Improving gearshift controllers for electric vehicles with reinforcement learning

TL;DR

This paper introduces a model-based reinforcement learning method to efficiently calibrate gearshift controllers for electric vehicles, reducing trial requirements and enabling faster exploration of control strategies.

Contribution

The paper presents a novel application of model-based reinforcement learning for gearshift controller calibration, significantly decreasing the number of trials needed compared to traditional methods.

Findings

Optimizes gearshift controller parameters with fewer trials

Accelerates exploration of gearshift control strategies

Effective for multi-speed transmissions in electric vehicles

Abstract

During a multi-speed transmission development process, the final calibration of the gearshift controller parameters is usually performed on a physical test bench. Engineers typically treat the mapping from the controller parameters to the gearshift quality as a black-box, and use methods rooted in experimental design -- a purely statistical approach -- to infer the parameter combination that will maximize a chosen gearshift performance indicator. This approach unfortunately requires thousands of gearshift trials, ultimately discouraging the exploration of different control strategies. In this work, we calibrate the feedforward and feedback parameters of a gearshift controller using a model-based reinforcement learning algorithm adapted from Pilco. Experimental results show that the method optimizes the controller parameters with few gearshift trials. This approach can accelerate the exploration of gearshift control strategies, which is especially important for the emerging technology of multi-speed transmissions for electric vehicles.