Battery (electricity)

Quantum mechanics

Engineering

Lithium (medication)

Reliability engineering

Biology

Statistics

Particle filter

Endocrinology

Degeneracy (biology)

Telecommunications

Power (physics)

Computer science

Filter (signal processing)

Geology

Oceanography

Empirical research

Physics

Nonlinear system

Bioinformatics

Simulation

Mathematics

Empirical modelling

Degradation (telecommunications)

Mathematical analysis

Reliability (semiconductor)

Artificial intelligence

Particle (ecology)

Medicine

Artificial neural network

Generalization

Computer vision

IEEE Access

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.

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Network and Bat-Based Particle Filter