Big data driven Cyber-Physical System based Lithium-ion battery modeling method with battery aging considered

As the bottleneck technology of electric vehicles (EVs), the battery has complex and hardly observable inside chemical reactions. Therefore, a precise mathematical model is crucial for the battery management system to ensure the secure and stable operation of the battery. Aiming at achieving a flexible, self-configuring, reliable Battery Management System (BMS), this paper mainly focuses on the following research points: Firstly, a Cyber-Physical system (CPS) based BMS is presented for a better use of battery data. Next, a data cleaning method based on machine learning algorithm is applied to the big data of batteries in electric vehicles. Finally, a rain-flow cycle counting algorithm-based battery degradation quantification method is proposed and a Stacked Denoising Autoencoders-Extreme Learning Machine (SDAE-ELM) algorithm-based battery modeling method is also built to deal with the influence of battery aging phenomenon. Using the battery data extracted from electric buses, model effectiveness and accuracy are validated. The error of the estimated battery terminal voltage estimator is within 2.5%.