Estimation of smart battery capacity using deep learning methods

Lithium-ion batteries play an irreplaceable role in various areas of production and life as an efficient energy storage element. The state of health (SOH) of lithium-ion batteries is critical to the safe operation of the energy storage system. Deterioration of the health status of lithium-ion batteries can lead to a decrease in battery performance, a decrease in the current maximum capacity, a shortening of the service life, a decrease in the driving range of electric vehicles and even security vulnerabilities in the use of electric vehicles. In this paper, measurable data such as voltage, current and temperature profiles coming from the battery management system, which change due to aging, were used to obtain the capacity change vector with these data and a capacity estimation framework was proposed with the Gated Recurrent Unit with Attention Mechanism method. Based on these data, the relationship between capacity and charging profiles is learned by neural networks. The experimental results we reached in this study are based on NASA lithium-ion battery data sets from both cold, hot and room temperature conditions. The proposed GRU with attention mechanism method has been found to be successful in terms of average absolute percentage error in estimating the health of the battery up to 35%, 27%, 20%, 16% and 10% better than the deep learning methods LSTM, GRU, BiLSTM, LSTM-AM and BiLSTM-AM, respectively. The simulation studies were carried out using the deep learning toolbox in the MATLAB environment. In recent years, attention mechanisms have emerged as a powerful tool to improve the performance of time series forecasting models. In this work, LSTM, BiLSTM and GRU, which are used in solving time series problems, were tested on the same NASA data sets and their performances were measured by combining each with the attention mechanism. GRU was preferred because it is faster and simpler than these three methods. The mechanism proposed in this study is the SoH prediction mechanism created by combining GRU and Attention Mechanism.