YongZhi Zhang1,2, Rui Xiong1, HongWen He1, Xiaobo Qu2 and Michael G. Pecht3
1 National Engineering Laboratory for Electric Vehicles, Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China
2 Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden
3 CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20742, USA
Abstract:
This paper developed methods for improving the practicability of battery health diagnosis and remaining useful life prognostics. Battery state of health was estimated using a feature extraction-based method based on the charging voltage curve. Battery remaining useful life was predicted by identifying recognizable aging stages. Acceleration aging test data for 9 cells at constant current rates including 0.5C, 1C, 1.5C, and 2C, and dynamic current rates were used to validate the developed methods. The capacity estimates were accurate with estimation errors less than 1% at most cycles. The remaining useful life was predicted within 0.3 s at dynamic current rates, with the prediction errors at most cycles less than 10 after 300 cycles and the 95% confidence intervals covering about 20 cycles for each prediction.