Personalized Optimal Gearshift Schedule for Multi-Speed Battery Electric Vehicles

To further enhance the comprehensive performance of multi-speed battery electric vehicles (MSBEVs) and meanwhile represent the driver’s expectation on performance, we need to consider the power performance, energy economy and driver’s intention together in the optimization of gearshift schedule. An optimization approach for personalized optimal gearshift schedule (POGS) for MSBEVs is put forward to tackle this issue. A quantification method for driver’s intention based on fuzzy neural network is proposed at first, which takes velocity, strength of accelerator pedal and its change rate as input, and takes driver’s expectations on performance as output. The optimization problem for POGS is then formulated with comprehensive evaluation function as goal, in which the driver’s expectations on performance are used as weights of sub-objective functions corresponding to the power performance and the energy economy, respectively. And a particle swarm optimization algorithm (PSO) is put forward to solve the optimization problem. To evaluate the proposed optimization approach, we completed parameters design for a two-speed drivetrain and a single-speed drivetrain with similar power performance, respectively. The POGS’s for the two-speed BEV are optimized, which are finally used in the power performance and WLTC driving cycle tests. Results show that the acceleration time from 0-100 km/h decrease with the increase of driver’s expectation on power performance, the energy consumption under WLTC driving cycle decrease with the increase of driver’s expectation on energy economy, and the energy consumption of the two-speed BEV is quite less than that of the single-speed BEV.