Jellyfish optimized recurrent neural network for state of health estimation of lithium-ion batteries

Abstract

The state of health (SOH) of lithium-ion batteries is considered an important health indicator to evaluate different features such as efficiency, robustness, and accuracy. The SOH of the lithium-ion battery is investigated to determine the advent of failures to mitigate the battery risk, avoid uneven fault occurrences and ensure safe battery operation. Nonetheless, the accurate assessment of SOH is difficult due to the occurrence of capacity degradation and performance deviation with temperature and aging impacts. Therefore, this work presents an improved hybrid recurrent neural network (RNN) and jellyfish optimization (JFO) to estimate the SOH of lithium-ion batteries. A total of 124 lithium-ion battery cells classified in three batches, namely ‘2017-05-12’, ‘2017-06-30’, and ‘2018-04-12’ from the MIT Stanford- Toyota Research Institute battery database, are used for the proposed work. The systematic sampling method is utilized for developing a 31-dimensional data format. The 31-dimensional data format is developed considering various critical battery parameters such as capacity, voltage, current, and temperature. The JFO is applied to achieve the best suitable RNN model hyperparameters for accurate estimation outcomes. The validation of the hybrid RNN-JFO method is conducted with other JFO-optimized neural network (NN) models, such as feedforward neural network (FFNN) and backpropagation neural network (BPNN). Compared with FFNN-JFO and BPNN-JFO models, the experiment conducted with the RNN-JFO method delivered accurate outcomes. For the batches ‘2017-05-12’, ‘2017-06-30’, and ‘2018-04-12’, the average root mean square error (RMSE) error achieved with the RNN-JFO model was 0.0529, 0.2674, and 0.1638. The SOH estimation outcomes confirm the effectiveness of the RNN-JFO method for SOH estimation on different lithium-ion batteries.