An optimized Relevance Vector Machine with incremental learning strategy for lithium-ion battery remaining useful life estimation

• Published in: 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) pp. 561 - 565
• Main Authors: Jianbao Zhou, Datong Liu, Yu Peng, Xiyuan Peng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2013
• Subjects: Algorithm design and analysis; Batteries; Estimation; Heuristic algorithms; Incremental Learning; Lithium-ion battery; Prediction algorithms; Relevance Vector Machine; Remaining Useful Life; Support vector machines; Training
• ISBN: 9781467346214; 1467346217
• ISSN: 1091-5281
• DOI: 10.1109/I2MTC.2013.6555479

In most industrial fields, it needs to evaluate the performance degradation and remaining useful life (RUL) of lithium-ion battery. With the uncertainty representation of the RUL, the Relevance Vector Machine (RVM) becomes an effective approach in lithium-ion battery RUL prognostics. But, the small sample size and low precision of multi-step prediction will bring district to RUL prediction for sparse RVM algorithm. With the on-line monitoring data updating, the dynamic training ability and online algorithm are necessary to improve the prediction precision for battery RUL model. Moreover, the operating efficiency and computing complexity are needed for on-line and real-time processing. A simple and effective on-line training strategy is introduced for RVM algorithm to realize high prediction performance. An incremental optimized RVM algorithm is proposed to achieve efficient online training for model updating. Furthermore, with the on-line training strategy, the prediction precision can increase for battery RUL estimation. Using proposed method, we carry out experiments with NASA battery data and the results show that our method has excellent performance on predicting the RUL of lithium-ion battery.