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The traditional traction control system (TCS) based on hydraulic braking only works when the wheels are slipping, which will cause the problem of slow response to extreme slip. In addition, the TCS of four‐wheel‐independent‐drive electric vehicle (4WIDEV) is often based on road adhesion characteristics identification or optimal slip ratio identification to implement active control, which is difficult to achieve in engineering. Aiming at this problem, a practical active TCS is proposed in this paper. Firstly, according to the wheel slip state of the front and rear axles, the dynamic transfer of torque between axles is realized to maintain the vehicle propulsion power. Second, the adhesion conditions between road and tire are classified, and two sets of target slip ratio thresholds are formulated for high and low adhesion pavement, respectively. Then the current road adhesion coefficient is estimated by using the advantage that the in‐wheel motor torque can be obtained in real‐time. Thirdly, the overall framework of the control strategy is established, the logic threshold control algorithm is adopted for tracking the wheel target slip ratio. Finally, the simulation results show that the proposed active TCS can improve the vehicle power and avoid excessive wheel slipping. [ABSTRACT FROM AUTHOR]

The driving range of electric vehicles (EVs) is still an important factor restricting their development. Although the rising battery energy density has reached a bottleneck, which is a key constraint, the drive energy management strategy also has a significant effect and can improve the driving range of EVs, since wheel traction torque control can directly optimize the driving energy consumption of EVs. In order to comprehensively analyze the current research status of driving energy management and clarify its development direction, this review focuses on the driving energy management strategy of EVs and systematically summarizes the configurations and power distribution strategies of the dual-motor coupling drive system (DCDS), as well as torque vectoring control strategies of the decentralized drive system. Firstly, driving energy losses are analyzed in detail, which mainly include electric loss, tire slip energy dissipation, and the power of cornering resistance. Secondly, typical configurations of the DCDS are introduced, and the power distribution strategies of the DCDS are comprehensively reviewed. Finally, as an interesting energy-saving technology, energy-saving torque vectoring, generally applied to decentralized drive systems, is reviewed in detail in terms of its energy-saving pathways and control strategies, which are classified as front-and-rear torque vectoring and left-and-right torque vectoring. Research findings indicate that the driving range of EVs can be effectively increased by applying a driving energy management strategy based on several novel multi-power source drive systems. The development of a driving energy management strategy and the required novel drive systems will be a valuable and crucial direction for further energy conservation in EVs. [ABSTRACT FROM AUTHOR]

The differential drive assist steering (DDAS) system improves the handiness of steering by torque difference between two-side wheels. However, the DDAS and the vehicle stability control (VSC) will interfere with each other due to the same actuators. This paper proposes a hierarchical coordination control approach for the DDAS and VSC. The system is divided into four layers: the parameter estimation layer, the control region division layer, the coordination decision layer, and the control allocation layer. In the control region division layer, the stable area is firstly determined on the $\beta - \dot{\beta }$ phase portrait, and the coordinated control region is obtained by applying the simulated annealing algorithm inwardly and outwardly to the boundary of the stable area. In the coordination decision layer, the direct control strategy of steering hand wheel torque is used to establish the controller of the DDAS based on the fuzzy adaptive PID algorithm. At the same time, the controller of yaw moment is established based on the sliding mode control theory. In the stable region, the DDAS works separately. The VSC works separately in the unstable region. In the coordination region, the Sigmoid function is used to adjust the weight coefficient of the DDAS dynamically. Besides, the control allocation layer includes torque distribution controller and vehicle slip ratio controller. Finally, the vehicle model is established in CarSim, the control strategy, and the wheel motor model are established in Simulink. The joint simulation results of several typical conditions show that the designed control strategy can improve the handling stability of the vehicle effectively while DDAS working even in severe driving condition. [ABSTRACT FROM AUTHOR]

The anxiety of driving range and inconvenience of battery recharging has placed high requirements on the energy efficiency of electric vehicles. To reduce driving-wheel slip energy consumption while cornering, a torque vectoring control strategy for a rear-wheel independent-drive (RWID) electric vehicle is proposed. First, the longitudinal linear stiffness of each driving wheel is estimated by using the approach of recursive least squares. Then, an initial differential torque is calculated for reducing their overall tire slippage energy dissipation. However, before the differential torque is applied to the two side of driving wheels, an acceleration slip regulation (ASR) is introduced into the overall control strategy to avoid entering into the tire adhesion saturation region resulting in excessive slip. Finally, the simulations of typical manoeuvring conditions are performed to verify the veracity of the estimated tire longitudinal linear stiffness and effectiveness of the torque vectoring control strategy. As a result, the proposed torque vectoring control leads to the largest reduction of around 17% slip power consumption for the situations carried out above. [ABSTRACT FROM AUTHOR]

With the help of several in-wheel motors, four-wheel independent-drive electric vehicle (4WIDEV) has tremendous potential to improve vehicle performance. Except for the theme of stability enhancement during the past two decades, energy saving topic becomes more attractive recently. However, it is difficult to achieve stability and economy performance simultaneously. Given the commonly used control method of rigid yaw rate tracking may limit the energy saving potential of 4WIDEV in cornering. Therefore, be different from previous studies which often focus on a sole target, in this paper, a driving energy management strategy for 4WIDEV based on multi-objective online optimization of four-wheel torque distribution is proposed in this paper, which includes weighted yaw rate tracking error into its parameterized control objectives besides electric drive system efficiency, tire slip losses and wheel torque ripple. Meanwhile, for better coordination of stability and economy in different situation, fuzzy logic control is adopted to dynamically adjust the weight of the control objective indexes of the proposed multi-objective online optimization function. Then, a particle swarm optimization algorithm is adopted to solve the optimization function. Finally, the proposed strategy for energy-efficient driving is verified based on the built Simulink and CarSim co-simulation model. The results show that the proposed four-wheel torque distribution strategy based on the multi-objective online optimization is more effective than average distribution strategy and offline optimization strategy. [Display omitted] • Propose a driving energy management strategy based on multi-objective online optimization. • Integrate yaw rate error, in-wheel motor efficiency, and tire slip into optimization function. • Design a weight coefficient fuzzy controller to better coordinate the stability and economy. [ABSTRACT FROM AUTHOR]