During vehicle operation, tire cornering characteristics vary dynamically with driving conditions and road conditions, making it difficult for fixed tire cornering stiffness (TCS) parameters to accurately represent the actual lateral dynamic characteristics of the vehicle. To improve the path-tracking accuracy of intelligent electric vehicles, this paper proposes a longitudinal-lateral coordinated path-tracking control method considering tire cornering characteristics. First, a vehicle dynamics model is established, and the concept of equivalent tire cornering stiffness (ETCS) is introduced. The forgetting factor recursive least squares (FFRLS) method is then employed to estimate and update the front and rear equivalent tire cornering stiffness online. Second, in lateral control, model predictive control (MPC) is adopted as the core controller, and the velocity pausing particle swarm optimization (VPPSO) algorithm is used to optimize the weighting matrix. In longitudinal control, a double-layer PID controller is employed to track the target speed. Finally, simulations are carried out under the double-lane-change maneuver. The results show that, compared with the fixed MPC-fixed TCS method, the proposed method reduces the lateral error by 62.4%; compared with the fixed MPC-FFRLS method, the lateral error is reduced by 41.9%. These results verify the effectiveness of the proposed method.

An efficient vehicle thermal management system (VTMS) is essential for ensuring the performance, safety, and driving range of battery electric vehicles (BEVs), and for supporting vehicle-level range performance under representative operating conditions. Maintaining all major thermal subsystems within their optimal temperature ranges is therefore critical to overall vehicle performance and market competitiveness. Over the past decade, VTMS technologies for BEV applications have been extensively investigated. However, existing reviews primarily focus on system configurations, heat-transfer mechanisms, and material properties, with limited systematic analysis from a control-oriented perspective to provide unified theoretical guidance. Therefore, this paper presents a comprehensive review of VTMS control methods for BEVs from a control-theoretical viewpoint. Different from the conventional active/passive/hybrid hardware classification, the proposed control-oriented taxonomy classifies VTMS methods as preventive and corrective strategies according to intervention timing, information source, and dominant control mechanism. Preventive control methods, based on feedforward control principles, mitigate thermal issues through geometric optimization, parametric optimization, and hybrid optimization approaches. Corrective control methods, grounded in feedback concepts, achieve real-time thermal deviation correction through classical, modern, and intelligent control techniques. Comparative studies reveal that preventive control methods offer advantages including high design flexibility, superior temperature uniformity, and mature commercialization, but are constrained by high manufacturing costs, lengthy validation cycles, and increased system weight. Corrective control methods demonstrate excellent performance in battery-pack compatibility, adaptability to complex operating conditions, and multi-subsystem coordination, though their development is limited by computational burden and algorithmic complexity. Intelligent control methods exhibit significant advantages in multi-objective optimization and thermal safety assurance, and show strong potential for improving vehicle-level energy efficiency and driving-range performance under application-specific operating conditions, representing an important future direction of VTMS development. The proposed control-oriented classification framework provides unified theoretical guidance for the design, optimization, and control of BEV VTMS, thereby supporting the coordinated improvement of thermal safety, energy efficiency, and vehicle-level range performance under clearly defined operating conditions.

The temperature non-uniformity problem of battery packs under extreme operating conditions in hot regions has become one of the main obstacles for the development of automotive power batteries. Under the combined effects of high-temperature environments and large-current discharging, the temperature differences within battery modules can lead to localized hotspots, which seriously threatens battery safety and service life. However, existing research primarily focuses on the overall temperature control of battery packs, with less consideration given to temperature differences within battery modules and system energy consumption optimization, making traditional proportional-integral-derivative (PID) control strategies difficult to simultaneously balance three key objectives: temperature uniformity, control precision, and system energy consumption. In this study, an improved adaptive genetic algorithm (IAGA) control strategy for battery thermal management systems is proposed to achieve multi-objective balanced optimization. A battery pack thermal model considering heat transfer between modules is established, which comprehensively accounts for module battery thermal behavior, intermodule heat transfer, and the temperature gradient effect of liquid cooling systems. Through bench test validation, the temperature calculation accuracy of the proposed model improves by 11.13 % compared with traditional models. Based on this model, an improved adaptive genetic algorithm optimization strategy with coolant volumetric flow rate as the control variable is designed. The experimental results demonstrate that under maximum vehicle speed conditions at 40 degrees C ambient temperature, compared with traditional PID control strategies, the IAGA strategy reduces the maximum temperature difference between battery modules by 43.45 %, enhances temperature control precision from 1.01 degrees C to 0.03 degrees C, and shortens temperature stabilization time by 24.7 %. Compared with basic genetic algorithm control strategies, the IAGA strategy reduces the maximum temperature difference between battery modules by 42.25 % while decreasing coolant volumetric flow rate by 9.5 %, effectively reducing system energy consumption by 4.8 %. The IAGA control strategy proposed in this paper provides a new solution for temperature uniformity control of electric vehicle battery packs under extreme conditions, which has significant implications for improving the safety and reliability of electric vehicles in extremely hot regions.

Direct refrigerant cooling is a promising technology for battery thermal management systems (BTMSs) in electric vehicles (EVs), particularly under high-temperature operating conditions, owing to its compact system configuration and high heat transfer efficiency. However, its cooling capacity is strongly influenced by compressor speed, electronic expansion valve openings, refrigerant thermodynamic states, and air-speed-dependent external convective heat transfer. Therefore, reducing compressor energy consumption while maintaining the battery temperature within the allowable range remains challenging. In this study, a control-oriented lumped thermal model is developed for a direct refrigerant-cooled battery pack. The model accounts for battery heat generation, refrigerant-side cooling, ambient heat exchange, and air-speed-dependent forced convection. Key thermal parameters, including battery internal resistance, entropic heat coefficient, and an empirical weighting coefficient for estimating the equivalent refrigerant mass flow rate in the cold plates, are identified through cell-level and system-level experiments. The optimal weighting coefficient is determined to be 0.64. The root mean square error (RMSE) of the predicted average battery temperature is 0.556 °C under the identification condition, and the maximum absolute error is 0.53 °C under an independent validation condition. These results indicate that the developed model can accurately capture the dominant dynamic variation of the pack-level average battery temperature with low computational complexity. It should be noted that the present model is intended for energy-efficient average-temperature control rather than spatial temperature-field reconstruction. To account for possible temperature non-uniformity at the pack level, sufficient temperature safety margin is retained in the experimental validation, and the maximum measured battery temperature remains below the prescribed safety limit throughout the tests. Based on the validated model, a nonlinear model predictive control (MPC) strategy solved by sequential quadratic programming (SQP), namely the MPC-SQP strategy, is proposed to coordinate compressor speed and the openings of two electronic expansion valves. Vehicle-level experiments are conducted in an environmental chamber at 40 °C under a real-world driving cycle, the China light-duty vehicle test cycle (CLTC), and the worldwide harmonized light vehicles test cycle (WLTC). Compared with on-off and proportional-integral-derivative (PID) control, the proposed MPC-SQP strategy reduces the energy consumption of the battery cooling system by 7.99% and 4.10%, respectively, under the real-world driving cycle. Energy savings of up to 11.14% and 13.12% are achieved under the CLTC and WLTC driving cycles, respectively. Meanwhile, the average battery temperature is maintained below the prescribed safety limit throughout the tests. The results demonstrate that the proposed MPC-SQP strategy improves the matching between dynamic thermal load and refrigerant-side cooling capacity, thereby reducing redundant cooling and compressor energy consumption in direct refrigerant-cooled BTMSs.

Multi-objective coordinated control of integrated thermal management systems (ITMS) presents significant technical challenges for electric vehicles operating at high speeds in extremely hot regions. The coupling effects between battery and cabin thermal loads under high-temperature and heavy-load conditions reduce temperature control precision and increase energy consumption, compromising both vehicle safety and economic performance. However, existing research primarily focuses on individual subsystem temperature control and neglects both the coupling characteristics between batteries and passenger compartments and system-level multi-objective optimization. This paper proposes a Multi-Island Genetic Algorithm-Differential Evolution (MIGA-DE) control strategy for ITMS to achieve coordinated optimization of battery and passenger compartment temperature control. An ITMS mathematical model considering system coupling characteristics is established to analyze the interaction mechanisms among battery thermal behavior, passenger compartment thermal load, and refrigerant flow distribution. Based on this model, a MIGA-DE multi-objective optimization strategy is developed using three control variables: battery electronic expansion valve (EEV) opening, air conditioning EEV opening, and compressor speed. Experimental results demonstrate that under maximum vehicle speed conditions, the MIGA-DE strategy outperforms traditional proportional-integral-derivative (PID) control by achieving reductions of 80.2%, 90.6%, and 7.96% in battery temperature fluctuation, passenger compartment temperature fluctuation, and cumulative system energy consumption, respectively. The strategy effectively addresses the low control precision and instability issues caused by battery-air conditioning system coupling. This control approach provides a novel solution for multi-objective coordinated control of electric vehicle ITMS under extreme conditions, with significant implications for improving vehicle safety, reliability, and economic performance in extremely hot regions.

Magnetic gears, utilizing permanent magnet fields for contactless power transmission, offer significant advantages for wind power applications, including no mechanical loss, elimination of lubrication, and inherent overload protection, addressing key limitations of conventional gearboxes. However, their adoption is hindered by common topological drawbacks: weak magnetic field modulation, low torque density, structural complexity, magnetic saturation, and excessive flux leakage. To overcome these limitations, this study proposes a modulation-enhanced monotonic magnetic ring double-modulated three-rotor coaxial magnetic gear. Simulation analysis first compared modulation schemes and validated the proposed gear’s feasibility against a traditional coaxial design. Subsequently, single-factor and Plackett–Burman screening tests identified critical factors significantly influencing torque performance. These factors were then analyzed using Box–Behnken testing, establishing a corresponding response surface model. Following model accuracy verification, multi-objective optimization was performed. Results demonstrated substantial improvements: torque density increased by 19.05%, inner rotor torque fluctuation reduced to 2.74%, and outer rotor torque fluctuation reduced to 2.24%. A trade-off was observed, with intermediate rotor torque fluctuation rising to 17.8%. This work presents a novel structural concept for magnetic transmission design, highlighting the enhanced performance of the proposed topology and underscoring the significant potential of advanced magnetic gears in future wind power transmission systems.

Accurate vehicle state estimation and tire cornering stiffness are crucial for advanced chassis control systems. However, most existing vehicle state estimation methods rely on assumed or fixed values of tire cornering stiffness. Because of the highly nonlinear characteristics of vehicle tires and varying driving conditions, these parameters may undergo significant changes. This paper proposes a joint estimation method based on Forgetting Factor Recursive Least Squares (FFRLS) and Dung Beetle Optimizer-Square-root Cubature Kalman Filter (DBO-SCKF). The FFRLS algorithm is employed to estimate tire cornering stiffness in real time, enabling adaptation to changes in tire characteristics under different conditions. Subsequently, the estimated stiffness parameters are incorporated into the DBO-SCKF algorithm for vehicle state estimation, enhancing accuracy in dynamic driving environments by continuously updating the model parameters. Finally, simulation experiments are conducted for validation. The results demonstrate that, compared to the Cubature Kalman Filter (CKF), the proposed FFRLS-DBO-SCKF algorithm reduces the root mean square error (RMSE) of yaw rate, sideslip angle, and longitudinal speed by at least 65.7% in double-lane-change conditions. Similarly, compared to the Extended Kalman Filter (EKF), it improves estimation accuracy by at least 62.9% in serpentine conditions.

To address the challenge of optimizing system adaptability, disturbance rejection, control precision, and convergence speed simultaneously in four-wheel steering (4WS) stability control, a 4WS controller with a variable steering ratio (VSR) strategy and fast adaptive super-twisting (FAST) sliding mode control is proposed to control and output the steering angles of four wheels. The ideal VSR strategy is designed based on the constant yaw rate gain, and a cubic quasi-uniform B-spline curve fitting method is innovatively used to optimize the VSR curve, effectively mitigating steering fluctuations and obtaining precise reference front wheel angles. A controller based on FAST is designed for active rear wheel steering control using a symmetric 4WS vehicle model. Under double-lane change conditions with varying speeds, the simulations show that, compared with the constant steering ratio, the proposed VSR strategy enhances low-speed sensitivity and high-speed stability, improving the system’s adaptability to different operating conditions. Compared with conventional sliding mode control methods, the proposed FAST algorithm reduces chattering while increasing convergence speed and control precision. The VSR-FAST controller achieves optimization levels of more than 7.3% in sideslip angle and over 41% in yaw rate across different speeds, achieving an overall improvement in the stability control performance of the 4WS system.

In order to solve the problem of electromagnetic vibration and noise caused by torque ripple, cogging torque and radial electromagnetic force of V-type permanent magnet synchronous motor for vehicle, a multi-objective optimization method of electric vehicle drive motor based on genetic algorithm is proposed in this paper to improve the electromagnetic vibration and noise level of the motor. Firstly, the finite element model of the target motor is established, and the electromagnetic force characteristics are deduced and analyzed. Then genetic algorithm is used to optimize the Angle of V-shaped magnetic pole, the length and width of magnetic bridge and the thickness of permanent magnet to obtain the best comprehensive performance of the motor. Finally, the electromagnetic vibration noise characteristics of the motor before and after optimization are obtained through simulation analysis. The comparison shows that the maximum amplitude of the motor after optimization is reduced by 27.4%, and the electromagnetic noise is reduced by 6.9 dBA under rated working conditions and 9.1 dBA under full speed working conditions. The experimental results are basically consistent with the simulation results. The results show that the multi-objective genetic algorithm optimization method used in this paper is accurate and feasible to optimize the design of V-type permanent magnet synchronous motor, and provides a reference value for the performance optimization of electric vehicle drive motor.

Under extreme operating conditions, addressing the issues of insufficient torque distribution and stability in Distributed Wheel Hub Motor-Driven Electric Vehicles (DWHDEV) caused by coupled nonlinear characteristics and safety constraints, this paper proposes an adaptive nonlinear model predictive control strategy for coordinated stability and anti-slip torque distribution. First, a 7-degree-of-freedom vehicle dynamics model is established using Matlab/Simulink. Subsequently, within the designed nonlinear model predictive controller, considering the nonlinear dynamic characteristics of slipping which is easy to occur on roads with low road adhesion coefficients, an objective function is devised that satisfies both vehicle stability and dynamic performance requirements. Based on the stability criterion of the phase plane method, a weight adaptive regulator is designed. Finally, a co-simulation model is established using Carsim and Matlab/Simulink. Simulation results show that the weight adaptive adjustment torque distribution control strategy proposed in this paper improves the handling stability by 7.6% and 10.5% over the fixed-parameter control strategy under double lane-change condition and sine steering condition, respectively, which not only meets the demand of vehicle dynamics performance, but also reduces the tire slip ratio. Therefore, the control strategy can effectively improve the stability and anti-slip capability of the vehicle under extreme working conditions.

To address the issue of reduced tracking accuracy caused by time-varying characteristic parameters during the path-tracking process of intelligent electric vehicles, this paper proposes a control strategy that integrates an adaptive linear quadratic regulator (IALQR) with a radial basis function neural network (RBFNN)-based adaptive feedforward control, effectively mitigating the impact of time-varying parameter variations. Based on linear quadratic regulator (LQR) theory, an adaptive linear quadratic regulator (ALQR) is designed, which employs an adaptive sliding mode observer (ASMO) to estimate tire lateral forces and incorporates the forgetting factor recursive least squares (FFRLS) method to dynamically adjust tire cornering stiffness. Furthermore, considering road curvature and changes in lateral tracking error, an adaptive feedforward controller based on an RBFNN is developed to generate the feedforward steering angle. Finally, a co-simulation platform is established using CarSim/Simulink. Under double-lane-change conditions, the proposed strategy demonstrates a 38.8% improvement in tracking accuracy compared with ALQR alone and an 83.9% enhancement over conventional LQR, validating the feasibility of this integrated control approach.

Excessive core temperature in lithium-ion batteries for new energy vehicles can accelerate electrochemical corrosion, thereby degrading battery lifespan. In extreme cases, it may even induce thermal runaway. However, the inherent complexity of internal electrochemical reactions and the battery's sealed structure make direct measurement of its core temperature highly challenging. To address the challenge in precise core temperature measurement, this paper proposes a maximum correntropy criterion improved adaptive extended Kalman filter (MCC-AEKF) for accurate battery core temperature estimation. The method establishes a battery thermal model with parameters identified by adaptive forgetting factor recursive least squares, then enhances the traditional EKF by incorporating MCC and Sage-Husa adaptive criterion to mitigate the effects of non-Gaussian noise and initial parameter settings, thereby improving estimation accuracy. Validation through co-simulations in AMESim and MATLAB/Simulink under urban dynamometer driving schedule and highway fuel economy test conditions demonstrates that the proposed MCC-AEKF achieves at least 41.7% higher accuracy than conventional methods. This approach effectively resolves the low-accuracy issue in traditional algorithms for core temperature estimation of new energy vehicle power batteries, significantly enhancing battery safety.

To address the challenge of directly and accurately measuring the internal temperature of lithium-ion power batteries in electric vehicles, this paper proposes an online precise estimation method for battery internal temperature based on the Aquila Optimizer-optimized Adaptive Strong Tracking Extended Kalman Filter (AO-ASTEKF). Building on a battery equivalent thermal model with parameters identified using the Genetic Algorithm (GA), the Aquila Optimizer (AO) is employed to optimize the initial noise covariance settings of the traditional Extended Kalman Filter (EKF), thereby mitigating the impact of improper initialization. To resolve the estimation deviation caused by fixed noise covariance in EKF, the Sage-Husa adaptive filtering technique is introduced to enable adaptive adjustment of noise covariance values. Furthermore, to counteract the estimation accuracy degradation of the filter due to sudden temperature changes in high-temperature environments, the Strong Tracking (ST) filter is incorporated to enhance the tracking capability of the EKF. Through co-simulation in AMESim and MATLAB/Simulink, the accuracy of the proposed AO-ASTEKF algorithm in estimating battery internal temperature is validated under different ambient temperatures and operating conditions. Experimental results demonstrate that the AO-ASTEKF algorithm improves estimation accuracy by at least 58.46% compared to both the traditional EKF and the Strong Tracking Extended Kalman Filter (STEKF). This method effectively overcomes the limitations of conventional algorithms in accurately estimating battery internal temperature, holding significant importance for ensuring battery safety and enhancing battery performance.

To cope with the challenges of inadequate precision and weak robustness of tire-road friction coefficient (TRFC) and vehicle state estimation under mixed Gaussian noise circumstances, this research puts forward a method combining maximum correntropy criterion (MCC) and generalized high-degree cubature Kalman filter (GHCKF). A coupled lateral and longitudinal vehicle model and the Dugoff tire model are established. The vehicle mass is estimated using the recursive least squares approach based on a forgetting factor (FFRLS). Low-cost onboard sensors are utilized to design observers for vehicle state and TRFC. The observers' effectiveness is tested by Carsim/Simulink co-simulation tests under the conditions of sine steering input and steering wheel angle step input. The research results indicate that in handling mixed Gaussian noise environments, maximum correntropy generalized high-degree cubature Kalman filter (MCGHCKF) method outperforms maximum correntropy cubature Kalman filter (MCCKF), GHCKF, and cubature Kalman filter (CKF) methods concerning robustness and estimation accuracy, providing stable and reliable support for systems of vehicle active safety control.

To boost the precision of estimating the vehicle driving state (VDS) and tire-road friction coefficient (TRFC) in a non-Gaussian noise environment (NGNE) and reduce the dependence on wheel angular velocity (WAV) sensors, this research suggests a technique for estimating the VDS and TRFC through control without sensors of the permanent magnet synchronous motor (PMSM). Firstly, a three-degree-of-freedom (3-DOF) automobile dynamics model and the mathematical model of PMSM are formulated, leading to the derivation of maximum correntropy singular value decomposition cubature Kalman filter (MCSVDCKF). Subsequently, a sensor-less control system (SLCS) for PMSM is designed utilizing MCSVDCKF algorithm to precisely estimate the rotor velocity and location of PMSM. The rotor speed obtained from this system substitutes the WAV signal, thus validating its effectiveness. Ultimately, the practicality of our method for estimating VS and TRFC in a NGNE is verified via simulated tests. The RMSE values for sideslip angle and TRFC prediction outcomes derived from our MCSVDCKF method improved by 77.7% and 49.1%, respectively. This demonstrates greater exactness and increased sturdiness compared against existing methods such as maximum correntropy cubature Kalman filter (MCCKF), singular value decomposition cubature Kalman filter (SVDCKF), and cubature Kalman filter (CKF).

To improve the stability and acceleration of electric vehicles with four in-wheel motors under various road conditions, an anti-slip regulation method considering the identification of slip ratios is proposed. In this paper, the model of the whole vehicle is built, and the road identifier based on the tire model established by Burckhardt is designed. Then, an anti-slip controller that utilizes the variable universe fuzzy proportional integral derivative (PID) algorithm is established to adjust the driving torques of the four in-wheel motors according to the road conditions. The simulation results show that the designed control strategy can quickly and accurately identify the optimal slip ratio under each typical road condition and make the tire slip ratio approach the optimal slip ratio in a short time, so as to effectively improve the driving stability and dynamic performance of the vehicle.

Aiming at the problems of slow response, poor comfort and high fuel consumption of the existing adaptive cruise control system under complex operating conditions, a multi-objective adaptive cruise control method of intelligent vehicles based on multi-mode switching is proposed. Firstly, the overall scheme of the adaptive cruise control system is designed by using the hierarchical control structure, and the multi-mode switching strategy is designed by using the fuzzy control theory to realize the division and switching of the working modes during vehicle cruise. Secondly, based on the variable spacing strategy and longitudinal kinematics model, the multi-objective of safety, following performance, fuel economy and comfort is analyzed and carried out, and a quadratic multi-objective optimization function based on multi constraints is obtained. Then, the model predictive control algorithm based on particle swarm optimization (PSO) is used to transform multi-objective function into a standard form with predictive control increment as the optimization variable, and the optimal control rate is solved. Finally, the simulation experiment is carried out by setting three complex working conditions: the preceding vehicle uniform speed change, the preceding vehicle rapid speed change and the adjacent vehicle cut in. The results show that the proposed method can meet the requirements of safety, comfort and fuel economy, and can improve the adaptability and friendliness of intelligent vehicle cruise control system.

To overcome the issue of unreachable targets and local optima in traditional artificial potential fields in multi-type scenarios, this paper introduces a method for obstacle avoidance path planning for intelligent vehicles based on the sparrow potential field (SPF). First, by integrating gravity and repulsion adjustment factors into the traditional artificial potential field, we propose a new intermediate potential field and target repulsive potential field. The resulting potential field is then optimized through the vehicle’s heading angle to resolve issues present in structured scenes. Second, we propose an adaptive velocity function and consider dynamic constraints in path planning. Next, we combine the improved artificial potential field with the sparrow search algorithm to resolve local path optimization problems in unstructured scenarios. Finally, simulation experiments are conducted using Simulink and Carsim co-simulation platform. The results show that in the unstructured scenario, the evaluation function score of SPF algorithm is the best, and the number of algorithm iterations is reduced by about half on average. In a structured scenario, the maximum lateral acceleration of the path planned by the SPF algorithm is generally reduced by about 0.1 g, and the average front wheel angle is reduced by about 2.3