As the world faces increasingly urgent environmental challenges, fuel cell hybrid electric vehicles powered by fuel cells, Maxwell supercapacitors, and Li-ion batteries have been considered a promising solution and li...
详细信息
As the world faces increasingly urgent environmental challenges, fuel cell hybrid electric vehicles powered by fuel cells, Maxwell supercapacitors, and Li-ion batteries have been considered a promising solution and likely candidate to replace internal combustion engine vehicles to save fossil fuels and reduce greenhouse gas emissions. Two of the big challenges in fuel cell hybrid electric vehicles are the optimal sizing of components (fuel cell, battery, supercapacitor), and the thermal control of PEMFCs stack temperatures always close to 90 & DEG;C. The purpose of this paper is to develop an in-house optimization code for creating a fuel cell hybrid electric vehicle (FCHEV) application for simulating a fuel cell-powered hybrid electric vehicle using MATLAB code based on the multi-objective particle swarm optimization algorithm. In this paper, to answer these two challenges, we demonstrate the FCHEV application in two case studies. In the first case study from the FCHEV application database, the Artemis driving cycle is offered to assess the influence of driving cycle conditions on fuel consumption. It should be noted that fuel consumption is closely related to three main parameters: the maximum speed of the vehicle 111.5 km/h, which directly affects the volume of the fuel cell, the average speed of the vehicle 38.37 km/h during the cycle, which affects the power required from the vehicle, which directly affects the fuel that is consumed, and mainly the speed profile mode (acceleration mode or braking mode). In the second case study, which is considered the most important contribution of this paper, the effects of the temperature of the fuel cell on the fuel consumption in the electric vehicle was shown, we create a thermal controller to maintain a fuel cell temperature at 90 & DEG;C and then its note effects on fuel consumption. It should be noted that the addition of the thermal controller reduced fuel consumption by more than 3.47% in the Artemis driving cycle of
In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urba...
详细信息
In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO.
暂无评论