Nonlinear Control of Hybrid Electric Vehicle with Regenerative Fuel Cell, Supercapacitor and Battery

Abstract

The efficient implementation of contemporary renewable energy sources is imperative within the transportation sector to enhance energy efficiency. The adoption of renewable energy in the transportation domain has been hindered by the prevalence of vehicles powered by internal combustion engines. To use cleaner and greener forms of transportation, urban and suburban planners are working together to ensure that new developments and modifications to existing ones are built with sustainable multi-modal transportation in mind. Hybrid Electric Vehicles (HEVs) have emerged as a promising alternative to conventional vehicles, offering a means to mitigate the climate crisis and reduce fossil fuel consumption. While battery-powered vehicles are popular, they suffer from limitations such as gradual capacity degradation and lengthy charging times, making them less suitable for long-haul travel. In contrast, hydrogen fuel cell-powered vehicles hold promise for extended journeys but face challenges related to low power density and hydrogen infrastructure requirements. Supercapacitors, with their high power density, can address some of these issues but exhibit limited energy storage capabilities per unit mass. This study centers on the modeling and control of a Regenerative Energy Storage System (RESS) for Hybrid Electric Vehicles. The system incorporates multiple energy sources, including fuel cells, supercapacitors, batteries, and hybrid photoelectrochemical and photovoltaic cells (HPEV). HPEV cells simultaneously produce clean hydrogen and electricity for supporting the fuel cell. Instead of using simple supercapacitors, hybrid supercapacitors are utilized. The power block of the proposed HPEV-RESS encompasses two boost converters for the fuel cell and HPEV, along with two buck-boost converters for the battery and supercapacitor, all interconnected via a common DC bus linked to a DC-AC inverter. viiiA rigorous mathematical model has been developed, and nonlinear controllers have been meticulously designed to ensure precise source-current tracking, DC bus voltage regulation, and global asymptotic stability of the system. The HPEV’s maximum power point is achieved through an innovative artificial neural network-based technique, facilitating simultaneous hydrogen and electricity production. To validate the efficacy of the proposed HPEV-RESS, comprehensive simulations were conducted using MATLAB/Simulink. Furthermore, real-time controller hardware in the loop experiments were performed under varied extra-urban driving cycle load conditions, demonstrating the system’s robustness and performance. The proposed HPEV-RESS offers a sustainable solution by effectively harnessing fuel cells, supercapacitors, batteries, and HPEV technology to power hybrid electric vehicles. This innovative approach not only supports long-haul transportation through green hydrogen production but also enhances acceleration and handles high-load conditions. By optimizing energy storage and utilization, this research contributes to the broader goal of sustainable development by harnessing decarbonized renewable energy sources.