Analysis and Prediction of the Discharge Characteristics of the Lithium-ion Battery Based on the Grey System Theory

The capacity (state of charge/SoC) and voltage of lithium-ion batteries are of prime importance in electric vehicles, so their condition-monitoring techniques are extensively studied. This paper focuses on the application of the grey system theory to the parameters analyzing and predicting behavior during the discharge/charge cycles of the battery. Firstly, Grey Relation Analysis (GRA) is applied to study and analyze the relationship between capacity (SoC) and various influencing factors. Secondly, the Segment Grey Prediction Model is proposed in order to test and improve the accuracy of the capacity (SoC) prediction. Lastly, based on the aging data from the NASA Prognostics Data Repository, the effects of different Grey Theory Models, such as the GM(1,1), the Verhulst model and the Segment Grey Prediction Model, are investigated. The results show that: (1) the Grey Relation Analysis is efficient in figuring out the relationship between the capacity (SoC) and various influencing factors; (2) the Segment Grey Prediction Model is an effective mode of prediction for EV batteries, because its accuracy is more reliable than other two Grey Models; (3) the Segment Grey Prediction Model is suitable for predicting the capacity (SoC) of batteries under various loading conditions.