LAPSE:2023.14664
Published Article

LAPSE:2023.14664
Energy Management Control Strategy for Saving Trip Costs of Fuel Cell/Battery Electric Vehicles
March 1, 2023
Abstract
Fuel cell vehicles (FCVs) should control the energy management between two energy sources for fuel economy, using the stored energy in a battery or generation of energy through a fuel cell system. The fuel economy for an FCV includes trip costs for hydrogen consumption and the lifetime of two energy sources. This paper proposes an implementable energy management control strategy for an FCV to reduce trip costs. The concept of the proposed control strategy is first to analyze the allowable current of a fuel cell system from the optimal strategies for various initial battery state of charge (SOC) conditions using dynamic programming (DP), and second, to find a modulation ratio determining the current of a fuel cell system for driving a vehicle using the particle swarm optimization method. The control strategy presents the on/off moment of a fuel cell system and the proper modulation ratio of the turned-on fuel cell system with respect to the battery SOC and the power demand. The proposed strategy reduces trip costs in real-time, similar to the DP-based optimal strategy, and more than the simple energy control strategy of switching a fuel cell system on/off at the battery SOC boundary conditions even for long-term driving cycles.
Fuel cell vehicles (FCVs) should control the energy management between two energy sources for fuel economy, using the stored energy in a battery or generation of energy through a fuel cell system. The fuel economy for an FCV includes trip costs for hydrogen consumption and the lifetime of two energy sources. This paper proposes an implementable energy management control strategy for an FCV to reduce trip costs. The concept of the proposed control strategy is first to analyze the allowable current of a fuel cell system from the optimal strategies for various initial battery state of charge (SOC) conditions using dynamic programming (DP), and second, to find a modulation ratio determining the current of a fuel cell system for driving a vehicle using the particle swarm optimization method. The control strategy presents the on/off moment of a fuel cell system and the proper modulation ratio of the turned-on fuel cell system with respect to the battery SOC and the power demand. The proposed strategy reduces trip costs in real-time, similar to the DP-based optimal strategy, and more than the simple energy control strategy of switching a fuel cell system on/off at the battery SOC boundary conditions even for long-term driving cycles.
Record ID
Keywords
dynamic programming, energy management control strategy, fuel cell system operation optimization, fuel cell vehicles, optimal rule extraction
Subject
Suggested Citation
Gim J, Kim M, Ahn C. Energy Management Control Strategy for Saving Trip Costs of Fuel Cell/Battery Electric Vehicles. (2023). LAPSE:2023.14664
Author Affiliations
Gim J: School of Electrical, Electronic and Control Engineering, Changwon National University, Changwon 51140, Korea
Kim M: School of Mechanical Engineering, Pusan National University, Busan 46241, Korea
Ahn C: School of Mechanical Engineering, Pusan National University, Busan 46241, Korea [ORCID]
Kim M: School of Mechanical Engineering, Pusan National University, Busan 46241, Korea
Ahn C: School of Mechanical Engineering, Pusan National University, Busan 46241, Korea [ORCID]
Journal Name
Energies
Volume
15
Issue
6
First Page
2131
Year
2022
Publication Date
2022-03-15
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en15062131, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.14664
This Record
External Link

https://doi.org/10.3390/en15062131
Publisher Version
Download
Meta
Record Statistics
Record Views
168
Version History
[v1] (Original Submission)
Mar 1, 2023
Verified by curator on
Mar 1, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.14664
Record Owner
Auto Uploader for LAPSE
Links to Related Works
[0.24 s]
