Estimation of internal states in a Li-ion battery using BiLSTM with Bayesian hyperparameter optimization

Abstract

An accurate state estimator is critical to Battery Management Systems (BMSs). In more advanced BMSs, state estimators based on electrochemical-thermal battery models are preferred because they can predict the internal states of the battery. However, these physics-based models are computationally costly and time-demanding, preventing them from being directly used for real-time estimation. As an alternative approach, data-driven models, like Deep Neural Networks (DNNs), can be trained on datasets generated by electrochemical models. Increasing the accuracy of data-driven models can be achieved by applying more complex architectures of DNN. However, such DNNs are computationally time-demanding, which makes them inefficient for online state estimation algorithms. To address this issue, we have proposed a BiLSTM model as a state estimator whose hyperparameters are automatically optimized using a Bayesian Optimization (BO) framework. We demonstrate that by applying Bayesian inference, one can have a highly accurate state estimator while using a less complex DNN architecture which allows for being computationally efficient as well. The high accuracy of the BiLSTM model with the optimized combination of hyperparameters is demonstrated by the low Root Mean Squared Errors (RMSE) levels on the test data set.