E-COSM 2021 Paper Abstract

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Paper MoBT2.5

Narita, Shohei (Sophia· University), Zhang, Jiangyan (Dalian Minzu University), Zhang, Weidong (Shanghai Jiaotong Univ.), Shen, Tielong (Sophia University)

Acceleration Control Design of HEVs with Comfortability Evaluation Based on IRL

Scheduled for presentation during the Regular session "Optimization and control for electrified vehicles" (MoBT2), Monday, August 23, 2021, 17:50−18:10, Room T2

6th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, August 23-25, 2021, Tokyo, Japan

This information is tentative and subject to change. Compiled on March 28, 2024

Keywords Hybrid and Electric Vehicles, Powertrain Control, Control Design

Abstract

For passenger cars, comfortability is an important issue to consider in powertrain control. However, it is not easy to take comfortability into account when designing a powertrain control strategy because of its subjective nature and difficulty in being quantified. This paper presents a solution by using the inverse reinforcement learning (IRL) method. An acceleration scenario of a hybrid electric powertrain is considered to show this design approach. With a sample acceleration profile scored by an expert evaluating module, a reward function is obtained by training an extreme learning machine. Using the analytical representation of comfortability, an estimation of distribution algorithm (EDA) is used to seek the optimal acceleration reference. Given the reference acceleration signal, the control law for the electric motors that provide power assistance during the acceleration phase is obtained by solving a optimal tracking control problem. A numerical example is shown to evaluate the design approach.

 

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