Search Results (136)

This paper presents an experimental study of vibration energy recovery from a horizontal seat suspension system in which a brushless direct current (BLDC) motor is used as both an active force actuator and a controllable regenerative braking element. The novelty of the study lies in the experimental validation of an active/regenerative switching strategy for a horizontal seat suspension and in the quantitative comparison of passive, fully active and regenerative operating modes under random vibration excitation and different inertial loads. The proposed system was evaluated using transmissibility functions, seat effective amplitude transmissibility (SEAT) factors, suspension travel, and electrical quantities generated in the braking branch. The results show that the fully active mode provides the highest vibration attenuation, whereas the regenerative mode reduces the SEAT factor compared with the passive suspension while simultaneously producing measurable electrical power in the braking resistor network. The maximum measured electrical power in the braking branch reached 7.692 W for the WN3 excitation signal and an 80 kg load. The obtained results confirm the practical potential of regenerative braking for potentially reducing the net energy demand of active seat suspension systems, while also highlighting the trade-off between vibration attenuation, suspension travel, and recoverable electrical power. Full article

This paper proposes a control method for electric vehicles’ powertrains that concentrates on improving the efficiency of the drivetrain as a whole by targeting regenerative energy recovery. The design of the system is based on a traditional six transistor voltage source inverter using an asymmetric control scheme together with a hybrid CNN-TD3 controller for speed control purposes. The asymmetric control method distinguishes the dynamics of the two operating modes of the inverter, that is, the traction and regenerative braking modes, by controlling the gain values based on the operating mode of the system. The TD3 algorithm adjusts the values of three continuous control parameters, Kpv, Kiv, and Ks, in real time. A multi-objective reward function aims to maximize the system’s energy efficiency, regenerative energy recovery efficiency, speed control accuracy, driving comfort, current harmonics reduction, and battery safety. Simulations performed using the WLTP Class 3 driving cycle show energy efficiency of 15.3% (25.2 to 21.44 kWh/100 km), 59.5% speed regulation error reduction, 19.6% increase in regenerative recovery efficiency from 65.2% to 77.34%, and 34.2% reduction in current THD from 8.58% to 5.65%. Full article

Electric vehicle (EV) powertrains involve complex interactions between speed regulation, energy consumption, regenerative braking, and battery thermal behavior. Most existing approaches address controller tuning and energy management separately, which may limit the overall system performance. This paper proposes an integrated deep reinforcement learning (DRL) strategy in which a single Twin Delayed Deep Deterministic Policy Gradient (TD3) agent simultaneously adjusts the proportional and integral gains of the speed controller (K_pv, K_iv), the torque modulation coefficient (K_s), and the regenerative braking factor (β_reg). A multi-objective reward formulation is adopted to account for speed tracking performance, energy efficiency, regenerative energy recovery, battery thermal constraints, and driving comfort. The framework is implemented through a MATLAB R2022b/Simulink–Python 3.10 co-simulation environment that enables online interaction between the EV model and the learning agent. Performance is evaluated using the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). Compared with a conventional fixed-gain PI controller, the approach reduces gross energy consumption by 16.2%, decreases speed tracking error by 43.7%, increases regenerative energy recovery by 21.4%, limits battery temperature rise by 30.4%, and lowers RMS jerk by 33.7%. The results indicate that jointly optimizing control and energy management variables can improve both vehicle dynamic performance and energy utilization. The methodology offers a practical framework for the development of adaptive and intelligent control systems in future electric vehicles. Full article

This study proposes an integrated self-aligning-torque energy recovery and DC-bus voltage stabilization strategy for a permanent-magnet synchronous motor (PMSM)-driven steer-by-wire system in new energy vehicles. During the front-wheel return-to-center process, self-aligning torque may provide excess mechanical energy to the steering actuator. Instead of dissipating this energy through a braking resistor, the proposed strategy converts part of the self-aligning-torque-induced mechanical energy into electrical energy and feeds it back to the low-voltage DC bus. To avoid ambiguity in the operating-mode description, this paper distinguishes the standard PMSM torque–speed quadrants from the mechanical stages of the steering process. Regenerative operation is defined according to the condition ($T_e{\omega }_m<0$), corresponding to the second or fourth quadrant of the PMSM torque–speed plane, whereas the return-to-center regenerative stage refers to the self-aligning-torque-dominated stage of the steer-by-wire motion. Based on this definition, an electromechanical energy-flow model is established to describe the transfer path from self-aligning torque to the PMSM and then to the DC bus. Considering that regenerative energy injection may cause DC-bus voltage fluctuation or braking-resistor activation, a single-loop bus-voltage stabilization method based on active disturbance rejection control is developed. A third-order linear extended state observer is adopted to estimate the lumped disturbance caused by self-aligning-torque variation, current coupling, load variation, parameter uncertainty, and inverter loss. The observer bandwidth, controller gains, current limitation, and overvoltage protection mechanisms are further discussed to improve the practical implementability of the proposed control strategy. In addition, an energy-accounting method is introduced to distinguish total steering energy consumption, available self-aligning-torque mechanical energy, gross recovered electrical energy, system losses, net recovered energy, and recovery efficiency. Simulation and experimental results show that the proposed strategy can suppress DC-bus voltage rise, reduce braking-resistor energy dissipation, and achieve measurable steering-actuator-level energy recovery during repeated return-to-center maneuvers. The results verify the feasibility of using self-aligning-torque-induced regenerative energy in PMSM-driven steer-by-wire systems, while the actual vehicle-level energy benefit depends on the driving cycle, low-voltage load demand, battery charging acceptance, and converter efficiency. Full article

The capability of recovering a portion of braking energy during vehicle deceleration is one of the distinctive advantages of new energy vehicles (EVs) over Conventional Internal Combustion Engine Vehicles (ICEVs). In existing production vehicles, regenerative braking control is commonly implemented using rule-based lookup table methods. Although such approaches are simple, reliable, and easy to implement, they lack the ability to adaptively adjust the braking force allocation according to varying driving conditions, thereby limiting the potential for high efficiency energy recovery. To improve regenerative energy recovery while simultaneously maintaining braking stability, this study introduces an ANFIS-optimized Sliding Mode–Fuzzy Joint Hierarchical Control Strategy (S-FJHCS) for regenerative braking systems. In the upper control layer, an improved tire road friction coefficient estimation algorithm is integrated with a sliding mode controller to ensure consistent slip ratio regulation between the front and rear wheels. In the lower control layer, a fuzzy control algorithm is employed to coordinate the distribution of braking torque between the hydraulic braking system and the hub motors. Furthermore, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized to perform offline optimization of the fuzzy controller, enabling the adaptive adjustment of fuzzy rules and membership functions based on historical operating conditions. Simulation and experimental results demonstrate that the proposed regenerative braking control strategy can improve regenerative energy recovery efficiency by approximately 5–10% compared with a conventional rule based regenerative braking strategy, while maintaining satisfactory braking performance and vehicle stability under various driving conditions. Full article

The article deals with the further development of national innovative subway cars with asynchronous electric drives and energy recovery systems through the introduction of capacitive energy storage. It has been determined that the assessment of the effectiveness of existing concepts for energy processes control of subway cars with asynchronous electric drives and capacitive energy storage under identical specified conditions remains a relevant issue. Five of the most promising concepts for managing energy processes were selected and idealized. Oscillograms of energy flows for the selected concepts are presented. Parameters for evaluating the effectiveness of the selected control concepts are presented. The scientific novelty lies in the development of a procedure for selecting a rational concept for controlling energy processes in subway rolling stock with asynchronous electric drives and CES, based on the application of a unified comparative analysis system using a comprehensive evaluation criterion. A scheme for replacing subway cars with asynchronous electric drives and capacitive energy storage is presented, and a mathematical model of energy flow processes for traction and regenerative braking modes has been developed based on this scheme. Algorithms for controlling energy processes between asynchronous electric drives, capacitive energy storage devices, and contact networks have been developed for each of the selected concepts. The efficiency of each of the five selected concepts for the same specified operating conditions of the subway cars, parameters of the asynchronous traction electric drive and capacitive energy storage device has been investigated using the developed mathematical model and the formulated comprehensive evaluation criterion. It was established that it is possible to save up to 18% of the electricity consumed from the contact network per braking-acceleration cycle under the specified operating conditions, parameters of the subway cars, asynchronous traction electric drive, and capacitive energy storage device. An additional possibility exists to reduce the installed power of the power supply system equipment by up to 33.5% under the specified operating conditions of a subway train with the proposed technical characteristics. It has been determined that the most rational concept for controlling energy processes in subway cars with asynchronous electric drives and capacitive energy storage is the fifth concept, which allows the use of stored energy from regenerative braking in both normal and emergency operation of the subway power supply system. Full article

Considering that conventional heavy-duty mining trucks equipped with centralized drive systems suffer from low transmission efficiency and limited flexibility in power distribution, this study focuses on distributed independent-drive heavy-duty mining trucks and develops energy-saving control strategies from two perspectives: drive torque control and regenerative braking. For the drive torque control, based on the principle of optimal driving efficiency, the overall efficiency of the drive motors is selected as the objective function, and an adaptive genetic algorithm (AGA) is employed to optimize the torque distribution coefficients among the axles offline. For regenerative braking, a fuzzy-control-based electromechanical braking distribution strategy and a dynamic-load-based inter-axle braking force allocation strategy are proposed. Finally, a co-simulation was conducted using MATLAB/Simulink and TruckSim based on specific open-pit mining conditions. Compared with the conventional baseline without energy-saving control, the simulation results demonstrate that under the single-cycle operation, the proposed strategy increases the driving energy utilization rate by 5.69% and achieves a braking energy recovery rate of 39.41%. Furthermore, under the full-mine cyclic operation, the proposed strategy extends the vehicle’s operational duration on a single charge by 200%. These findings demonstrate the strong potential of the proposed strategy to improve overall driving efficiency and fully exploit the regenerative braking capabilities of heavy-duty mining trucks, thereby providing theoretical support for enhancing their economic efficiency and driving range. Full article

This study presents an energy-based assessment of a battery electric sport utility vehicle (SUV) tested under high-altitude and high-gradient real-world conditions in Ambato, Ecuador, at approximately 2500 m above sea level. A low-cost instrumentation setup composed of a Global Navigation Satellite System (GNSS) device, a Fluke 393 FC clamp meter, and an On-Board Diagnostics II (OBD-II) interface was used to evaluate zero, positive, and negative road-gradient conditions in Normal and Sport driving modes. The results show that positive gradients increased the acceleration energy from 0.0454 to 0.0658 kWh in Normal mode and from 0.0351 to 0.0535 kWh in Sport mode. In contrast, negative gradients favored regenerative braking, with Normal mode reaching a net energy balance of $-0.0249$ kWh and a segment-level recovery ratio of 194.38%. This value reflects the contribution of gravitational potential energy. Sport mode showed lower regenerative performance, particularly during uphill operation, where the recovery ratio decreased to 8.96%. These findings demonstrate that low-cost instrumentation can capture representative route-level energy trends and support real-world electric vehicle (EV) energy assessment in topographically complex high-altitude environments. Full article

As urban rail transit systems evolve within the Industrial Internet of Things (IIoT), the intelligent recovery of regenerative braking energy becomes critical for energy efficiency. However, the existing train operation optimizations primarily focus on time-domain synchronization, frequently neglecting the spatial impedance constraints of the DC traction network. This oversight creates a discrepancy between theoretical energy matching and actual absorption. To address this, this paper proposes a spatiotemporal synergistic optimization framework integrating the analysis of electrical energy transmission factors and train relative motion. First, a dynamic multi-node circuit model based on Kirchhoff’s laws is established to characterize train fleet operations. By evaluating electrical energy transmission factors, the current distribution ratio and line impedance loss are identified as primary determinants of absorption efficiency. This physically quantifies the coupling among instantaneous energy distribution, transmission loss, and source-load relative distance. Second, a time-domain integration-based gradient analysis framework is formulated to deconstruct the energy gradient into amplitude and directional components. By mapping the relative position and speed of interacting trains, their relative motion states are systematically categorized. Subsequently, an adaptive gradient optimization strategy based on these motion states is introduced, which fine-tunes dwell times to precisely guide train trajectories into a low-impedance “optimal window” for energy absorption. Finally, a case study using operational data from Luoyang Metro Line 1 validates the proposed framework. Results demonstrate that the framework achieves dual spatiotemporal matching of braking and traction trains, outperforming the traditional fixed timetable and improving the regenerative braking energy absorption rate by approximately 13%. Full article

Energy crises and environmental pollution continue to constrain sustainable development of the automotive industry. Large-scale deployment of electric vehicles (EVs) provides an effective pathway to reduce energy consumption and emissions. Regenerative braking technology plays a central role in improving energy utilization and extending EV driving range has sustained research attention. This study examines the operating principles, control strategies, and energy performance characteristics of regenerative braking systems (RBS). The historical development of brake-by-wire systems is reviewed, including system classification, structural configuration, and operating mechanisms, with an emphasis on their application in electric vehicles. On this basis, the working principles of regenerative braking systems are analyzed. A systematic review of regenerative braking control strategies is conducted across four categories: conventional control, fuzzy control, neural network control, and intelligent optimization algorithms. The analysis focuses on optimization methods for improving energy recovery efficiency and on the key factors governing energy transfer performance. Technical challenges associated with integrating regenerative braking systems into electric vehicles are further examined to provide a reference for future research and engineering development. Full article

The friction-induced stick-slip vibration (FISSV) generated by intense friction between the brake disc and brake pads of high-speed trains is a critical issue affecting braking stability, the service life of foundational braking components, and ride comfort. The floating friction block structure, which effectively regulates interfacial contact characteristics through the elastic deformation of disc springs, thereby improving tribological behavior, represents an effective approach for mitigating FISSV. However, the topic of how to design the floating structure of the friction block to produce the best suppression impact on FISSV emerges, using the choice of disc spring material as an example. Thus, the purpose of this study is to look at how disc spring material affects stick-slip vibration (SSV) at the high-speed train floating brake interface. Four typical disc spring materials—304 stainless steel, Mubea-specific spring steel, 50CrVA high-alloy spring steel, and 60Si₂MnA silicon-manganese spring steel—were selected. Through braking tribological tests and explicit dynamics-wear coupling simulations, the effects of material differences on interfacial friction-wear characteristics and SSV behavior were systematically studied. The findings show that the stiffness of the disc spring material greatly influences the dynamic responsiveness of the system and the contact pressure distribution at the braking interface, elasticity, and damping characteristics. 60Si₂MnA spring steel, owing to its excellent elastic recovery and load equalization capability, promoted the formation of uniformly dispersed medium-to-small contact platforms on the interface, resulting in the mildest wear. Concurrently, its system vibration energy exhibited a more dispersed distribution in the frequency domain, with low SSV intensity and weak nonlinear behavior, demonstrating the best comprehensive performance. Materials with poorer compatibility, such as 304 stainless steel, tended to cause localized stress concentration, exacerbating wear and intensifying severe high-frequency SSV. The influence mechanism of disc spring material at the interface is shown by this work, providing an important basis for material optimization and vibration suppression design in floating brake pad structures. Full article

Fully leveraging the four-wheel independent drive characteristics of distributed-drive electric vehicles has become essential for enhancing their driving range. However, conventional regenerative braking strategies applied to such vehicles often fail to consider individual wheel slip ratios, which can easily lead to wheel lock and low energy recovery efficiency. To address these issues, this paper proposes a novel energy management method that integrates hybrid braking control with intelligent connected speed planning. A hierarchical control strategy for the hybrid braking system is first developed, explicitly accounting for the slip ratio of each wheel. The upper-level controller calculates the slip ratio for each wheel based on vehicle speed and wheel speed information and subsequently determines the braking torque distribution between the front and rear axles. The lower-level controller then allocates the motor braking torque and hydraulic braking torque to each wheel, subject to system constraints such as battery status and motor torque limits. Building on this framework, vehicle state and road information are incorporated as inputs to formulate a Markov decision process, which optimizes traffic efficiency, energy economy, and ride comfort as multiple objectives. The deep deterministic policy gradient (DDPG) algorithm is employed to achieve collaborative optimization of speed planning and energy management. Simulation results demonstrate that the proposed DDPG-based control strategy outperforms both rule-based control methods and classical dynamic programming algorithms in terms of comprehensive performance across traffic efficiency, energy consumption, and ride comfort. These findings validate its superiority in complex traffic conditions. Full article

To address the challenge of balancing regenerative braking efficiency and braking safety in rear-wheel-drive electric motorcycles, this study proposes a genetic algorithm-based electromechanical hybrid fuzzy braking control strategy. First, a three-dimensional fuzzy controller is designed with braking force, motorcycle speed, and battery state of charge (SOC) as input variables to adjust the regenerative braking ratio in real-time. To further improve the fuzzy logic, which typically relies on engineering experience, a genetic algorithm (GA) is employed to optimize the controller’s parameter space. Co-simulation results using BikeSim 2013.1 and MATLAB/Simulink R2022a demonstrate that, under WMTC and NEDC standard driving cycles, the proposed GA-optimized fuzzy control system increases energy recovery rates by 6.59% and 11.65%, respectively, compared with the unoptimized fuzzy control strategy. Full article

The eddy current braking system (ECBS) is a crucial non-contact technology for high-speed railway. Conventional DC-excited systems face significant challenges such as excessive rail heating and high-capacity power supply requirements. This paper proposes a novel ECBS with AC excitation and auxiliary capacitor to achieve integrated energy recovery and power supply optimization. To evaluate its performance, a rigorous analytical framework is developed. First, a 2D subdomain model is established by incorporating the longitudinal end effect to solve the magnetic field distribution. Subsequently, an equivalent circuit is derived from the subdomain results to investigate steady-state braking characteristics and power flow. Analysis results demonstrate that the proposed system not only generates controllable braking force but also converts a portion of kinetic energy into storable electrical energy, effectively mitigating secondary rail heating. Most significantly, the implementation of an optimal auxiliary capacitor (134 μF) is found to reduce the required inverter capacity compared to inverter-only conditions. These findings provide a theoretical foundation and a practical design tool for developing high-performance, energy-efficient braking systems in high-speed transportation. Full article

This paper presents the design, development, and evaluation of a Reinforcement Learning (RL)–based torque-split controller for the regenerative braking system (RBS) in battery electric vehicles (BEVs). The controller employs a Deep Deterministic Policy Gradient (DDPG) agent to distribute the braking demand between regenerative and frictional braking systems with the aim of maximizing energy recovery while adhering to the physical and operational constraints. To capture the charging limitation of the battery, a State-of-Power (SoP) calculation mechanism is incorporated, providing a time-varying bound on the regenerative charge power. The agent is trained in a MATLAB/Simulink environment representing the digital twin of a BEV drivetrain, and considers a mix of different braking scenarios, i.e., light braking, medium braking, hard braking, and emergency braking. The RL’s reward shaping promotes efficient utilization of the SoP-limited regenerative capability while discouraging constraint violations and aggressive control behavior. Across a range of State-of-Charge (SoC) conditions and driving cycles, including the Worldwide Harmonized Light–Vehicle Test Procedure (WLTP) and synthetic random-rich driving cycle, the RL controller consistently delivers promising performance, yielding energy recovery of up to ~98% of the total braking energy available on WLTP type 3 driving cycle while being able to operate closely to the battery SoP limit. The results demonstrate the proposed controller’s capability for adaptive, constraint-aware energy management in BEVs and underline its potential for future intelligent braking strategies. Full article