TY - JOUR
T1 - Optimum Selection of Lithium Iron Phosphate Battery Cells for Electric Vehicles
AU - Akyildiz, Arda
AU - Ergun, Bati E.
AU - Uzun, Ege
AU - Zehir, Mustafa A.
AU - Kucuktezcan, Cavit F.
AU - Kocaarslan, Ilhan
AU - Gulbahce, Mehmet O.
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper presents a systematic approach to selecting lithium iron phosphate (LFP) battery cells for electric vehicle (EV) applications, considering cost, volume, aging characteristics, and overall performance. A battery selection algorithm is developed, and to investigate its functionality, a case study to evaluate four different LFP battery cell models based on their long-term behavior in a 40 kWh battery pack is conducted. The algorithm integrates a vehicle energy consumption model to better account for the aging impacts of different cell choices, where battery performance is analyzed based on the Worldwide Harmonised Light Vehicles Test Procedure (WLTP) over a 10-year simulated period, considering five driving cycles per day. In order to ensure a fair assessment, the model accounts for variations in battery pack weight as the sole influencing factor on vehicle dynamics. The results compare vehicle range, battery pack mass, cost, cell degradation, and volume for each battery option. The case study findings indicate that the developed method found A123 Systems ANR 26650m1 battery cell superior among the considered four options offering the best trade-off between longevity and cost-effectiveness, making it a highly suitable choice for durable and efficient EV battery packs. This study underscores the importance of considering several critical factors including aging based on detailed driving cycles, together for the most suitable battery selection in designing cost-effective, long-lasting EV energy storage solutions.
AB - This paper presents a systematic approach to selecting lithium iron phosphate (LFP) battery cells for electric vehicle (EV) applications, considering cost, volume, aging characteristics, and overall performance. A battery selection algorithm is developed, and to investigate its functionality, a case study to evaluate four different LFP battery cell models based on their long-term behavior in a 40 kWh battery pack is conducted. The algorithm integrates a vehicle energy consumption model to better account for the aging impacts of different cell choices, where battery performance is analyzed based on the Worldwide Harmonised Light Vehicles Test Procedure (WLTP) over a 10-year simulated period, considering five driving cycles per day. In order to ensure a fair assessment, the model accounts for variations in battery pack weight as the sole influencing factor on vehicle dynamics. The results compare vehicle range, battery pack mass, cost, cell degradation, and volume for each battery option. The case study findings indicate that the developed method found A123 Systems ANR 26650m1 battery cell superior among the considered four options offering the best trade-off between longevity and cost-effectiveness, making it a highly suitable choice for durable and efficient EV battery packs. This study underscores the importance of considering several critical factors including aging based on detailed driving cycles, together for the most suitable battery selection in designing cost-effective, long-lasting EV energy storage solutions.
KW - Battery aging
KW - Battery selection algorithm
KW - Electric vehicles
KW - Lithium-ion batteries
KW - Worldwide Harmonised Light Vehicles Test Procedure (WLTP)
UR - http://www.scopus.com/inward/record.url?scp=105002180254&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2025.3553081
DO - 10.1109/ACCESS.2025.3553081
M3 - Article
AN - SCOPUS:105002180254
SN - 2169-3536
VL - 13
SP - 55070
EP - 55080
JO - IEEE Access
JF - IEEE Access
ER -
