State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles under Dynamic Load Conditions

Ethelbert Ezemobi, Mario Silvagni, Ahmad Mozaffari, Andrea Tonoli and Amir Khajepour
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Ethelbert Ezemobi: Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy
Mario Silvagni: Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy
Ahmad Mozaffari: Mechanical and Mechatronics Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Andrea Tonoli: Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy
Amir Khajepour: Mechanical and Mechatronics Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada

Energies, 2022, vol. 15, issue 3, 1-20

Abstract: Among numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estimation minimizes failure risk and improves reliability by predicting battery health conditions. The challenge of accurate estimation of SOH is based on the uncertain dynamic operating condition of the EVs and the complex nonlinear electrochemical characteristics exhibited by the lithium-ion battery. This paper presents an artificial neural network (ANN) classifier experimentally validated for the SOH estimation of lithium-ion batteries. The ANN-based classifier model is trained experimentally at room temperature under dynamic variable load conditions. Based on SOH characterization, the training is done using features such as the relative values of voltage, state of charge (SOC), state of energy (SOE) across a buffer, and the instantaneous states of SOC and SOE. At implementation, due to the slow dynamics of SOH, the algorithm is triggered on a large-scale periodicity to extract these features into buffers. The features are then applied as input to the trained model for SOH estimation. The classifier is validated experimentally under dynamic varying load, constant load, and step load conditions. The model accuracies for validation data are 96.2%, 96.6%, and 93.8% for the respective load conditions. It is further demonstrated that the model can be applied on multiple cell types of similar specifications with an accuracy of about 96.7%. The performance of the model analyzed with the confusion matrices is consistent with the requirements of the automotive industry. The classifier was tested on a Texas F28379D microcontroller unit (MCU) board. The result shows that an average real-time execution speed of 8.34 µs is possible with a negligible memory occupation.

Keywords: lithium-ion battery; energy storage; state of health—SOH; prediction; classification; automotive; electric vehicle; artificial neural network; dynamic load condition (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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