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Comparative Studies on Batteries for the Electrochemical Energy Storage in the Delivery Vehicle

Author

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  • Piotr Szewczyk

    (Department of Ship Automation, Gdynia Maritime University, Morska 83 Str., 81-225 Gdynia, Poland)

  • Andrzej Łebkowski

    (Department of Ship Automation, Gdynia Maritime University, Morska 83 Str., 81-225 Gdynia, Poland)

Abstract

The publication presents a proposal of methodology for the evaluation of electric vehicle energy storage, based on examples of three types of batteries. Energy stores are evaluated in different categories such as cost, reliability, total range, energy density, battery life, weight, dependency on ambient temperature, and requirements of battery conditioning system. The performance of the battery systems were analyzed on exemplary 4 × 4 vehicle with 4 independent drives systems composed of inverters and synchronous in-wheel motors. The studies showed that the best results were obtained for energy storage built on LFP prismatic batteries, and the lowest ranking was given to energy storage built on cylindrical NMC batteries. The studies present the method of aggregation of optimization criteria as a valuable methodology for assessing design requirements and the risk of traction batteries in electric vehicles.

Suggested Citation

  • Piotr Szewczyk & Andrzej Łebkowski, 2022. "Comparative Studies on Batteries for the Electrochemical Energy Storage in the Delivery Vehicle," Energies, MDPI, vol. 15(24), pages 1-28, December.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9613-:d:1007213
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    References listed on IDEAS

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    1. Bo Pang & Li Chen & Zuomin Dong, 2022. "Data-Driven Degradation Modeling and SOH Prediction of Li-Ion Batteries," Energies, MDPI, vol. 15(15), pages 1-12, August.
    2. Piotr Dukalski & Roman Krok, 2021. "Selected Aspects of Decreasing Weight of Motor Dedicated to Wheel Hub Assembly by Increasing Number of Magnetic Poles," Energies, MDPI, vol. 14(4), pages 1-27, February.
    3. Tomasz Neumann, 2021. "The Impact of Carsharing on Transport in the City. Case Study of Tri-City in Poland," Sustainability, MDPI, vol. 13(2), pages 1-24, January.
    4. Xin Lai & Ming Yuan & Xiaopeng Tang & Yi Yao & Jiahui Weng & Furong Gao & Weiguo Ma & Yuejiu Zheng, 2022. "Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing," Energies, MDPI, vol. 15(19), pages 1-20, October.
    5. Arkadiusz Małek & Agnieszka Dudziak & Ondrej Stopka & Jacek Caban & Andrzej Marciniak & Iwona Rybicka, 2022. "Charging Electric Vehicles from Photovoltaic Systems—Statistical Analyses of the Small Photovoltaic Farm Operation," Energies, MDPI, vol. 15(6), pages 1-18, March.
    6. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
    7. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    8. Miroslaw Śmieszek & Nataliia Kostian & Vasyl Mateichyk & Jakub Mościszewski & Liudmyla Tarandushka, 2021. "Determination of the Model Basis for Assessing the Vehicle Energy Efficiency in Urban Traffic," Energies, MDPI, vol. 14(24), pages 1-18, December.
    9. Piotr Dukalski & Jan Mikoś & Roman Krok, 2022. "Analysis of the Simulation of the Operation of a Wheel Hub Motor Mounted in a Hybrid Drive of a Delivery Vehicle," Energies, MDPI, vol. 15(21), pages 1-39, November.
    10. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    11. Yang Yang & Libo Lan & Zhuo Hao & Jianyou Zhao & Geng Luo & Pei Fu & Yisong Chen, 2022. "Life Cycle Prediction Assessment of Battery Electrical Vehicles with Special Focus on Different Lithium-Ion Power Batteries in China," Energies, MDPI, vol. 15(15), pages 1-23, July.
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    Cited by:

    1. Tadeusz Białoń & Roman Niestrój & Wojciech Skarka & Wojciech Korski, 2023. "HPPC Test Methodology Using LFP Battery Cell Identification Tests as an Example," Energies, MDPI, vol. 16(17), pages 1-21, August.

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