Lithium Battery State-of-Health Estimation Based on Sample Data Generation and Temporal Convolutional Neural Network

Abstract

Accurate estimation of battery health is an effective means of improving the safety and reliability of electrical equipment. However, developing data-driven models to estimate battery state of health (SOH) is challenging when the amount of data is restricted. In this regard, this study proposes a method for estimating the SOH of lithium batteries based on sample data generation and a temporal convolutional neural network. First, we analyzed the charge/discharge curves of the batteries, from which we extracted features that were highly correlated with the SOH decay. Then, we used a Variational Auto-Encoder (VAE) to learn the features and distributions of the sample data to generate highly similar data and enrich the number of samples. Finally, a temporal convolutional neural network (TCN) was built to mine the nonlinear relationship between features and SOH by combining the source and extended domain data to realize SOH estimation. The experimental results show that the proposed method in this study has less than 2% error in SOH estimation, which improves the accuracy by 64.9% based on its baseline model. The feasibility of using data-driven models for battery health management in data-constrained application scenarios is demonstrated.