Energy management of a fuel cell/ultracapacitor hybrid power system using an adaptive optimal-control method

• Published in: Journal of power sources Vol. 196; no. 6; pp. 3280 - 3289
• Main Authors: Lin, Wei-Song, Zheng, Chen-Hong
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.03.2011; Elsevier
• Subjects: Adaptive optimal control; Algorithms; Applied sciences; Capacitors. Resistors. Filters; Direct energy conversion and energy accumulation; Dynamical systems; Electric power generation; Electric vehicles; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Energy; Energy management; Energy. Thermal use of fuels; Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc; Exact sciences and technology; Fuel cell; Fuel cells; Hybrid power system; Nonlinear dynamics; Optimization; Strategy; Various equipment and components; Hybrid power system; ESS; AOC; SoC; FCHPS; FCS; RBF; VCC; EMS; Energy management; Adaptive optimal control; Fuel cell; Optimal control; Control system; Supercapacitor; Hybrid power systems; Electrolytic capacitor; Adaptive control; Adaptive method
• ISSN: 0378-7753; 1873-2755
• DOI: 10.1016/j.jpowsour.2010.11.127

▶ The energy management problem is formulated as a constrained optimal control problem. ▶ A penalty function method is applied for the constrained optimal problem. ▶ Adaptive optimal-control method is developed to synthesize an optimal EMS. Energy management of a fuel cell/ultracapacitor hybrid power system aims to optimize energy efficiency while satisfying the operational constraints. The current challenges include ensuring that the non-linear dynamics and energy management of a hybrid power system are consistent with state and input constraints imposed by operational limitations. This paper formulates the requirements for energy management of the hybrid power system as a constrained optimal-control problem, and then transforms the problem into an unconstrained form using the penalty-function method. Radial-basis-function networks are organized in an adaptive optimal-control algorithm to synthesize an optimal strategy for energy management. The obtained optimal strategy was verified in an electric vehicle powered by combining a fuel-cell system and an ultracapacitor bank. Driving-cycle tests were conducted to investigate the fuel consumption, fuel-cell peak power, and instantaneous rate of change in fuel-cell power. The results show that the energy efficiency of the electric vehicle is significantly improved relative to that without using the optimal strategy.