Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data

Abstract

The accuracy of the state of health (SoH) estimation and prediction is of great importance to the operational effectiveness and safety of electric vehicles. Present approaches mostly employ data-driven analysis with laboratory measurements to determine these parameters. Here a novel method is proposed using discrete incremental capacity analysis based on real-life driving data, which enables to estimate the battery SoH without any prior detailed knowledge of battery internal specifics such as current capacity/resistance information. The method accounts for the battery characteristics. It is robust, highly compatible, and has a short computing time and low memory requirement. It's capable to evaluate the SoH of various type of electric vehicles under different charging strategies. The short computing time and low memory needed for the SoH estimation also demonstrates its potential for practical use. Moreover, the clustering analysis is presented, which provides SoH comparison information of certain EV to that of EVs belonging to same type.