, with trained models demonstrating reduced overshoot and steady-state error, adapting effectively to irradiance variations and outperforming traditional controllers [25]. Modern buck–boost topologies offer better voltage regulation and efficiency than conventional positive and negative step designs [26].

2.1.2. Ćuk Converter

The Ćuk converter is valued for its ability to deliver continuous input and output currents, making it suitable for applications with stringent low-ripple and low-EMI requirements. Its bidirectional version, illustrated in Figure 4, replaces the diode and single switch with two actively controlled switches, enabling efficient bidirectional energy transfer while preserving the benefits of ripple suppression. Coupled-inductor configurations further enhance performance by reducing current ripple, which is particularly beneficial in noise-sensitive applications such as renewable energy systems and battery-powered platforms.
Novel approaches have extended the use of bidirectional Ćuk converters in modular and battery equalization applications. A modular power converter for shore power systems employs an isolated Ćuk submodule to interface renewable sources with energy storage and grid-connected loads, achieving high efficiency and voltage flexibility suitable for shipping ports [27]. In battery management systems, an improved bidirectional Ćuk-based equalization circuit reduces size and complexity by minimizing inductor count, enabling faster balancing across series-connected cells [28]. In addition, a novel soft-switching Ćuk equalizer incorporates a resonant tank to achieve zero-current switching (ZCS), enhancing efficiency by 5–8% compared to traditional hard-switched circuits at 300 kHz [29]. These developments underscore the growing versatility of Ćuk converters in high-performance power management applications.

2.1.3. Zeta/Sepic Converters

The bidirectional Zeta/Sepic converters provide continuous input–output currents and a positive output polarity, making them ideal for low-ripple and low-EMI applications. As illustrated in Figure 5, they operate in Sepic mode when transferring power from V s to V o , and in Zeta mode during reverse flow, enabled by actively controlled switches.
Contemporary advancements have made these converters highly adaptable. A hybrid Sepic/Zeta converter with a modified switched capacitor cell reduces voltage stress on semiconductors and supports battery integration in DC microgrids [30]. A single-stage bidirectional Zeta/Sepic DC-AC converter has been developed for brushless DC motor (BLDC) traction systems, featuring a mode-switching control strategy for high-efficiency operation in both motoring and regenerative braking [31]. Furthermore, optimization of the control configuration for dual-input Zeta/Sepic converters has been explored using interaction measures, leading to improved input–output variable pairing and robust closed-loop control under varying conditions [32].

2.1.4. Cascaded Converters

Cascaded bidirectional converters connect multiple basic stages (e.g., buck–boost) in series to increase voltage gain and improve component utilization. As shown in Figure 6, this structure suits EV powertrains and high-voltage applications requiring robust performance and high conversion ratios. Although it involves more switches, inductors, and capacitors than single-stage designs, it achieves a higher gain at the same duty cycle and reduces the current stress on the components. An auxiliary capacitor ( C a ) may be added to suppress the ripple of the output and stabilize the responses of dynamic loads.
Emerging techniques demonstrate the versatility of cascaded converters. A step-up interleaved design with a central capacitor enables bidirectional charging stations with accurate modeling and robust control [33]. Another topology integrates energy storage modules and cell equalization via half-bridge cells, reducing complexity and inductor ripple [34]. A five-level ANPC converter improves power quality during both charging and discharging operations, balancing capacitor voltage and enhancing efficiency [35]. A switched-capacitor design with coupled inductors simplifies control while maintaining bidirectional energy flow for storage systems [36].Together, these innovations enhance the adaptability and efficiency of cascaded converter architectures for modern energy systems.

2.1.5. Switched-Capacitor Converters

Switched-capacitor (SC) converters use only capacitors and semiconductor switches to achieve voltage conversion through charge redistribution, eliminating the need for magnetic components. As illustrated in Figure 7, bidirectional SC converters utilize interleaved switching cells operating in anti-phase to approximate continuous input current. Their compactness, low weight, and ease of integration make them ideal for embedded and portable systems.
Recent advances have introduced modular SC architectures that integrate series–parallel, regulating, and resonant modules, achieving peak efficiencies of up to 97.15% by separating regulation and voltage transformation functions [37]. Hybrid topologies with common-ground configurations offer high voltage conversion ratios while minimizing passive component sizes and maintaining uninterrupted ground paths, enhancing compatibility with supercapacitor-based storage systems [38]. Other high-gain configurations incorporate complementary PWM control and low-voltage side inductance to suppress ripple and extend battery life in electric vehicle applications [39]. Additionally, cascaded boost stages paired with high-voltage side SC modules effectively reduce current stress and support high voltage gain in both directions of power flow [40]. These advancements highlight the versatility of SC converters for modern bidirectional systems requiring high efficiency, compactness, and low electromagnetic interference.

2.1.6. Interleaved Topology

Interleaved BDC topologies consist of parallel-connected phases operating with interleaved switching cycles. Figure 8 presents a schematic of an interleaved converter topology, illustrating the parallel-phase structure commonly used in such designs. This configuration offers several performance advantages, including significant ripple reduction, improved transient response, and more uniform thermal distribution among components. It is highly scalable and adaptable for high-power applications such as electric vehicles (EVs), battery charging systems, and power management units. Advanced control strategies such as phase shedding allow for dynamic adjustment of active phases based on load current to reduce switching losses under light-load conditions [41]. Current-mode and average current-mode controls are commonly employed, with coupled inductors used to mitigate the current imbalance between phases [42]. To further improve efficiency, discontinuous conduction mode (DCM) and critical conduction mode (CRM) operations have been adopted, enabling soft-switching without the need for auxiliary components [43,44,45]. Additionally, ZCS and ZVS techniques have been implemented using auxiliary or snubber circuits to reduce switching losses, with reported efficiencies exceeding 97% [46,47]. These enhancements position interleaved converters as highly effective solutions for bidirectional energy conversion in modern power electronics.

2.1.7. Multilevel Converter

Multilevel bidirectional DC-DC converters use cascaded cells or ladder networks to achieve high voltage gain while minimizing voltage stress between components. Their inductor-less architecture simplifies design and reduces weight, making them suitable for dual-voltage EV architectures and modular power units. Modular multilevel topologies with isolated submodules connected in series have been introduced to provide wide voltage-boosting ranges and galvanic isolation, enhancing performance in fuel cell-based EV systems [48]. Furthermore, multilevel BDCs have been integrated into bipolar DC grids for EVs, where they ensure reliable operation under both normal and fault conditions [49]. These advances highlight the increasing relevance of multilevel converters in scalable and high-efficiency energy conversion applications.
Table 1 presents a comparative summary of non-isolated bidirectional converters, highlighting their component requirements along with key features and typical EV application scenarios.

2.2. Isolated Topologies

Isolated BDCs employ high-frequency transformers to provide galvanic isolation, which is critical for safety and compliance in high-voltage systems. These converters also offer flexibility in voltage gain through adjustable transformer turns ratios.

2.2.1. Flyback Converter

The bidirectional flyback converter, derived from the buck–boost topology, replaces the inductor with a high-frequency transformer to enable galvanic isolation. As shown in Figure 9, it supports bidirectional power transfer using a single switch on each side, offering a compact and cost-effective solution for low-power applications. However, transformer leakage inductance can cause voltage overshoot and efficiency losses, necessitating the use of snubber or clamp circuits for mitigation.
Recent implementations have demonstrated the versatility of the flyback converters. In battery management systems, module-to-cell equalization using this topology has achieved over 94% peak efficiency [50]. In EVs and hybrid storage systems, multiphase configurations with sliding mode control enhance transient response and simplify control [51]. The adoption of ZVS further reduces losses and increases power density [52]. The cascaded designs that integrate step-up/down stages improve the voltage gain and reduce transformer turn ratio, thereby minimizing leakage effects and enhancing efficiency in energy storage systems [53].

2.2.2. Push–Pull Converter

The bidirectional push–pull converter employs a center-tapped transformer with alternately driven switches on each side to facilitate efficient two-way energy transfer. As shown in Figure 10, this topology achieves improved core utilization and supports high step-up/down voltage conversion ratios, making it suitable for medium-to-high-power applications including EVs, aerospace systems, and isolated power supplies. However, imbalanced switching can lead to core saturation, requiring precise control strategies. Recent advancements have addressed these challenges through resonant and soft-switching techniques, enabling near-zero voltage or current switching across wide voltage ranges [54]. Integration of RC snubber circuits has been shown to reduce voltage spikes by more than 75% and improve efficiency by up to 1.46% [55]. To improve power density and reduce control complexity, single-stage bidirectional PFC converters have been proposed using current-fed push–pull architectures [56]. In nanogrid applications, composite push–pull converters achieve high efficiency through optimized transformer design and parallel regulation paths [57]. These innovations highlight the topology’s continued relevance in achieving high efficiency, compact design, and robust performance under bidirectional operation.

2.2.3. Forward Converter

The bidirectional forward converter extends the conventional forward topology by incorporating additional switches and transformer reset mechanisms to support controlled bidirectional power flow with galvanic isolation. As illustrated in Figure 11, it offers a compact and efficient solution for isolated applications, particularly in aerospace systems and telecom modules. Modern innovations have improved performance through optimal control strategies and soft-switching techniques. Active clamp configurations with synchronous rectifiers enable cell-to-storage balance with improved speed and energy efficiency, using model predictive control without complex efficiency modeling [58]. To reduce losses and switching stress, designs that employ ZVS leverage transformer leakage inductance and auxiliary capacitors, achieving high efficiency across a wide load range using simple PWM control [59]. These innovations demonstrate the adaptability of forward converters increasingly viable for compact, high-performance power systems.

2.2.4. Dual Active Bridge (DAB) Converter

The dual active bridge (DAB) converter is a prominent isolated bidirectional topology known for its modularity, high efficiency, and suitability for high-power applications. As shown in Figure 12, it comprises two full-bridge circuits linked by a high-frequency transformer, with phase-shift modulation controlling both power flow and ZVS/ZCS behavior [60,61]. Wide-bandgap semiconductors such as SiC and GaN have improved DAB efficiency and power density by allowing higher switching frequencies [62]. A GaN-based DAB achieved 36% greater power transfer than a silicon design [63]. Advanced modulation methods, including triple-phase-shift (TPS) and variable frequency control have been employed, with TPS reaching 97.7% efficiency [64] and hybrid SPS-variable frequency control approaching 99% in a 10 kW SiC prototype [65,66]. AI-based control strategies have further optimized DAB operation. A hybrid extended phase shift (HEPS) method using AI achieved full-range ZVS and 97.1% efficiency in a 1 kW prototype [67]. Together, these innovations confirm the vital role of the DAB converter in EVs, V2G systems, and advanced energy storage that requires high efficiency and galvanic isolation.

2.2.5. Dual Half-Bridge Converter

The dual half-bridge (DHB) bidirectional converter offers a streamlined alternative to the DAB topology by reducing the number of active switches from eight to four, thereby simplifying the circuit and lowering cost and control complexity. As shown in Figure 13, the DHB typically employs voltage-fed half-bridge configurations on both the primary and secondary sides of a high-frequency transformer. Variants include asymmetric combinations, such as a current-fed half-bridge on one side and a voltage-fed half-bridge on the other, enabling continuous current profiles, which are desirable in certain applications. Interleaved dual half-bridge architectures have also been explored to increase voltage gain, reduce transformer turn ratios, and lower current stress [68]. These features make the dual half-bridge topology well-suited for compact, efficient, and isolated low-to-medium-power conversion systems [69].
Recent research has demonstrated the versatility of this topology. A nonlinear control allocation strategy was proposed to improve transient performance and minimize peak-to-peak current in hybrid energy storage systems [70]. For battery management, a low-cost balancing module using DHB links and coreless transformers achieved a 22% cost reduction without sacrificing functionality [71]. Furthermore, a multiport DHB architecture with self-balancing capability has been developed for distributed photovoltaic systems, allowing high voltage gain and efficient power flow control [72].

2.2.6. LLC Resonant Converter

The LLC resonant converter is an evolution of the DAB topology, where a resonant network is introduced between the full-bridge stage and the high-frequency transformer. This modification enables soft-switching across a wider load range, significantly improving efficiency and reducing switching losses [73,74]. By shaping the voltage and current waveforms through resonance, the converter achieves ZVS or ZCS, which also helps minimize electromagnetic interference (EMI) [75,76]. In bidirectional applications, such resonance-assisted switching enhances performance in both directions of power flow [77]. The LLC resonant configuration is especially beneficial in scenarios that demand high-frequency operation and high conversion efficiency, such as wireless charging systems, data center power supplies, and EV onboard chargers [78,79].

2.2.7. Multilevel Bidirectional Converter

Multilevel bidirectional DC-DC converters regulate the output voltage by appropriately combining multiple DC voltage sources through specific switching configurations. By selecting different switching states, a stepped voltage waveform with various amplitude levels can be synthesized at the output [80,81,82]. These converters do not require inductive components, which makes them lightweight and compact, an advantage in space-constrained and cost-sensitive applications.
The common multilevel topologies include Neutral Point Clamped (NPC), Flying Capacitor (FC), and Cascaded H-Bridge (CHB), each shown in Figure 14. Each topology offers unique benefits and limitations. For instance, the CHB structure allows for easier voltage sharing across switches, redundant switching paths, and scalability in the number of voltage levels [80,81]. Modular extensions of these topologies improve the voltage gain and scalability but can introduce challenges such as higher ripple, stress, and efficiency drop at light loads [82]. Nevertheless, multilevel converters are crucial in HVDC systems, modular storage, and renewable energy integration [80,81].
Table 2 presents a comparison of isolated bidirectional converters, highlighting their component requirements, operational features, and typical application scenarios in electric vehicle systems.
In addition to the detailed descriptions, Table 1 and Table 2 provide a comparative view of component count, control complexity, and typical EV applications across different converter types. This comparison highlights the trade-offs between size, efficiency, voltage gain, isolation, and control requirements. For example, non-isolated topologies such as interleaved or switched-capacitor converters offer compact designs and low EMI, ideal for auxiliary or low-voltage systems, whereas isolated architectures like DAB and LLC are better suited for high-power or safety-critical systems due to their galvanic isolation and soft-switching capabilities. These insights help guide converter selection based on the specific requirements of power levels, voltage ranges, cost constraints, and safety standards.

3. Regenerative Braking Systems

As EVs continue to gain traction globally, improving their energy efficiency and extending their range remain critical design objectives. One of the most impactful technologies supporting these goals is regenerative braking. Unlike traditional braking systems that waste kinetic energy as heat, regenerative braking recovers and converts it into electrical energy that recharges the battery. Studies indicate that regenerative braking can increase the range of electric vehicles by up to 25%, depending on driving conditions [83]. Furthermore, the integration of regenerative braking with electric motor systems and power electronics enables EVs to efficiently recycle energy during deceleration, thereby enhancing overall energy efficiency.

3.1. Principles and Components

The effectiveness of regenerative braking in electric vehicles depends on both fundamental energy conversion principles and advanced hardware systems. This section explains the mechanisms through which kinetic energy is recovered and stored and emphasizes the key role of electric motors and power electronics in enabling bidirectional energy flow. A comprehensive understanding of these aspects is crucial to appreciate how regenerative braking contributes to improved vehicle performance, range extension, and seamless integration with vehicle control systems.

3.1.1. Effectiveness Across Electric Vehicle Types

The effectiveness of regenerative braking varies substantially across different types of EVs, primarily due to differences in vehicle mass, braking dynamics, motor configurations, and driving profiles. In lightweight EVs such as electric bicycles and scooters, the braking force and energy available for recovery are limited due to their lower inertia and simpler drivetrain architectures [84]. As a result, range extensions are generally modest, often in the range of around 15% [85,86]. Nevertheless, in hilly urban environments with frequent stop-and-go movement, even small gains in efficiency can be meaningful for such vehicles.
In contrast, electric passenger cars typically exhibit higher kinetic energy during deceleration and are equipped with more sophisticated regenerative braking systems. Studies have reported potential range improvements of 10–25% depending on vehicle weight, driving style, terrain, and regenerative system control strategy [87,88]. Vehicles that operate in urban settings with frequent braking opportunities tend to benefit the most.
Heavy-duty electric vehicles, such as buses and delivery trucks, show the highest potential for regenerative braking gains due to their substantial mass and high braking energy. Moreover, their drive cycles often involve frequent acceleration and deceleration phases, making them ideal candidates for energy recovery. In such applications, regenerative braking extends the range around 30% [89].
The degree of benefit also depends on the energy storage system. For instance, EVs with hybrid energy storage systems (e.g., battery + supercapacitor) can capture and reuse braking energy more effectively, and reported range extension is up to 35% [90]. Table 3 summarizes the regenerative braking potential across different EV types.

3.1.2. Energy Conversion and Storage Mechanisms

Regenerative braking enables EVs to recover kinetic energy during deceleration by operating the electric motor as a generator. Unlike internal combustion engine vehicles, which dissipate kinetic energy as heat, EVs convert it into electrical energy through electromagnetic induction, consistent with Faraday’s law, and store it in a lithium-ion battery or supercapacitor [91]. In advanced systems, energy recovery is optimized by managing motor torque and vehicle dynamics [92]. The energy harvesting potential can be further increased in all-wheel-drive dual-motor configurations using coordinated braking control [93]. However, such configurations may encounter torque distribution conflicts between the front and rear axles during braking. To address this, advanced strategies such as torque vectoring [94] and predictive control algorithms [95,96] are used to optimize front–rear torque allocation, improve braking stability, and maximize energy recovery efficiency.
Figure 15 illustrates this process, showing the transition from mechanical braking to conversion, storage, and reuse of electrical energy. The captured energy is stored according to dynamic evaluations performed by the battery management system (BMS), which continuously tracks key metrics including state of charge (SoC), thermal conditions, and permitted power levels [97]. Efficient energy storage control is essential to prevent overcharging and thermal degradation of the battery [21]. Although supercapacitors are less common due to their cost and limited energy density, they offer rapid charge–discharge capability and are particularly effective in heavy-duty electric buses or frequent stop–start braking scenarios [98]. Many modern EVs adopt a blended braking strategy, where regenerative braking manages initial deceleration, and mechanical friction brakes engage when additional stopping force is required or when energy recovery is constrained by battery conditions [21].

3.1.3. Types of Energy Storage for Regenerative Braking

Energy storage technologies significantly influence the performance and efficiency of regenerative braking systems. This subsection outlines key types of storage solutions used in electric vehicles (EVs), highlighting their characteristics and suitability.
Batteries
Batteries are the most common storage option in EVs due to their high energy capacity. However, their relatively low power density and limited ability to handle rapid charge/discharge cycles reduce their effectiveness in short regenerative events. Additionally, frequent cycling can accelerate degradation, impacting overall battery life [99].
Supercapacitors
Supercapacitors offer excellent power density and fast charge/discharge capability, making them ideal for capturing short bursts of braking energy. Though they have lower energy density compared to batteries, their ability to support frequent cycling without performance degradation makes them suitable for urban EVs and transit systems [100].
Hybrid Energy Storage Systems (HESSs)
HESSs combine batteries and supercapacitors to leverage the strengths of both: high energy capacity from batteries and high power density from supercapacitors. This integration optimizes regenerative efficiency, reduces stress on individual components, and extends system lifespan [101,102].
Flywheel Energy Storage Systems (FESSs)
Flywheels store energy mechanically and release it quickly, providing excellent performance in high-power, short-duration applications. Modern electrically driven flywheels are compact, efficient, and require minimal maintenance [103,104]. Table 4 summarizes the key characteristics of various energy storage technologies used in regenerative braking systems, including their power and energy densities, as well as application suitability.

3.1.4. Role of Electric Motors and Power Electronics

Electric motors serve a dual purpose in EVs. They provide propulsion during acceleration and act as generators during braking [105]. The regenerative braking capability, however, differs across motor types. Permanent Magnet Synchronous Motors (PMSMs) support efficient and high-torque regenerative operation, even at low speeds, due to their precise control and low losses [106]. Induction Motors (IMs) are widely used for their robustness and low cost, but they typically offer lower regenerative efficiency and reduced braking controllability [107]. Switched Reluctance Motors (SRMs) can also enable energy recovery, but high torque ripples and acoustic noise limit their effectiveness in smooth regenerative braking scenarios [108]. Brushless DC Motors (BLDCs) enable efficient regeneration with relatively simple control, though performance may vary with speed and load [109]. Wound Field Synchronous Motors (WFSMs) allow for control of the excitation field, which can optimize regeneration under varying conditions, but require more complex control circuitry [110]. Therefore, the choice of motor directly influences the energy recovery potential, braking smoothness, and control flexibility of EVs.
Power electronics, particularly the inverter and associated controllers, are central to managing energy flow in EVs. During propulsion, it converts direct current (DC) from the battery, typically at voltage levels of 400 V or 800 V, into three-phase alternating current (AC) to drive the motor efficiently. In regenerative braking mode, the inverter performs the inverse operation by converting the AC generated by the motor, acting as a generator, into DC power. This recovered energy is subsequently conditioned by the DC-DC converter, which ensures voltage and current compatibility with the battery charging profile, thereby safeguarding against overcharging and thermal stress. Beyond power conversion, the inverter plays a central role in regulating torque generation, implementing brake blending strategies, and coordinating energy recovery under dynamic driving conditions [111]. These functions are managed by high-level controllers such as the vehicle control unit (VCU) or motor control unit (MCU), which interface with both the inverter and the DC-DC converter to achieve real-time power management [112]. Advanced inverter architectures incorporate wide-bandgap (WBG) semiconductor devices, including silicon carbide (SiC) and gallium nitride (GaN), enabling higher switching frequencies, improved thermal performance, and compact packaging without compromising efficiency [113]. Furthermore, modern inverters are equipped with embedded protection schemes against overcurrent, undervoltage, short-circuit, and over-temperature conditions, contributing to the overall functional safety of the traction system [114]. In the context of autonomous driving and advanced driver-assistance systems (ADASs) [115,116],t he inverter also supports adaptive regenerative braking and smooth deceleration trajectories by dynamically modulating energy recovery according to real-time feedback from vehicle sensors and control logic [117]. Therefore, the inverter is not merely a power converter but a highly integrated control and energy management unit essential to the efficiency, safety, and intelligence of next-generation EV platforms.
In advanced RBSs, control algorithms dynamically adjust the regeneration level based on real-time driving conditions, driver input, and vehicle load. Many EVs enable drivers to select from multiple levels of regenerative braking ranging from mild to aggressive, tailored to driving preferences or terrain features such as downhill slopes [118]. Furthermore, vehicle dynamics control units can independently regulate regeneration on each axle to maintain traction and stability, particularly under low-friction conditions [119]. Adaptive regenerative braking strategies that take into account the conditions of the road surface and the driving styles have demonstrated significant improvements in energy recovery efficiency and vehicle stability [120].

3.2. Control Strategies

The effectiveness of RBSs in EVs is significantly influenced by the adopted control strategies, which are crucial for optimizing energy recovery while maintaining vehicular stability and ensuring braking safety. Given the nonlinear and multi-objective nature of braking dynamics, various classical and intelligent control methods have been developed. This section reviews key RBS control strategies, highlighting their underlying principles, implementation, and effectiveness across diverse operating conditions.

3.2.1. Fuzzy Logic Control

The performance of RBSs in EVs is determined not only by the hardware configuration but also by the sophistication of the deployed control strategies [15]. These strategies govern braking force distribution, energy recuperation, vehicle stability, and ride comfort [121]. Among them, Fuzzy Logic Control (FLC) is widely adopted for its capacity to handle nonlinearities, parameter variations, and uncertain conditions without requiring precise system models. FLC enables real-time decision-making by applying rule-based inference to inputs such as braking intensity, vehicle speed, motor response, and battery state of charge (SoC). Typically embedded within hierarchical control architectures, the fuzzy logic layer computes the regenerative braking ratio, while lower-level controllers distribute axle-specific braking forces according to ECE regulations and ideal braking force curves [122]. This layered coordination ensures both effective energy recovery and braking safety. Figure 16 illustrates a representative fuzzy logic controller architecture for an RBS. The diagram outlines the process flow from driver input and vehicle states through fuzzification, inference, and defuzzification stages, leading to the generation of optimal braking commands managed by the power control unit. The integration with braking control and mode decision units ensures a coordinated allocation between motor and hydraulic braking systems.
Recent developments in RBSs further demonstrate the role of advanced fuzzy-based algorithms in facilitating smooth transitions between regenerative and mechanical braking, enhancing overall drivability and system robustness [123]. FLC’s adaptability extends to a range of drivetrain topologies, including central, wheel-side, and in-wheel motors [124,125,126], as well as various energy storage configurations such as electric batteries, hydraulic units, and flywheels [127]. Simulation-based evaluations confirm that FLC improves braking smoothness and energy recovery rates, particularly under dynamic conditions involving deceleration and drive cycle transitions [128,129,130,131]. The integration of FLC into multi-layered RBS frameworks offers high adaptability, scalability, and compliance with performance and safety standards, making it a practical solution for modern electric vehicle platforms.

3.2.2. Neural Network-Based Control

Neural networks (NNs) are increasingly employed in regenerative braking systems due to their capability to learn from data and model complex, nonlinear, and time-varying dynamics [132]. These controllers process inputs such as vehicle speed, brake pedal position, and battery SoC through interconnected layers of artificial neurons to generate braking torque commands. Once trained, NNs enable accurate and adaptive allocation of braking force between regenerative and mechanical subsystems under diverse operating conditions. A typical NN-based control architecture is illustrated in Figure 17. The structure comprises a driver module unit that generates command inputs, a data acquisition unit that collects vehicle information, and a neural network controller trained through an optimization loop. The trained neural controller then delivers braking commands to the power control unit, which in turn manages the distribution of braking force. This configuration supports online learning, adaptation to dynamic environments, and improved system responsiveness.
Recent advances in NN-based control focus on hybrid architectures that enhance recovery efficiency and robustness. Examples include integration with sliding mode control [133], fuzzy neural networks [134], and neural networks combined with PI control strategies. Unlike fuzzy logic controllers, which rely on predefined rule sets, NNs adaptively map complex input–output relationships, facilitating more precise control of energy recuperation [135]. Architectural design significantly influences control performance. For instance, a three-layer NN with five hidden neurons applied in a switched reluctance motor (SRM) system achieved improved energy recovery during high-torque braking events [136]. Adaptive methods that combine backpropagation with radial basis functions have been used to fine-tune PID parameters in real-time, further enhancing regenerative control performance [137]. NN-based control has also been implemented in multi-motor EVs, including four-wheel drive systems, to coordinate torque distribution across axles [138,139]. Nevertheless, the efficacy of NN-based control depends critically on training quality; poorly trained networks or inadequate designs can reduce recovery efficiency and stability of the system [140]. To address these limitations, predictive algorithms are often combined with NNs to mitigate control delays and enhance steady-state tracking in dynamic scenarios.

3.2.3. Model Predictive Control

Model Predictive Control (MPC) is widely applied in regenerative braking systems for its ability to manage multi-objective constraints through predictive optimization. By forecasting system states over a defined horizon and updating control inputs at each step, MPC ensures accurate torque distribution while enhancing energy recovery and driving stability [141]. Figure 18 illustrates a typical MPC-based control architecture used in such applications. Recent developments include adaptive MPC frameworks that adjust braking torque in real time based on slip ratio and road conditions, achieving improved recovery efficiency and longitudinal control [141,142]. Integrated ABS-regenerative control has demonstrated energy recovery gains exceeding 50% during ABS events, while parameter estimation techniques have improved torque smoothness and system coordination [143]. To address computational challenges, simplified and gain-scheduled MPCs have been proposed for real-time feasibility [144]. Despite its strengths, MPC depends heavily on accurate modeling; any mismatch may reduce control precision. Computational load remains a key limitation, particularly in highly dynamic scenarios where solving optimization problems in real time may not be feasible [145,146]. Hybrid strategies combining predictive and adaptive control layers are being explored to balance optimality with responsiveness under real-world constraints [147].

3.2.4. Sliding Mode Control

Sliding Mode Control (SMC) is a nonlinear robust control technique increasingly applied in regenerative braking due to its high accuracy, disturbance rejection capability, and resilience to system uncertainties [148,149]. By driving system trajectories onto a defined sliding surface using discontinuous control signals, SMC provides precise control over braking torque distribution, even under varying operating conditions. A representative block diagram of a typical SMC-based control structure is presented in Figure 19 to illustrate the key control elements and feedback mechanisms involved.
Figure 18. An MPC-based control system for regenerative braking in EVs.
Figure 18. An MPC-based control system for regenerative braking in EVs.
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Figure 19. A Sliding Mode Control system for regenerative braking in EVs.
Figure 19. A Sliding Mode Control system for regenerative braking in EVs.
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Recent developments focus on enhancing braking efficiency and reducing chattering, a known limitation of conventional SMC caused by high-frequency oscillations that can affect system stability and mechanical components [150]. Adaptive SMC and fuzzy SMC methods have been introduced to suppress chattering and improve control precision. These approaches optimize controller parameters in real time based on slip ratio error, braking torque demand, and motor dynamics [151,152]. Integration of fuzzy logic allows smoother transitions and improved regenerative torque estimation while maintaining robust slip tracking [153]. Advanced implementations include intelligent sliding mode schemes (ISMSs), which incorporate torque limiting to prevent battery overcharge and ensure stability during extreme braking events [154,155]. In traction motor applications such as switched reluctance motors (SRMs) and BLDC motors, modified SMC frameworks like time-varying SMC and model-based observers are employed to manage high-speed dynamics and torque ripple [117,156,157,158]. Fractional-order SMC has also emerged, using metaheuristic optimization to minimize tracking errors and overshoot, particularly under complex trajectories [159,160]. While SMC offers robustness, its effectiveness depends on accurate modeling of system dynamics and proper tuning of control parameters. External disturbances, slip variation, and chattering remain critical challenges. Adaptive and intelligent SMC strategies continue to evolve to improve energy recovery, braking smoothness, and long-term system performance in electric vehicle applications [161,162,163,164]. A comparative summary of the discussed control approaches, highlighting their key advantages and limitations, computational complexity, and application scenarios is presented in Table 5.

3.2.5. Other Control Techniques

A range of advanced control strategies have been developed to improve the performance and energy recuperation effectiveness of RBSs in EVs. One significant approach is eco-driving, which seeks to optimize vehicle speed profiles to minimize energy consumption during driving while maximizing energy recovered during braking. Various optimization techniques have been applied to this problem, including sequential quadratic programming [166], interior point algorithms [167], receding horizon control [168], and co-optimization using Pontryagin’s minimum principle [169]. A genetic algorithm-based eco-driving framework has also been proposed to determine the most energy-efficient driving trajectories by evaluating regenerative and consumptive energy over multiple generations [170].
Learning-based strategies have also emerged as effective tools for adaptive regenerative braking control. Fuzzy Q-learning has been applied to derive optimal braking torque across different driving scenarios [171,172], while iterative learning control has addressed issues such as torque ripple and slip regulation by updating control actions based on past errors [173]. Reinforcement learning has been used to control motor torque based on deceleration demands, improve the regeneration factor estimation, and manage energy flows to and from the battery in real time [174]. A hybrid learning-based method was introduced to estimate brake intensity and pressure, enhancing the decision-making efficiency in regenerative braking without relying on physical sensors [171]. Additionally, Q-learning algorithms have been used to dynamically optimize speed profiles for improved braking energy recovery in traffic environments supported by vehicle-to-infrastructure communication [175].
In hub-motor electric vehicles, several game-theoretic and multi-objective optimization strategies have been proposed for braking torque distribution. These include approaches that allocate braking forces between regenerative and mechanical systems based on ideal braking curves and safety regulations [176,177,178,179]. Optimization techniques such as genetic algorithms [180], particle swarm optimization [181], and ant colony-based methods have been used to develop braking strategies that improve both energy recovery and vehicle stability. A game theory-based approach has been introduced to balance multiple objectives, including energy efficiency, braking comfort, and vehicle stability under various braking conditions [182].
Further advancements have been made in connected vehicle environments using eco-approach and departure (EAD) strategies. These systems rely on signal phase and timing (SPaT) information to predict optimal speed profiles for efficient braking and energy recovery in urban scenarios [183,184]. Dynamic programming has been employed to optimize these trajectories while accounting for passenger comfort and energy efficiency [185]. Evolutionary algorithms, such as genetic algorithms, have shown improved braking energy recovery compared to human drivers [186]. Reinforcement learning has also been validated in this context, enabling adaptive and explainable braking decisions in simulation-based environments [187]. These techniques enhance regenerative braking by improving energy recovery, adaptability, and control efficiency across various EV applications.

3.3. Impact on Battery Life

The implementation of regenerative braking in EVs brings significant benefits in terms of energy recovery and extended driving range. However, these gains must be balanced against the potential implications for battery health and longevity. Regenerative braking introduces frequent, high-power charging events that can affect battery temperature, state of charge (SoC), and cycle depth key factors influencing battery degradation [188]. Therefore, understanding both the detrimental and beneficial impacts of regenerative braking on battery life is essential. This section discusses the effects of regenerative braking on battery aging mechanisms and outlines control strategies and system-level enhancements designed to mitigate wear and prolong battery lifespan in modern EV architectures.

3.3.1. Effects of Regenerative Braking on Battery Degradation

Regenerative braking enhances energy efficiency and extends vehicle range, but also presents challenges related to battery degradation. High charging currents associated with regenerative braking can accelerate the aging of lithium-ion batteries if not carefully managed. Experimental studies have shown that cells cycled at 25 °C provide an optimal balance between calendar and cyclic aging, while lower temperatures reduce calendar aging but make cycle aging more sensitive to load profiles [189].
Over a driving distance of 200,000 km, it was observed that regenerative braking can reduce depth of discharge (DOD), thereby extending battery life [175]. In fact, higher levels of regenerative braking can inhibit degradation by avoiding high-DOD events and mitigating the risk of lithium plating, particularly at high SoC and in low-temperature conditions. Experimental data [190] over 50,000 km further support this, showing that higher levels of regenerative braking reduce degradation through shallower DOD and lower lithium plating risk. Under optimized conditions, capacity fade can remain around 10% after 100,000 km. Another extensive experimental study [191] shows that regenerative braking significantly improves battery life by reducing cycle depth and degradation. After 2000 equivalent full cycles (∼200,000 km), cells with regenerative braking showed only ∼10% capacity fade at low SoC and 25 °C, compared to over 20% without it at 61% DOD. Regenerative braking lowered DOD from 24.8% to 20.4%, reducing capacity fade across all conditions. At 10 °C and high SoC, it reduced capacity fade from 12.5% to around 6%. Resistance increase remained low ( R dc , 10 s < 15%), confirming that regenerative braking extends battery life by lowering stress during charging. Moreover, research indicates that total capacity fade is more influenced by conventional charging station use than by regenerative events [191]. However, regenerative braking also increases battery temperature, potentially accelerating thermal degradation. A fuzzy logic-based control strategy has been proposed to modulate the regenerative braking ratio in response to SoC and battery temperature, successfully limiting the temperature rise during braking [192].

3.3.2. Strategies to Mitigate Battery Wear

To mitigate battery degradation associated with regenerative braking, several advanced strategies have been developed. One effective approach involves the use of HESSs, in which ultracapacitors complement batteries by managing high-power, short-duration energy demands. This configuration reduces electrochemical stress on the battery and enhances the overall efficiency of energy recovery. For example, in a representative HESS configuration, braking energy is routed through an induction motor and a full-bridge inverter, governed by fuzzy logic and pulse width modulation (PWM), to minimize conversion losses and protect battery health [193]. A hierarchical control architecture has also been proposed to address battery aging while preserving braking performance. In this scheme, the high-level controller focuses on maximizing energy recuperation and minimizing degradation, whereas the low-level controller coordinates the electric and pneumatic braking actuators. Controller-in-the-loop testing has demonstrated consistent braking behavior with and without explicit aging compensation, although torque output was reduced for aged motor conditions [194]. Another promising technique is the application of H-infinity (H∞) control to energy management, which enables optimal regulation of braking torque and battery power constraints. Compared to conventional proportional–integral–derivative (PID) control, this method improved energy recovery by approximately 5.3% while maintaining battery longevity [195].
For systems employing switched reluctance motors (SRMs), a multi-objective optimization framework has been developed to enhance braking performance while reducing system wear. The strategy considers control objectives such as torque ripple suppression, current smoothness, and power stability, thus achieving a trade-off between safety, regenerative efficiency, and battery life [108]. The integration of ultracapacitors has also demonstrated benefits in extending battery service life. In scenarios involving high-power bursts, ultracapacitors supplement propulsion demands, particularly when the battery’s state of charge is low. This reduces peak current load on the battery, improves drivetrain efficiency, and contributes to lower life cycle operating costs [196]. Intelligent control strategies and system-level enhancements, including HESS integration and adaptive braking logic, effectively mitigate battery degradation while enhancing energy efficiency and long-term system reliability in electric vehicles.

4. Emerging Trends and Future Directions

4.1. Advanced Materials and Technologies

The evolution of electric vehicle (EV) performance and powertrain efficiency is strongly linked to advancements in power electronics, particularly in the adoption of wide-bandgap (WBG) semiconductor materials. Silicon Carbide (SiC) and Gallium Nitride (GaN) devices have emerged as critical enablers in achieving higher efficiency, compactness, and thermal performance in EV traction inverters.

4.1.1. Wide-Bandgap Semiconductors (SiC, GaN)

Silicon Carbide (SiC) Devices
SiC-based traction drives have become the dominant solution for high-voltage electric vehicle applications. SiC devices possess a wide energy bandgap, high thermal conductivity, and elevated breakdown voltages, allowing them to operate efficiently at high temperatures and voltages [197,198]. Compared to conventional silicon devices, SiC power devices exhibit lower conduction and switching losses, which enables higher efficiency in inverter operation [199]. For instance, SiC-based two-level (2L) voltage source inverters (VSIs) can achieve increased efficiency at both low- and high-speed driving conditions [200].
To further enhance efficiency and reduce electromagnetic interference (EMI), multi-level inverter topologies are increasingly paired with SiC devices. These configurations help reduce voltage stress on switching devices and decrease total harmonic distortion (THD), ultimately extending the life of traction motors [201,202]. Despite these technical advantages, the widespread adoption of SiC devices is constrained by their high cost, estimated to be 8–10 times more than traditional silicon semiconductors. Therefore, ongoing research is focused on hybrid configurations that combine Si and SiC to strike a balance between performance and cost [203].
Gallium Nitride (GaN) Devices
GaN-based power devices are gaining traction in low-to-medium-voltage applications due to their superior switching characteristics and high electron mobility. Although GaN devices are less mature than SiC, recent developments have introduced GaN devices rated up to 1200 V, making them more competitive for EV applications [203]. GaN devices are especially suitable for low-voltage applications such as compact urban EVs or electric motorbikes, where size and weight are crucial design constraints. Multi-level inverter topologies are often employed to exploit GaN’s fast-switching ability while keeping voltage stress within safe limits [204]. However, challenges such as limited short-circuit withstand time, device packaging complexities, and scalability issues related to mass production still hinder their widespread integration into high-power EV traction systems. [205].

4.1.2. High-Efficiency Motor Designs

Improving the efficiency of electric motors is central to enhancing the performance and sustainability of electric vehicles (EVs). Modern motor designs are evolving rapidly, driven by the need for increased torque density, better thermal stability, reduced material usage, and minimized energy losses across various operating conditions [206].
Among the most promising innovations are in-wheel motors with surface-mounted permanent magnets. These motors eliminate the need for a traditional drive transmission, integrating the motor directly into the wheel hub. This architectural simplification reduces mechanical losses and enhances the system’s reliability and energy conversion efficiency [207]. One significant advancement in this area is the use of 20-pole–24-slot surface permanent magnet synchronous motors, which provide a high-efficiency solution for low-speed direct-drive applications while minimizing the amount of permanent magnet material required [208].
Additionally, motor geometries are being optimized for better thermal stability and reduced torque ripple. For instance, segmented stator cores and rectangular cross-section conductors improve slot area utilization, lowering current density and minimizing Joule losses [209]. Moreover, incorporating an uneven air gap—a design technique shown to reduce cogging torque and torque ripple—further enhances rotational smoothness and driver comfort [210].
Another promising development is the adoption of axial-flux motor designs, which offer high power density and compact axial layouts. These motors are particularly advantageous for EVs requiring lightweight and compact drivetrains, such as solar-powered or city commuting vehicles [211]. Optimization of axial-flux motors often involves adjusting stator tooth geometry and magnet dimensions, resulting in better torque characteristics and reduced electromagnetic noise [212]. In one study, a motor with three stator disks exhibited the highest torque density and the lowest copper losses, demonstrating the benefits of multi-disc coreless configurations [213].
Further, dual-rotor and dual-stator designs have emerged as effective ways to increase average torque and reduce material consumption. These designs leverage sophisticated pole-slot combinations and winding layouts to address limitations imposed by rim dimensions and axial core length [214]. Careful adjustment of these parameters ensures optimal performance while accommodating the spatial constraints of light electric vehicles (LEVs) [213].

4.2. AI-Based Control Systems

Artificial Intelligence (AI) is emerging as a pivotal tool in improving energy optimization and augmenting the operational intelligence of EVs. [19]. AI-based control systems, particularly those leveraging machine learning (ML), are being employed to develop optimized energy management strategies that adapt to real-time driving patterns, battery health, and user behavior [215]. These systems enable dynamic allocation of power between regenerative braking, battery storage, and traction needs, improving overall energy utilization [215].
Predictive algorithms form a core component of AI-based systems by forecasting braking events and charging opportunities [216]. Using data such as vehicle speed, traffic conditions, and terrain profiles, these algorithms allow for preemptive control of regenerative braking and battery charging power, thereby maximizing energy recovery while reducing thermal and electrical stress on the battery [217].
Moreover, AI contributes to the development of intelligent braking systems that ensure safety and comfort while maximizing regenerative efficiency [218]. By integrating AI with traditional vehicle control architectures, EVs are becoming smarter, more efficient, and better equipped to handle the complexities of real-world driving scenarios [116]. Recent advancements in AI-driven braking strategies have demonstrated improvements in energy recovery rates and braking stability [147].
AI-driven control strategies are revolutionizing EV technology by optimizing energy management, predictive braking, and intelligent vehicle control. As research continues to evolve, AI will play a crucial role in advancing EV performance, improving battery longevity, and enhancing overall driving efficiency.

4.3. Integration with Autonomous Driving

In autonomous driving scenarios, where acceleration, deceleration, and braking are fully automated, regenerative braking systems (RBSs) must be designed to ensure both energy efficiency and ride comfort. While conventional EVs typically optimize regenerative braking for maximum energy recovery, autonomous vehicles require braking strategies that also maintain smooth and predictable deceleration. Passenger comfort, system responsiveness, and integration with vehicle dynamics control systems become essential factors in the development of intelligent regenerative braking algorithms.
To address this, a comfort regenerative braking system (CRBS) has been proposed that integrates artificial neural networks (ANNs) to intelligently manage braking commands [18]. The CRBS strategy considers variables such as vehicle acceleration, jerk (rate of change of acceleration), and road conditions, predicting optimal deceleration rates via backpropagation techniques. By employing this AI-based approach, braking force is modulated in real time, ensuring that the deceleration remains within the limits defined by ISO 2631–1997 for human comfort while still allowing significant energy regeneration [219].

5. Conclusions

This review has presented a unified analysis of recent developments in bidirectional converters and regenerative braking systems in electric vehicles. These technologies are fundamental for enhancing energy efficiency, driving performance, and battery durability. Bidirectional converters enable energy to flow both to and from the battery, supporting not only regenerative braking but also vehicle-to-grid operations. The paper examined a variety of converter topologies including non-isolated types such as buck–boost, Ćuk, Sepic/Zeta, interleaved, cascaded, and switched-capacitor designs. Isolated topologies such as flyback, push–pull, forward, dual active bridge, dual half-bridge, and LLC resonant converters were also discussed. Each configuration offers specific benefits based on efficiency, voltage gain, size, and isolation requirements. The integration of wide-bandgap semiconductors like Silicon Carbide and Gallium Nitride further improves switching performance and thermal characteristics. These materials allow higher operating frequencies and power density, although they present challenges in cost and packaging. Regenerative braking systems have progressed beyond simple energy recovery to include advanced control features that improve braking force distribution, torque coordination, and system stability. Various control strategies such as fuzzy logic, sliding mode control, model predictive control, and neural networks have been used to manage braking under changing conditions. These techniques help balance energy recovery with safety and comfort. Recent approaches combine adaptive learning and predictive algorithms to improve response and reduce energy loss. The paper also examined how regenerative braking affects battery health. High-frequency charging events during braking can increase stress and lead to faster aging. However, solutions such as hybrid energy storage systems and thermal-aware controllers reduce these effects and extend battery life. In addition to hardware and control improvements, this review highlighted emerging trends in electric motor design and artificial intelligence. New motor architectures offer higher torque density and better cooling, while AI-based systems enable predictive braking and smarter energy management. Regenerative braking is also becoming more important in autonomous driving, where it supports smoother deceleration and improved passenger comfort. All of these developments point toward a future where electric vehicles are more efficient, intelligent, and reliable. The close integration of bidirectional converters, regenerative braking strategies, and intelligent control systems is essential to achieve this goal. Continued research and innovation in these areas will be key to advancing the next generation of electric vehicle technology.

Author Contributions

Conceptualization, H.N.; software, H.N.; formal analysis, J.-K.S.; investigation, H.N.; data curation, H.N.; writing, original draft preparation, H.N.; review and editing, H.N. and J.-K.S.; supervision, J.-K.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Felter, K. The US and The UN Framework Convention on Climate Change (UNFCCC); Harvard Model Congress: Cambridge, MA, USA, 2023. [Google Scholar]
  2. Hou, M.Z.; Luo, J.; Huang, L.; Beck, H.P.; Mehmood, F.; Wang, Q.; Wu, X.; Wu, L.; Yue, Y.; Fang, Y.; et al. Strategies toward carbon neutrality: Comparative analysis of China, USA, and Germany. Carbon Neutral Syst. 2025, 1, 3. [Google Scholar] [CrossRef]
  3. Department for Transport; Alexander, H. Phasing Out the Sale of New Petrol and Diesel Cars from 2030 and Support for Zero Emission Vehicle (ZEV) Transition. Written Statement to Parliament, House of Commons; 2025. Available online: https://www.gov.uk/government/speeches/phasing-out-the-sale-of-new-petrol-and-diesel-cars-from-2030-and-support-for-zero-emission-vehicle-zev-transition (accessed on 23 April 2025).
  4. Blaabjerg, F.; Wang, H.; Vernica, I.; Liu, B.; Davari, P. Reliability of power electronic systems for EV/HEV applications. Proc. IEEE 2020, 109, 1060–1076. [Google Scholar] [CrossRef]
  5. Naseem, H.; Seok, J.K. Reactive Power Controller for Single Phase Dual Active Bridge DC–DC Converters. IEEE Access 2023, 11, 141537–141546. [Google Scholar] [CrossRef]
  6. Panchanathan, S.; Vishnuram, P.; Rajamanickam, N.; Bajaj, M.; Blazek, V.; Prokop, L.; Misak, S. A comprehensive review of the bidirectional converter topologies for the vehicle-to-grid system. Energies 2023, 16, 2503. [Google Scholar] [CrossRef]
  7. International Energy Agency. Global EV Data Explorer. 2025. Available online: https://www.iea.org/data-and-statistics/data-tools/global-ev-data-explorer (accessed on 23 April 2025).
  8. Dias, N.; Naik, A.J.; Shet, V.N. A Novel Tri-Mode Bidirectional DC–DC Converter for Enhancing Regenerative Braking Efficiency and Speed Control in Electric Vehicles. World Electr. Veh. J. 2024, 15, 12. [Google Scholar] [CrossRef]
  9. Bahrami, A. EV charging definitions, modes, levels, communication protocols and applied standards. Changes 2020, 1, 1–10. [Google Scholar]
  10. Aghabali, I.; Bauman, J.; Kollmeyer, P.J.; Wang, Y.; Bilgin, B.; Emadi, A. 800-V electric vehicle powertrains: Review and analysis of benefits, challenges, and future trends. IEEE Trans. Transp. Electrif. 2020, 7, 927–948. [Google Scholar] [CrossRef]
  11. Acharige, S.S.; Haque, M.E.; Arif, M.T.; Hosseinzadeh, N.; Hasan, K.N.; Oo, A.M.T. Review of electric vehicle charging technologies, standards, architectures, and converter configurations. IEEE Access 2023, 11, 41218–41255. [Google Scholar] [CrossRef]
  12. Avraham, T.; Dhyani, M.; Bernstein, J.B. Reliability Challenges, Models, and Physics of Silicon Carbide and Gallium Nitride Power Devices. Energies 2025, 18, 1046. [Google Scholar] [CrossRef]
  13. Buffolo, M.; Favero, D.; Marcuzzi, A.; De Santi, C.; Meneghesso, G.; Zanoni, E.; Meneghini, M. Review and outlook on GaN and SiC power devices: Industrial state-of-the-art, applications, and perspectives. IEEE Trans. Electron Devices 2024, 71, 1344–1355. [Google Scholar] [CrossRef]
  14. Alatai, S.; Salem, M.; Ishak, D.; Das, H.S.; Alhuyi Nazari, M.; Bughneda, A.; Kamarol, M. A review on state-of-the-art power converters: Bidirectional, resonant, multilevel converters and their derivatives. Appl. Sci. 2021, 11, 10172. [Google Scholar] [CrossRef]
  15. Yang, C.; Sun, T.; Wang, W.; Li, Y.; Zhang, Y.; Zha, M. Regenerative braking system development and perspectives for electric vehicles: An overview. Renew. Sustain. Energy Rev. 2024, 198, 114389. [Google Scholar] [CrossRef]
  16. Salari, A.H.; Mirzaeinejad, H.; Mahani, M.F. A new control algorithm of regenerative braking management for energy efficiency and safety enhancement of electric vehicles. Energy Convers. Manag. 2023, 276, 116564. [Google Scholar] [CrossRef]
  17. Wang, H.; Wang, J.; Pi, D.; Wang, Q.; Sun, X.; Liu, Y.; Xue, P. Optimization of Commercial Vehicle Mechatronics Composite ABS Braking Control Considering Braking Efficiency and Energy Regeneration. IEEE Trans. Transp. Electrif. 2024, 11, 2332–2343. [Google Scholar] [CrossRef]
  18. Gupta, G.; Sudeep, R.; Ashok, B.; Vignesh, R.; Kannan, C.; Kavitha, C.; Alroobaea, R.; Alsafyani, M.; AboRas, K.M.; Emara, A. Intelligent Regenerative Braking Control with Novel Friction Coefficient Estimation Strategy for Improving the Performance Characteristics of Hybrid Electric Vehicle. IEEE Access 2024, 12, 110361–110384. [Google Scholar] [CrossRef]
  19. Hwang, M.H.; Lee, G.S.; Kim, E.; Kim, H.W.; Yoon, S.; Talluri, T.; Cha, H.R. Regenerative braking control strategy based on AI algorithm to improve driving comfort of autonomous vehicles. Appl. Sci. 2023, 13, 946. [Google Scholar] [CrossRef]
  20. Revathy, R.; Balaji, B.; Mohasin, A.A.; Gobinath, A. Supercapacitor and bldc motor-based regenerative braking for an electric vehicles. In Proceedings of the 2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN), Villupuram, India, 21–22 April 2023; pp. 1–5. [Google Scholar]
  21. Teasdale, A.; Ishaku, L.; Amaechi, C.V.; Adelusi, I.; Abdelazim, A. A study on an energy-regenerative braking model using supercapacitors and DC motors. World Electr. Veh. J. 2024, 15, 326. [Google Scholar] [CrossRef]
  22. Suyanto; Darwito, P.A.; Wahyuono, R.A.; Arifin, M.S.; Sudarmanta, B. Design of regenerative braking system for electric motorcycle based on supercapacitor with fuzzy PID. Int. J. Automot. Technol. 2023, 24, 187–194. [Google Scholar] [CrossRef]
  23. Hosseinpour, M.; Heydarvand, M.; Azizkandi, M.E. A new positive output DC–DC buck–boost converter based on modified boost and ZETA converters. Sci. Rep. 2024, 14, 20675. [Google Scholar] [CrossRef]
  24. Okati, M.; Eslami, M.; Khan, B. A novel semi-quadratic buck-boost structures with continuous input current for PV application. Sci. Rep. 2024, 14, 14134. [Google Scholar] [CrossRef]
  25. Muhammad, A.; Amin, A.; Qureshi, M.A.; Bhatti, A.R.; Ali, M.M. Deep learning based buck-boost converter for PV modules. Heliyon 2024, 10, e27405. [Google Scholar] [CrossRef] [PubMed]
  26. Truong, V.A.; Nguyen, Q.T.; Quach, T.H. A New Buck-Boost Converter Structure with Improved Efficiency. In Proceedings of the 2023 International Conference on System Science and Engineering (ICSSE), Ho Chi Minh, Vietnam, 27–28 July 2023; pp. 593–597. [Google Scholar]
  27. Darwish, A. A Bidirectional Modular Cuk-Based Power Converter for Shore Power Renewable Energy Systems. Energies 2022, 16, 274. [Google Scholar] [CrossRef]
  28. Yi, Q.; Ling, R.; Wang, P.; Tong, Z. An improved equalization circuit with bidirectional CUK converter for series-connected battery strings. In Proceedings of the 2023 5th International Conference on Energy, Power and Grid (ICEPG 2023), Guangzhou, China, 22–24 September 2023; Volume 2703, p. 012069. [Google Scholar]
  29. He, X.; Ling, R.; Li, D. A Novel ZCS Bidirectional CUK Equalizer for Energy Balance of Battery Cells Connected in Series. In Proceedings of the 2021 IEEE Energy Conversion Congress and Exposition (ECCE), Vancouver, Canada, 10–14 October 2021; pp. 174–179. [Google Scholar]
  30. Sebaje, A.S.; da Silva Martins, M.L.; Font, C.H.I. A hybrid bidirectional DC-DC converter based on a SEPIC/Zeta converter with a modified switched capacitor cell. In Proceedings of the 2021 Brazilian Power Electronics Conference (COBEP), João Pessoa, Brazil, 7–10 November 2021; pp. 1–6. [Google Scholar]
  31. Shchur, I. Bidirectional single-stage Zeta-SEPIC DC-AC converter for traction BLDC motors. In Proceedings of the 2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek), Kharkiv, Ukraine, 3–7 October 2022; pp. 1–6. [Google Scholar]
  32. Thota, P.; Bhimavarapu, A.R.; Chintapalli, V.B.R. Selection of input–output pairing and control structure configuration using interaction measures for DC–DC dual input zeta-SEPIC converter. Electrica 2022, 23, 95–106. [Google Scholar] [CrossRef]
  33. Leal, W.C.; Godinho, M.O.; Bastos, R.F.; de Aguiar, C.R.; Fuzato, G.H.; Machado, R.Q. Cascaded interleaved DC–DC converter for a bidirectional electric vehicle charging station. IEEE Trans. Ind. Electron. 2023, 71, 3708–3717. [Google Scholar] [CrossRef]
  34. Uno, M.; Cheng, D.; Onodera, S.; Sasama, Y. Bidirectional buck-boost converter using cascaded energy storage modules based on cell voltage equalizers. IEEE Trans. Power Electron. 2022, 38, 1249–1261. [Google Scholar] [CrossRef]
  35. Lara, J.; Masisi, L.; Hernandez, C.; Arjona, M.A.; Chandra, A. Novel five-level ANPC bidirectional converter for power quality enhancement during G2V/V2G operation of cascaded EV charger. Energies 2021, 14, 2650. [Google Scholar] [CrossRef]
  36. Mei, J.; Gao, Q.; Cai, X. Switched capacitor cascaded bidirectional DC-DC converter suitable for energy storage system. In Proceedings of the 2021 IEEE 12th Energy Conversion Congress &, Exposition-Asia (ECCE-Asia), Singapore, 24–27 May 2021; pp. 2205–2210. [Google Scholar]
  37. Han, S.; Wang, Y.; Xie, Z.; Guan, Y.; Alonso, J.M.; Xu, D. Continuously adjustable modular bidirectional switched-capacitor DC–DC converter. IEEE Trans. Power Electron. 2022, 37, 12944–12948. [Google Scholar] [CrossRef]
  38. Gireadă, M.; Hulea, D.; Muntean, N.; Cornea, O. A common-ground bidirectional hybrid switched-capacitor DC–DC converter with a high voltage conversion ratio. Energies 2023, 16, 1337. [Google Scholar] [CrossRef]
  39. Nagabushanam, K.M.; Mahto, T.; Tewari, S.V.; Udumula, R.R.; Alotaibi, M.A.; Malik, H.; Márquez, F.P.G. Development of high-gain switched-capacitor based bi-directional converter for electric vehicle applications. J. Energy Storage 2024, 82, 110602. [Google Scholar] [CrossRef]
  40. Mei, J.; Gao, Q.; Cai, X. High gain bidirectional DC-DC converter with three boost converters and switched capacitor. In Proceedings of the 2021 IEEE 1st International Power Electronics and Application Symposium (PEAS), Shanghai, China, 12–15 November 2021; pp. 1–6. [Google Scholar]
  41. Kumar, R.; Behera, P.K.; Pattnaik, M. A comparative analysis of two-phase and three-phase interleaved bidirectional dc-dc converter. In Proceedings of the 2023 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India, 18–19 February 2023; pp. 1–5. [Google Scholar]
  42. Wibisono, A.; Facta, M.; Setiawan, I. An average current control method in multiphase interleaved bidirectional dc/dc converter connected on dc microgrids. In Proceedings of the 2021 12th International Renewable Engineering Conference (IREC), Amman, Jordan, 14–15 April 2021; pp. 1–6. [Google Scholar]
  43. Fantino, R.A.; Christian, S.F.; Balda, J.C. Synchronous-variable-frequency control of bidirectional DCM interleaved DC–DC converter for wide-range enhanced efficiency. IEEE Trans. Ind. Electron. 2021, 69, 5844–5853. [Google Scholar] [CrossRef]
  44. Liu, J.; Qiu, D.; Zhang, B.; Chen, Y.; Xie, F.; Xiao, W.; Wang, Y. A High-Gain Interleaved Soft-Switching Bidirectional Flyback Converter. Int. J. Circuit Theory Appl. 2025, 1–11. [Google Scholar] [CrossRef]
  45. Zhao, K.; Zhang, L.; Zeng, T. High Step-Up Bidirectional DC-DC Converter with Interleaved Resonant Soft-Switching. J. Electr. Eng. Technol. 2025, 1–17. [Google Scholar] [CrossRef]
  46. Sun, L.; Zhuo, F.; Wang, F.; Yi, H.; Zhu, Y. New no-isolated interleaved bidirectional soft-switching dc-dc converter with a novel auxiliary ZVT cell. In Proceedings of the 2018 IEEE Energy Conversion Congress and Exposition (ECCE), Portland, OR, USA, 23–27 September 2018; pp. 2843–2848. [Google Scholar]
  47. Yao, Z.; Lu, S. A simple approach to enhance the effectiveness of passive currents balancing in an interleaved multiphase bidirectional DC–DC converter. IEEE Trans. Power Electron. 2018, 34, 7242–7255. [Google Scholar] [CrossRef]
  48. Aditama, R.D.; Ramadhani, N.; Ardriani, T.; Furqani, J.; Rizqiawan, A.; Dahono, P.A. New modular multilevel dc–dc converter derived from modified buck–boost dc–dc converter. Energies 2023, 16, 6950. [Google Scholar] [CrossRef]
  49. Monteiro, V.; Oliveira, C.F.; Afonso, J.L. Experimental validation of a bidirectional multilevel dc–dc power converter for electric vehicle battery charging operating under normal and fault conditions. Electronics 2023, 12, 851. [Google Scholar] [CrossRef]
  50. Milas, N.T.; Tatakis, E.C. Fast battery cell voltage equalizer based on the bidirectional flyback converter. IEEE Trans. Transp. Electrif. 2022, 9, 4922–4940. [Google Scholar] [CrossRef]
  51. Waghmare, T.; Chaturvedi, P. Sliding mode controller for multiphase bidirectional flyback converter topology in hybrid electric vehicle applications. Energy Rep. 2023, 9, 40–47. [Google Scholar] [CrossRef]
  52. Kumar, C. Bi-directional DC-DC flyback converter using zero voltage switching for hybrid electric vehicle application. In Proceedings of the 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 17–18 March 2023; Volume 1, pp. 894–899. [Google Scholar]
  53. Zhu, J.; He, Y.; Gu, T.; Li, B.; Wang, Y.; Zhang, Y.; Qiu, D.; Zhang, B. Bidirectional Step-up/down Flyback Converter for Energy Storage System. In Proceedings of the 2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia), Chengdu, China, 17–20 May 2024; pp. 1440–1445. [Google Scholar]
  54. Lim, J.W.; Hassan, J.; Kim, M. Bidirectional soft switching push–pull resonant converter over wide range of battery voltages. IEEE Trans. Power Electron. 2021, 36, 12251–12267. [Google Scholar] [CrossRef]
  55. Zelan, M.N.; Hidayat, N.M.; Umair, M.; Ali, N.N.; Abdullah, E.; Rahmat, M. Enhancing the Performance of Bidirectional DC-DC Push Pull Converter with RC Snubber Circuits for Electric Vehicle Applications. In Proceedings of the 2024 IEEE 22nd Student Conference on Research and Development (SCOReD), Selangor, Malaysia, 19–20 December 2024; pp. 50–54. [Google Scholar]
  56. Patel, N.; Lopes, L.A.; Rathore, A.K.; Khadkikar, V. High-efficiency single-stage single-phase bidirectional pfc converter for plug-in ev charger. IEEE Trans. Transp. Electrif. 2023, 10, 5636–5649. [Google Scholar] [CrossRef]
  57. Faistel, T.M.K.; Jank, H.; Ribeiro, G.C.; da Silva Martins, M.L.; Lopes, L.A.C. Composite DC-DC Converter with Bipolar/bidirectional Current-Fed Push-pull Voltage Regulator. In Proceedings of the 2021 Brazilian Power Electronics Conference (COBEP), João Pessoa, Brazil, 7–10 November 2021; pp. 1–5. [Google Scholar]
  58. Shi, K.; Bui, T.; Marco, J. Optimal control of bidirectional active clamp forward converter with synchronous rectifier based cell-to-external-storage active balancing system. J. Energy Storage 2021, 41, 102851. [Google Scholar] [CrossRef]
  59. Bahrami, H.; Allahyari, H.; Adib, E. An improved wide ZVS soft-switching range PWM bidirectional forward converter for low power applications with simple control circuit. IET Power Electron. 2022, 15, 1652–1663. [Google Scholar] [CrossRef]
  60. Blinov, A.; Kosenko, R.; Vinnikov, D.; Parsa, L. Bidirectional isolated current-source DAB converter with extended ZVS/ZCS range and reduced energy circulation for storage applications. IEEE Trans. Ind. Electron. 2019, 67, 10552–10563. [Google Scholar] [CrossRef]
  61. Li, N.; Zhang, C.; Liu, Y.; Zhuo, C.; Liu, M.; Yang, J.; Zhang, Y. Single-Degree-of-Freedom Hybrid Modulation Strategy and Light-Load Efficiency Optimization for Dual-Active-Bridge Converter. IEEE J. Emerg. Sel. Top. Power Electron. 2024, 12, 3936–3947. [Google Scholar] [CrossRef]
  62. Zhang, Z.; Huang, J.; Xiao, Y. GaN-based 1-MHz partial parallel dual active bridge converter with integrated magnetics. IEEE Trans. Ind. Electron. 2020, 68, 6729–6738. [Google Scholar] [CrossRef]
  63. Yu, C.; Jang, S.; Kim, H.; Kim, T.; Son, S.; Kwon, C.; Jang, I.; Cha, H. High efficiency bidirectional dual active bridge (DAB) converter adopting boost-up function for increasing output power. IEEE Trans. Power Electron. 2022, 37, 14678–14691. [Google Scholar] [CrossRef]
  64. Kuo, S.h.; Chiu, H.J.; Chiang, C.W.; Huang, T.W.; Chang, Y.C.; Bachman, S.; Piasecki, S.; Jasinski, M.; Turzyński, M. High Efficiency Dual-Active-Bridge Converter with Triple-Phase-Shift Control for Battery Charger of Electric Vehicles. Energies 2024, 17, 354. [Google Scholar] [CrossRef]
  65. Cinik, S.; Zhao, F.; De Falco, G.; Wang, X. Efficiency and Cost Optimization of Dual Active Bridge Converter for 350kW DC Fast Chargers. In Proceedings of the 2024 Energy Conversion Congress & Expo Europe (ECCE Europe), Darmstadt, Germany, 2–6 September 2024; pp. 1–8. [Google Scholar]
  66. Esteve, V.; Bellido, J.L.; Jordán, J.; Dede, E.J. Improving the Efficiency of an Isolated Bidirectional Dual Active Bridge DC–DC Converter Using Variable Frequency. Electronics 2024, 13, 294. [Google Scholar] [CrossRef]
  67. Li, X.; Zhang, X.; Lin, F.; Sun, C.; Mao, K. Artificial-intelligence-based hybrid extended phase shift modulation for the dual active bridge converter with full ZVS range and optimal efficiency. IEEE J. Emerg. Sel. Top. Power Electron. 2023, 11, 5569–5581. [Google Scholar] [CrossRef]
  68. Aghajani, A.A.; Zare Kashani, N.; Eldoromi, M.; Moti Birjandi, A.A. A Novel Half-Full-Bridge Split-Capacitor DC-DC Converter Based On Dual-Active-Bridge Topology. In Proceedings of the 2022 13th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), Tehran, Iran, 1–3 February 2022; pp. 240–244. [Google Scholar]
  69. Zhu, X.; Zhao, X.; Li, Y.; Liu, S.; Yang, H.; Tian, J.; Hu, J.; Mai, R.; He, Z. High-Efficiency WPT System for CC/CV Charging Based on Double-Half-Bridge Inverter Topology with Variable Inductors. IEEE Trans. Power Electron. 2022, 37, 2437–2448. [Google Scholar] [CrossRef]
  70. de Castro, R.; Araujo, R.E.; Brembeck, J. A Nonlinear Control Allocation Strategy for Dual Half Bridge Power Converters. IEEE Trans. Autom. Sci. Eng. 2025, 22, 16091–16107. [Google Scholar] [CrossRef]
  71. Wang, W.; Fahmy, Y.A.; Preindl, M. A Low-Cost Battery-Balancing Auxiliary Power Module with Dual-Active Half Bridge Links and Coreless Transformers. IEEE Trans. Transp. Electrif. 2023, 9, 3801–3809. [Google Scholar] [CrossRef]
  72. Zhu, X.; Hou, P.; Zhang, B. A Multiport Current-Fed IIOS Dual-Half-Bridge Converter for Distributed Photovoltaic MVDC Integration System. IEEE Trans. Ind. Electron. 2024, 71, 3788–3800. [Google Scholar] [CrossRef]
  73. Zeng, J.; Zhang, G.; Yu, S.S.; Zhang, B.; Zhang, Y. LLC resonant converter topologies and industrial applications—A review. Chin. J. Electr. Eng. 2020, 6, 73–84. [Google Scholar] [CrossRef]
  74. Han, W.; Ren, J.; Liu, X.; He, S.; Diao, L. Design and Simulation Verification of Full-Bridge LLC Resonant Converter. In International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT); Springer: Singapore, 2021; pp. 451–458. [Google Scholar]
  75. Li, L.; Hong, F.; Chen, M.; Qian, Q. A high power density magnetically integrated scheme of LLC converter. IET Power Electron. 2025, 18, e70006. [Google Scholar] [CrossRef]
  76. Luo, Z.; Wu, Z.; Quan, X.; Xie, X.; Dou, X.; Hu, Q. Synchronous rectification of LLC resonant converters based on resonant inductor voltage. Front. Energy Res. 2023, 11, 1–11. [Google Scholar] [CrossRef]
  77. Shahsevani, J.; Beiranvand, R. Application-Oriented Review of the LLC-Based Resonant Converters. IEEE Access 2024, 12, 52687–52726. [Google Scholar] [CrossRef]
  78. Li, Y.; Cheng, X.F.; Zhao, J.; Zhao, Q.; Li, H.; Zhang, Y.; Hao, X. State-of-the-Art Review on Topology and Deductive Methods of LLC Resonant Converter. J. Electr. Eng. Technol. 2024, 19, 2217–2237. [Google Scholar] [CrossRef]
  79. Mortazavizadeh, S.A.; Palazzo, S.; Amendola, A.; De Santis, E.; Di Ruzza, D.; Panariello, G.; Sanseverino, A.; Velardi, F.; Busatto, G. High Frequency, High Efficiency, and High Power Density GaN-Based LLC Resonant Converter: State-of-the-Art and Perspectives. Appl. Sci. 2021, 11, 1–22. [Google Scholar] [CrossRef]
  80. Martinez-Vera, E.; Bañuelos-Sanchez, P. Review of Bidirectional DC-DC Converters and Trends in Control Techniques for Applications in Electric Vehicles. IEEE Lat. Am. Trans. 2024, 22, 144–155. [Google Scholar] [CrossRef]
  81. Wu, Q.; Li, G.; Wang, Y.; Shi, Y. A Bidirectional Modular Multilevel DC-DC Converter with Inherent Voltage Balance Capability. In Proceedings of the 19th Annual Conference of China Electrotechnical Society; Springer: Singapore, 2025; pp. 307–315. [Google Scholar]
  82. Farajdadian, S.; Hajizadeh, A.; Soltani, M. Recent Developments of Multiport DC/DC Converter Topologies, Control Strategies, and Applications: A Comparative Review and Analysis. Energy Rep. 2024, 11, 1019–1052. [Google Scholar] [CrossRef]
  83. Chidambaram, R.K.; Chatterjee, D.; Barman, B.; Das, P.P.; Taler, D.; Taler, J.; Sobota, T. Effect of regenerative braking on battery life. Energies 2023, 16, 5303. [Google Scholar] [CrossRef]
  84. Kim, J.; Park, S.H.; Kim, I.D. A Study on the Regenerative Braking of Electric Scooter Using BLDCM. In Proceedings of the 2022 IEEE 5th Student Conference on Electric Machines and Systems (SCEMS), Busan, Republic of Korea, 24–26 November 2022; pp. 1–5. [Google Scholar]
  85. Serykhanovna, B.Z.; Utebayev, R.; Baktybayev, M.; Temirzhanov, A.; Kunelbayev, M.M. Development of a method for regenerative braking of an electric scooter. Int. J. Power Electron. Drive Syst. (IJPEDS) 2023, 14, 1331–1344. [Google Scholar] [CrossRef]
  86. Yang, M.J.; Jhou, H.L.; Ma, B.Y.; Shyu, K.K. A cost-effective method of electric brake with energy regeneration for electric vehicles. IEEE Trans. Ind. Electron. 2009, 56, 2203–2212. [Google Scholar] [CrossRef]
  87. Szumska, E.M. Regenerative Braking Systems in Electric Vehicles: A Comprehensive Review of Design, Control Strategies, and Efficiency Challenges. Energies 2025, 18, 2422. [Google Scholar] [CrossRef]
  88. Carter SB, R.; S, T. Regenerative Braking in PV-Mounted Electric Vehicle With Reduced Switch VSI-Driven BLDC Motor and HAP-FUP Controller. Int. Trans. Electr. Energy Syst. 2024, 2024, 6465530. [Google Scholar] [CrossRef]
  89. Luo, D.; Yang, W.; Wang, Y.; Han, Y.; Liu, Q.; Yang, Y. Investigation of regenerative braking for the electric mining truck based on fuzzy control. Int. J. Veh. Perform. 2024, 10, 73–95. [Google Scholar] [CrossRef]
  90. Kumar, C.N.; Subramanian, S.C. Cooperative control of regenerative braking and friction braking for a hybrid electric vehicle. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 2016, 230, 103–116. [Google Scholar] [CrossRef]
  91. Heydari, S.; Fajri, P.; Shadmand, M.; Sabzehgar, R. Maximizing harvested energy through regenerative braking process in dual-motor all-wheel drive electric vehicles. In Proceedings of the 2020 IEEE Transportation Electrification Conference & Expo (ITEC), Chicago, IL, USA, 23–26 June 2020; pp. 1246–1250. [Google Scholar]
  92. Li, L.; Ping, X.; Shi, J.; Wang, X.; Wu, X. Energy recovery strategy for regenerative braking system of intelligent four-wheel independent drive electric vehicles. IET Intell. Transp. Syst. 2021, 15, 119–131. [Google Scholar] [CrossRef]
  93. Park, Y.; Park, S.; Ahn, C. Performance potential of regenerative braking energy recovery of autonomous electric vehicles. Int. J. Control. Autom. Syst. 2023, 21, 1442–1454. [Google Scholar] [CrossRef]
  94. Wang, J.; Zhang, Z.; Guo, D.; Ni, J.; Guan, C.; Zheng, T. Torque Vectoring and Multi-Mode Driving of Electric Vehicles with a Novel Dual-Motor Coupling Electric Drive System. Automot. Innov. 2024, 7, 236–247. [Google Scholar] [CrossRef]
  95. Muduli, U.R.; Beig, A.R.; Behera, R.K.; Al Jaafari, K.; Alsawalhi, J.Y. Predictive control with battery power sharing scheme for dual open-end-winding induction motor based four-wheel drive electric vehicle. IEEE Trans. Ind. Electron. 2021, 69, 5557–5568. [Google Scholar] [CrossRef]
  96. Tang, Q.; Yang, Y.; Luo, C.; Yang, Z.; Fu, C. A novel electro-hydraulic compound braking system coordinated control strategy for a four-wheel-drive pure electric vehicle driven by dual motors. Energy 2022, 241, 122750. [Google Scholar] [CrossRef]
  97. Adib, A.; Dhaouadi, R. Modeling and analysis of a regenerative braking system with a battery-supercapacitor energy storage. In Proceedings of the 2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), Sharjah, United Arab Emirates, 4–6 April 2017; pp. 1–6. [Google Scholar]
  98. Guo, J.; He, H.; Wei, Z.; Li, J. An economic driving energy management strategy for the fuel cell bus. IEEE Trans. Transp. Electrif. 2022, 9, 5074–5084. [Google Scholar] [CrossRef]
  99. Iclodean, C.; Varga, B.; Burnete, N.; Cimerdean, D.; Jurchiş, B. Comparison of different battery types for electric vehicles. Iop Conf. Ser. Mater. Sci. Eng. 2017, 252, 012058. [Google Scholar] [CrossRef]
  100. Pandey, D.; Sambath Kumar, K.; Henderson, L.N.; Suarez, G.; Vega, P.; Salvador, H.R.; Roberson, L.; Thomas, J. Energized composites for electric vehicles: A dual function energy-storing supercapacitor-based carbon fiber composite for the body panels. Small 2022, 18, 2107053. [Google Scholar] [CrossRef]
  101. Lemian, D.; Bode, F. Battery-supercapacitor energy storage systems for electrical vehicles: A review. Energies 2022, 15, 5683. [Google Scholar] [CrossRef]
  102. bin Zulkarnian Gafoor, M.N.A.; Sadeq, T.M.A. Enhance the Efficiency of the Regenerative Braking System in Electric Vehicles using a Hybrid Energy Storage System. Res. Prog. Mech. Manuf. Eng. 2024, 5, 393–409. [Google Scholar]
  103. Wang, W.; Li, Y.; Shi, M.; Song, Y. Optimization and control of battery-flywheel compound energy storage system during an electric vehicle braking. Energy 2021, 226, 120404. [Google Scholar] [CrossRef]
  104. Kurtulmuş, Z.N.; Karakaya, A. Efficiency Analysis of Regenerative Brake System Using Flywheel Energy Storage Technology in Electric Vehicles. Teh. Vjesn. 2024, 31, 442–448. [Google Scholar]
  105. Li, C.; He, C.; Yuan, Y.; Zhang, J. Control, modeling and simulation on a novel regenerative brake system of electric vehicle. In Proceedings of the 2018 IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE), Wuhan, China, 21–23 August 2018; pp. 90–94. [Google Scholar]
  106. Esfahani, F.N.; Darwish, A. Regenerative Braking for EVs Using PMSM with CHB as Bidirectional Traction Converter. In Proceedings of the 2022 IEEE 16th International Conference on Compatibility, Power Electronics, and Power Engineering (CPE-POWERENG), Birmingham, UK, 29 June–1 July 2022; pp. 1–7. [Google Scholar]
  107. Youssef, O.E.; Hussien, M.G.; El-Wahab Hassan, A. A Robust Regenerative-Braking Control of Induction Motors for EVs Applications. Int. Trans. Electr. Energy Syst. 2024, 2024, 5526545. [Google Scholar] [CrossRef]
  108. Zhu, Y.; Wu, H.; Zhang, J. Regenerative braking control strategy for electric vehicles based on optimization of switched reluctance generator drive system. IEEE Access 2020, 8, 76671–76682. [Google Scholar] [CrossRef]
  109. Soni, N.; Barai, M. Performance study of regenerative braking of BLDC motor targeting electric vehicle applications. In Proceedings of the 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), Ravet, India, 26–28 August 2022; pp. 1–6. [Google Scholar]
  110. Ho-Jin, O.; Jae-Hoon, C.; Young-Ho, H.; Yongmin, K.; Sang-Yong, J. Optimization of WFSM for EV Propulsion Considering Regenerative Braking Based on Driving Conditions. In Proceedings of the 2023 26th International Conference on Electrical Machines and Systems (ICEMS), Zhuhai, China, 5–8 November 2023; pp. 1370–1373. [Google Scholar]
  111. Subramaniyam, K.V.; Subramanian, S.C. Impact of regenerative braking torque blend-out characteristics on electrified heavy road vehicle braking performance. Veh. Syst. Dyn. 2021, 59, 269–294. [Google Scholar] [CrossRef]
  112. Ma, Z.; Sun, D. Energy recovery strategy based on ideal braking force distribution for regenerative braking system of a four-wheel drive electric vehicle. IEEE Access 2020, 8, 136234–136242. [Google Scholar] [CrossRef]
  113. Mehrotra, S.; Ray, R.K.; Pandey, D.; Naithani, H. Comparison Between State of art Performance of GaN and SiC Converters for Electric Vehicle Application; Technical Report; SAE Technical Paper: Warrendale, PA, USA, 2024. [Google Scholar]
  114. Wang, Y.C.; Lee, C.S.; Kuo, P.C.; Lin, Y.L. Overcurrent protection design, failure mode and effect analysis of an electric vehicle inverter. In Proceedings of the 2016 IEEE International Conference on Industrial Technology (ICIT), Taipei, Taiwan, 14–17 March 2016; pp. 1287–1292. [Google Scholar]
  115. Subramaniyam, K.V.; Subramanian, S.C. Electrified vehicle wheel slip control using responsiveness of regenerative braking. IEEE Trans. Veh. Technol. 2021, 70, 3208–3217. [Google Scholar] [CrossRef]
  116. Ziadia, M.; Kelouwani, S.; Amamou, A.; Agbossou, K. An adaptive regenerative braking strategy design based on naturalistic regeneration performance for intelligent vehicles. IEEE Access 2023, 11, 99573–99588. [Google Scholar] [CrossRef]
  117. Geraee, S.; Mohammadbagherpoor, H.; Shafiei, M.; Valizadeh, M.; Montazeri, F.; Feyzi, M.R. Regenerative braking of electric vehicle using a modified direct torque control and adaptive control theory. Comput. Electr. Eng. 2018, 69, 85–97. [Google Scholar] [CrossRef]
  118. EVKX. Adaptive Regen. 2023. Available online: https://evkx.net/technology/regen/ (accessed on 30 May 2025).
  119. Ziadia, M.; Kelouwani, S.; Amamou, A.; Agbossou, K. Weather-Adaptive Regenerative Braking Strategy Based on Driving Style Recognition for Intelligent Electric Vehicles. Sensors 2025, 25, 1175. [Google Scholar] [CrossRef]
  120. Chen, Q.; Lv, Z.; Xu, W.; Shu, Q.; Liu, S.; Xu, L. Regenerative braking control strategy based on pavement recognition controller for electric vehicle. Energy Technol. 2023, 11, 2300299. [Google Scholar] [CrossRef]
  121. Liu, H.; Lei, Y.; Fu, Y.; Li, X. Multi-objective optimization study of regenerative braking control strategy for range-extended electric vehicle. Appl. Sci. 2020, 10, 1789. [Google Scholar] [CrossRef]
  122. Xin, Y.; Zhang, T.; Zhang, H.; Zhao, Q.; Zheng, J.; Wang, C. Fuzzy Logic Optimization of Composite Brake Control Strategy for Load-Isolated Electric Bus. Math. Probl. Eng. 2019, 2019, 9735368. [Google Scholar] [CrossRef]
  123. Chen, Y.C.; Tu, C.H.; Lin, C.L. Integrated electromagnetic braking/driving control of electric vehicles using fuzzy inference. IET Electr. Power Appl. 2019, 13, 1014–1021. [Google Scholar] [CrossRef]
  124. De Pinto, S.; Camocardi, P.; Chatzikomis, C.; Sorniotti, A.; Bottiglione, F.; Mantriota, G.; Perlo, P. On the comparison of 2-and 4-wheel-drive electric vehicle layouts with central motors and single-and 2-speed transmission systems. Energies 2020, 13, 3328. [Google Scholar] [CrossRef]
  125. Ju, J.; Li, W.; Liu, Y.; Zhang, C. Research on bifurcation and control of electromechanical coupling torsional vibration for wheel-side direct-driven transmission system. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 2021, 235, 93–104. [Google Scholar] [CrossRef]
  126. Deepak, K.; Frikha, M.A.; Benômar, Y.; El Baghdadi, M.; Hegazy, O. In-wheel motor drive systems for electric vehicles: State of the art, challenges, and future trends. Energies 2023, 16, 3121. [Google Scholar] [CrossRef]
  127. Raman, S.R.; Cheng, K.W.; Xue, X.D.; Fong, Y.C.; Cheung, S. Hybrid energy storage system with vehicle body integrated super-capacitor and li-ion battery: Model, design and implementation, for distributed energy storage. Energies 2021, 14, 6553. [Google Scholar] [CrossRef]
  128. Liu, F. A PMSM fuzzy logic regenerative braking control strategy for electric vehicles. J. Intell. Fuzzy Syst. 2021, 41, 4873–4881. [Google Scholar] [CrossRef]
  129. Zhu, Y.; Wu, H.; Zhen, C. Regenerative braking control under sliding braking condition of electric vehicles with switched reluctance motor drive system. Energy 2021, 230, 120901. [Google Scholar] [CrossRef]
  130. Li, W.; Xu, H.; Liu, X.; Wang, Y.; Zhu, Y.; Lin, X.; Wang, Z.; Zhang, Y. Regenerative braking control strategy for pure electric vehicles based on fuzzy neural network. Ain Shams Eng. J. 2024, 15, 102430. [Google Scholar] [CrossRef]
  131. Pugi, L.; Favilli, T.; Berzi, L.; Locorotondo, E.; Pierini, M. Brake blending and torque vectoring of road electric vehicles: A flexible approach based on smart torque allocation. Int. J. Electr. Hybrid Veh. 2020, 12, 87–115. [Google Scholar] [CrossRef]
  132. Cao, J.; Cao, B.; Chen, W.; Xu, P. Neural network self-adaptive PID control for driving and regenerative braking of electric vehicle. In Proceedings of the 2007 IEEE International Conference on Automation and Logistics, Jinan, China, 18–21 August 2007; pp. 2029–2034. [Google Scholar]
  133. Ruz-Canul, M.A.; Djilali, L.; Ruz-Hernandez, J.A.; Sanchez-Camperos, E.N. Neural Sliding mode control of a regenerative braking system for electric vehicles. J. Innov. Des 2022, 6, 6–15. [Google Scholar] [CrossRef]
  134. Wang, M.; Yu, H.; Dong, G.; Huang, M. Dual-mode adaptive cruise control strategy based on model predictive control and neural network for pure electric vehicles. In Proceedings of the 2019 5th International Conference on Transportation Information and Safety (ICTIS), Liverpool, UK, 14–17 July 2019; pp. 1220–1225. [Google Scholar]
  135. Cheng, C.H.; Ye, J.X. GA-based neural network for energy recovery system of the electric motorcycle. Expert Syst. Appl. 2011, 38, 3034–3039. [Google Scholar] [CrossRef]
  136. Gounis, K.; Bassiliades, N. Intelligent momentary assisted control for autonomous emergency braking. Simul. Model. Pract. Theory 2022, 115, 102450. [Google Scholar] [CrossRef]
  137. Shijil, P.; Sindhu, M. Braking control strategies based on single-pedal regenerative braking and neural network for Electric Vehicles. In Proceedings of the 2021 IEEE International Power and Renewable Energy Conference (IPRECON), Kollam, India, 24–26 September 2021; pp. 1–7. [Google Scholar]
  138. Kanchev, H.; Hinov, N.; Gilev, B.; Francois, B. Modelling and control by neural network of electric vehicle traction system. Elektron. Ir Elektrotech. 2018, 24, 23–28. [Google Scholar] [CrossRef]
  139. Ge, G.; Wang, T.; Lv, Y.; Zou, X.; Song, W.; Zhang, G. Energy-efficient braking torque distribution strategy of rear-axle drive commercial ev based on fuzzy neural network. SAE Int. J. Adv. Curr. Pract. Mobil. 2021, 3, 2136–2145. [Google Scholar] [CrossRef]
  140. Quintero-Manríquez, E.; Sanchez, E.N.; Antonio-Toledo, M.E.; Muñoz, F. Neural control of an induction motor with regenerative braking as electric vehicle architecture. Eng. Appl. Artif. Intell. 2021, 104, 104275. [Google Scholar] [CrossRef]
  141. Zhang, Y.; Wang, W.; Yang, C.; Han, L.; Zhang, Z.; Liu, J. An effective regenerative braking strategy based on the combination algorithm of particle swarm optimization and ant colony optimization for electrical vehicle. In Proceedings of the 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), Vancouver, Canada, 12–14 June 2019; pp. 1905–1910. [Google Scholar]
  142. Chu, L.; Li, J.; Guo, Z.; Jiang, Z.; Li, S.; Du, W.; Wang, Y.; Guo, C. RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control. Sensors 2024, 24, 3076. [Google Scholar] [CrossRef]
  143. Aparow, V.R.; Ahmad, F.; Hudha, K.; Jamaluddin, H. Modelling and PID control of antilock braking system with wheel slip reduction to improve braking performance. Int. J. Veh. Saf. 2013, 6, 265–296. [Google Scholar] [CrossRef]
  144. Hu, D.; Li, G.; Deng, F. Gain-Scheduled Model Predictive Control for a Commercial Vehicle Air Brake System. Processes 2021, 9, 899. [Google Scholar] [CrossRef]
  145. Yong, J.; Dong, Y.; Zhang, Z.; Feng, N.; Li, W. Integrated control of anti-lock and regenerative brak-ing for in-wheel-motor-driven electric vehicles. Complex Eng. Syst. 2024, 4, 2. [Google Scholar] [CrossRef]
  146. Tavernini, D.; Metzler, M.; Gruber, P.; Sorniotti, A. Explicit nonlinear model predictive control for electric vehicle traction control. IEEE Trans. Control Syst. Technol. 2018, 27, 1438–1451. [Google Scholar] [CrossRef]
  147. Mei, M.; Cheng, S.; Mu, H.; Pei, Y.; Li, B. Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles. Front. Robot. AI 2023, 10, 1078253. [Google Scholar] [CrossRef] [PubMed]
  148. Sakri, D.; Laib, H.; Farhi, S.E.; Golea, N. Sliding mode approach for control and observation of a three phase AC-DC pulse-width modulation rectifier. Electr. Eng. Electromech. 2023, 2, 49–56. [Google Scholar] [CrossRef]
  149. Mohammed, H.A.; Alsammak, A.N.B. An intelligent hybrid control system using ANFIS-optimization for scalar control of an induction motor. J. Eur. Des. Syst. Autom. 2023, 56, 857. [Google Scholar] [CrossRef]
  150. El Idrissi, A.L.; Bouchnaif, J.; Mokhtari, M.; Bensliman, A. Comparative Study Between PI Speed Control and Sliding Mode Control of BLDC Motor. In Proceedings of the Advances in Smart Technologies Applications and Case Studies: Selected Papers from the First International Conference on Smart Information and Communication Technologies, SmartICT 2019, Saidia, Morocco, 26–28 September 2019; pp. 309–317. [Google Scholar]
  151. Rajendran, S.; Spurgeon, S.; Tsampardoukas, G.; Hampson, R. Intelligent sliding mode scheme for regenerative braking control. IFAC-PapersOnLine 2018, 51, 334–339. [Google Scholar] [CrossRef]
  152. He, L.; Ye, W.; He, Z.; Song, K.; Shi, Q. A combining sliding mode control approach for electric motor anti-lock braking system of battery electric vehicle. Control Eng. Pract. 2020, 102, 104520. [Google Scholar] [CrossRef]
  153. Parra, A.; Zubizarreta, A.; Pérez, J. An energy efficient intelligent torque vectoring approach based on fuzzy logic controller and neural network tire forces estimator. Neural Comput. Appl. 2021, 33, 9171–9184. [Google Scholar] [CrossRef]
  154. Khan, M.S.; Ahmad, I.; Armaghan, H.; Ali, N. Backstepping sliding mode control of FC-UC based hybrid electric vehicle. IEEE Access 2018, 6, 77202–77211. [Google Scholar] [CrossRef]
  155. Yuan, J.; Gao, S.; Wang, L.; Xiu, G. Sliding Mode Control for Two-Degree-of-Freedom Fractional Zener Oscillator. J. Dyn. Syst. Meas. Control 2022, 144, 021004. [Google Scholar] [CrossRef]
  156. Zhang, L.; Cai, X. Control strategy of regenerative braking system in electric vehicles. Energy Procedia 2018, 152, 496–501. [Google Scholar] [CrossRef]
  157. Ok, S.; Xu, Z.; Lee, D.H. A sensorless speed control of high-speed bldc motor using variable slope smo. IEEE Trans. Ind. Appl. 2023, 60, 3221–3228. [Google Scholar] [CrossRef]
  158. Prabhu, N.; Thirumalaivasan, R.; Ashok, B. Critical review on torque ripple sources and mitigation control strategies of BLDC motors in electric vehicle applications. IEEE Access 2023, 11, 115699–115739. [Google Scholar] [CrossRef]
  159. Kheel, A.M.; Al-Shamaa, N.K.; Hawas, M.N. Sliding mode controller enchancement for speed control of BLDC motor based on dragonfly algoritm. In Proceedings of the 2023 International Conference on Converging Technology in Electrical and Information Engineering (ICCTEIE), Bandar Lampung, Indonesia, 25–26 October 2023; pp. 135–141. [Google Scholar]
  160. Can, K.; Orman, K.; BAŞÇİ, A.; Derdiyok, A. A fractional-order sliding mode controller design for trajectory tracking control of an unmanned aerial vehicle. Elektron. Ir Elektrotech. 2020, 26, 4–10. [Google Scholar]
  161. Lee, H.; Utkin, V.I. Chattering suppression methods in sliding mode control systems. Annu. Rev. Control 2007, 31, 179–188. [Google Scholar] [CrossRef]
  162. Yang, C.; Liu, K.; Jiao, X.; Wang, W.; Chen, R.; You, S. An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles. Energy 2022, 239, 122120. [Google Scholar] [CrossRef]
  163. Li, K.; Ding, J.; Sun, X.; Tian, X. Overview of sliding mode control technology for permanent magnet synchronous motor system. IEEE Access 2024, 12, 71685–71704. [Google Scholar] [CrossRef]
  164. Bazi, S.; Benzid, R.; Bazi, Y.; Rahhal, M.M.A. A fast firefly algorithm for function optimization: Application to the control of BLDC motor. Sensors 2021, 21, 5267. [Google Scholar] [CrossRef]
  165. Li, L.; Zhang, Y.; Yang, C.; Yan, B.; Martinez, C.M. Model predictive control-based efficient energy recovery control strategy for regenerative braking system of hybrid electric bus. Energy Convers. Manag. 2016, 111, 299–314. [Google Scholar] [CrossRef]
  166. Padilla, G.; Weiland, S.; Donkers, M. A global optimal solution to the eco-driving problem. IEEE Control Syst. Lett. 2018, 2, 599–604. [Google Scholar] [CrossRef]
  167. Dongre, N.R.; Sindekar, A. Optimization of energy consumption in electric traction system by using interior point method. IOSR J. Electr. Electron. Eng. 2018, 13, 09–15. [Google Scholar]
  168. Maamria, D.; Gillet, K.; Colin, G.; Chamaillard, Y.; Nouillant, C. Optimal predictive eco-driving cycles for conventional, electric, and hybrid electric cars. IEEE Trans. Veh. Technol. 2019, 68, 6320–6330. [Google Scholar] [CrossRef]
  169. Kim, Y.; Figueroa-Santos, M.; Prakash, N.; Baek, S.; Siegel, J.B.; Rizzo, D.M. Co-optimization of speed trajectory and power management for a fuel-cell/battery electric vehicle. Appl. Energy 2020, 260, 114254. [Google Scholar] [CrossRef]
  170. Thibault, L.; De Nunzio, G.; Sciarretta, A. A unified approach for electric vehicles range maximization via eco-routing, eco-driving, and energy consumption prediction. IEEE Trans. Intell. Veh. 2018, 3, 463–475. [Google Scholar] [CrossRef]
  171. Ning, X.; Wang, J.; Yin, Y.; Shangguan, J.; Bao, N.; Li, N. Regenerative braking algorithm for parallel hydraulic hybrid vehicles based on fuzzy Q-Learning. Energies 2023, 16, 1895. [Google Scholar] [CrossRef]
  172. Maia, R.; Mendes, J.; Araújo, R.; Silva, M.; Nunes, U. Regenerative braking system modeling by fuzzy Q-Learning. Eng. Appl. Artif. Intell. 2020, 93, 103712. [Google Scholar] [CrossRef]
  173. Karan, V.K.; Alam, A.; Thakur, A. Hybrid control using fuzzy logic and adaptive space vector modulation for reduction of torque ripples in PM-BLDC motor drive. J. Eng. Appl. Sci. 2023, 70, 66. [Google Scholar] [CrossRef]
  174. Min, K.; Sim, G.; Ahn, S.; Park, I.; Yoo, S.; Youn, J. Multi-level deceleration planning based on reinforcement learning algorithm for autonomous regenerative braking of EV. World Electr. Veh. J. 2019, 10, 57. [Google Scholar] [CrossRef]
  175. Yin, Y.; Zhang, L.; Zhan, S.; Ma, Y.; Ma, S. A novel state energy spatialization regenerative braking control strategy based on Q-learning algorithm for a super-mild hybrid electric vehicle. Int. J. Green Energy 2021, 18, 1263–1276. [Google Scholar] [CrossRef]
  176. Nadeau, J.; Micheau, P.; Boisvert, M. Collaborative control of a dual electro-hydraulic regenerative brake system for a rear-wheel-drive electric vehicle. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 2019, 233, 1035–1046. [Google Scholar] [CrossRef]
  177. Kusuma, C.F.; Budiman, B.A.; Nurprasetio, I.P.; Islameka, M.; Masyhur, A.H.; Aziz, M.; Reksowardojo, I.K. Energy management system of electric bus equipped with regenerative braking and range extender. Int. J. Automot. Technol. 2021, 22, 1651–1664. [Google Scholar] [CrossRef]
  178. Zhao, X.; Li, L.; Wang, X.; Mei, M.; Liu, C.; Song, J. Braking force decoupling control without pressure sensor for a novel series regenerative brake system. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 2019, 233, 1750–1766. [Google Scholar] [CrossRef]
  179. Li, S.; Yu, B.; Feng, X. Research on braking energy recovery strategy of electric vehicle based on ECE regulation and I curve. Sci. Prog. 2020, 103, 0036850419877762. [Google Scholar] [CrossRef] [PubMed]
  180. Pei, X.; Pan, H.; Chen, Z.; Guo, X.; Yang, B. Coordinated control strategy of electro-hydraulic braking for energy regeneration. Control Eng. Pract. 2020, 96, 104324. [Google Scholar] [CrossRef]
  181. Zhang, Y.; Wang, W.; Xiang, C.; Yang, C.; Peng, H.; Wei, C. A swarm intelligence-based predictive regenerative braking control strategy for hybrid electric vehicle. Veh. Syst. Dyn. 2022, 60, 973–997. [Google Scholar] [CrossRef]
  182. Cheng, S.; Li, L.; Chen, X.; Fang, S.N.; Wang, X.Y.; Wu, X.H.; Li, W.B. Longitudinal autonomous driving based on game theory for intelligent hybrid electric vehicles with connectivity. Appl. Energy 2020, 268, 115030. [Google Scholar] [CrossRef]
  183. Qian, G.; Fu, T.; Sun, L. Research on the fuel consumption conservation potential of ADAS on passenger cars. In Proceedings of the E3S Web of Conferences; EDP Sciences: Wuhan, China, 2021; Volume 268, pp. 1–23. [Google Scholar]
  184. Simchon, L.; Rabinovici, R. Real-time implementation of green light optimal speed advisory for electric vehicles. Vehicles 2020, 2, 35–54. [Google Scholar] [CrossRef]
  185. Yan, Y.; Han, D.; Shen, T.; Wang, Z.; Wang, J.; Yin, G. Velocity Trajectory Planning of Electric Vehicles with Consideration of the Passenger’s Individual Preferences. In Proceedings of the 2022 6th CAA International Conference on Vehicular Control and Intelligence (CVCI), Nanjing, China, 28–30 October 2022; pp. 1–6. [Google Scholar]
  186. Li, N.; Yang, J.; Jiang, J.; Hong, F.; Liu, Y.; Ning, X. Study on speed planning of signalized intersections with autonomous vehicles considering regenerative braking. Processes 2022, 10, 1414. [Google Scholar] [CrossRef]
  187. Jiang, X.; Zhang, J.; Wang, B. Energy-efficient driving for adaptive traffic signal control environment via explainable reinforcement learning. Appl. Sci. 2022, 12, 5380. [Google Scholar] [CrossRef]
  188. Thakur, A.; Khan, K.U.Z.; Gupta, J.; Gupta, K.; Vats, M.; Mishra, C.; Roy, A. Impacts of Regenerative Braking on Li-Ion Battery. In Advances in Electromechanical Technologies: Select Proceedings of TEMT 2019; Springer: Singapore, 2020; pp. 831–841. [Google Scholar]
  189. Wang, W.; Yuan, B.; Sun, Q.; Wennersten, R. Analysis and Modeling of Calendar Aging and Cycle Aging of LiCoO2/Graphite Cells. J. Therm. Sci. 2024, 33, 1109–1118. [Google Scholar] [CrossRef]
  190. Keil, P.; Jossen, A. Aging of lithium-ion batteries in electric vehicles: Impact of regenerative braking. World Electr. Veh. J. 2015, 7, 41–51. [Google Scholar] [CrossRef]
  191. Keil, P.; Jossen, A. Impact of dynamic driving loads and regenerative braking on the aging of lithium-ion batteries in electric vehicles. J. Electrochem. Soc. 2017, 164, A3081. [Google Scholar] [CrossRef]
  192. Şen, M.; Özcan, M.; Eker, Y.R. Fuzzy logic-based energy management system for regenerative braking of electric vehicles with hybrid energy storage system. Appl. Sci. 2024, 14, 3077. [Google Scholar] [CrossRef]
  193. Naseri, F.; Farjah, E.; Ghanbari, T. An efficient regenerative braking system based on battery/supercapacitor for electric, hybrid, and plug-in hybrid electric vehicles with BLDC motor. IEEE Trans. Veh. Technol. 2016, 66, 3724–3738. [Google Scholar] [CrossRef]
  194. Wu, J.; Wang, X.; Li, L.; Qin, C.; Du, Y. Hierarchical control strategy with battery aging consideration for hybrid electric vehicle regenerative braking control. Energy 2018, 145, 301–312. [Google Scholar] [CrossRef]
  195. Ashok, B.; Kannan, C.; Deepak, C.; Ramesh, R.; Narendhra, T.M.; Farrag, M.E.; Ashok, S.D.; Vignesh, R.; Saiteja, P.; Kavitha, C. Model based integrated control strategy for effective brake energy recovery to extend battery longevity in electric two wheelers. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 2024, 238, 2843–2864. [Google Scholar] [CrossRef]
  196. Wasim, M.S.; Habib, S.; Amjad, M.; Bhatti, A.R.; Ahmed, E.M.; Qureshi, M.A. Battery-ultracapacitor hybrid energy storage system to increase battery life under pulse loads. IEEE Access 2022, 10, 62173–62182. [Google Scholar] [CrossRef]
  197. Ohno, T.; Haider, M.; Mirić, S. Performance review of state-of-the-art 1.2 kV SiC devices based on experimental figures-of-merit. E+ I Elektrotech. Informationstech. 2025, 142, 151–163. [Google Scholar] [CrossRef]
  198. Bhalla, A. Silicon carbide semiconductors with wide bandgap for electric vehicles. ATZelectronics Worldw. 2021, 16, 18–21. [Google Scholar] [CrossRef]
  199. Yu, H.; Tu, H.; Lukic, S. A passivity-based decentralized control strategy for current-controlled inverters in ac microgrids. In Proceedings of the 2018 IEEE Applied Power Electronics Conference and Exposition (APEC), San Antonio, TX, USA, 4–8 March 2018; pp. 1399–1406. [Google Scholar]
  200. Lin, H.; Xu, J. Performance Evaluation of SiC-Based Two-Level VSIs with Generalized Carrier-Based PWM Strategies in Motor Drive Applications. Electronics 2022, 11, 4136. [Google Scholar] [CrossRef]
  201. Cai, K.; Xiao, J.; Yang, Z.; Hu, R. Three-Level All-SiC High-Frequency High-Voltage Plasma Power Supply System. Energies 2025, 18, 1617. [Google Scholar] [CrossRef]
  202. Mirza, A.Y.; Bazzi, A.; Nguyen, H.H.; Cao, Y. Motor stator insulation stress due to multilevel inverter voltage output levels and power quality. Energies 2022, 15, 4091. [Google Scholar] [CrossRef]
  203. Kshatri, S.S.; Dhillon, J.; Mishra, S.; Haghighi, A.T.; Hunt, J.D.; Patro, E.R. Comparative Reliability Assessment of Hybrid Si/SiC and Conventional Si Power Module Based PV Inverter Considering Mission Profile of India and Denmark Locations. Energies 2022, 15, 8612. [Google Scholar] [CrossRef]
  204. Chevinly, J.; Rad, S.S.; Nadi, E.; Proca, B.; Wolgemuth, J.; Calabro, A.; Zhang, H.; Lu, F. Gallium nitride (GaN) based high-power multilevel h-bridge inverter for wireless power transfer of electric vehicles. In Proceedings of the 2024 IEEE Transportation Electrification Conference and Expo (ITEC), Chicago, IL, USA, 19–21 June 2024; pp. 1–5. [Google Scholar]
  205. Soomro, H.A.; Khir, M.H.B.M.; Zulkifli, S.A.B.; Abro, G.E.M.; Abualnaeem, M.M. Applications of wide bandgap semiconductors in electric traction drives: Current trends and future perspectives. Results Eng. 2025, 26, 104679. [Google Scholar] [CrossRef]
  206. Faiz, J.; Parvin, F. Trends and Technical Advancements on High-Efficiency Electric Motors: A Review. In Efficiency in Complex Systems: Self-Organization Towards Increased Efficiency; Springer Nature: Cham, Switzerland, 2022; pp. 81–95. [Google Scholar]
  207. Ulrich, L. Startup promises an electric-motor revolution: Linear labs says its superefficient motor could power cars, robots, and more. IEEE Spectr. 2019, 56, 10–11. [Google Scholar] [CrossRef]
  208. Aloeyi, E.F.; Ali, N.; Wang, Q. A review of in-wheel motors for electric vehicle propulsion. In Proceedings of the 2022 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), Haining, China, 28–31 October 2022; pp. 1–6. [Google Scholar]
  209. Paul, S.; Lee, J.G.; Han, P.W.; Chang, J.; Luong, X.T. Design Consideration of Rectangular Conductors and Slot Wedge for AC Winding Loss Reduction in High Speed Train Traction Motor. IEEE Trans. Veh. Technol. 2024, 73, 16543–16556. [Google Scholar] [CrossRef]
  210. Zhao, J.; Wang, Q.; Han, Q. Reducing torque ripple through innovative configuration of permanent magnet based on air gap field modulation theory in a novel axial flux reversal permanent magnet machine. IEEE Access 2024, 12, 54901–54912. [Google Scholar] [CrossRef]
  211. Truque Bertran, A. Design of a Vehicle for the World Solar Challenge. Bachelor’s Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, 2023. [Google Scholar]
  212. Nava, F. Sizing, Optimisation and Thermal Evaluation of an Axial Flux Permanent Magnet Motor in Comparison with a Radial Flux Motor. 2022. Available online: https://hdl.handle.net/10589/214963 (accessed on 23 April 2025).
  213. Gmyrek, Z. Optimal Electric Motor Designs of Light Electric Vehicles: A Review. Energies 2024, 17, 3462. [Google Scholar] [CrossRef]
  214. Farrokh, F.; Vahedi, A.; Torkaman, H.; Banejad, M. Design and comparison of dual-stator axial-field flux-switching permanent magnet motors for electric vehicle application. IET Electr. Syst. Transp. 2023, 13, e12074. [Google Scholar] [CrossRef]
  215. Cavus, M.; Dissanayake, D.; Bell, M. Next generation of electric vehicles: AI-driven approaches for predictive maintenance and battery management. Energies 2025, 18, 1041. [Google Scholar] [CrossRef]
  216. Sarker, R.; Nair, V.V.; Chinta, S. Energy Harvesting Techniques for Extending the Range of Electric Vehicles. Int. J. Emerg. Trends Comput. Sci. Inf. Technol. 2025, 6, 47–55. [Google Scholar]
  217. Jamil, H.; Naqvi, S.S.A.; Iqbal, N.; Khan, M.A.; Qayyum, F.; Muhammad, F.; Khan, S.; Kim, D.H. Analysis on the driving and braking control logic algorithm for mobility energy efficiency in electric vehicle. Smart Grids Sustain. Energy 2024, 9, 12. [Google Scholar] [CrossRef]
  218. Guo, J.; Li, W.; Wang, J.; Luo, Y.; Li, K. Safe and energy-efficient car-following control strategy for intelligent electric vehicles considering regenerative braking. IEEE Trans. Intell. Transp. Syst. 2021, 23, 7070–7081. [Google Scholar] [CrossRef]
  219. ISO 2631-1:1997; Mechanical Vibration and Shock—Evaluation of Human Exposure to Whole-Body Vibration—Part 1: General Requirements. International Organization for Standardization: Geneva, Switzerland, 1997.
Figure 2. Classification of bidirectional DC-DC converter topologies.
Figure 2. Classification of bidirectional DC-DC converter topologies.
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Figure 3. (a) Bidirectional buck and boost converter topology. (b) Bidirectional buck–boost converter topology.
Figure 3. (a) Bidirectional buck and boost converter topology. (b) Bidirectional buck–boost converter topology.
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Figure 4. Bidirectional Ćuk converter topology.
Figure 4. Bidirectional Ćuk converter topology.
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Figure 5. Bidirectional SEPIC/Zeta converter topology.
Figure 5. Bidirectional SEPIC/Zeta converter topology.
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Figure 6. Cascaded bidirectional buck–boost converter topology.
Figure 6. Cascaded bidirectional buck–boost converter topology.
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Figure 7. Switched-capacitor bidirectional DC-DC converter topology.
Figure 7. Switched-capacitor bidirectional DC-DC converter topology.
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Figure 8. Interleaved bidirectional DC-DC converter topology.
Figure 8. Interleaved bidirectional DC-DC converter topology.
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Figure 9. Isolated bidirectional flyback converter.
Figure 9. Isolated bidirectional flyback converter.
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Figure 10. Isolated bidirectional push–pull converter topology.
Figure 10. Isolated bidirectional push–pull converter topology.
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Figure 11. Isolated bidirectional forward converter topology.
Figure 11. Isolated bidirectional forward converter topology.
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Figure 12. Dual active bridge (DAB) converter topology.
Figure 12. Dual active bridge (DAB) converter topology.
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Figure 13. Dual half-bridge bidirectional isolated converter.
Figure 13. Dual half-bridge bidirectional isolated converter.
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Figure 14. Basic topologies of multilevel converters: Neutral Point Clamped (NPC), Flying Capacitor (FC), and Cascaded H-Bridge (CHB).
Figure 14. Basic topologies of multilevel converters: Neutral Point Clamped (NPC), Flying Capacitor (FC), and Cascaded H-Bridge (CHB).
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Figure 15. Regenerative braking cycle.
Figure 15. Regenerative braking cycle.
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Figure 16. A fuzzy logic-based control system for regenerative braking in EVs.
Figure 16. A fuzzy logic-based control system for regenerative braking in EVs.
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Figure 17. A neural network-based control system for regenerative braking in EVs.
Figure 17. A neural network-based control system for regenerative braking in EVs.
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Table 1. Comparison of non-isolated bidirectional converter topologies with their applications.
Table 1. Comparison of non-isolated bidirectional converter topologies with their applications.
TopologyInductorsCapacitorsSwitchesApplication and Key Features
Buck and Boost122Used for auxiliary power and battery management. Enables step-down and step-up operation with low complexity.
Buck–Boost222Applied in hybrid storage systems and voltage matching. Supports both step-up and step-down with polarity inversion.
Ćuk234Utilized in energy interface applications. Provides continuous input/output currents, low EMI, and ripple suppression.
Zeta/Sepic232Used in battery and supercapacitor integration. Offers positive output polarity, continuous currents, and low ripple.
Cascaded124Suitable for modular multi-source energy management. Delivers high voltage gain and enhanced dynamic load response.
Switched Capacitor034Deployed in compact and lightweight EV power stages. Utilizes inductor-less charge redistribution with high efficiency.
Interleavednn 2 n Applied in high-power drive systems and fast charging. Enables ripple reduction, phase shedding, and ZVS/ZCS operation.
Multilevel0 n ( n + 1 ) / 2 n ( n + 1 ) Used in high-voltage and high-power EV systems. Provides inductor-less design with modular scalability and low EMI.
Table 2. Comparison of isolated bidirectional converter topologies with applications.
Table 2. Comparison of isolated bidirectional converter topologies with applications.
TopologyInductorsCapacitorsSwitchesApplication and Key Features
Flyback022Used for low-power auxiliary applications in EVs. Compact and low cost, with transformer-based isolation.
Push–Pull114Employed in moderate-power EV converters. Provides symmetrical drive with continuous output current. Requires center-tap transformer.
Forward113Suited for mid-power onboard chargers. Offers compact and efficient conversion with transformer reset and reduced switching stress.
Dual Active Bridge028Used for battery-grid interface and V2G systems. Modular structure enables full-range ZVS and high efficiency.
Dual Half-Bridge044Utilized in compact and high-frequency EV converters. Features fewer switches and simpler control than DAB with interleaving for stress reduction.
LLC Resonant128Applied in isolated DC-DC converters and onboard chargers. Resonant behavior supports ZVS/ZCS, low EMI, and high efficiency across wide load ranges.
Table 3. Comparative analysis of regenerative braking effectiveness across EV types.
Table 3. Comparative analysis of regenerative braking effectiveness across EV types.
Vehicle TypeTypical MassRange ExtensionRemarks
E-Bikes and Scooters<100 kg5–10%Limited recovery due to low inertia. Effective in urban or hilly conditions with frequent stops.
Passenger Cars1000–2500 kg10–25%Greater braking energy supports recovery. Range gain varies with terrain and control logic.
Electric Buses and Trucks>5000 kgUp to 30%High mass enables substantial energy recovery. Suited for routes with repeated acceleration and braking.
Hybrid Storage EVsVariesUp to 35%Dual storage (battery + supercapacitor) enhances recovery speed and reduces stress on batteries.
Table 4. Comparison of energy storage technologies for regenerative braking.
Table 4. Comparison of energy storage technologies for regenerative braking.
Storage TypePower DensityEnergy DensityRemarks
BatteryModerateHighHigh energy capacity but limited in fast response. Frequent cycling shortens lifespan.
SupercapacitorHighLow–ModerateIdeal for quick charge/discharge. Suitable for urban EVs with frequent braking events.
HESSHighHighCombines strengths of batteries and supercapacitors. Enhances regeneration efficiency and durability.
FESSVery HighModerateMechanical storage with rapid response. Low maintenance and effective in short, high-power applications.
Table 5. Comparison of control strategies for regenerative braking: advantages, disadvantages, computational complexity, and application scenarios.
Table 5. Comparison of control strategies for regenerative braking: advantages, disadvantages, computational complexity, and application scenarios.
Control StrategyAdvantagesDisadvantagesComputational
Complexity and Application
Fuzzy Logic Control [15,122,123,128]Cost-effective. User-friendly and interpretable even for non-experts.Relies heavily on human expertise. Manual tuning is time-consuming for complex systems.Low complexity. Suitable for real-time implementation in embedded systems for small to medium EVs.
Neural Network [136,139,140]Can produce output even with incomplete input. Learns from past data patterns.Requires trial-and-error for network design. Demands processors with parallel capability.Medium to high complexity. Often used for adaptive learning in smart EVs and HESS.
Model Predictive Control [141,142,165]Handles multi-variable systems. Suitable for time-delay and open-loop unstable processes.Requires accurate system modeling. Derivation of control law is complex.High computational load. Suitable for centralized energy management in high-end EVs.
Sliding Mode Control [148,151,155]Robust against disturbances and model uncertainties. Effective for nonlinear systems.Chattering effect. Limited applicability in multi-input systems.Moderate complexity. Effective in traction control and motor braking in dynamic environments.
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Naseem, H.; Seok, J.-K. Recent Advances in Bidirectional Converters and Regenerative Braking Systems in Electric Vehicles. Actuators 2025, 14, 347. https://doi.org/10.3390/act14070347

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Naseem H, Seok J-K. Recent Advances in Bidirectional Converters and Regenerative Braking Systems in Electric Vehicles. Actuators. 2025; 14(7):347. https://doi.org/10.3390/act14070347

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Naseem, Hamid, and Jul-Ki Seok. 2025. "Recent Advances in Bidirectional Converters and Regenerative Braking Systems in Electric Vehicles" Actuators 14, no. 7: 347. https://doi.org/10.3390/act14070347

APA Style

Naseem, H., & Seok, J.-K. (2025). Recent Advances in Bidirectional Converters and Regenerative Braking Systems in Electric Vehicles. Actuators, 14(7), 347. https://doi.org/10.3390/act14070347

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