Power Management Strategies for a Fuel Cell/Supercapacitor Electric Vehicle

Author(s):  
Francisco J. Perez-Pinal ◽  
Ciro Nunez ◽  
Ricardo Alvarez ◽  
Ilse Cervantes
Author(s):  
Sujit Kumar Bhuyan ◽  
Prakash Kumar Hota ◽  
Bhagabat Panda

This paper presents the detailed modeling of various components of a grid connected hybrid energy system (HES) consisting of a photovoltaic (PV) system, a solid oxide fuel cell (SOFC), an electrolyzer and a hydrogen storage tank with a power flow controller. Also, a valve controlled by the proposed controller decides how much amount of fuel is consumed by fuel cell according to the load demand. In this paper fuel cell is used instead of battery bank because fuel cell is free from pollution. The control and power management strategies are also developed. When the PV power is sufficient then it can fulfill the load demand as well as feeds the extra power to the electrolyzer. By using the electrolyzer, the hydrogen is generated from the water and stored in storage tank and this hydrogen act as a fuel to SOFC. If the availability of the power from the PV system cannot fulfill the load demand, then the fuel cell fulfills the required load demand. The SOFC takes required amount of hydrogen as fuel, which is controlled by the PID controller through a valve. Effectiveness of this technology is verified by the help of computer simulations in MATLAB/SIMULINK environment under various loading conditions and promising results are obtained.


2020 ◽  
Vol 18 (2) ◽  
pp. 128-143
Author(s):  
Arigela Satya Veerendra ◽  
Mohd Rusllim Mohamed ◽  
Pui Ki Leung ◽  
Akeel Abbas Shah

Author(s):  
Ahmed M. Ali ◽  
Dirk Söffker

Abstract Power management in all-electric powertrains has a significant potential to optimally handle the limited energy and power density of electric power sources. Situation-based power management strategies (SB-PMSs), defining optimized solutions related to specific vehicle situations, offer the ability to reduce computational requirements and enhance the solution optimality of simple rule-based algorithms. Moreover, the local optimality of SB-PMSs can be addressed by considering online optimization of the situated solutions for limited horizons. This paper presents a novel PMSs using model predictive control (MPC) to define optimal control strategies based on situated solutions for fuel cell hybrid vehicles. Vehicle states are defined in terms of multiple characteristic variables and power management decisions are optimized offline for each vehicle states. Prediction of vehicle states is conducted using statistical predictive model based on state transitions in a number of driving cycles. Preoptimized solutions related to predicted states are iterated online to achieve better optimality over the look-ahead horizon. Results analysis from online testing revealed the ability of SB-MPC to improve the optimality of situation-based solutions and hence reduce total energy cost in different driving cycles.


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