Search Results (12,569)

The wide voltage range of energy storage batteries, as currently required in the electric vehicle industry, presents significant challenges for the optimal design of the dual active bridge (DAB) converters used in bidirectional DC–DC (BCD) plug-in electric vehicle (PEV) chargers and home energy management systems (HEMS) applications. This article proposes a DAB converter with an enhanced single-phase-shift (ESPS) modulation that extends the operating voltage range while maintaining zero-voltage-switching (ZVS) conditions by including a DC-blocking capacitor and modifying the trigger sequence of the bridge converter on the secondary side. The operational modes of this modulation scheme are presented, and a control strategy is developed to extend the ZVS range. To validate the concept, a 3.7 kW, 100 kHz prototype is designed and tested, interfacing a 400 V DC bus with a 400–800 V battery. Using 1200 V silicon carbide (SiC) devices, the prototype achieves a peak efficiency of 95.5%. Full article

The rapid increase of electric mobility is challenging the deployment design and operation of electric vehicle charging infrastructure in a scalable, sustainable, operationally reliable, and regulation-compliant manner. Although advances in both digitization and artificial intelligence in recent years have made smarter charging solutions possible, today’s approaches tend to concentrate on individual technical parts without considering holistic views. This paper introduces an AI-driven integrated management model for sustainable EV charging infrastructures, composed of four interconnected layers, namely, Eco-Design, Digital Tools, Risk Management, and Governance. In particular, each layer focuses on specific aspects of functionality, including environmentally friendly design decisions, digital monitoring capabilities, proactive risk reduction, and strategic coordination. Compared with existing approaches that address isolated technical or operational aspects, the proposed model provides an integrated, multi-layer architecture that unifies eco-design, digital intelligence, risk management and governance, offering a more holistic and scalable foundation for sustainable EV charging infrastructures. It represents the conceptual output of a structured integration of existing technologies, design principles and governance needs. Considering that fragmented, solution-specific advances are reduced by including interdependencies between layers, the model allows us to better integrate technical operations, resilience mechanisms and sustainability goals. The model is theoretical and offers a scalable point of reference for researchers, as well as infrastructure operators and politicians. Full article

Precise assessment of battery state of health (SoH) is vital for certifying consistent performance in order to enable maintenance of energy storage system. This work compares different deep learning methods to learn and predict the complex and nonlinear dynamics of battery. The models are developed and tested for predicting SoH using sequential degradation data from batteries. The effectiveness of these models is assessed using matrices such as RMSE, MAE and R², along with qualitative analysis. The experiment results show that the BiLSTM model performs better than the others. It has the lowest RMSE (0.90), the lowest MAE (0.72), and the highest R² (0.99), which highlights its enhanced ability to capture long-term temporal dependencies. The proposed models are validated using NASA lithium-ion battery aging dataset (B0005), which is widely used as a benchmark for battery health predictions studies. Overall, the findings indicate that bidirectional network architecture significantly improves the accuracy and consistency of SoH predictions when compared to unidirectional models. Full article

To enhance the trajectory tracking accuracy and lateral stability of distributed-drive electric vehicles, a hierarchical cooperative control strategy optimized by the Genetic–Firefly Algorithm (G-FA) is proposed. First, bottom-level controllers for trajectory tracking utilizing a Linear Quadratic Regulator (LQR) and stability relying on Sliding Mode Control (SMC) are jointly optimized offline using the G-FA to address the limitations of empirical parameter tuning and effectively mitigate chattering. Compared to traditional Nonlinear Model Predictive Control (NMPC), which relies on computationally demanding dynamic programming, the proposed G-FA acts as an efficient approximate optimization method that significantly reduces the online computational burden while maintaining high control accuracy. Second, an adaptive cooperative mechanism based on desired yaw rate correction is introduced. By constructing two reference benchmarks—“tracking-oriented” and “stability-oriented”—a cooperative weighting coefficient adapts the fusion of control objectives based on the vehicle’s stability state. Hardware-in-the-loop (HIL) simulation results demonstrate that, under high-adhesion double lane change maneuvers, the proposed strategy reduces peak lateral error and sideslip angle by 31.53% and 28.08%, respectively, compared to traditional LQR. In low-adhesion S-curve limit maneuvers, where traditional LQR fails, the proposed strategy outperforms the NMPC benchmark, further reducing these indices by 61.98% and 8.33%, respectively, significantly improving control performance under extreme conditions. Full article

The rapid expansion of e-commerce and on-demand logistics has intensified the need for cost-effective and reliable urban distribution systems. This paper investigates an energy-aware routing problem for electric vehicle fleets operating from multiple depots under time-varying traffic conditions. We propose a novel multi-depot vehicle routing model that jointly incorporates time-dependent travel speeds, simultaneous pickup and delivery operations, and time window constraints. The model explicitly captures key operational realities, including battery capacity limitations, load- and speed-dependent energy consumption, synchronized pickup-delivery requirements, and soft time windows. The objective is to minimize total operational cost by simultaneously optimizing depot assignments, vehicle routes, and service schedules. Given the NP-hard nature of the problem, we develop a two-stage heuristic solution framework. In the first stage, a spatio-temporal clustering strategy is employed to assign customers to depots efficiently. In the second stage, route construction and improvement are performed using an enhanced Adaptive Large Neighborhood Search (ALNS) algorithm equipped with problem-specific destroy and repair operators. Computational experiments on adapted benchmark instances demonstrate that the proposed approach consistently produces high-quality solutions and exhibits robust convergence behavior. In addition, sensitivity analyses provide managerial insights, revealing an optimal range of vehicle energy capacity and an economically efficient speed band that balances travel time and energy consumption. Full article

Task scheduling for vehicle–crane coordination is crucial for the operational efficiency of electrified automated container terminals (ACTs). However, under fully shared dispatching, existing studies rarely capture how charging-induced capacity fluctuations disrupt bidirectional service–arrival matching and propagate service-window shifts. To address this gap, this study proposes a comprehensive spatio-temporal optimization approach. Firstly, a bi-objective model is established to minimize service–arrival mismatch and vehicle energy consumption under state-of-charge (SOC) and charger-capacity constraints, explicitly quantifying vehicle–crane alignment at both handling interfaces. Secondly, an enhanced multi-objective algorithm (ST-NSGA-II) is developed, integrating a feasibility-preserving recursive decoding mechanism and a spatio-temporal variable neighborhood search (VNS) procedure. Finally, numerical experiments demonstrate that ST-NSGA-II significantly reduces mismatch and energy consumption compared to standard NSGA-II in large-scale scenarios. It also outperforms MOEA/D in Pareto-set quality, yielding a higher hypervolume (1.301 vs. 0.960) and a lower Spacing value (0.102 vs. 0.185). The results demonstrate that the proposed spatio-temporal optimization approach can effectively reduce handover mismatch compared to conventional scheduling modes, thereby achieving seamless task scheduling for vehicle–crane coordination. Full article

The push for higher energy density in electric vehicles has resulted in large-sized lithium-ion batteries, but their geometric upscaling exacts a heavy thermal price. Under high-rate discharge, these massive cells become heat traps, risking thermal runaway. To tame this instability, this paper engineered a hybrid management strategy fusing liquid cooling, Phase Change Materials (PCMs), and flow deflectors. With a primary focus on the structural optimization of the cooling channel, a three-dimensional numerical model, calibrated using experimentally determined thermophysical properties, was developed to overcome the thermal bottlenecks of conventional cooling architectures. Results indicated that the initial channel optimization effectively reduced the maximum temperature to 327.7 K, but it still remained near the safety threshold. Integrating PCM radically altered the thermal landscape, slashing the outlet temperature differential by 41.67% (from 2.76 K to 1.61 K) compared to pure liquid cooling and blunting peak thermal spikes. Furthermore, to overcome laminar stagnation, strategic deflector baffles were introduced to agitate the coolant, enhancing heat dissipation. Specifically, the optimal half-coverage (L = 1/2) baffle configuration successfully lowered the maximum temperature to 322.42 K while substantially reducing the system pressure drop from 948.16 Pa to 627.57 Pa, achieving a 33.33% reduction compared to the full-coverage scheme. Finally, a multi-variable sensitivity analysis confirmed the extraordinary engineering robustness of the optimized configuration, demonstrating a negligible maximum temperature fluctuation of less than 0.5% despite ±10% operational and material uncertainties. This synergistic system actively stabilizes the thermal envelope, offering a robust engineering blueprint for next-generation high-power battery packs. Full article

In order to provide an innovative form of urban air mobility, a new and versatile generation of small, highly automated aircraft is currently being developed. This is made feasible by the development of new technologies such as electrified powertrains, Vertical Take-Off and Landing capabilities and distributed propulsion systems. The operation of these novel aircraft types will generate a new source of air traffic noise. In particular, the perception of noise and the annoyance caused by these aircraft and their distributed propulsion systems are likely to deviate from those of conventional aircraft and will also depend on psychoacoustic effects. Thus, the noise emission and its subjective perception will be key factors for the success of urban air mobility vehicles and their acceptance by society. In order to investigate acoustic effects that enable low-noise aircraft design, a multidisciplinary approach is applied to develop new aircraft concepts for urban air mobility. This approach includes the conceptual design of two vehicles, one vehicle with tilt-rotors and one with tiltable, ducted fans; the sizing of an electric powertrain; the design and manufacturing of a wingtip rotor; and the design and manufacturing of the low-speed ducted fans. This paper presents the design of the two vehicle architectures, including their electric powertrain, as well as the aerodynamic and acoustic performance of the rotor and fan. Full article

Technological advances such as Vehicle-to-Grid (V2G) have the potential to support renewable energy integration and grid stability, but large-scale deployment depends on users’ willingness to participate, particularly in public charging environments. While prior research has examined V2G from technical feasibility and system-level perspectives, everyday public settings remain unexplored. This study investigates electric vehicle (EV) users’ willingness to engage in V2G services in public spaces, with a focus on incentives, expectations, and how participation aligns with existing routines and parking conditions. A mixed-method approach was applied, combining a survey of 544 car users with two waves of user-centered interviews. The survey data were analyzed using factor analysis and linear regression models, while the interview data were thematically analyzed. The results show that users’ evaluations of V2G are shaped by sustainability expectations, perceived efficiency, and uncertainties, and preferences for public V2G participation are strongly influenced by convenience, clarity of the offer, and perceived control. Home charging practices emerged as a key reference point shaping expectations of public V2G services. Across both methods, simple and transparent incentives, such as reduced charging or parking costs, were consistently preferred over more complex reward models, including point-based systems or dynamic energy trading. Concerns related to control over trips, battery degradation, trust in service providers, and added complexity remain important barriers to participation. The findings highlight the need for user-centered and socio-technical design of public V2G services that align with users’ everyday routines, parking conditions, and expectations to support broader adoption beyond the home context. Full article

During routine operation, the power batteries of electric buses (EBs) gradually age due to the combined effects of numerous factors, including charging/discharging cycles, load fluctuations, and ambient temperature. This paper focuses specifically on the problem of battery aging in the context of actual urban electric bus system operations. It explores how to comprehensively incorporate the battery degradation effect into optimization schemes for EB fleet scheduling. This paper proposes an integrated optimization methodology that combines a capacity degradation model, a scheduling optimization model, and a genetic algorithm. A comprehensive scheduling optimization model is constructed, incorporating vehicle procurement costs, operational charging costs, off-peak charging costs, and battery capacity degradation costs, subject to rigorously defined constraints. Subsequently, an improved genetic algorithm framework is developed. Finally, the constructed model is validated using operational data from the Chongqing bus system. An analysis of the optimization mechanisms is provided, and a sensitivity analysis is conducted on vehicle procurement costs and battery capacity degradation costs. Based on the results, the model can reduce the total cost to 92% of the original level, proving that it is effective to a certain extent in reducing the operating costs of electric buses. Full article

This study presents an integrated simulation framework that combines agent-based transport modeling with probabilistic load-flow analysis to quantify power system loading of long-haul heavy-duty electrification. The approach is applied to a case study considering fully electrified road freight in the Skåne region in Sweden, using high-resolution transport demand data and the actual power grid model used by the grid owner in the study area. The synthetic freight population covers the full long-haul truck segment intersecting Skåne. Both public en-route fast charging and end-of-trip depot charging are considered. The analysis reveals two fundamentally different charging demand profiles: a heavily fluctuating profile for public en-route charging, accounting on average for 82% of the total daily charging energy, and a stable profile for end-of-trip depot charging, covering on average the remaining 18%. The latter is achieved through a Linear Programming (LP) optimization model that flattens the load by scheduling charging across depot stay windows. These profiles serve as inputs to a probabilistic load-flow simulation that computes loading distributions for substation transformers. The simulation results show that in 4 of the 43 primary substations studied, the maximum transformer loading exceeds 100% following the introduction of truck charging, with peak loading at the most affected substation rising from 99% to 159%. This stress is primarily caused by the public charging demand, which peaks from late morning to noon, aligning with the early stages of logistics operations. However, there is no clear correlation between the magnitude of the truck charging load and the impact on transformer loading, since this is also highly dependent on local grid conditions. These findings highlight the value of integrated transport-energy simulations for planning resilient infrastructure and guiding targeted grid reinforcements. Full article

Lithium-ion batteries (LiBs) are fundamental to modern energy systems, particularly in electric vehicle (EV) applications, due to their high energy density, long cycle life, and low self-discharge characteristics. Accurate State-of-Charge (SoC) estimation is essential for ensuring reliable performance, efficient energy usage, and the safety of Battery Management Systems (BMSs). However, the nonlinear and time-varying characteristics of LiBs, along with the difficulty in directly measuring internal states, pose significant challenges for parameter identification and SoC estimation. This study presents an advanced approach based on the Weighted Mean of Vectors optimization algorithm to simultaneously identify the unknown parameters of an extended Thevenin Equivalent Circuit Model (ECM) and estimate the SoC. Unlike previous methods that use static parameters for specific battery modes, the proposed technique accounts for dynamic changes during both charging and discharging operations. The algorithm demonstrates superior adaptability by continuously adjusting model parameters to reflect real-time battery behavior under varying operational conditions. The algorithm also models the relationship between SoC and open-circuit voltage (Voc) using data collected from real lithium-ion cells tested under a controlled load profile in the laboratory. This experimental validation ensures the practical applicability and robustness of the proposed methodology. The simulation results confirm the effectiveness and precision of the proposed approach, showing excellent agreement between measured and estimated values, with minimal errors in both voltage and SoC prediction. The enhanced accuracy achieved through this dynamic parameter identification framework represents a significant advancement in battery state estimation technology. Full article

In the vehicle electrification sector, the precise and reliable control of e-motors is of the utmost importance for ensuring the efficient and safe operation of the whole electric vehicle drivetrain. Specifically, the assessment of the absolute rotor position of the permanent magnet-based synchronous motors is necessary for precise e-motor control, which is strongly determined by the precision of the sensing device used for the absolute rotor position assessment. Magnetic rotational position sensing devices/encoders are predominantly used in the automotive sector. The accuracy of a magnetic-based rotational position sensing device can be affected by defects/errors which may occur during its manufacturing and/or assembly process. These defects may in turn affect the accuracy of the e-motor’s control and operation. The primary objective of this study was to numerically and experimentally design and investigate the accuracy of a magnetic-based off-axis rotational position sensing device intended for the control of a new permanent magnet e-motor, which was developed for a two-wheeler electric vehicle drivetrain. First, a 3D parametric numerical model of a magnetic rotational position sensing device mounted on the motor shaft was built by virtue of the finite element method (FEM). Based on numerical simulations, the appropriate dimensions of the magnetic ring were determined and the possible errors which may have occurred during its manufacturing process have been numerically imposed and analyzed. Second, the rotor position sensing device was prototyped based on the recommendations obtained with the 3D FEM model. Finally, the accuracy of the designed rotational position device was then experimentally assessed by comparing it to a standardized end-of-shaft rotational position encoder. To evaluate the influence of the possible errors on the e-motor rotor position measurement, the output characteristics of the motor torque as a function of its rotational speed of a real permanent magnet e-motor were experimentally assessed using two different rotational position devices. Based on the numerical end experimental results, we identified the manufacturing errors of the magnetic ring and analyzed their influence on the resulting output characteristics of the e-motor. The results revealed that the magnetic ring eccentricity and its magnetization process could affect the accuracy of the e-motor’s output torque characteristics. Full article

With the increasing adoption of electric vehicles (EV), congestion at charging stations during peak hours has become a prominent issue, imposing significant pressure on station scheduling. Furthermore, the large-scale integration of photovoltaics (PV) introduces dual uncertainties in both generation and load, negatively impacting grid voltage. To tackle the above problems, a strategy for demand response balancing and congestion alleviation of charging stations under grid–road network partition mapping is proposed in this paper. Firstly, a user demand response capability assessment method based on the Fogg Behavior Model is proposed to evaluate the demand response potential of individual users in each zone. The results are aggregated to obtain the demand response participation capability of each zone, thereby realizing capability-based allocation and achieving demand response balancing. Secondly, the road network is divided into several zones and mapped to the power grid, and a two-layer cross-zone collaborative autonomy model is established. The upper layer aims to alleviate inter-zone congestion and balance inter-station power, taking into account the grid voltage level. A tripartite benefit model involving the power grid, charging stations and users is constructed, and an inter-zone mutual-aid model for the upper layer is established and solved optimally. The lower layer establishes an intra-zone self-consistency model, which subdivides different functional zone types within the road network zone, allocates and accommodates the cross-zone power from the upper-layer output inside the zone, and synchronously performs intra-zone cross-zone judgment to avoid congestion at charging stations. Simulation verification is carried out on the IEEE 33-bus system. The results show that the proposed method can effectively alleviate the congestion of charging stations, the balance degree among all zones is increased by 43.58%, and the power grid voltage quality is improved by about 38%. This study offers feasible guidance for exploring large-scale planned participation of electric vehicles in power system demand response. Full article

In urban driving cycles, battery electric vehicles are subject to frequent start–stop operations, which lead to substantial braking energy losses. Although fuzzy control (FC) strategies are commonly employed for regenerative braking, their performance is often constrained by subjectively defined membership functions and rules. To address this limitation, this paper proposes an improved FC strategy that is optimized using the particle swarm optimization (PSO) algorithm. Focusing on a front-wheel-drive BEV, a three-input single-output fuzzy controller is developed in accordance with ECE regulations, where braking intensity, battery state of charge (SOC), and vehicle speed serve as inputs, and the motor braking force ratio serves as the output. A co-simulation platform based on AVL-Cruise 2019 and Matlab/Simulink 2017a is established to evaluate the strategy under the New European Driving Cycle (NEDC) and the Worldwide Light Vehicles Test Cycle (WLTC). Additionally, hardware-in-the-loop (HIL) tests are conducted to validate the practical feasibility and accuracy of the optimized strategy. The results demonstrate that the PSO-optimized FC strategy achieves a performance in real-world controllers that is comparable to that observed in a simulation, confirming its real-time applicability. Specifically, under the NEDC, the optimized strategy reduces battery SOC from 0.90 to 0.8795, representing improvements of 0.2515% and 0.4670% over the unoptimized FC strategy and the ideal distribution strategy, respectively. The regenerative braking efficiency is enhanced by 2.45% and 10.48%. Under the WLTC, the final SOC with the optimized strategy is 0.8488, reflecting gains of 0.5202% and 0.8380% over the two reference strategies, while regenerative braking efficiency improves by 2.32% and 8.95%. These findings indicate that the proposed strategy offers a safe and effective solution for improving the regenerative braking performance in electric vehicles. Full article