Data-driven battery health prognosis with partial-discharge information

The unpredictability of battery degradation behavior is a challenging issue impeding the development of battery applications, due to the complexity of the degradation and the limitation of state measurement methods. Nowadays, with accessible battery aging datasets and machine learning algorithms, there are opportunities for data-driven battery health prognosis. However, most of the previous work is restricted in the scope of full -discharge capacity records extrapolation, which has insufficient prospects in real-life applications. In this work, we propose using partial discharge information for degradation estimation and prediction. Our Gaussian process regression model achieves good performance by limited partial discharge information without re-quirements of feature selection. The accurate battery health prognosis in 300 cycles can be carried out by one partial-discharge cycle at any degradation stage. The capacity estimation gives around 1 % root mean square error (RMSE) when using 30 % information on the discharge process. As full-cycle discharge is not required, the proposed model can diagnose the battery state of health (SOH) with a limited portion of battery operation in-formation extracted during the discharge process and reduce the effort of capacity tests. Further development of this method brings opportunities for battery state evaluation and prediction in real applications with better applicability and accuracy.