Commercial truck path tracking is strongly affected by large mass and yaw moment of inertia, steering-rate constraints, and signal delays, whereas optimization-based predictive controllers can impose high online-computational costs. This study proposes a real-time path-tracking method based on Constraint-Handling Trajectory Prediction (CHTP). A dynamic model predicts the vehicle’s future pose, and a Stanley-based tracking law computes the desired steering angle from the predicted state. A constraint-handling module then explicitly limits the steering angle and its rate of change. The proposed CHTP method requires no online optimization. The method was evaluated through MATLAB/Simulink-TruckSim co-simulations and hardware-in-the-loop (HIL) tests. Under the low-speed unladen condition, CHTP reduced the maximum absolute-displacement error by 74.60% compared with the conventional Stanley controller. Under the high-speed unladen, low-speed heavy-load, and high-speed heavy-load conditions, CHTP completed the lane-change maneuver with bounded tracking errors, whereas the conventional Stanley controller failed to maintain convergent tracking. Across the four basic path-tracking conditions, the maximum absolute displacement and heading errors of CHTP did not exceed 0.3930 m and 0.1133 rad, respectively. Although nonlinear model predictive control (NMPC) generally achieved higher tracking accuracy, CHTP reduced the mean solution time by 94.17–95.96% relative to NMPC, with a maximum solution time of 1.1416 ms. Additional robustness tests showed that CHTP maintained bounded tracking errors and stable lateral dynamic responses under positioning errors, low road adhesion, and random response delays, while preserving its real-time computational performance. In the HIL test with a total loop delay of approximately 0.22 s, extending the prediction time from 0.20 s to 0.42 s limited the maximum displacement and heading errors to 0.2290 m and 0.1046 rad, respectively, with a maximum solution time of only 0.9895 ms. Additional prediction-time tests showed a trend consistent with the simulation results, further supporting the proposed delay-compensation mechanism. These results demonstrate that CHTP provides a favorable balance among tracking accuracy, robustness, and real-time performance.
Vehicle-to-Grid (V2G) enables energy and information exchange between electric vehicles (EVs) and the power grid. Large amounts of distributed data are generated in V2G systems. The charging data of EVs connected to charging piles (CPs) are aggregated by charging stations (CSs), charging service operators (COs), and load aggregation platforms. The aggregated results support grid regulation and electricity market trading. However, some CPs within a station are idle or offline due to hardware failures or communication disruptions, preventing their data from being aggregated in time. Consequently, traditional schemes that rely on full-pile participation result in incomplete ciphertext aggregation results due to the absence of data contributions from some CPs, making correct decryption unreliable. In addition, most existing data aggregation schemes only provide a single result. They cannot satisfy the differentiated data access demands of V2G business entities. To address these challenges, a user-driven differentiated fault-tolerant data aggregation scheme for V2G interaction is proposed. First, the EC-ElGamal encryption scheme is employed to preserve data confidentiality, while the ECDSA batch signature verification mechanism is adopted to ensure data integrity. Second, a mask compensation mechanism is designed to restore the completeness of the aggregated ciphertext under the failures of some CPs. Finally, on-demand differential de-aggregation and controlled authorized decryption are designed based on Shamir’s secret sharing scheme. Data users (DUs) are allowed to access target ciphertexts on demand, only upon obtaining sufficient authorization from CPs. Security and performance analyses demonstrate that the proposed scheme effectively resists chosen-plaintext and key-collusion attacks with practical efficiency. The proposed scheme provides a secure and reliable solution for V2G data aggregation.
The widespread adoption of EVs is expected to intensify peak demand, stress distribution networks, and increase carbon emissions if traditional grid-based charging models continue. Therefore, this study proposes a smart-grid-connected microgrid (MG) architecture for EV charging stations (EVCSs) that integrates renewables and energy storage, while incorporating incentive-based response (iDR) and net energy metering schemes into a single optimization framework. To maintain the quality of EV charging services, optimize grid interactions and MG reliability, and explore the socioeconomic impact of establishing such infrastructures, a comprehensive 4E optimization framework incorporating energy, environmental, employment, and economic metrics is developed. The suggested framework is applied to an urban case study in Riyadh, including realistic load profiles, tariff structures, EV charging behavior, network outage scenarios, NEM conditions, and an assumed iDR incentive scenario. A grid-dependent BES/Conv/Grid configuration optimized without iDR is adopted as the common reference for consistently evaluating all system configurations. Compared with this common reference, the results show that while renewable hybridization improves performance, the assumed iDR scenario provides further economic and operational benefits. The optimal iDR-enabled configuration achieves an 87.5% reduction in TNPC and a standard HOMER Grid LCOE of $0.0287/kWh, corresponding to a 67% reduction relative to the common grid-dependent reference. When electricity exports are excluded from the LCOE normalization, the corresponding load-serving LCOE is approximately $0.0372/kWh, which remains approximately 57.3% lower than the reference value of $0.0872/kWh. In addition, the optimal MG generates $63,128 in revenue/year through participation in iDR events without degrading EV charging service performance relative to the non-iDR scenario. Employment analysis indicates that the optimal system supports approximately 49 job-years of direct project-associated employment over the 25-year project lifetime through infrastructure deployment, operation, and maintenance, while ecologically, it limits annual grid-related CO2 emissions to approximately 280.18 tons, representing about an 84.7% reduction relative to the reference scenario. These findings confirm that coordinated demand-side flexibility and intelligent storage dispatch can partially substitute for infrastructure oversizing, enabling cost-effective, low-carbon, and investor-attractive EVCS-based MGs aligned with sustainability targets.
As China’s new energy vehicle (NEV) market shifts from basic electrification toward competition over integrated technological capability and in-cabin user experience, understanding how consumers evaluate green technology and smart-cockpit configurations has become increasingly important. This study examines how perceived green technological innovation (PGTI) and smart-cockpit scenario-based configuration (SCSC) shape young Chinese consumers’ NEV purchase intentions. Drawing on the Theory of Consumption Values and signaling theory, the study combines hierarchical regression with fuzzy-set qualitative comparative analysis (fsQCA). Data were collected in multiple Chinese cities through Wenjuanxing and on-site surveys at authorized NEV dealerships and brand experience centers. Of the 500 questionnaires collected from consumers aged 18–35, 420 valid responses were retained, yielding a valid response rate of 84.0%. The regression results show that perceived value is the strongest positive correlate of purchase intention, while SCSC, technology trust, PGTI, and charging convenience also have significant positive associations. The negative PGTI × SCSC interaction indicates diminishing marginal returns rather than simple additive synergy. The fsQCA results identify eight alternative configurations leading to high purchase intention, including integrated flagship, technology-assurance, and smart-cabin-oriented pathways. The study contributes by distinguishing average net effects from equifinal configurational mechanisms and by showing that smart-cockpit value depends on its combination with technology, charging, price, and trust conditions.
Dynamic inductive charging (DIC) combined with hybrid energy storage systems (HESSs) and vehicle-to-grid (V2G) capabilities offers a promising pathway toward extended-range electric vehicles with grid integration benefits. However, real-time optimal energy management remains challenging due to multi-axis coil misalignment, component aging, and bidirectional power flow uncertainty. This paper proposes an adaptive digital twin driven artificial intelligence (AI) energy management framework integrating physics-informed neural networks (PINNs), soft actor critic (SAC) deep reinforcement learning, and model predictive control (MPC) for optimal power distribution among a proton exchange membrane fuel cell (PEMFC), lithium-ion battery, supercapacitor, dynamic wireless charging, and grid interface in four-wheel drive electric vehicles (4WD-EVs). The framework features: (1) a self-evolving digital twin with online learning via Elastic Weight Consolidation (EWC) updating every 50 cycles; (2) a PINN-based state estimator for battery-state estimation, with an average inference time of 1.1 ms and a worst-case latency of 2.8 ms; (3) a hierarchical SAC–MPC strategy with high-level mode selection and low-level power optimization; (4) real-time five-degree-of-freedom WPT misalignment compensation, achieving a mean efficiency of 91.5% under the evaluated dynamic lateral misalignment conditions, with a 50 mm displacement amplitude; (5) degradation-aware V2G optimization generating €582.50/year in revenue while reducing battery aging by 31.8%; and (6) comprehensive techno-economic analysis yielding a discounted payback period of approximately 5.57 years and a net present value of approximately €3777 over a 10-year horizon. Validated through 200+ hours of hardware-in-the-loop (HIL) simulation on the dSPACE/NVIDIA Jetson platform, the proposed approach achieves a 24.3% cost reduction and 31.8% lower battery degradation. The MPC controller exhibits an average execution time of 32.1 ms, a 95th-percentile latency of 44.8 ms, and a worst-case latency of 62.4 ms, while remaining within the 100-ms real-time control deadline. Results demonstrate the viability of adaptive digital twins for next-generation EVs with autonomous charging and multi-source architectures.
In applications with a wide constant power speed range, the use of conventional permanent magnet machines is complicated by their uncontrolled magnetic flux, increased losses at high speeds, increased inverter current, and dangerous open circuit back EMF. For this reason, synchronous machines without magnets and with a field winding on the rotor are increasingly being used in traction applications. However, due to high electrical losses in the field winding, rotor cooling becomes a critical issue. An alternative is to use hybrid excited machines, which retain the advantages of electrically excited machines while significantly reducing rotor losses. This paper presents a comparison between a conventional electrically excited machine and a novel hybrid excited machine for traction applications with a wide constant power speed range of 9.3:1 (4200 to 450 rpm, mechanical power 23.6 kW). Both machines have the same external dimensions and were optimized using the same optimization algorithm. It is shown that the hybrid excited machine provides a reduction in rotor losses by 2.0–3.25 times depending on load conditions. Its total loss is also reduced, although its cost of active materials increased by a factor of 1.6 due to the use of permanent magnets.
Multiphase permanent magnet synchronous motors (PMSMs) have been widely used in renewable energy, electric vehicle, and aviation applications. Model predictive control has emerged as a mainstream technique for multiphase drives, although conventional implementations suffer from the complexity of weighting factor tuning and excessive computational burden. This paper proposes a dimensionless multi-objective model predictive current control (MPCC) strategy for six-phase PMSM drives that operate without conventional weighting factors. Instead of penalizing harmonic subspace x-y currents through weighted cost terms, the proposed method pre-filters the 64 candidate voltage vectors to a selective set of 12 vectors whose α-β and x-y plane projections are inherently balanced, thereby constraining harmonic excitation at the source. A dimensionless multi-objective cost function is subsequently formulated, integrating current tracking, switching reduction, torque ripple suppression, and voltage/current constraints without additional weighting factors. The proposed strategy is evaluated under startup, steady-state, disturbance, and parameter sensitivity scenarios, with comparative analysis against conventional MPCC. Simulation results demonstrate that the phase current total harmonic distortion is reduced from 29.64% to 6.00%. The q-axis current settling time is reduced from 0.12 s to 0.025 s and the startup overshoot is reduced from 22 A to 11 A. The x-y subspace current ripple and average switching frequency are also reduced.
In electric vehicles (EVs), the battery management system (BMS) plays a central role in ensuring safe, efficient, and reliable battery operation under varying driving and environmental conditions. The effectiveness of a BMS largely depends on the availability of an accurate battery model, whose performance is strongly influenced by the precision of its identified parameters. However, estimating these parameters remains a difficult nonlinear optimization problem, especially under low state of charge (SOC) operation. Classical identification approaches often have limited robustness under such conditions, while metaheuristic algorithms provide a promising alternative because of their ability to handle nonlinear and multimodal search spaces. Even so, many existing methods still encounter drawbacks related to convergence speed and susceptibility to local optima. Motivated by these challenges, this study investigates the recently introduced Artificial Lemming Algorithm (ALA) for parameter identification of a second-order equivalent circuit model (2RC-ECM) under EV-oriented low-SOC operating conditions. Experimental validation is conducted using two independent dynamic datasets, namely the High Dynamic Profile (HDP) at 25 °C and the Urban Dynamometer Driving Schedule (UDDS) at −5 °C, involving different lithium-ion cells and operating conditions. ALA is benchmarked against nine competing metaheuristic algorithms under identical search boundaries and computational settings. Performance is assessed using RMSE, MAE, MaxAE, bias, convergence behavior, error distributions, execution time, and sensitivity to the number of independent runs, population size, and maximum number of iterations. The results show that ALA achieves the lowest minimum, mean, and maximum RMSE for both datasets, with minimum RMSE values of 0.01075 V for HDP and 0.03534 V for UDDS. Unseen-data validation further yields RMSE and MAE values of 0.0082 and 0.0061 V, respectively, for HDP, and 0.0416 and 0.0299 V, respectively, for UDDS. In addition, convergence, error-distribution, and sensitivity analyses show that ALA maintains competitive and consistent performance across the investigated configurations. Overall, the results demonstrate that ALA provides a favorable balance between estimation accuracy, robustness, convergence behavior, and computational cost for offline lithium-ion battery parameter identification.
The electrification of transportation pushes traction inverters, on-board chargers, and aircraft propulsion systems toward higher power density, increasingly enabled by wide-bandgap devices, thereby concentrating ever-larger heat fluxes on power semiconductor dies as package-level cooling nears its sustainable limit. Junction temperature is the resulting bottleneck: it caps the usable rating and, through the thermal cycling that fatigues module interconnects, governs reliability and service life. Managing junction temperature therefore requires a coupled chain of processes spanning the device loss that generates the heat, the models that predict it, the parameters which these models require as input, the cooling that removes it, and the control that bounds its excursion. This review traces heat transfer along this chain, organizing each link by method class and comparing representative methods against the limitation that marks its open problem. The methods within each link are relatively mature, whereas the couplings between links and their validation against realistic missions are not. The cross-cutting gaps, among them sparse drive-cycle validation, weak coupling between electro-thermal and aging models, and the absent co-design of cooling and control, are consolidated into a forward research agenda.
Electric vehicles (EVs) are crucial for mitigating greenhouse gas emissions in urban transportation. However, their integration requires efficient charging infrastructure and allocation strategies. In this paper, five heuristic algorithms were developed to allocate EVs to urban charging stations. This allocation process incorporates critical constraints, such as user preferences and charger type compatibility, while respecting station capacities governed by power output rules. The proposed methods include four initial allocation heuristics, ranging from capacity-centric and nearest-neighbor approaches to random assignments, complemented by a local search algorithm for solution refinement. To evaluate these heuristics, an optimization model minimizing station establishment and vehicle travel costs was adapted from the literature. Computational experiments were performed on both synthetic instances and real-world case studies. The results indicate that the developed heuristics, especially when enhanced by local search, deliver high-quality, near-optimal solutions within highly competitive computational times. Consequently, this study offers a scalable decision-support tool for urban planners, demonstrating how the joint optimization of infrastructure costs and user preferences can foster sustainable urban mobility and accelerate EV adoption. Ultimately, these findings offer actionable insights for scaling heterogeneous EV infrastructure, fostering urban sustainability and mitigating transport-related carbon emissions.
Against the background of the global low-carbon transformation, battery charging and battery swap (BaaS) have become two mainstream energy replenishment modes for battery electric vehicles (BEVs). Existing studies lack comparative Stackelberg game analysis covering fuel vehicle manufacturers, BEV manufacturers, and independent battery operators under heterogeneous consumer preferences including purchase price sensitivity, mileage cost perception, and vehicle depreciation attention. This study establishes a tripartite game framework covering three industrial scenarios: independent charging operation, third-party exclusive battery-swap R&D, and vehicle battery joint swap station construction. By solving closed-form equilibrium solutions and conducting parameter comparative statics plus numerical surface simulation, this paper systematically identifies how multi-dimensional consumer preferences affect product pricing, market demand, battery service level, and supply chain profit distribution. The results indicate that under the given baseline parameter settings, battery swap mode yields higher vehicle prices but lower market demand relative to charging mode; joint R&D only achieves bilateral profit win–win when consumers attach high importance to driving cost and automakers undertake low cost-sharing ratios. This study expands the theoretical framework of BEV energy replenishment supply chain games and provides quantitative decision references for vehicle and battery enterprises to select optimal operational cooperation paths.
With the increasing deployment of EV charging infrastructure, large numbers of power electronic converters are being integrated into distribution networks, making small-signal stability a key issue for converter-intensive charging facilities. In practical EV charging stations, grid-following (GFL) and grid-forming (GFM) converters may coexist and operate in parallel. Due to their different synchronization mechanisms and control structures, the stability of such mixed systems is affected not only by grid strength but also by EV charging power levels and power sharing among converters. This paper investigates the impedance characteristics and small-signal stability of three representative parallel configurations: all-GFL, all-GFM, and mixed GFL/GFM systems. Unified dq-domain impedance models are established for both converter types, and the critical short-circuit ratio (CSCR) boundaries are determined through eigenvalue-based analysis. The effects of charging power level and power-sharing ratio on the stability boundaries are further analyzed. The results show that the all-GFL configuration becomes unstable under low-SCR conditions due to phase-locked-loop-dominated dynamics, whereas the all-GFM configuration loses stability under high-SCR conditions due to reactive-power-loop dynamics. In contrast, the mixed GFL/GFM configuration exhibits both lower and upper stability boundaries and remains stable only within an intermediate SCR range. These findings reveal the combined influence of converter control type, grid strength, and charging-power operating conditions, providing guidance for the planning and stable operation of converter-intensive EV charging facilities.
This study presents an experimental assessment of how thermal management strategies influence the degradation kinetics of lithium-ion battery modules. To enhance temperature control at the system level, a novel partial Direct Liquid Cooling (DLC) configuration was developed, establishing direct physical contact between the cell surfaces and a dielectric fluid. The efficacy of the proposed DLC design in mitigating battery aging was validated through a rigorous comparative analysis against conventional Indirect Liquid Cooling (ILC), the current benchmark technology in electric vehicles. The experimental data demonstrate that the proposed partial DLC strategy significantly enhances the battery system’s thermal control, maintaining it within the optimal temperature range under identical electrothermal stress. Apart from providing higher thermal management control than conventional strategies, linear extrapolation of the measured capacity-fade trends indicates a projected increase in battery durability of 67.4%, highlighting the potential of the proposed partial DLC strategy to substantially extend system lifespan and withstand extreme operating conditions. Furthermore, while the ILC subsystem exhibited severe degradation with a 24% increase in internal resistance, the proposed partial DLC system remained remarkably resilient, retaining a stable resistance profile. These findings validate the technical viability of the proposed partial direct liquid cooling strategy as a high-performance thermal management solution for e-mobility applications.
Hydrogen-powered heavy-duty vehicles (HHDVs) operating under multiple driving conditions are subjected to coupled longitudinal, vertical, and lateral dynamic excitations, which significantly affect their dynamic performance and rollover stability. To investigate these characteristics, a coupled vehicle dynamic model consisting of a vertical dynamic model and a yaw–roll dynamic model was established, and numerical simulations were conducted under multiple operating conditions. The effects of operating condition, road roughness, initial braking speed, and braking deceleration on ride comfort and dynamic tire load were systematically analyzed. Furthermore, rollover stability was evaluated under J-turn, Fishhook, and Double Lane Change (DLC) maneuvers using yaw rate, slip angle, lateral acceleration, and lateral load transfer ratio (LTR) as evaluation indices. The simulation results show that braking causes the greatest deterioration in ride comfort, with the peak human–seat vertical acceleration increasing by 33.10% compared with the constant-speed condition, while acceleration results in a 27.55% increase. Road roughness substantially affects both ride comfort and dynamic tire load. Under braking, the peak front and rear tire dynamic loads on a Class D road are approximately 3.1 and 2.9 times those on a Class B road, respectively. Increasing the initial braking speed intensifies dynamic responses, whereas increasing the braking deceleration effectively suppresses tire dynamic load fluctuations. Among the three steering maneuvers, the Fishhook maneuver exhibits the highest rollover propensity, with the maximum absolute LTR approaching 0.8. These simulation-based findings provide insights into chassis parameter optimization, vehicle dynamic performance evaluation, and rollover prevention of HHDVs under multiple operating conditions. The present study is limited by the lack of experimental validation of the developed dynamic models, and experimental or hardware-in-the-loop validation will be considered in future work.
A transition towards sustainable transportation requires efficient integration of electric vehicles (EVs) with renewable energy sources. This work proposes a two-stage Parking Lot Energy Management Scheme (PLEMS) to minimize charging costs while maximizing solar photovoltaic utilization in commercial parking facilities. In the first stage, the optimization phase is formulated using a mixed-integer linear programming (MILP) that minimizes the overall cost of EV charging while ensuring maximum utilization of locally available PV energy. In the second stage, a gated recurrent unit (GRU)-based deep learning model performs state of charge (SOC) forecasting for EVs parked in the parking lot. Using the predicted SOC for the next time step, the system decides whether each EV will be charged or discharged, ensuring consistency with the cost-optimal MILP strategy from the first stage. The proposed PLEMS achieves up to 62% daily cost savings in charging compared to uncoordinated direct grid charging. However, this cost saving is the outcome of proposed optimization as well as the integration of PV panels in power grid. When compared with nine similar vehicles to grid (V2G)-enabled approaches from the literature, which report cost savings ranging from 9.73% to 52%, the proposed framework shows an improvement of 10% to 52% over these methods. This hybrid MILP-GRU framework offers practical V2G operation and high scalability for large EV fleets in solar-powered smart parking lots.
The rapid adoption of electric vehicles, together with increased battery mass and altered load distribution, is placing greater demands on ride comfort and suspension adaptability, while controller optimization may still request forces beyond the instantaneous capability of the physical semi-active actuator if experimentally supported force limits are not explicitly enforced. This study proposes a unified experimentally constrained optimization framework for a magnetorheological damper (MRD)-based semi-active suspension system using a two-degree-of-freedom quarter-car model. The damper is characterized at eleven current levels and represented by a branch-dependent lookup model that provides the zero-current baseline and instantaneous feasible force range. Proportional–integral–derivative (PID) and linear quadratic regulator (LQR) controllers are independently tuned using a genetic algorithm (GA) and particle swarm optimization (PSO) under identical vehicle dynamics, objective functions, tuning excitation, and MRD force constraints. Each candidate force demand is projected onto the experimentally derived feasible range throughout optimization. The controllers are tuned on a composite B–C–D profile and subsequently evaluated over nine road–speed scenarios. PID-PSO reduces the RMS sprung-mass acceleration by 15.91% and achieves the best acceleration performance in six cases, whereas LQR-PSO provides more balanced improvements in body motion, suspension travel, tire response, and force feasibility. The proposed framework therefore provides a more physically constrained basis for the comparative design and evaluation of MRD-based semi-active suspension control.
At highway merging sections, merging vehicles must enter the mainline traffic flow within a limited acceleration section. Under high traffic demand, this process often involves rapid acceleration or deceleration. Conventional merging assistance control has mainly focused on reducing acceleration and ensuring safe inter-vehicle gaps. However, when electric vehicles (EVs) are considered, battery energy consumption is also an important evaluation perspective. This study evaluates the effects of different speed adjustment strategies for merging vehicles on EV battery energy consumption and smooth merging performance at a single highway merging section. Four cases are compared: Acceleration-Minimizing Merging Control (AMC), which minimizes the absolute value of the required acceleration; Fixed-Arrival-Time Energy-Minimizing Merging Control (FEMC), which minimizes battery energy consumption under a fixed arrival time; Variable-Arrival-Time Energy-Minimizing Merging Control (VEMC), which minimizes battery energy consumption without fixing the arrival time; and a no-control case. EV battery energy consumption is calculated by integrating battery-side power over time, considering driving resistance, inertial force, drivetrain efficiency, regenerative braking efficiency, maximum regenerative power, and auxiliary power. The simulation results show that AMC is advantageous in terms of smooth merging performance, whereas VEMC achieves the lowest overall average battery energy consumption. FEMC and VEMC reduced battery energy consumption by up to 10.7% and 39.5%, respectively, compared with AMC under the evaluated initial-speed conditions, although their smooth merging performance decreased under some conditions; however, their smooth merging performance remains lower than that of AMC, and the energy-saving effect depends on the initial speed and traffic demand conditions. These results indicate that EV-oriented merging assistance control requires a control design that considers the trade-off between energy efficiency and smooth merging performance.
The decarbonisation of heavy-duty road transport is central to European climate policy, yet the economic viability of battery-electric trucks under real-world route conditions remains uncertain. This study presents a route-specific total cost of ownership (TCO) analysis comparing an 18-tonne battery-electric truck with a comparable diesel truck across four Danish distribution routes. An eight-year net present value (NPV) model at 5% discount rate captures acquisition, energy, taxation, and maintenance costs. The analysis incorporates seasonal variation and distinguishes between depot charging (0.75 DKK/kWh) and public fast charging (3.00 DKK/kWh). The electric truck achieves NPV advantages of DKK 1.01–2.14 million across all routes, with break-even periods of 1.8 to 3.3 years. Charging strategy is the most influential economic factor. The findings demonstrate that fleet-averaged TCO estimates are insufficient and route-level analysis is essential for informed electrification decisions, though results are contingent on the specific policy and operational context of this single-operator case study.
Permanent magnet synchronous motors (PMSMs) are widely used in AC drive systems, and their control performance depends strongly on accurate motor parameters. Conventional proportional-integral model reference adaptive system (PI-MRAS) observers use fixed adaptation gains, resulting in a trade-off between rapid convergence and low steady-state fluctuation. To address this limitation, this paper proposes a fuzzy proportional integral (Fuzzy-PI)-tuned MRAS observer for the simultaneous online identification of stator resistance (Rs) and stator inductance (Ls). The parameter-error dynamics are formulated from the PMSM model, and the adaptation laws are derived using Popov hyperstability theory. A fuzzy tuner uses the absolute identification error and its rate of change to schedule the proportional and integral gains online, thereby accelerating transient error convergence when the identification error is large and reducing estimation oscillations during steady-state operation. The method is evaluated through simulation and laboratory experiments involving rated operation, speed variation, parameter perturbation, and load disturbance. Under the investigated conditions, the identification errors of Rs and Ls are 3.8% and 0.18%, respectively. Compared with the conventional PI-MRAS, the reported Rs identification error decreases from 8.1% to 3.8% and the Ls identification error decreases from 0.91% to 0.18%. The results demonstrate an improved identification accuracy and disturbance recovery within the tested operating range. The implementation on an Infineon TC233 platform also demonstrates real-time feasibility, while broader validation under temperature variation, magnetic saturation, inverter nonlinearity, and measurement noise remains necessary.
To address the insufficient control accuracy of traditional vehicle stability control methods under nonlinear conditions, this paper proposes a combined stability control strategy for distributed-drive electric vehicles based on the phase plane method. A two-degree-of-freedom vehicle dynamics model incorporating the Magic Formula tire model is established. The β−β˙ phase plane is selected, and a dynamic stability boundary function is constructed through saddle point analysis and road adhesion coefficient fitting. An instability index is defined to quantify the deviation from the stable state. Based on this index, a hierarchical control strategy is designed: within the stable region, model predictive control (MPC) is employed for yaw moment optimization via differential torque distribution among the four in-wheel motors; when the vehicle enters the unstable region, sliding mode control-based active rear-wheel steering (ARS) is activated. The strategy is validated through CarSim-Simulink co-simulation under step steering and slalom maneuvers. Results show that under the high-speed step steering condition, compared with the uncontrolled case, the combined control reduces the peak yaw rate by 5.3%, the overshoot from 32.86% to 27.38%, the settling time from 9.87 s to 8.02 s, and the oscillation amplitude by 36.5%; under the slalom condition, the yaw rate amplitude is reduced by 5.4%. The proposed strategy effectively improves vehicle handling stability.
