Search Results (463)

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

Axial Flux Permanent Magnet Synchronous Motors (AFPMSMs) are gaining increasing attention for their application in electric vehicle (EV) drive systems. Their high torque density and compact axial geometry make them attractive for high-performance EV drive systems. However, cogging torque remains a major challenge, degrading low-speed drivability, noise performance, and control stability. This article proposes a magnet skew on rotor modulation structure using a genetic algorithm (GA) to reduce cogging torque in AFPMSMs utilizing a 12/15 non-integer pole/slot arrangement. The objective of optimization is to simultaneously reduce cogging torque under identical electromagnetic constraints. A complete three-dimensional finite element model (3D-FEM) incorporating nonlinear magnetic material properties has been developed to evaluate the electromagnetic field distribution and torque components. The results indicate that a 12/15 non-integer pole/slot arrangement improves harmonic distribution and extends the operating range with lower cogging torque compared to integer pole/slot designs. Combined with GA-optimized skew angles, this reduces peak-to-peak cogging torque to less than 50%. This design is ideally suited for the traction requirements of electric vehicles, including premium electric vehicles where smooth operation at low speeds is critical. Full article

In recent years, electric cargo (e-cargo) bikes have been increasingly adopted as a sustainable alternative for urban logistics and last-mile delivery, particularly in densely populated areas where traditional vehicles face traffic congestion and access limitations. This study aims to develop a representative driving cycle for e-cargo bikes based on real-world cycling data. An instrumented Long John-type e-cargo bike was used to collect naturalistic data from four different riders covering a total of 50 km along a predefined route in the city center of Florence, selected in collaboration with the Italian postal service provider (i.e., Poste Italiane) to reflect typical delivery operations. The driving cycle was generated using a Markov chain Monte Carlo (MCMC) method, modeling the stochastic transitions of vehicle speed and acceleration values. The resulting driving cycle, defined as the Florence cargo bike driving cycle (FCBDC), achieved an error of 2.1% on the Speed Acceleration Probability Distribution (SAPD) root sum square difference; although minor losses in peak acceleration values were observed due to data smoothing and discretization, the synthesized driving cycle effectively reproduces the dynamic characteristics of e-cargo bike riding. While the study is limited to a single route and is equivalent to simulated postman behavior, it provides valuable insights to guide the future development and optimization of e-cargo bikes for sustainable mobility operations. Full article

In real-world operation, battery electric buses often encounter conditions with significant and rapid load variations. To improve regenerative braking energy recovery efficiency under such dynamic load conditions, this paper proposes a braking control strategy based on dynamic load estimation. First, a load estimation method based on a time-varying interactive multiple-model unscented Kalman filter (TVIMM-UKF) is developed by leveraging the vehicle longitudinal dynamics model and IMU sensor data, achieving high-accuracy online load estimation. Second, a multi-objective constrained optimization model is established, and an improved artificial bee colony algorithm is introduced to realize optimal brake force distribution under time-varying loads. Based on this, a regenerative braking control strategy is designed by incorporating motor characteristics and system-level operational constraints, enabling precise adjustment of braking torque across the full load range. Finally, simulation studies are conducted under two typical driving cycles, CHTC-B and C-WTVC, to verify the effectiveness of the proposed strategy. The results show that under dynamic load conditions, the proposed strategy can effectively improve braking energy recovery efficiency in both driving cycles. 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

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

To further explore the energy-saving potential of power-split hybrid electric vehicles, this paper addresses issues in traditional Radial Basis Function (RBF) neural network-based vehicle speed prediction methods, which rely solely on time-varying information from historical speed sequences of the host vehicle, leading to problems such as idle overestimation, large local prediction errors, and low prediction accuracy across different time horizons. An improved RBF neural network-based vehicle speed prediction method that integrates multi-sensor information is proposed. This method identifies the driver’s driving intention through a fuzzy inference system, extracts historical speed sequences within a fixed time window in a rolling manner, and integrates inter-vehicle motion characteristic parameters obtained through fusion of millimeter-wave radar and camera data. These multi-dimensional influencing factors are used as inputs to the RBF neural network for vehicle speed prediction. Based on this, an energy management optimization model for the vehicle is established, with the goal of optimizing fuel economy. The model predictive control (MPC) strategy is employed, and the Dynamic Programming (DP) algorithm is used to solve for the real-time optimal torque distribution among various power sources within a limited time horizon. Finally, simulation validation is conducted on the MATLAB/Simulink platform under the CHTC-B driving cycle, CCBC driving cycle, and actual road driving cycle. The results show that, compared with the traditional method adopting Radial Basis Function (RBF) neural network-based vehicle speed prediction and rule-based energy management, the proposed method improves the vehicle’s fuel economy by 4.11%. Full article

Environmental regulations, rapid technological advancements, and evolving mobility trends have led to a significant transformation of the automotive industry in recent years. The adoption of battery-electric vehicles (BEVs) has been accelerated by these developments, which are becoming increasingly efficient and widely deployed. Evaluating BEV energy consumption and performance is essential for optimizing energy efficiency, extending driving range, and developing effective control strategies under real-world operating conditions. The analysis is based on the WLTP Class 3 driving cycle, in which the vehicle operating points are projected onto the motor efficiency map to evaluate the influence of real-world operating conditions on overall propulsion efficiency. Two operating scenarios are considered: with regenerative braking and without regenerative braking. The inverter and battery are modeled using quasi-static energy-based representations to ensure system-level energetic consistency while maintaining computational efficiency. The results show that rotor topology significantly influences vehicle-level energy consumption. The dual-layer IPM configuration reduces net WLTP energy demand by approximately 9% and increases the estimated driving range from about 489 km to 535 km compared to the single-layer V-shaped configuration. Variations in rotor topology led to different efficiency distributions, which leads to systematic differences in battery energy demand and achievable driving range. The results highlight the importance of aligning traction motor design with realistic operating-point distributions rather than optimizing solely for peak efficiency or marginal improvements in regenerative braking performance. Full article

To address the core challenges of low energy utilization efficiency and limited range in front-wheel-drive electric vehicles (FWD EVs), this study proposes a dynamic series braking energy recovery strategy featuring adaptive braking force distribution and multi-factor correction. A comprehensive simulation model integrating five core modules—Cycle, Driver, Controller, Vehicle, and Display—was developed using Matlab/Simulink, combining the dynamic series recovery strategy with traditional parallel recovery strategies. Model reliability was validated through chassis dynamometer test data (maximum error ≤ 3.2%), followed by simulation comparisons under CLTC conditions. Results demonstrate that compared to parallel strategies, the dynamic series approach increases range by 25.8% (from 318 km to 400 km). Key innovations include real-time adaptive front axle braking coefficients based on braking intensity and a correction mechanism integrating vehicle speed and state of charge (SOC), achieving a balance between recovery efficiency, braking stability, and battery protection. This study provides actionable design guidance for FWD EV powertrain optimization while establishing a validated regenerative braking simulation framework. Full article

To address the challenges of facility–demand mismatch, aggravated congestion, and imbalanced benefit distribution caused by the interdependence between dynamic wireless charging systems (DWCS) and transportation networks, this study proposes an optimization scheme that coordinates DWCS planning, travel flow guidance for electric vehicle (EV) owners, and transportation network operations. We develop a bi-level dynamic collaborative optimization model. The upper-level model aims to maximize the annual net profit of DWCS operators and determines DWCS planning by optimizing the traffic flow distribution. The lower-level model, based on the user equilibrium principle, guides EV route choices via a traffic flow guidance mechanism to mitigate peak-hour congestion and minimize vehicle owners’ travel costs. We validate the model using a test network comprising 9 nodes and 13 links. Results indicate that, compared with a full-coverage planning scenario, the proposed bi-level optimization scheme significantly reduces operational losses by accounting for owners’ optimal travel flow distribution. Introducing a traffic flow guidance mechanism further improves traffic flow distribution, enhances operator revenue, and effectively reduces owners’ travel time costs. Sensitivity analysis reveals that increased battery capacity decreases construction and maintenance costs, thereby improving annual net profit, while lower energy consumption reduces charging demand and weakens dependence on charging infrastructure. These factors are interrelated; specifically, lower energy consumption implies reduced battery capacity requirements for the same driving range. Additionally, the effectiveness of the traffic flow guidance mechanism becomes more pronounced as traffic flow increases. Overall, the proposed framework integrates DWCS planning and traffic flow guidance to achieve a win–win outcome for both operators and owners. These findings demonstrate the practicality and economic feasibility of interactive optimization between DWCS and transportation networks. Full article

To address the challenge that existing vehicle chassis coordinated control methods struggle to balance the nonlinear couplings and control conflicts among Four-Wheel Steering (4WS), Direct Yaw-moment Control (DYC), and Active Suspension Systems (ASS), this paper proposes a Cooperative Distributed Model Predictive Control (Co-DMPC) strategy. First, the 4WS, DYC, and ASS are modeled as three interacting agents that effectively mitigate inter-subsystem control conflicts through information exchange and coupling compensation. Second, a Gaussian Mixture Model (GMM) is utilized to extract features from vehicle state data to enable the real-time grading of instability risks, which dynamically adjusts the control weights of the 4WS, DYC, and ASS agents. Finally, a distributed iterative optimization algorithm is designed to ensure that all agents converge to a global Pareto-optimal solution through rapid negotiation, achieving a balance between control performance and computational burden. Simulation results demonstrate that compared with No-Control and CMPC, the proposed Co-DMPC strategy significantly enhances the comprehensive performance of the vehicle. In terms of path tracking accuracy, the maximum tracking errors under high- and low-adhesion road conditions are reduced by 32.73% and 17%, respectively. Regarding roll stability, the peak roll angles of the vehicle are 0.27 rad and 0.01 rad under the respective conditions. For lateral stability, the proposed method maintains a more compact sideslip angle-yaw rate phase plane envelope, effectively achieving the coordinated optimization of chassis subsystems. Hardware-in-the-Loop (HIL) experiments further validate the performance and effectiveness of the controller. Full article

Accurate estimation of vehicle sideslip angle is vital for the stability and safety of four-wheel independent drive electric vehicles (4WIDEVs), but it faces challenges, including model uncertainties caused by tire yaw stiffness variations and system delays. This paper proposes a novel adaptive fusion strategy that combines the dynamic robust observer (DRO) and the improved adaptive square-root unscented Kalman filter (ASUKF). The DRO is designed based on a two-degrees-of-freedom vehicle model and ensures stability through linear matrix inequalities (LMIs), effectively handling parameter uncertainties and time delays; the ASUKF utilizes a three-degrees-of-freedom model and the magic formula tire model, combined with Sage–Husa adaptive filtering, to address the nonlinear tire dynamics. The key innovation of this paper is the introduction of a fuzzy-rule-based adaptive weighting mechanism that dynamically adjusts the fusion weights of the DRO and ASUKF in real time, thereby exploiting their complementary advantages under uncertainty and nonlinear conditions. The simulation and experimental validations demonstrate that this method significantly improves estimation accuracy, reducing the estimation error of vehicle sideslip angle by an average of 9.36%, and maintains robust performance and dynamic adaptability in various conditions, providing a reliable solution for the real-time state estimation of intelligent electric vehicles. Full article

Multi-motor electric heavy-duty vehicles face significant energy efficiency challenges due to the complex coordination of discrete gear selection and continuous energy flow allocation in the powertrains. This study aims to address this coordination dilemma by proposing a multi-agent deep reinforcement learning energy management strategy (EMS). The framework employs collaborative control across three agents to simultaneously optimize middle axle/rear axle gear shifts (DQN) and power distribution (DDPG), effectively handling the hybrid action space. A specialized rule is integrated to accelerate convergence and enhance real-cycle adaptability. Simulation results on CHTC-TT and CHTC-HT cycles show the proposed strategy achieves only 3.14% and 4.65% higher energy consumption, respectively, compared to a rule-optimized benchmark. This validates its practicality and robustness for real-world electric heavy-duty transportation applications. Full article

Dynamic Wireless Power Transfer (DWPT) is emerging as critical smart city infrastructure for sustainable urban mobility, enabling electric vehicle charging while driving. However, DWPT introduces complex fault scenarios requiring intelligent monitoring. Existing fault diagnosis approaches for wireless power transfer systems face three key complexities: (1) they are limited to static charging with only 2–4 fault categories, failing to address the time-varying coupling dynamics and segmented coil handover transients inherent in dynamic charging; (2) they lack integration with the host distribution grid, ignoring grid-side disturbances that propagate to charging stations; and (3) they offer only reactive detection without predictive capability for incipient fault management. This paper presents a deep neural network (DNN)-based fault diagnosis framework utilizing multi-station sensor fusion for DWPT systems integrated with the IEEE 13-bus distribution network to address these limitations. The system monitors 36 sensor features across three charging stations, employing feature-level concatenation with station-specific normalization for multi-station fusion, achieving 97.85% classification accuracy across eight fault types. Unlike static charging, the framework explicitly models time-varying coupling dynamics due to vehicle motion, including segmented coil handover effects. A digital twin provides dual-horizon prediction: long-term forecasting (24–72 h) for incipient faults and real-time detection under 50 ms for critical protection, with fault probability outputs and ranked fault lists enabling actionable maintenance decisions. The DNN outperforms SVM (92.45%), Random Forest (94.82%), and LSTM (96.54%) with statistical significance ($p<0.001$), while maintaining model inference latency of 4.2 ms, suitable for edge deployment. Circuit-based analysis provides analytical justification for fault signatures, and practical parameter acquisition methods enable real-world implementation. Five case studies validate robustness across highway, urban, and grid disturbance scenarios with detection accuracies exceeding 95%. Full article

This study proposes an electromagnetic design strategy to improve the energy efficiency of electric-vehicle (EV) traction motors by defining an operating region with high energy contribution using Urban Dynamometer Driving Schedule (UDDS) data and targeting efficiency improvement within that region. For distributed-winding (DW) and concentrated-winding (CW) IPMSM models, the stator-to-rotor diameter ratio varied, and the resulting change in the loading ratio was used as an indicator to evaluate loss and efficiency variations in the energy-weighted region of the efficiency map via two-dimensional finite element analysis (2D FEA). The results show that the losses within the weighted region decreased by up to 16.64% compared with the reference model, and the UDDS-cycle-based overall energy efficiency improved by up to 0.423%. These findings demonstrate that combining electromagnetic geometric design with driving-cycle data can serve as a practical metric for improving EV energy efficiency. Full article