Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

Abstract

This paper presents a study on Lithium-ion battery aging behaviors/patterns and related State-of-Health (SOH) indicators before presenting the development of data-driven based SOH estimators. Battery charge/discharge cycling experiments are conducted in order to obtain needed data for this work. The battery ageing behavior patterns until the battery cell reaches highly deteriorated health conditions are investigated and characterized in this paper by analyzing the aggregated battery ageing data. The observed battery ageing behavior patterns include: (1) the rate at which the battery voltage decreases during discharging increases as the battery ages, (2) the speed at which the battery terminal voltage increases during Constant Current (CC) charging increases as the battery’s health deteriorates, (3) the time period for CC charging operation decreases as the battery ages, (4) the rate at which the battery current decreases during Constant Voltage (CV) charging increases as the battery ages, and (5) the speed at which the battery temperature drops during CV charging increases as the battery ages. Corresponding SOH indicators are developed to quantify these battery ageing behavior patterns for the development of SOH estimators. Deep Neural Network (DNN) is utilized to extract and model the nonlinear and complex correlation between the defined SOH indicators and SOH values of the Lithium-ion battery. Multiple DNN-based SOH estimators are developed in this paper. The SOH estimation results from different DNN-based SOH estimators indicate that the diversity of SOH indicators used for the development of SOH estimator can substantially improve the estimation performance.

Keywords

• Battery
• battery capacity
• battery management system
• data-driven
• lithium-ion
• neural network