A Bayesian Optimized Deep Learning Approach for Accurate State of Charge Estimation of Lithium Ion Batteries Used for Electric Vehicle Application

Battery technology used in Electric Vehicles has recently drawn numerous researchers’ attention. Monitoring of battery condition, especially the state of charge, is necessary to ensure the safe and reliable operation of the battery. Even though researchers have proposed numerous SOC estimation techniques, exploration is still required to find a suitable technique that can adapt versatile lithium-ion battery chemistries. Deep learning (DL) is a well-known machine learning strategy that has been shown to outperform many other approaches for SOC estimation in recent studies. However, choosing the right hyperparameters and appropriate use of suitable input parameters is crucial to get the best performance out of DL models. Currently, researchers use well-established heuristics approaches to choose hyperparameters by manual tuning or using thorough search techniques like grid search and random search. This leads the models to be inefficient and less accurate. This paper suggests a methodical, automated procedure for choosing hyperparameters using a Bayesian optimisation algorithm. In addition to that, average voltage and average current are used as the important input parameters along with battery parameters (current, voltage and temperature) for accurate SOC prediction as they involve the past and present history of voltages and load conditions, respectively. The proposed methods are validated and tested for varying hidden neuron count with four different datasets involving different temperatures, namely, -10°C, 0°C, 10°C and 25°C. The findings demonstrate that, for all three RNN types (LSTM, GRU and BiLSTM), the ideal configuration yields SOC estimations with less than 2% root mean square and 5% maximum error. Among the three, BiLSTM with 70 hidden neurons estimates SOC with reduced estimation error compared to other methods. By utilizing the suggested approach, battery management systems that monitor the condition of batteries in various environmental circumstances can become more reliable.