Empirical Electrical and Degradation Model for Electric Vehicle Batteries

Saldana, Gaizka; San Martin, Jose Ignacio; Zamora, Inmaculada; Asensio, Francisco Javier; Onederra, Oier; Gonzalez, Mikel · 2020 · DOAJ

DOI: 10.1109/ACCESS.2020.3019477

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Summary

This paper addresses the critical challenge of battery degradation in electric vehicles (EVs), which limits range, residual value, and widespread adoption. The authors develop an empirical model to estimate the State of Health (SoH) of lithium-ion batteries, specifically focusing on cycling aging effects. The study aims to provide a computationally efficient tool for predicting capacity fade (CF) and power fade (PF) under varying operating conditions, thereby enabling strategies for lifetime maximization. The research focuses on the LG Chem E63 nickel-cobalt-manganese (NCM) pouch cell used in Renault Zoe vehicles. The methodology combines an electrical submodel with a degradation submodel. The electrical model utilizes an internal resistance structure where terminal voltage is calculated based on open-circuit voltage and internal resistance, both dependent on State of Charge (SoC) and SoH. The degradation model is derived from experimental cycling tests across various temperatures, depths of discharge (DoD), and C-rates. Data interpolation is performed using Hermite Cubic Interpolation Polynomial (PCHIP) for most variables, while the impact of cycle count is modeled using a potential law. The model decouples capacity and resistance health states, allowing for independent tracking of CF and PF. Key findings indicate that temperature and C-rate are the most influential factors in battery aging. The model demonstrates high precision, achieving a root-mean-square error (RMSE) of 1.12% for capacity fade and 2.63% for power fade. The analysis reveals non-linear degradation behaviors relative to DoD, with rapid degradation at low DoD ranges (0–20%) and slower rates at intermediate levels. The model is computationally efficient, requiring only 2.03 seconds to calculate degradation metrics on a standard processor. Validation against experimental data confirms the model's accuracy across different thermal and electrical stress conditions. The significance of this work lies in its application to real-world driving scenarios. The authors applied the model to simulate battery degradation under urban (FTP75), mixed (WLTP3), and highway driving conditions. This demonstrates the model's utility in assessing how different usage patterns affect battery longevity. By providing a precise, low-complexity empirical model that accounts for key operational variables, this research supports the development of advanced battery management systems and operational strategies aimed at extending EV battery life and improving vehicle reliability.

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