A Health Monitoring Method Based on Multiple Indicators to Eliminate Influences of Estimation Dispersion for Lithium-Ion Batteries

The state-of-health (SOH) is a critical parameter to determine the degradation degree of lithium-ion batteries, thus plays an important part in energy management problems of electric vehicles (EVs). Due to the estimation dispersion of health indicators, it is difficult to monitor conveniently and robustly the SOH of on-board batteries. In this paper, a health monitoring method for the application of battery management system (BMS) has been presented to solve this issue. First of all, the necessity of multiple indicators (MIs) is demonstrated through the analysis of estimation dispersion of single indicator (SI), and three health indicators are extracted from actual operating conditions of EVs. After that, the mixed membership function (MMF) according to fuzzy logic theory is applied to integrate the estimation effects of different health indicators. Then, the analytic hierarchy process (AHP) using dispersion information of health indicators is introduced to calculate the weight coefficient of each indicator. Finally, the performance and robustness of the proposed method have been verified by numerous experiments. The results indicate the average estimation errors of five tested batteries are less than 3%, and the influences of estimation dispersion are largely eliminated when compared with the SI-based and other MIs-based methods.