Multi-Objective Optimal Gearshift Control for Multispeed Transmission Electric Vehicles

Yanwei Liu,Kegang Zhao,Ziyue Lin,Yuzhong Chen,Zhenye Li

doi:10.1109/access.2020.3009481

Abstract

Electric vehicles are being increasingly adopted worldwide to support the sustainable development of society. However, various technologies that are related to electric vehicles still require further investigation. In this study, we consider a gearshift control architecture that combines offline trajectory planning and online control for improved shifting in multispeed transmission electric vehicles. For gearshift trajectory planning, we establish a dynamic model and apply the multi-objective optimization Radau pseudospectral method. Simulations of this method provide a Pareto solution set under three optimization objectives, namely duration, friction work, and jerk, thereby establishing a new approach for satisfying the requirements for shift quality. Moreover, the relations among these objectives are detailed on the Pareto solution set. We conducted a hardware-in-the-loop simulation to verify the performance of the proposed control architecture and trajectory planning method. The simulation results indicate that the gearshift control delivers a suitable response for different driving intentions based on the obtained Pareto solution set.

Highlights

Fossil fuel is currently an indispensable resource for transportation; its intensive consumption is leading to severe shortage and environmental pollution
We performed the optimization and conducted simulations to verify the performance of the proposed gearshift trajectory planning method and control architecture through an implementation on MathWorks MATLAB/Simulink
Because we set the throttle at the 50% position, we regard them as 50% DTD and 50% DPD in the following part 1) and 2), which represents two of the three optimal scenes

Summary

INTRODUCTION

Fossil fuel is currently an indispensable resource for transportation; its intensive consumption is leading to severe shortage and environmental pollution. The electric motor of an EV does not operate at optimal conditions, thereby increasing energy consumption Both speed and acceleration are difficult to improve, limiting the EV dynamic performance [6]–[8]. Chai et al [14] proposed a genetic algorithm (GA) to plan the multi-objective optimal trajectory for spacecraft in the reentry phase Their approach generates well-distributed Pareto fronts and has excellent convergence performance. Compared with PSO, GA, and PMP, the Radau pseudospectral method (RPM) has a high iteration speed and low sensitivity to initial values in multi-objective optimization [16] This method has been proven feasible [17] for solving the global nonlinear problem transformed from the optimal gearshift control.

GEARSHIFT CONTROL ARCHITECTURE AND POWERTRAIN MODEL

SOLUTION OF RPM

RESULTS AND DISCUSSION