Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine

Energies

Yang Zhang¹  Bo Guo² 

[1] College of Information Systems and Management, National University of Defense Technology, No. 109 De Ya Street, Kai Fu District, Changsha 410073, China

关键词: lithium-ion battery;    multi-kernel relevance vector machine;    accelerated particle swarm optimization;    feature extraction;    model verification;    online capacity estimation;   

DOI  :  10.3390/en81112320

来源: mdpi

【 摘 要 】

Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for online capacity estimation. First, six novel features are extracted from cyclic charge/discharge cycles and used as indirect health indicators. An adaptive multi-kernel relevance machine (MKRVM) based on accelerated particle swarm optimization algorithm is used to determine the optimal parameters of MKRVM and characterize the relationship between extracted features and battery capacity. The overall estimation process comprises offline and online stages. A supervised learning step in the offline stage is established for model verification to ensure the generalizability of MKRVM for online application. Cross-validation is further conducted to validate the performance of the proposed model. Experiment and comparison results show the effectiveness, accuracy, efficiency, and robustness of the proposed approach for online capacity estimation of lithium-ion batteries.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.