Accuracy Comparison of State-of-Health Estimation for Lithium-ion Battery Based on Forklift Aging Profile

Abstract

Lithium-ion batteries have been widely applied in e-mobilities and energy storage devices. Fast and accurate state of health (SOH) estimation is crucial to ensure the reliable operation and timely maintenance of these devices. This work proposed multiple linear regression (MLR) models to estimate the SOH of battery applied in forklift load profile and compared the estimation accuracy between extracting features from complete discharging-charging voltage curves and only charging voltage curves. Two features were extracted from two kinds of voltage curves respectively firstly, and the third feature was then extracted from many-step voltage curves to improve the generalization performance. The MLR was used to build the relationship between SOH and features. Finally, root mean square error (RMSE) was employed to evaluate the model accuracy. Results show that the MLR can effectively estimate SOH based on the three features and the estimation accuracy is higher when extracting features from only charging voltage curves.