Dynamic Analysis of Accessory Load and Driving Cycles on Depth of Discharge of Ni-Cd Battery

This paper evaluates the impact of accessory load and various drive cycles on the range of electric vehicles (EVs) by analyzing depth of discharge (DoD) and cumulative distance traveled. The research focuses on correlating key parameters such as battery capacity, Peukert coefficient, rolling resistance, and vehicle dynamics with the presence of accessories and different drive cycles. A simulation model was developed to predict EV range under different driving conditions. Over a period of one-year, extensive simulations and data analysis were conducted to ensure robustness and accuracy of the model. The proposed model introduces a novel approach to analyzing the dynamic behavior of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Ni}-\text{Cd}$</tex> batteries under various accessory loads and driving cycles. By integrating realtime data and advanced simulation techniques, this study offers a unique perspective on battery performance and degradation patterns, providing valuable insights for optimizing battery usage in automotive applications. The model considers factors like mass, drag coefficient, air density, frontal area, gear ratio, rolling resistance, accessory power, and battery characteristics. Drive cycles were loaded to simulate real-world driving conditions. For each drive cycle, the depth of discharge and cumulative distance were calculated iteratively until the battery reached 80 % DoD. Different scenarios with accessories ON and OFF were analyzed to understand their impact on the range. The switching on of accessories impacts the range significantly and reduces it by 13.2 % to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 8 \%}$</tex> for Highway Fuel Economy Test (HWFET) and Supplemental Federal Urban Driving Schedule (SFUDS) cycles respectively. The decrease in range for the different cycles is attributed to increase in Peukert coefficient. The exceptional decrease in the range for US06 cycle to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 0 \%}$</tex> of HWFET is due to its aggressiveness. The analysis also showed varying impacts of different driving cycles on the EV range, highlighting the efficiency of highway driving and the high energy consumption of aggressive driving patterns. Noninvasive predictors like drive cycle characteristics and accessory load significantly affect the EV range. Understanding these factors can improve EV design and provide users with accurate range estimates, enhancing the overall management and usability of electric vehicles.