A novel power allocation strategy considering multi-objective comprehensive optimization for hybrid electric vehicles. (15th June 2023)

• Record Type: Journal Article
• Authors: Hua, Zhiguang; Wang, Tianhong; Li, Xianglong; Zhao, Dongdong; Wang, Yuanlin; Dou, Manfeng
• Is Part Of: Energy conversion and management. Volume 286(2023)
• Publication Date: 2023-06-15
• Subjects: Hybrid electric vehicle -- Fuel cell -- Power distribution strategy -- Battery; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2023.117037
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A comprehensive optimization power distribution strategy for FCHEV is presented. A SQP algorithm is utilized in this paper to solve the objective function. Degradation of power sources operation performance is considered. Experimental results verify the effectiveness of the presented method. Abstract: This research introduces a multi-objective comprehensive optimization power distribution strategy (PDS), specifically designed for a hybrid electric vehicle (HEV) that utilizes a combination of fuel cell (FC) and battery technology. As hybrid power systems face challenges such as high operating costs and limited stack durability, which hinder the widespread adoption of FCs, this study proposes a strategy that focuses on optimizing both the operational cost and service life of the stack. To enhance the longevity of the stack, this research investigates the coupling relationship between the operational lifespan of the fuel cell and its output power. In addition, this study formulates a mathematical formula to depict the relationship between the FC's output power and the loss of its lifespan. Furthermore, the lifetime loss of the energy storage system, as well as limitations on the fluctuation of its state of charge (SOC), are also taken into account. Additionally, this study aims to enhance fuel efficiency by optimizing the hydrogen consumption of the system. Based on the above considerations, a cost function has been formulated for the purpose of multi-objective optimization. To expedite the convergence speed when solving the cost function, a sequential quadratic programming (SQP) algorithm has been employed. Based on the hardware-in-the-loop (HIL) bench, the effectiveness of the presented multi-objective comprehensive optimization PDS is validated. The results demonstrate that the proposed strategy has advantages over other benchmark PDSs in many aspects, including operating cost, hydrogen consumption, battery SOC fluctuation range, and power sources operating life loss.