Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network

• Main Authors: Wang, Y. (Author), Yang, B. (Author), Zhan, Y. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Battery management systems; Battery state of charge; Bayesian optimization; Bayesian optimization algorithm; Bayesian optimization algorithms; bidirectional long short-term memory neural network; Bidirectional long short-term memory neural network; Brain; Charging (batteries); Data characteristics; Lithium batteries; lithium battery; Long short-term memory; Mean square error; Memory modeling; Network topology; Neural-networks; Optimization; Parameter estimation; state of charge; State-of-charge estimation; States of charges
• Online Access: View Fulltext in Publisher

 State of charge (SOC) is the most important parameter in battery management systems (BMSs), but since the SOC is not a directly measurable state quantity, it is particularly important to use advanced strategies for accurate SOC estimation. In this paper, we first propose a bidirectional long short-term memory (BiLSTM) neural network, which enhances the comprehensiveness of information by acquiring both forward and reverse battery information compared to the general one-way recurrent neural network (RNN). Then, the parameters of this network are optimized by introducing a Bayesian optimization algorithm to match the data characteristics of lithium batteries with the network topology. Finally, two sets of lithium battery public data sets are used to carry out experiments under different constant temperature and variable temperature environments. The experimental results show that the proposed model can effectively fit the actual measurement curve. Compared with traditional long short-term memory network (LSTM) and BiLSTM models, the prediction accuracy of the Bayes-BiLSTM model is the best, with a root mean square error (RMSE) within 1%, achieving a better ability for capturing long-term dependencies. Overall, the model exhibits high accuracy, adaptability, and generalization for the SOC estimation of batteries with different chemical compositions. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.