State-of-Health Estimation and End of Life Prediction for the Lithium-Ion Battery by Correlatable Feature-based Machine Learning Approach

doi:10.23940/ijpe.21.09.p9.825836

Abstract

Abstract: A robust and straightforward prognostic framework is proposed to estimate the state-of-health (SOH) and accurate prediction of the end of life (EOL) for lithium-ion batteries. Two commonly used machine learning (ML) models, feed-forward neural network (FNN) and long short-term memory (LSTM), are used to estimate the SOH. Firstly, some features which are easy to calculate on every discharge cycle are observed, and their correlation with SOH is calculated. Secondly, two scenarios with two inputs and three inputs respectively are created to provide the inputs to these models where SOH is the output. Thirdly, the model's optimal structure is derived based on testing mean absolute percentage error (MAPE). Finally, SOH estimation is done by the model, which shows the highest accuracy. Two models considering both the scenarios are used for EOL prediction, and one is chosen as it shows early forecast. Compared with other ML-based methods, it is easier to implement as the input features are totally based on the initial and final status of the discharge cycle. This methodology is applied to the NASA battery dataset, which shows an average MAPE of 1.86% for SOH estimation and an early prediction of EOL for most of the batteries.

Key words: lithium-ion battery, state-of-health, feed forward neural network, long short term memory, end of life

Himadri Sekhar Bhattacharyya, Sindhu Seethamraju, Amalendu Bikash Choudhury, chandan Kumar Chanda. State-of-Health Estimation and End of Life Prediction for the Lithium-Ion Battery by Correlatable Feature-based Machine Learning Approach [J]. Int J Performability Eng, 2021, 17(9): 825-836.

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