A Convolutional Neural Network Approach for Estimation of Li-Ion Battery State of Health from Charge Profiles

Ephrem Chemali, Phillip J. Kollmeyer, Matthias Preindl, Youssef Fahmy and Ali Emadi
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Ephrem Chemali: McMaster Automotive Resource Centre (MARC), McMaster University, Hamilton, ON L8P 0A6, Canada
Phillip J. Kollmeyer: McMaster Automotive Resource Centre (MARC), McMaster University, Hamilton, ON L8P 0A6, Canada
Matthias Preindl: Department of Electrical Engineering, Columbia University in the City of New York, New York, NY 10027, USA
Youssef Fahmy: Department of Electrical Engineering, Columbia University in the City of New York, New York, NY 10027, USA
Ali Emadi: McMaster Automotive Resource Centre (MARC), McMaster University, Hamilton, ON L8P 0A6, Canada

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

Abstract: Intelligent and pragmatic state-of-health (SOH) estimation is critical for the safe and reliable operation of Li-ion batteries, which recently have become ubiquitous for applications such as electrified vehicles, smart grids, smartphones, as well as manned and unmanned aerial vehicles. This paper introduces a convolutional neural network (CNN)-based framework for directly estimating SOH from voltage, current, and temperature measured while the battery is charging. The CNN is trained with data from as many as 28 cells, which were aged at two temperatures using randomized usage profiles. CNNs with between 1 and 6 layers and between 32 and 256 neurons were investigated, and the training data was augmented with noise and error as well to improve accuracy. Importantly, the algorithm was validated for partial charges, as would be common for many applications. Full charges starting between 0 and 95% SOC as well as for multiple ranges ending at less than 100% SOC were tested. The proposed CNN SOH estimation framework achieved a mean average error (MAE) as low as 0.8% over the life of the battery, and still achieved a reasonable MAE of 1.6% when a very small charge window of 85% to 97% SOC was used. While the CNN algorithm is shown to estimate SOH very accurately with partial charge data and two temperatures, further studies could also investigate a wider temperature range and multiple different charge currents or constant power charging.

Keywords: battery management systems; convolutional neural networks; deep learning; Li-ion batteries; machine learning; state-of-health estimation (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 (4)

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