A Comparative Study on Different Online State of Charge Estimation Algorithms for Lithium-Ion Batteries

Zeeshan Ahmad Khan, Prashant Shrivastava, Syed Muhammad Amrr, Saad Mekhilef, Abdullah A. Algethami, Mehdi Seyedmahmoudian and Alex Stojcevski
Additional contact information
Zeeshan Ahmad Khan: CARAID SE, Volkswagen AG, 85053 Ingolstadt, Germany
Prashant Shrivastava: Centre for Automotive Research and Tribology (CART), Indian Institute of Technology Delhi, New Delhi 110016, India
Syed Muhammad Amrr: Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
Saad Mekhilef: Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Abdullah A. Algethami: Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Mehdi Seyedmahmoudian: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia
Alex Stojcevski: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia

Sustainability, 2022, vol. 14, issue 12, 1-19

Abstract: 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.

Keywords: lithium-ion battery; state of charge; electric vehicle; battery model; estimation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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