Simulation-based Comparative Study of EV Energy Consumption and Effects on the Lithium-ion Battery Aging under Different Driving Cycles

Lithium-ion batteries (LiBs) used in electric vehicles (EVs) undergo capacity degradation primarily influenced by charge and discharge currents (C-rates) during acceleration and deceleration of the vehicle, as well as the operating temperature. Therefore, this study aims to comprehensively investigation single cell of LiFePO4 capacity degradation resulting from different driving patterns, which exhibit diverse average speeds and acceleration profiles representing various daily usage scenarios. Additionally, the electrical energy required from the auxiliary unit has been considered. To achieve this, the battery current profiles are necessary and calculated using a forward-facing simulation implemented in AUTONOMIE. Since this software is a MATLAB Simulink-based, allowed to use the Simscape battery model which enables 2-RC battery dynamics simulation by configuring the lookup tables of the equivalent circuit parameters. The driving traffic representing city, combined, and highway are captured by the JC08, WLTP3B, and US06 standard cycles, respectively. The cycling aging of the battery over an equivalent year is computed using an empirical aging model that accounts for the C-rates, battery cycles, and temperature effects. This model can predict the capacity loss with an error less than 5% compared to the data from the experiment in previous literature. In addition, the calendar aging is incorporated into the battery degradation model. The results shown city, combined, and highway driving consumed energy approximately 199.74 Wh/km., 237.13 Wh/km., and 292.1 Wh/km. respectively. The prediction of EV usable lifetime on city, combined, and highway driving are 6.6, 5, and 3.5 year respectively based-on assumption in this work. The findings can be utilized to estimate the lifetime of EV battery under various usage conditions.

I. Introduction

Road transportation is a significant contributor to increase the overall fuel consumption by 13% in 2022 compared to 2021 [1]. In an effort to mitigate fuel and energy consumption in the transportation sector, the Ministry of Energy has actively promoted electric vehicles (EVs) due to their environmentally friendly and highly efficient drivetrains [2]. Currently, there is a growing interest from worldwide in switching from old vehicles to new EVs. However, to make informed decisions regarding selecting of an appropriate EV model or designing components for EV conversions, an fast-accurate predictions of energy consumption and Lithium-ion battery (LiB) lifespan in the daily usage scenarios are essential. Numerous studies have focused on developing LiB capacity fade model based on large experimental data [3]. presented the capacity fade model for a commercial LiFePO₄ battery, derived from cycling aging test. The model incorporates time, temperature, DOD, and C-rates as factors, with the results highlighting time and temperature as the primary contributors to capacity fade. By curve fitting method to the LiB aging data, the model can be used to accurately predicts capacity fade resulting from different fade factors. Building upon this work, [4] have improved the model by adding the effect of SOC and presented the sensitivity analysis of LiB aging parameters. The resulting capacity fade model demonstrated applicability in system-level optimization for EV energy management controller design. [5] utilized the LiB aging prediction model from [4] to investigate the battery performance and cycling capacity fade prediction under different daily driving patterns by involved calculating the traction battery power and current profile using a developed MATLAB-based EV simulation tool. The results indicated that aggressive driving under the same route significantly reduces battery life due to high peak current rates and high battery temperature. Notably, there is a gap in the literature concerning the adoption and comparative study of coupling the battery dynamic model with an empirical battery capacity fade model within the EV simulation framework that covers the energy consumption calculation and battery aging prediction. This research aims to address this gap by developing a fast and adaptable approach in various driving patterns. In this work, the dynamic model of the LiFePO₄ battery has been utilized by configuring the equivalent circuit parameters and adapting the LiFePO₄ battery aging prediction empirical model from [4] and [6]. The model relies on data to accurately capture the LiB response under different loads and capacity loss due to aging factors. Therefore, this study focuses on simulating an electric vehicle and its battery, considering both the energy consumption and capacity fade prediction. The battery load varies based on different daily driving scenarios: city, combined, and highway. Additionally, the impact of the additional energy required by the auxiliary unit are considered. To achieve this, the AUTONOMIE vehicle simulation software will be utilized, coupled with the Simscape table-based battery dynamic model and MATLAB capacity loss calculation model.