State-of-health estimation based on real data of electric vehicles concerning user behavior

Abstract

State of health (SOH) of lithium-ion battery pack directly determines the driving mileage and output power of the electric vehicle. With the development of big data storage and analysis technology, using big data to off-line estimate battery pack SOH is more feasible than before. This paper proposes a SOH estimation method based on real data of electric vehicles concerning user behavior. The charging capacity is calculated by historical charging data, and locally weighted linear regression (LWLR) algorithm is used to qualitatively characterize the capacity decline trend. The health features are extracted from historical operating data, maximal information coefficient (MIC) algorithm is used to measure the correlation between health features and capacity. Then, long and short-term memory (LSTM)-based neural network will further learn the nonlinear degradation relationship between capacity and health features. Bayesian optimization algorithm is used to ensure the generalization of the model when different electric vehicles produce different user behaviors. The estimation method is validated by the 300 days historical dataset from 100 vehicles with different driving behavior. The results indicates that the maximum relative error of estimating SOH is 0.2%.