A Study on LSTM-Based Lithium Battery SoH Estimation in Urban Railway Vehicle Operating Environments

This paper presents the composition of a lithium polymer battery and a battery management system used in the configuration of 10-car urban railway vehicles on Seoul Line 4 in the Republic of Korea. The operating environment and load usage conditions are analyzed. The initial capacity was measured through full charge/full discharge experiments, and the battery capacity after the experiment was measured and used as training data. During railway vehicle operation, the battery load characteristic is that power is supplied through the charger, except in certain sections. Battery usage is extremely short compared to the operating time, and floating charging is constantly performed through the charger. Under these conditions, a battery aging experiment was conducted over 500 cycles. To minimize the impact of aging due to capacity measurements, the battery’s capacity was measured every 100 cycles. The experiments were performed at room temperature to replicate conditions similar to those of actual vehicles. The acquired data were preprocessed to enhance the accuracy of state of health (SoH) estimation. The SoH of the battery was then estimated using a long short-term memory model, which is a type of deep learning recurrent neural network.