A comparative study on different online state of charge estimation algorithms for lithium-ion batteries

Khan, Zeeshan Ahmad and Shrivastava, Prashant and Amrr, Syed Muhammad and Mekhilef, Saad and Algethami, Abdullah A. and Seyedmahmoudian, Mehdi and Stojcevski, Alex (2022) A comparative study on different online state of charge estimation algorithms for lithium-ion batteries. Sustainability, 14 (12). ISSN 2071-1050, DOI https://doi.org/10.3390/su14127412.

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With an accurate state of charge (SOC) estimation, lithium-ion batteries (LIBs) can be protected from overcharge, deep discharge, and thermal runaway. However, selecting appropriate algorithms to maintain the trade-off between accuracy and computational efficiency is challenging, especially under dynamic load profiles such as electric vehicles. In this study, seven different widely utilized online SOC estimation algorithms were considered with the following goals: (a) to compare the accuracy of the different algorithms; (b) to compare the computational time in the simulation. Since the 2-RC battery model is highly accurate and not very computationally complex, it was selected for implementing the considered algorithms for the model-based SOC estimation. The considered online SOC estimation performance was evaluated using measurement data obtained from experimental tests on commercial lithium manganese cobalt oxide batteries. The experimental analysis consisted of a dynamic current profile comprising a worldwide harmonized light vehicle test procedure (WLTP) cycle and constant current discharging pulses. In addition, the performance of the considered different algorithms was compared in terms of estimation error and computational time to understand the challenges of each algorithm. The results indicated that the extended Kalman filter (EKF) and sliding mode observer (SMO) were the best choices because of their estimation accuracy and computation time. However, achieving the SOC estimation accuracy depended on the battery modeling. On the other hand, the estimated SOC root means square error (RMSE) using a backpropagation neural network (BPNN) was less than that using a Luenberger observer (LO). Moreover, with the advantages of BPNNs, such as no need for battery modeling, the estimation error could be further reduced using a large size dataset.

Item Type: Article
Funders: Ministry of Higher Education, Malaysia, under the Long Term Research Grant Scheme (LRGS), LRGS/1/2019/UKM-UM/01/6/3
Uncontrolled Keywords: Lithium-ion battery; State of charge; Electric vehicle; Battery model; Estimation
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 19 Nov 2023 12:12
Last Modified: 19 Nov 2023 12:12
URI: http://eprints.um.edu.my/id/eprint/41954

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