Real‐time optimal energy management for a fuel cell/battery hybrid system

Abstract

An energy management strategy is proposed for a class of fuel cell∕battery hybrid systems. In such hybrid systems, a fuel cell system is the main power source, and a lithium-ion battery is the auxiliary power source. In order to manage the system power at the next moment in a reasonable way, a load current filter with bounded estimation errors is designed to estimate the load current.Then, a real-time optimal energy management algorithm is proposed to optimize economy consumption of the hybrid system. By taking current change rate of the fuel cell and the state of charge into consideration and taking reasonable model simplifications, the optimization problem can be described as a quadratic programming problem. Then a general purpose solver is proposed to solve the quadratic programming problem based on the alternating direction method of multipliers. The efficiency of the proposed solver is much faster than computing interior point method or active set method. Simulation results in MATLAB/SIMULINK are carried out to validate the significant effectiveness and efficiency of the proposed management strategy.

Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies

“…The most important feature of the real-time optimization strategies is the processing power of the information gathered from the ESS for the purpose of energy control automa-tion to prevent the aging of the components. Even though the design of such algorithms is more difficult to achieve, compared to the other energy management strategies, real-time strategies are important because the realization of FCEVs must have a competitive finish on the world market [116,131].…”

MPC‐based robust contouring control for a robotic machining system

“…In this paper, the optimization problem (28) is solved by the active‐set method [37]. In fact, other optimization algorithms, for example, fast gradient method and interior point method, can also solve such optimization problem [38].…”

Power Allocation Strategy Based on Decentralized Convex Optimization in Modular Fuel Cell Systems for Vehicular Applications

“…Moreover, the additional degrees of freedom in the MESs make the centralized algorithms substantially complicated and time-consuming to be solved. In this respect, some papers have focused on the distributed optimization algorithms to solve the PAS optimization problems [30][31][32][33][34]. In [30], a projected interior point method is proposed under the framework of model predictive control (MPC) to solve the power allocation problem and concluded that this strategy is faster than CVX tool, which is a general-purpose convex optimization software.…”

“…In [31], CVX tool is utilized to solve a formulated convex optimization problem for a plug-in FCV, and it is shown that the proposed approach can effectively distribute the power between the power sources and also find the optimal sizes of each source. In [32], the slew rate of the PEMFC current and the battery state of charge (SoC) are considered to formulate the PAS in the form of quadratic programming (QP). Subsequently, a solver is utilized to solve the QP problem based on the alternating direction method of multipliers (ADMM).…”

“…The comparison of this distributed algorithm with a centralized convex optimization problem shows that the proposed algorithm can result in the same fuel economy as the centralized method while its computational time is declined up to 1825 times. Although the discussed papers in [30][31][32][33][34] have improved the PAS formulation to a further step regarding the accuracy and computational time reduction, they are not still fully decentralized, and are sensitive to the occurrence of malfunction in their systems. In [35,36], a decentralized approach based on non-cooperative game theory is proposed to formulate the PAS in a multi-source hybrid vehicle.…”