Junqiu Li

Vehicle creep control is widely used for vehicle following in congested traffic and maneuvering in confined areas, and it can greatly ease the burden of driving in these situations, especially for heavy vehicles. In this paper, a hierarchical creep control strategy is proposed for multi-axle distributed electric vehicles. To address the problem of multiple operating conditions and external disturbances, the upper level establishes an event-triggered gain scheduling controller to obtain the required creep torque. The lower level proposes a torque distribution strategy that takes into account motor faults and achieves adaptive fault-tolerant torque distribution to ensure that there is no degradation of the dynamic response of the vehicle under creep conditions. Furthermore, a cost function incorporating the expected dynamic response constraints is designed, and the relationship between interpretability coefficients and dynamic response constraints is established. Controller parameters that achieve the expected creep dynamic response are obtained offline by adjusting the interpretability coefficients. Simulations and real-vehicle experiments show that the proposed control strategy and parameter tuning method can allow the vehicle to achieve the desired dynamic response constraints of creep.

Dual-side independently driven electric tracked vehicles have become an important research focus owing to their flexible and efficient propulsion configuration. However, variations in payload and terrain conditions complicate the coordination of longitudinal and steering demands and degrade vehicle-motion robustness. To address these issues, this paper proposes a hierarchical motion-control strategy. In the upper layer, an interpretable rule-based mapping converts the pedal input and current motor speed into a common longitudinal-torque demand, whereas a soft actor–critic (SAC) agent generates a continuous steering differential-torque demand from the steering input and measurable vehicle states. A subsequent torque-coordination and allocation module combines the two demands subject to the speed-dependent torque limits of the bilateral motors and reconstructs mutually compatible longitudinal-speed and yaw-rate references through the nominal model. In the lower layer, Tube model predictive control tracks the reconstructed references and corrects the bilateral torques under bounded disturbances and actuator constraints. Hardware-in-the-loop tests conducted under the prescribed operating and disturbance conditions indicate that the complete architecture realizes the commanded steering response with smooth torque outputs and provides a favorable balance among state-tracking accuracy, torque smoothness, and disturbance attenuation.

To reduce vehicular emission pollution in cold regions and maximize sustainable development of transportation, AC self-heating of electric vehicles is acknowledged as an efficient approach to mitigate the decline in Li-ion battery performance under low-temperature conditions. This paper introduces a novel battery self-heating approach based on reconfiguration of the drive circuit, which is compatible with both preheating and on-route heating. The undesired torque generated by the heating current can be inherently nullified regardless of the rotor position. The control of heating and driving currents is entirely decoupled, facilitating straightforward adaptation to a range of heating strategies. Furthermore, a battery electro-thermal model is proposed and integrated with the drive system model to estimate the battery temperature evolution. Comprehensive experiments are designed to validate the operating principle and the accuracy of battery temperature estimation under various working conditions. The results present a high fidelity between the experimental data and the simulation outcomes. The root mean square errors of the predicted battery temperature under all the constant and combined driving conditions are less than 1 °C.

Efficient cooling systems are crucial for achieving high efficiency and power density in electric vehicle motors. To enhance motor cooling performance, a novel oil spray cooling system was developed, referred to as the centripetal-inclined oil spray (CIOS) cooling system. The CIOS cooling system features axial oil channels evenly distributed on the surface of the stator core, with each channel connected at both ends to stepped oil channels. This configuration allows for direct oil spraying towards the center at specific inclined angles without the need for additional components such as nozzles, oil spray rings, and oil spray tubes, which reduces costs, minimizes the risk of oil leakage, and enhances motor reliability. Electromagnetic and computational fluid dynamic simulations were conducted on the motor with the CIOS cooling system. The results indicated that the CIOS cooling system adversely impacted core losses and torque, while these effects were minimized after optimization, with losses increasing by up to 0.29% and torque decreasing by up to 0.45%. The CIOS cooling system achieved stable oil spraying, forming oil films on the end-winding with a maximum formation rate of 49.4% and an average thickness of 1.56 mm. Compared to the motor with oil spray rings, the motor with the CIOS cooling system exhibited lower temperatures across all components and more uniform cooling. Finally, the cooling performance of the CIOS cooling system was verified through experiments, and the results showed that the measured temperature closely matched the simulated results, with a maximum error of 5.9%. The findings in this study are expected to provide new insights for optimizing oil cooling systems in electric vehicle motors.

Power coordination strategies (PCSs) are crucial for enhancing the dynamic performance of heavy-duty electric drive vehicles. In an electric drive system (EDS) for such vehicles with minimal battery configuration, system states fluctuate frequently due to the hybrid system’s nonlinearity, time-varying constraints, limited energy storage, and the controller complexity. To tackle the issue, this article presents a dynamic event-triggered model predictive control (DETMPC) method with a two-parameter adaptive update strategy (TPAUS) for heavy-duty electric drive vehicles. Departing from traditional component-independent control methodologies, this study establishes an integrated control model considering front and rear power chains of EDS. The control model is updated each calculation cycle to reduce dimensionality and is subsequently reformulated into a linear optimization control problem with component time-varying constraints. To lighten the computational load and improve system stability, a dynamic event-triggered mechanism (DETM) with adaptive prediction horizon and sampling interval is introduced. Finally, an EDS physical model for the heavy-duty vehicle using the Simscape-Electrical library is built, and the effectiveness of the proposed strategy is validated via hardware-in-the-loop experiments. Results show the proposed strategy effectively manages EDS state fluctuations and reduces the solving frequency by 85.13% and single-step time by approximately 56.67% compared to conventional MPC.

Autonomous obstacle avoidance (OA) has garnered increasing attention these years, which is a demanding task especially for heavy vehicles with maneuvering difficulties. A hierarchical OA framework that consists of a new virtual state planning optimizer (VSPO) and a dynamic path follower (DPF) considering dynamic properties is proposed for an autonomous heavy vehicle. In the upper layer, a new path virtual state predictor based on the nonlinear tire is proposed, with high-fidelity modeling for path dynamics (PDs) description. A rolling optimization is solved under dynamic constraints related to vehicle lateral safety and planning barrier functions. Additionally, a multidimensional safety evaluator is designed considering collision risk anisotropy, by which the optimal state sequence is obtained iteratively through the discrete planning equation. In the lower layer, a dynamic following algorithm with dynamic cost is proposed based on NMPC, in which a high-fidelity predictive model is built to depict multiple degrees of freedom (DOF) and reflect wheel slip. In order to improve the adaptation of the optimal control, a variable weight regulation strategy is formulated with a threshold sensitivity function. The constraints related to vehicle lateral safety and wheel slip are constructed, and the initial control law based on the Lyapunov function is designed, which are respectively converted to the limitation of vehicle states for stability enhancement. Finally, the simulation platform is established, and the validation is conducted in different cases, which proves the effectiveness of reliable OA and dynamic performance improvement.

This article proposes a handling-stability control strategy for distributed drive electric vehicles (EVs) to improve motion performance. A motion supervisor, using only front steering angle feedback, is developed to evaluate the driving state and generate a unified yaw rate reference for handling-stability coordination. To ensure tracking convergence, a Lyapunov-based nonlinear model predictive control (LNMPC) strategy is proposed for direct yaw moment control (DYC), incorporating a contraction constraint to guarantee closed-loop stability, with rigorous proofs provided. For rapid problem-solving, a modified iterative linear quadratic regulator (iLQR) algorithm is developed, leveraging a relaxed log barrier function and double-loop iteration to handle inequality constraints, preventing violations and theoretically ensuring convergence to the original problem’s solution. Additionally, an auxiliary control law is applied to generate the initial solution in iLQR, reducing sensitivity. Using a Karush-Kuhn-Tucker (KKT) conditions-based approach, the virtual control distribution is optimized efficiently, and the torque command of in-wheel motors (IWMs) can be gained. Simulations and hardware-in-the-loop (HIL) experiments demonstrate superior handling-stability performance and high computational efficiency with the proposed strategy.

Multi-axle distributed electric drive heavy-duty vehicles have the characteristics of high transmission efficiency, strong maneuverability, and good passability, making them widely used in large cargo transportation. However, the current driving torque control strategies of multi-axle distributed electric drive heavy-duty vehicles lack comprehensive consideration of their longitudinal and lateral dynamic characteristics, making it difficult to comprehensively optimize multiple performances such as power economy, comfort, and stability. In order to solve the above problems, This work focuses on a five-axle distributed electric drive heavy-duty vehicle. Firstly, given the differences in dynamics between two-axle vehicles and multi-axle vehicles, the dynamic model of the multi-axle distributed electric drive heavy-duty vehicle and its critical components is constructed. Then, by analyzing the characteristics of power economy, comfort, and stability of the multi-axle distributed electric drive heavy-duty vehicle, an optimal driving torque control strategy based on multiple performance coordination is proposed. Finally, on the hardware-in-the-loop (HiL) platform, the performance of the optimal driving torque control strategy proposed in this paper is verified by using the China Heavy-Duty Commercial Vehicle Test Cycle for Truck (CHTC-HT) and a straight-line acceleration driving condition on a split friction road. The simulation test results show that, compared with the traditional torque average distribution strategy, the proposed optimal driving torque control strategy can reduce the energy consumption rate by 3.45% in CHTC-HT. The strategy is attributed to the driving torque distribution based on the vehicle’s optimal instantaneous energy consumption, and vehicle comfort is also ensured by the driving mode switching frequency suppression. Subsequently, the vehicle’s stability on the split friction road is effectively improved by the torque coordination compensation strategy. This control strategy significantly improves the comprehensive performance of multi-axle distributed electric drive heavy-duty vehicles.

Mechanical abuse can lead to internal short circuits and thermal runaway in lithium-ion batteries, causing severe harm. Therefore, this paper systematically investigates the thermal runaway behavior and safety assessment of lithium iron phosphate (LFP) batteries under mechanical abuse through experimental research. Mechanical abuse experiments are conducted under different conditions and battery state of charge (SOC), capturing force, voltage, and temperature responses during failure. Subsequently, characteristic parameters of thermal runaway behavior are extracted. Further, mechanical abuse conditions are quantified, and the relationship between experimental conditions and battery characteristic parameters is analyzed. Finally, regression models for battery safety boundaries and the degree of thermal runaway risk are established. The research results indicate that the extracted characteristic parameters effectively reflect internal short circuit (ISC) and thermal runaway behaviors, and the regression models provide a robust description of the battery's safety boundaries and thermal runaway risk degree. This work sheds light on understanding thermal runaway behavior and safety assessment methods for lithium-ion cells under mechanical abuse.