Estimating the State of Health for Lithium-ion Batteries: A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach
Guijun Ma, Zidong Wang, Weibo Liu, Jingzhong Fang, Yong Zhang, Han Ding, Ye Yuan
Abstract
The state of health (SOH) is a critical factor in evaluating the performance of the lithium-ion batteries (LIBs). Due to various end-user behaviors, the LIBs exhibit different degradation modes, which makes it challenging to estimate the SOHs in a personalized way. In this article, we present a novel particle swarm optimization-assisted deep domain adaptation (PSO-DDA) method to estimate the SOH of LIBs in a personalized manner, where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy. The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method. The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials, ambient temperatures and charge-discharge configurations. Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method. The PyTorch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.