On-Road Experimental Campaign for Machine Learning Based State of Health Estimation of High-Voltage Batteries in Electric Vehicles

Edoardo Lelli, Alessia Musa (), Emilio Batista, Daniela Anna Misul () and Giovanni Belingardi
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Edoardo Lelli: Hyundai Motor Europe Technical Center GmbH, Hyundai-Platz, 65428 Ruesselsheim, Germany
Alessia Musa: Department of Energy (DENERG), Politecnico di Torino, 10129 Torino, Italy
Emilio Batista: Hyundai Motor Europe Technical Center GmbH, Hyundai-Platz, 65428 Ruesselsheim, Germany
Daniela Anna Misul: Department of Energy (DENERG), Politecnico di Torino, 10129 Torino, Italy
Giovanni Belingardi: Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, 10129 Torino, Italy

Energies, 2023, vol. 16, issue 12, 1-21

Abstract: The present study investigates the use of machine learning algorithms to estimate the state of health (SOH) of high-voltage batteries in electric vehicles. The analysis is based on open-circuit voltage (OCV) measurements from 12 vehicles with different mileage conditions and focuses on establishing a correlation between the OCV values, the energy stored in the battery, and the battery SOH. The experimental campaign was conducted at the Hyundai Motor Europe Technical Center GmbH (Germany), and the data collection process took advantage of the ETAS Integrated Calibration and Application Tool (INCA) and the ETAS Measure Data Analyzer (MDA) software. Six machine learning algorithms are evaluated and compared, namely linear regression, k-nearest neighbors, support vector machine, random forest, classification and regression tree, and neural network. Among the evaluated algorithms, random forest (RF) exhibits the best performance in predicting the state of health of high-voltage batteries, both for the OCV and the capacity (C) estimation. Specifically, if compared to the worst algorithm (i.e., linear regression), RF achieves a remarkable improvement with a reduction of 96% and 97% in the mean absolute error for the OCV and the C estimation, respectively. Furthermore, the comparison highlighted the main differences in the performance, complexity, interpretability, and specific features of the six algorithms. The findings of the present study will contribute to the development of efficient maintenance strategies, thus reducing the risk of unexpected battery failures.

Keywords: high-voltage batteries; state-of-health (SOH); machine learning (ML) algorithms (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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