Optimal Calibration of an Adaptive and Predictive Energy Management Strategy for Fuel Cell Electric Trucks

Alessandro Ferrara, Saeid Zendegan, Hans-Michael Koegeler, Sajin Gopi, Martin Huber, Johannes Pell and Christoph Hametner
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Alessandro Ferrara: Institute of Mechanics and Mechatronics, Division of Process Control and Automation, TU Wien, 1060 Vienna, Austria
Saeid Zendegan: Institute of Mechanics and Mechatronics, Division of Process Control and Automation, TU Wien, 1060 Vienna, Austria
Hans-Michael Koegeler: AVL List GmbH, 8020 Graz, Austria
Sajin Gopi: AVL List GmbH, 8020 Graz, Austria
Martin Huber: AVL List GmbH, 4407 Steyr, Austria
Johannes Pell: AVL List GmbH, 4407 Steyr, Austria
Christoph Hametner: Institute of Mechanics and Mechatronics, Division of Process Control and Automation, TU Wien, 1060 Vienna, Austria

Energies, 2022, vol. 15, issue 7, 1-20

Abstract: Energy management strategies have a significant impact on the hydrogen economy of fuel cell trucks and the lifetime of battery and fuel cell systems. This contribution presents the design and optimal calibration of an energy management strategy that is adaptive to the battery and ambient temperatures. Indeed, fuel cell trucks face critical operating conditions due to high ambient temperatures or high loads on long uphill roads. However, the presented adaptive energy management strategy shifts the electric loads to the fuel cell system to limit the battery usage, avoiding accelerated degradation due to battery temperature peaks without hindering the hydrogen economy. The strategy design and calibration involves a multi-objective optimization of performance indicators related to hydrogen consumption, fuel cell degradation, battery thermal state, equivalent charge/discharge cycles, and charge control. This work uses AVL CAMEO to systematically vary the adaptive curve parameters to explore the trade-off between the key performance indicators. The calibration considers real-world driving cycles of road freight vehicles, including measured speed, road elevation, and variable vehicle mass. Moreover, the energy management design is robust because the performance indicators are evaluated over 8935 km, covering an extensive range of real-world driving scenarios. Eventually, the adaptive and predictive energy management strategy proposed in this work can meet all the performance targets thanks to the optimal calibration, and it is particularly effective in avoiding battery temperature peaks.

Keywords: energy management strategy; fuel cell truck; optimal calibration; adaptive control; battery temperature; fuel cell degradation; battery thermal management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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