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.

State of Health Estimation of Lithium‐Ion Batteries in Electric Vehicles under Dynamic Load Conditions / Ezemobi, E.; Silvagni, M.; Mozaffari, A.; Tonoli, A.; Khajepour, A.. - In: ENERGIES. - ISSN 1996-1073. - ELETTRONICO. - 15:3(2022), p. 1234. [10.3390/en15031234]

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

Ezemobi E.;Silvagni M.;Tonoli A.;
2022

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2958679