Improve Battery Safety for Hybrid Electric Vehicles through Multi-Objective Optimization of Battery Design and Hybridization Level

Abstract

Balancing fuel consumption and battery safety is critical for hybrid electric vehicles (HEVs). Both hybrid power control strategies and battery design parameters can significantly affect battery degradation. Battery degradation is caused by the irreversible side reactions, which can lead to further safety issues. This paper aims to reduce battery degradation for HEVs through multi-objective optimization. The proposed approach adopts our reduced order electrochemical battery model to predict battery degradation (e.g, SEI layer growth and capacity loss) and estimate the battery state (e.g., State of charge and State of Health). Hysteresis phenomenon and temperature dependence are also considered in the battery model so as to predict heat generation and dynamic voltage response. Battery degradation is complicated and can be affected by various factors. Different battery design parameters (e.g., electrode thickness, particle size, and electrode porosity) have influence on battery degradation, energy density, and power density. In addition, hybrid power control strategy affects the hybrid power output from power sources i.e., hybridization level. Various strategies of using battery power can also influence battery degradation. Therefore, optimization of battery design parameters and hybridization level in HEVs are able to be mitigate battery degradation and improve battery safety. In this paper, a HEV is simulated under a standard driving cycle (e.g., FTP-72). Fuel consumption, vehicle drivability, and battery degradation are set as control objectives. In order to optimize the hybrid system, battery design parameters and hybridization level are iterated after each simulation until minimized battery degradation is found. Then, the corresponding design parameters and hybridization level are considered as the optimal results for battery design and power control strategy. Furthermore, optimal results under different driving cycles are investigated. The results of battery degradation and fuel consumption are also analyzed to show the overall optimization outcome for improving battery safety.