The online estimation of the state of health (SOH) is an essential part of a lithium battery management system. Most data-driven lithium battery SOH estimation methods are computationally intensive and difficult to use in real-time in battery management system microcontrollers. Therefore

a rapid estimation method of lithium battery SOH based on novel health feature is proposed in this paper. The charging data of the battery is firstly analyzed in the method

and based on the existing health characteristics of time interval of an equal charging voltage difference (TIECVD) in the constant current charging process of the battery

constructs a new health feature

that is

the health feature of charging voltage at the same starting point and charging time interval. Then

a fast estimation method of lithium battery SOH based on the novel health feature and multiple linear regression (MLR) is proposed. Next

by analyzing the oxford battery aging dataset and the random usage dataset of lithium ion batteries used by NASA

the method traverses the constant current charging voltage range in steps of 0.01 V and determines the optimal starting voltage of the lithium battery by maximizing the Pearson correlation coefficient. Finally

considering different time intervals

the method uses the ordinary least squares (OLS) regression analysis method to determine the optimal time interval parameter of the lithium battery. The training set divided by two datasets is used to establish a multiple linear regression model

and the validation set divided by two datasets is used to verify the method. The experimental results show that the proposed method and novel health feature can greatly reduce the calculation volume

and can achieve fast estimation of lithium battery SOH while ensuring prediction accuracy.