An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction. (February 2018)

• Record Type: Journal Article
• Authors: Zhang, Heng; Miao, Qiang; Zhang, Xin; Liu, Zhiwen
• Is Part Of: Microelectronics and reliability. Volume 81(2018)
• Page Start: 288
• Page End: 298
• Publication Date: 2018-02
• Subjects: Remaining useful life prediction -- Lithium-ion battery -- Particle filter -- Unscented Kalman filter -- Linear optimizing combination resampling; Electronic apparatus and appliances -- Reliability -- Periodicals; Miniature electronic equipment -- Periodicals; Appareils électroniques -- Fiabilité -- Périodiques; Équipement électronique miniaturisé -- Périodiques; Electronic apparatus and appliances -- Reliability; Miniature electronic equipment; Periodicals; 621.3815
• DOI: 10.1016/j.microrel.2017.12.036
• Languages: English
• ISSNs: 0026-2714

Abstract

Abstract: Lithium-ion rechargeable batteries are widely used as power sources for mobile phones, laptops and electric cars, and gradually extended to military communication, navigation, aviation, aerospace and other fields. Accurate remaining useful life (RUL) prediction of lithium-ion battery plays an important role in avoiding serious security and economic consequences caused by failure to supply required power levels. Thus, the RUL prediction for lithium-ion battery has become a critical task in engineering practices. With its superiority in handling nonlinear and non-Gaussian system behaviors, the particle filtering (PF) technique is widely used in the remaining life prediction. However, the choice of importance function and the degradation of diversity in sampling particles limit the estimation accuracy. This paper presents an improved PF algorithm, that is, the unscented particle filter (UPF) based on linear optimizing combination resampling (U-LOCR-PF) to improve the prediction accuracy. In one aspect, the unscented Kalman filter (UKF) is used to generate a proposal distribution as an importance function for particle filtering. In the other aspect, the linear optimizing combination resampling (LOCR) algorithm is used to overcome the particle diversity deficiency. It should be noted that the step coefficient K can affect the performance of LOCR algorithm, and the fuzzy inference system is applied to determine the value of step coefficient K. According to the analysis results, it can be seen that the proposed prognostic method shows higher accuracy in the RUL prediction of lithium-ion battery, compared with the existing PF-based and UPF-based prognostic methods. Highlights: An improved UPF method, namely, the U-LOCR-PF, is proposed to realize battery RUL prediction. Linear optimizing combination resampling technique is used to overcome the particle diversity deficiency problem in UPF. Comparison study shows the proposed U-LOCR-PF method has higher accuracy in battery RUL prediction.