Abstract
Accurately forecasting a battery’s remaining useful life (RUL) plays an important role in the prognostics and health management of rechargeable batteries. An effective forecast is reported using a particle filter (PF), but it currently suffers from particle degeneracy and impoverishment deficiencies in RUL evaluations. In this paper, an inheritance PF is developed to predict lithium-ion battery RUL for the first time. A battery degradation model is first mapped onto a PF problem using the genetic algorithm (GA) framework. Then, a Lamarckian inheritance operator is designed to improve the light-weight particles by heavy-weight ones and thus to tackle particle degeneracy. In addition, the inheritance mechanism retains certain existing information to tackle particle impoverishment. The performance of the inheritance PF is compared with an elitism GA-based PF. The former has fewer tuning parameters than the latter and is less sensitive to tuning parameters. Both PFs are applied to the prediction of lithium-ion battery RUL, which is validated using capacity degradation data from the NASA Ames Research Center. The experimental results show that the inheritance PF method offers improved RUL prediction and wider applications. Further improvement is obtained with one-step ahead prediction when the charging and discharging cycles move along.
Highlights
Optimized lithium-ion batteries play an important role in daily applications, from mobile phones, through microgrids, to satellites [1,2]
To optimize the energy deployment of a battery, avoid its premature failure, and improve its reliability and durability, it is important to monitor its degradation process accurately. This involves evaluating the state of health (SOH) [5] and predicting the state of charging (SOC) and remaining useful life (RUL) [6] in a battery management system (BMS) [7], which can be used for automated and optimized scheduling of maintenance actions that, in turn, ensure safe operation and use of lithium-ion batteries
To determine the probabilistic characteristics of the estimation, the state variables are estimated conditionally based on observation information, which can be achieved via particle filtering
Summary
Optimized lithium-ion batteries play an important role in daily applications, from mobile phones, through microgrids, to satellites [1,2]. It is known that the estimation error for PF can be large for several instances during the model parameter training stage due to the degeneracy and impoverishment of particles [21] To solve these problems, improved particle filtering methods have been developed recently. These have led to improved RUL estimation for lithium-ion batteries in evaluating SOH. We propose an AI-based inheritance particle filtering approach to the prediction of lithium-ion battery RUL with high accuracy, using the genetic algorithm (GA) framework.
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