Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process

Abstract

Due to the complex changes in physicochemical properties of lithium-ion batteries during the process from degradation to failure, it is difficult for methods based on physical or data-driven models to fully characterize this nonlinear process, and existing methods that hybridize physical and data-driven models suffer from ambiguous hybridization, which results in the vast majority of existing methods for predicting the RUL of lithium-ion batteries suffering from a lack of accuracy and robustness. In this study, a novel hybrid approach based on empirical modeling and data-driven techniques is proposed for predicting the remaining useful life (RUL) of lithium-ion batteries. To better capture its complexity, stochasticity, and state transition, and improve the modeling accuracy and RUL prediction precision, Gamma stochasticity and state-space modeling are used to empirically model the complex Li-ion battery degradation process. Moreover, the expectation maximization (EM) method of particle filtering (PF) was used to estimate the hidden parameters of the empirical model, and the estimated parameters were corrected using an optimized support vector regression (SVR) method to enhance the generalization performance and robustness of the data-driven model. The results show that the gamma state-space model is effective in capturing the inherent stochastic properties of the battery degradation and the proposed hybrid method outperforms the existing prediction methods in RUL prediction. The experiments show that the Sparrow Search Algorithm (SSA) optimized SVR is considered to be the most effective correction method for the estimated parameters, while the new EM-PF-SSA-SVR hybrid method provides better performance for state assessment and RUL prediction of lithium-ion batteries. It is indicated that the proposed EM-PF-SSA-SVR method with Gamma stochastic process has hybrid validity and superior performance with equal performance and less parameter computation relative to the existing state-of-the-art deep learning RUL prediction methods.

A hybrid remaining useful life prediction method for lithium-ion batteries based on transfer learning with CDRSN-BiGRU-AM

“…As mentioned in the literature [5], characteristic factors during discharge such as temperature, voltage, current, and time contribute to the battery capacity degradation. Therefore, for the four batteries labeled B5, B6, B7, and B18, the temperature, voltage, current and time during discharge, the corresponding capacity data, were utilized in the experiment.…”

“…However, batteries are subject to aging stress during actual use, and their performance will gradually degrade with an increase in charge and discharge cycles, leading to a heightened risk of failure [4]. Therefore, it is crucial to accurately predict the remaining useful life (RUL) of LIBs [5,6], a task that is challenging in online scenarios and in the field of scientific research. In recent years, model-based, data-driven, and hybrid approaches have been created for the RUL prediction of LIBs [7].…”

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“…To evaluate the prediction effect of the proposed model, this research applies mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R 2 ) as the evaluation metrics to evaluate the difference between the real value and the predicted value [44]. The specific formulas are shown in equation 18 to 20…”

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