State of Health (SOH) Diagnosis and Remaining Useful Life (RUL) Prediction of lithium-ion batteries (LIBs) are subject to low accuracy due to the complicated aging mechanism of LIBs. This paper investigates a SOH diagnosis and RUL prediction method to improve prediction accuracy by combining multi-feature data with mechanism fusion. With the approach of the normal particle swarm optimization, a support vector regression (SVR)-based SOH diagnosis model is developed. Compared with existing works, more comprehensive features are utilized as the feature variables, including three aspects: the representative feature during a constant-voltage protocol; the capacity; internal resistance. Further, the optimized regularized particle filter (ORPF) model with uncertainty expression is integrated to obtain more accurate RUL prediction and SOH diagnosis. Experiments validate the effectiveness of the proposed method. Results show that the proposed SOH diagnosis and RUL prediction method has higher accuracy and better stability compared with the traditional methods, which help to realize the decision of the maintenance process.

To be prepared for the capacity diving phenomena in future capacity deterioration, a hybrid method for predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is proposed. First, a novel empirical degradation model is proposed in this paper to improve the generalization applicability and accuracy of the algorithm. A particle filter (PF) algorithm is then implemented to generate the original error series using prognostic results. Next, a discrete wavelet transform (DWT) algorithm is designed to decompose and reconstruct the original error series to improve the data validity by reducing the local noise distribution information. A relatively less approximate component is selected as the reconstructed error series, which preserves the primary evolutionary information. Finally, to make full use of the information contained in the PF algorithm’s prognosis results, the support vector regression (SVR) algorithm is utilized to correct the PF prognosis results. The results indicate that long–short-term deterioration progress and RUL prediction tasks can both benefit from significant performance improvements.

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With the widespread use of lithium-ion batteries in various fields, battery Prognostics and Health Management (PHM) technologies are gaining more and more attention. Repeated use of batteries can lead to degradation of battery performance and thus affect battery life. Accurate prediction of the remaining useful life (RUL) of batteries is crucial and is the most central issue in battery PHM. In this paper, a method based on a combination of fuzzy information granulation (FIG) and support vector regression with artificial bee colony optimization (ABC-SVR) is proposed to estimate the RUL of Lithium-ion batteries. First, the capacity degradation data are divided into several windows using the FIG method. Second, the maximum and minimum values of each window are predicted separately using the ABC-SVR algorithm to obtain the information of the prediction window. Finally, the missing values of the prediction windows are complemented by the linear interpolation method to obtain the complete capacity prediction values, and the remaining useful life of the battery can be calculated according to the failure threshold. The results show that the proposed method obtains the RUL value with high accuracy.