Comprehensive Comparative Analysis of Deep-Learning-Based State-of-Charge Estimation Algorithms for Cloud-Based Lithium-Ion Battery Management Systems
Dominic Karnehm, Akash Samanta, Latha Anekal, Sebastian Pohlmann, Antje Neve, Sheldon S. Williamson
Abstract
A modern battery management system (BMS) in electric vehicles plays a crucial role in enhancing battery pack safety, reliability, and performance, particularly in e-transportation applications. To achieve more accurate estimation methods, combining battery digital twinning with cloud computing for computational power and data storage capabilities proves beneficial. Over the last decade, various data-driven state-of-charge (SOC) estimation methods, such as machine learning and deep learning approaches, have been introduced to provide highly precise estimations. The widely used SOC estimation method in the industry is the extended Kalman filter (EKF). To explore and analyze the potential use of SOC estimation in a cloud platform, this paper develops and conducts a comparative analysis of four SOC estimation methods: EKF, Feedforward Neural Network (FNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). These models are deployed in two cloud computing infrastructures, and their accuracy and computing time are thoroughly examined in this study. The study concludes that the EKF method is the fastest and most accurate among all considered methods. It boasts an average execution time of 54.8 ms and a mean absolute error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {2\times 10^{-4}}$</tex-math></inline-formula> when measured over a physical distance of approximately 450 km via the mobile network Long-Term Evolution (LTE).