Bi-objective optimization strategy of energy consumption and shift shock based driving cycle-aware bias coefficients for a novel dual-motor electric vehicle

A novel dual-motor electric vehicle (DMEV) with superior energy efficiency depends on its diverse operation modes. However, the operation mode shift will lead to the ride comfort problem owing to the uncertainty of the actual driving cycles. To address this issue, bi-objective optimization strategy-based driving cycle-aware bias coefficients is proposed to tradeoff between energy consumption and shift shock. Firstly, the system efficiency and the shift shock models are developed for DMEV. The torque distribution coefficient is defined to be a control variable for the bi-objective optimization of system efficiency and ride comfort. Furthermore, this study combines the bi-objective optimization algorithm with driving cycle recognition to introduce the optimized bias coefficient to determine the driving cycleaware bias coefficient for the mentioned optimized target objectives. Then, the non-dominated sorting genetic algorithm-II is applied for the bi-objective optimization, which produces a group of the Pareto solutions including the energy consumption and shift shock. Moreover, the proposed bi-objective optimization strategy is conducted by controlling the optimized bias lines for the optimal torque distribution of the novel dual-motor powertrain system. Ultimately, numerous validations and comparisons demonstrate that the proposed strategy effectively accomplishes the trade-off optimization between
energy consumption and shift shock in real-time.