A Model Predictive Controller with Adaptive Tuning Weights for Energy Management in Fuel Cell Hybrid Electric Vehicles

Fuel cell hybrid electric vehicles (FCHEVs) are recognized as a promising solution for vehicle electrification. However, the adoption of FCHEVs is relatively slow due to various factors such as the high cost of hydrogen and the limited lifespan of fuel cells. Therefore, effective energy management s...

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Bibliographic Details
Published in2024 IEEE Transportation Electrification Conference and Expo (ITEC) pp. 1 - 5
Main Authors Xun, Qian, Li, Qiuyu, Yang, Hengzhao
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.06.2024
Subjects
Adaptation models
Batteries
Electrification
energy management
Fuel cell hybrid electric vehicles
Fuel cells
Hydrogen
Linear programming
model predictive control
optimal weight adaptation
Transportation
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Summary:Fuel cell hybrid electric vehicles (FCHEVs) are recognized as a promising solution for vehicle electrification. However, the adoption of FCHEVs is relatively slow due to various factors such as the high cost of hydrogen and the limited lifespan of fuel cells. Therefore, effective energy management strategies are of great interest. Model predictive control (MPC) is widely employed to deal with energy management in FCHEVs. However, conventional MPC often relies on subjective selection of control weights in the objective function and the performance may be compromised. This paper proposes an optimal weight adaptation method within the MPC framework to enhance its effectiveness. The weights in the objective function are dynamically adjusted online using a moving horizon. Optimization techniques are then applied to fine tune these weights. The effectiveness of the proposed MPC controller with adaptive tuning weights is validated under the UDDS drive cycle.
ISSN:2473-7631
DOI:10.1109/ITEC60657.2024.10598909