Torque Vectoring Control of RWID Electric Vehicle for Reducing Driving-Wheel Slippage Energy Dissipation in Cornering

Abstract

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.

Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

“…However, there are few rover or vehicle verification studies in this research field, particularly studies on regulating the wheel slip independently while maintaining a fixed orientation on slippery roads under high-speed conditions (during acceleration and deceleration) via dynamic FLCs. In order to further verify the proposed metaheuristic FLCs' effect, studies involving a reference estimation model [111], an adaptive fuzzy type-2 control mechanism [112], H ∞ with the Moore-Penrose theory [113], a torque distribution control [114], an electrical drive wheel speed using a machine learning approach [115], a longitudinal vehicle speed estimator based on fuzzy logic control [116], a torque vector control of a rear-wheel independent-drive (RWID) electric vehicle [117] and an anti-skid fuzzy PID control strategy for a four-wheel independent-drive electric vehicle (4WDIEV) [118] are selected to be compared and a validation simulation is carried out, except for [116], which is validated via both the simulation setup and hardware setup. A comprehensive performance comparison is shown in Table 12.…”

Energy-Saving Algorithm Considering Cornering Resistance of a Four-Wheel Independent Drive Electric Vehicle With Vehicle-to-Vehicle (V2V) Information

“…However, most studies on cornering resistance have focused on interpreting it. Although certain studies analyzed the driving energy of a vehicle according to the steering angle [18] and tire slippage energy [19], the cornering resistance was not considered in their approaches.…”