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Predicting the remaining useful life (RUL) is an effective way to indicate the health of lithium-ion batteries, which can help to improve the reliability and safety of battery-powered systems. To predict the RUL, the line of research focuses on using the empirical degradation model followed by the particle filter (PF) algorithm, which is used for online updating the model's parameters. However, this works well for specific batteries under specific discharge conditions. When the degradation trends cannot be presented by the chosen empirical model or the standard PF encounters impoverishment and degeneracy problem, the RUL prediction would be inaccurate. To improve the RUL prediction accuracy, we propose a novel approach by enhancing the existing method from two aspects. First, we introduce a neural network (NN) to model battery degradation trends under various operation conditions. As NN's generalization and nonlinear representing ability, it outperforms the typical empirical degradation model. Second, the NN model's parameters are recursively updated by the bat-based particle filter. The bat algorithm is used to move the particles to the high likelihood regions, which optimizes the particle distribution and thus reduces the degeneracy and impoverishment of PF. In this paper, quantitative evaluation is presented using two datasets with different batteries under different aging conditions. The results indicate that the proposed the approach can achieve higher RUL prediction accuracy than conventional empirical model and standard PF. INDEX TERMS Lithium-ion batteries, neural network, capacity degradation, remaining useful life prediction, bat algorithm, particle filter.

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