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Innovative battery thermal management system based on hydrogen storage in metal hydrides for fuel cell hybrid electric vehicles

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  • Di Giorgio, Paolo
  • Di Ilio, Giovanni
  • Jannelli, Elio
  • Conte, Fiorentino Valerio

Abstract

This study proposes a new approach for dealing with the thermal management of batteries in fuel cell hybrid electric vehicles, by introducing a new concept of on-board energy storage system which integrates the battery pack with a metal hydride tank. The rationale behind this solution is to use the exothermic absorption and endothermic desorption processes of hydrogen in metal hydrides for heating and cooling the battery pack, respectively, thus ensuring an optimal thermal management control. An experimental investigation is carried out on a prototype in order to proof the concept and preliminary assess its feasibility for actual implementations. The results show that the system is capable to effectively control the temperature variations within the battery pack: for 1C and 1.5C constant-current discharge tests, a desorption of 30%–40% of hydrogen, over the total amount stored into the metal hydride tank of the system, allows the final average temperature of the battery pack to be around 15 °C lower than the one reached without thermal management. Moreover, it is found that the system is potentially capable of providing a suitable thermal management for more than four hours under realistic driving conditions. The proposed energy storage system also achieves inherently high gravimetric and volumetric energy densities, with theoretical values equal to 182 Wh/kg and 530 Wh/L, respectively. These estimates represent reference values for further design improvements.

Suggested Citation

  • Di Giorgio, Paolo & Di Ilio, Giovanni & Jannelli, Elio & Conte, Fiorentino Valerio, 2022. "Innovative battery thermal management system based on hydrogen storage in metal hydrides for fuel cell hybrid electric vehicles," Applied Energy, Elsevier, vol. 315(C).
    • Handle: RePEc:eee:appene:v:315:y:2022:i:c:s0306261922003543
    DOI: 10.1016/j.apenergy.2022.118935
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    1. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
    2. Farsi, Aida & Rosen, Marc A., 2023. "Performance analysis of a hybrid aircraft propulsion system using solid oxide fuel cell, lithium ion battery and gas turbine," Applied Energy, Elsevier, vol. 329(C).
    3. Kotowicz, Janusz & Uchman, Wojciech & Jurczyk, Michał & Sekret, Robert, 2023. "Evaluation of the potential for distributed generation of green hydrogen using metal-hydride storage methods," Applied Energy, Elsevier, vol. 344(C).

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