Research On State Of Health Estimation And Remaining Useful Life Prediction Of The Lithium Ion Battery

Lithium ion battery has been widely used in civil and military fields. Estimating state of health(SOH) and predicting remaining useful life(RUL) exactly have a great significance to improve the safety and extend the useful life of battery. In this paper, we took the lithium ion battery as the research object and investigated the SOH estimation methods and RUL prediction methods. The specific contents are as follows:1) We elucidated the working principle and the basic concept of performance parameters of lithium ion battery. The charging mode, the influence of the environment temperature and discharge current on the battery terminal voltage, and the battery capacity degradation rules are also introduced.2) At present, the method to estimate SOH of lithium ion battery with Kalman Filter mostly belong to the offline estimation, this cannot to meet the needs of practical engineering. In order to solve this problem,we studied a online Dual Extended Kalman Filter method. Firstly, parameters of lithium ion battery are identified by Least Squares and model of lithium ion battery was builded.Secondly, we used two Kalman Filter to estimate the SOC and ohmic resistance altemately. In order to further improve the estimation precision, an estimation method of li-ion battery SOH based on fuzzy inference system-adaptive dual extended Kalman filter(FIS-ADEKF) was proposed, we used Sage-Husa adaptive algorithm and fuzzy inference system to correct the covariance matrix of state noise and the covariance matrix of measurement noise respectively. In the end, this paper designed the DST(Dynamic Stress Test) condition experiment to verify this method, experiment result demonstrated that this method can online predict SOH of lithium ion battery, and neither initial values nor precalculated performance parameters are needed, the improved Dual Extended Kalman Filter method has a higher accuracy, convergence and feasibility.3) In order to predict the RUL of lithium ion battery,the curve fitting method and the grey model is studied. To verify the three prediction methods, test data obtained from the NASA Ames research center and other data obtained from our self-built experimental platform were used. Experiment result demonstrated that the liner fitting and grey model can predict the RUL better, and the prediction accuracy will increase with the increasing of training data.