Energy efficiency optimization of Simpson planetary gearset based dual-motor powertrains for electric vehicles

Abstract

The improvement of powertrain energy efficiency is of great significance to improve the driving range of electric vehicles. This paper proposes four configurations of Simpson planetary gearset-based dual-motor powertrain (SPGDMP) and optimizes their parameters. The powertrain energy efficiency is obtained through the scalable motor model and the gear ratio-dependent transmission efficiency model. The influence of powertrain parameters is then analyzed to determine the design variables. An improved economic performance indicator with less computational cost is proposed, which can reduce the computational time by 98.82% compared with the conventional economic indicator calculated by the dynamic programming (DP) algorithm. Considering the trade-off between dynamic and economic performance, the multi-objective optimization model is proposed, which gains the discrete Pareto front by the NSGA-Ⅱ and makes the discrete Pareto front continuous by neural network fitting (NNF). The optimization results show that compared with a widely studied torque coupled dual-motor powertrain (TCDMP), SPGDMPs can reduce the energy consumption and motors power by at least 5.02% and 14.56%, respectively. The energy efficiency of SPGDMPs has a little improvement when the powertrain equips with more brakes.