Remaining Life Prediction Model And Software Implementation For Lithium-ion Battery Based On Data-driven

With the development of social economics and energy storage technology, Lithium-ionbattery is widely used in various fields of the whole society because of its light weight, lowdischarge rate, longevity and other advantages. To establish an accurate capacity degradationmodel and to accurately predict the remaining life(RL) of Lithium-ion battery are foundationsto guarantee the reliability and safety of Lithium-ion battery. It is also an important part in thefield of Lithium-ion battery prognostics and health management(PHM) technology.The capacity degradation is chosen as characteristic variable to deal with the Lithium-ionbattery remaining life prediction problem. This paper is developed in the following folds.First, based on data-driven idea, the Lithium-ion battery remaining life prediction modelsare built using curve fitting, grey model and extended Kalman filter, respectively.Second, the above three data-driven models (including curve fitting, grey model andextended Kalman filter) testing are experimented on the Lithium-ion battery data available onthe NASA Ames Research Center website, respectively, and the applicability of differentprediction models is analyzed.Third, the remaining life of Lithium-ion battery prediction software is designedaccording to the above three data-driven models (including curve fitting, grey model andextended Kalman filter), and the software was realized on the Microsoft Visual Studio2008development platform.The verification results show that three prediction models have high precision and theycan predict the Lithium-ion battery capacity degradation process in a good way. In particular,the grey model and extended Kalman filter have higher accuracy in short-term prediction,respectively, and the liner fitting method has better performance than others in long-termprediction.