A Li-Ion Battery Capacity Estimation Method Based on Multi-Kernel Relevance Vector Machine Optimized Model
Lü Zhiqiang1,2, Gao Renjing1,2, Huang Xianguo3
1. State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian 116000 China; 2. Ningbo Institute of Dalian University of Technology Ningbo 315016 China; 3. Laiwu Branch of Shandong Iron & Steel Co. Ltd Laiwu 271104 China
Abstract With the large-scale promotion and popularization of electric vehicles, power lithium-ion batteries are widely employed owing to their high energy density, long cycle life, and environmental friendliness. However, the performance of power lithium-ion batteries decreases with continuous use, leading to the increasing range anxiety and safety anxiety of electric vehicles, which requires the prognostic and health management of power batteries. Therefore, the development of key methods for battery management systems has become a prerequisite for electric vehicles. Accurate online capacity estimation of power batteries is one of the core functions of the battery management system for prognostic and health management, which is of great significance to improve the refined management of batteries in electric vehicles. During the driving process of electric vehicles, power batteries in electric vehicles are affected by various external factors, such as noise and vibration. Therefore, the battery capacity estimation based on the discharging process is vulnerable to complex factors in dynamic discharging conditions. To solve this problem of online capacity estimation of power batteries in electric vehicles, this study proposes a method for battery capacity estimation during the charging process. First, based on the partial charging data of the power battery, the incremental capacity analysis method is used to obtain its charging incremental capacity curve. To eliminate the influence of the noise of the sampling process on the differential process, a locally weighted scatterplot smoothing method is proposed to smooth and denoise the original incremental capacity analysis to obtain a smooth curve with a clear boundary. Further, based on the processed charging incremental capacity curve, the maximum peak and area on the curve are extracted as the two aging features to characterize the battery capacity degradation. The validity of the aging features based on the partial capacity increment curves is verified by the correlation analysis, and the correlation coefficients show that the extracted aging features can effectively characterize the battery capacity degradation. Then, a multi-kernel relevance vector machine optimization model with the aging features as the input and the battery capacity as the output is established. Meanwhile, the optimal kernel function weights and kernel parameters in the multi-kernel relevance vector machine are determined by the grey wolf optimization to improve the global and local learning and generalization capability of the multi-kernel relevance vector machine, then, the battery capacity is estimated online by combining the online extracted aging features. Finally, a joint simulation using Matlab and LabVIEW is performed to establish a simulated hardware-in-the-loop battery management system based on the multi-kernel relevance vector machine optimization model. Meanwhile, the developed battery management system is also validated by the University of Maryland single-cell battery aging datasets and the Dalian University of Technology battery pack aging datasets. The estimation results show that the proposed online battery capacity estimation method based on the multi-kernel relevance vector machine optimization model can achieve accurate battery capacity estimation with a maximum estimation error of 2.3% or less for both single-cell and battery pack capacity estimation. The proposed method provides a new research idea for the algorithm development of a battery management system.
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2023, Vol. 38 
