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Abstract
Plug-in hybrid fuel cell vehicles provide a promising option for environmentally responsible land freight transportation. Energy management is needed to control the power split between fuel cell and battery power. Eight energy management strategies were designed and studied including online and offline strategies, as well as optimization and rule-based strategies. A model was built in MATLAB Simulink and these strategies were tested with three drive cycles. The strategies were evaluated in terms of energy consumption and fuel cell and battery component degradation factors. Results showed that optimization strategies generally outperformed rule-based ones. Among the online strategies, the equivalent consumption minimization strategy performed best overall. Optimization was most valuable when average power requirement was high, with the nonlinear optimization strategy showing promising results. When the average power requirement was low, it was most important that the energy management run the fuel cell near its highest efficiency point, turning the fuel cell on and off as needed to ensure it is either operating efficiently, or not at all. In this case, a simple rule-based strategy proved effective. The success of energy management strategies depended both on the drive cycle characteristics and parameters used for each strategy, highlighting the importance of understanding the route when selecting and tuning a strategy.
Original language | English |
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Supervisors/Advisors |
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Publisher | |
Publication status | Published - 27 Sept 2024 |
MoE publication type | G2 Master's thesis, polytechnic Master's thesis |
Keywords
- energy management
- equivalent consumption minimization strategy
- fuel cell
- heavy-duty vehicle
- optimization
- plug-in hybrid electric vehicle
- Pontryagin’s minimization principle
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Dive into the research topics of 'Energy management for a fuel cell plug-in hybrid heavy-duty vehicle'. Together they form a unique fingerprint.Projects
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ESCALATE: Powering EU Net Zero Future by Escalating Zero Emission HDVs and Logistic Intelligence
Pihlatie, M. (Manager), Antila, M. (Participant), Ihonen, J. (Participant), Åman, R. (Participant), Vänttinen, L. (Participant), Arora, S. (Participant), Huang, X. (Participant), Hajduk, P. (Participant) & Ranta, M. (Participant)
1/01/23 → 30/06/26
Project: EU project