Optimized Multi-Source Fusion Based State of Health Estimation for Lithium-Ion Battery in Fast Charge Applications

Abstract

Knowing the health state of the batteries would enhance the energy storage system's reliability and safety, especially for fast charge applications. Here we propose a synergetic method with the help of the genetic algorithm (GA) and the support vector regression (SVR) for SOH estimation. Firstly, features for battery aging process description are selected from the multi-source data, including current, voltage, and temperature, in the battery charging process. The SVR is then employed to establish a battery aging model and estimate the SOH with the generated features. Afterward, the feature set which can optimize the pre-set objective, namely minimize the SOH estimation error and the defined difficulty of feature acquisition, are selected by the GA via an iterative process. Experimental results indicate that the selected feature set generated from the charged capacity and temperature rise data may perform a better SOH estimation. Moreover, by collaborating with the chosen features, the SVR is found to have a similar SOH estimation accuracy to a more complex algorithm while using less computation power. Furthermore, it should be noted that the selected features are obtainable in about 95% of the charging operations according to the voltage distribution resulting from more than 40,000 actual charging bills.