A Quantum Particle Filter Based Remaining Useful Life Prediction for Lithium-ion Battery

Accurate prediction of remaining useful life (RUL) is crucial for Lithium-ion batteries to ensure safety and extend lifetime. Aiming at to solve the accuracy issue induced by particle degradation and scarcity in traditional particle filters, this work proposes an improved quantum particle filter (QPF) based RUL prediction method. The spin states in quantum computing are used to improve the particle states in traditional particle filters. Through quantization, the randomness of the sampling space in PF is increased to ensure particle diversity, thereby improving the prediction accuracy. The paper first introduces a bimodal exponential degradation model. And then a Monte Carlo(MC) improved QPF based RUL prediction algorithm (MQPF) is introduced. In this method, the initial state of QPF is optimized to improve stability of QPF. Simulation results show that the QPF method has better prediction accuracy and robustness than traditional particle filters, and the MQPF benefits better stability than QPF.