The Sliding Window and SHAP Theory—An Improved System with a Long Short-Term Memory Network Model for State of Charge Prediction in Electric Vehicle Application

Xinyu Gu, See Kw, Yunpeng Wang, Liang Zhao and Wenwen Pu
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Xinyu Gu: Faculty of Engineering, Institute for Superconducting & Electronic Materials, University of Wollongong, Innovation Campus, Wollongong, NSW 2500, Australia
See Kw: Faculty of Engineering, Institute for Superconducting & Electronic Materials, University of Wollongong, Innovation Campus, Wollongong, NSW 2500, Australia
Yunpeng Wang: Azure Mining Technology, CCTEG, Level 19, 821 Pacific Highway, Chatswood, NSW 2067, Australia
Liang Zhao: Azure Mining Technology, CCTEG, Level 19, 821 Pacific Highway, Chatswood, NSW 2067, Australia
Wenwen Pu: College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China

Energies, 2021, vol. 14, issue 12, 1-15

Abstract: The state of charge (SOC) prediction for an electric vehicle battery pack is critical to ensure the reliability, efficiency, and life of the battery pack. Various techniques and statistical systems have been proposed in the past to improve the prediction accuracy, reduce complexity, and increase adaptability. Machine learning techniques have been vigorously introduced in recent years, to be incorporated into the existing prediction algorithms, or as a stand-alone system, with a large amount of recorded past data to interpret the battery characteristics, and further predict for the present and future. This paper presents an overview of the machine learning techniques followed by a proposed pre-processing technique employed as the input to the long short-term memory network (LSTM) algorithm. The proposed pre-processing technique is based on the time-based sliding window algorithm (SW) and the Shapley additive explanation theory (SHAP). The proposed technique showed improvement in accuracy, adaptability, and reliability of SOC prediction when compared to other conventional machine learning models. All the data employed in this investigation were extracted from the actual driving cycle of five different electric vehicles driven by different drivers throughout a year. The computed prediction error, as compared to the original SOC data extracted from the vehicle, was within the range of less than 2%. The proposed enhanced technique also demonstrated the feasibility and robustness of the prediction results through the persistent computed output from a random selection of the data sets, consisting of different driving profiles and ambient conditions.

Keywords: SOC prediction; LSTM; SHAP; time-based sliding window (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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