Abstract
For enhancing the prediction accuracy of remaining useful life (RUL) of lithium-ion batteries (LIBs), an innovative LIBs' RUL prediction framework based on whale optimization algorithm (WOA) and long-short term memory (LSTM) is proposed. The validity and exactitude are proved by NASA batteries data set. The WOA has faster convergence speed and higher convergence accuracy than Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. Explanatory results manifest the proposed WOA-LSTM model can markedly improve the prediction accuracy of RUL, which solves the problem that hyperparameter settings have widely different effects on LSTM prediction. The RMSE and MAE are basically less than 0.03 and 0.02, respectively. It can be selected as a recommended model for accurately predicting the RUL of LIBs.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.