Hierarchical energy management strategy for fuel cell/ultracapacitor/ battery hybrid vehicle with life balance control

Fuel cell (FC) is an ideal power source for electric vehicles with high efficiency and little pollution. However, with its weak dynamic reaction, it needs to be used in combination with other energy storage devices, and the coupling of multiple energy sources makes it difficult to fully utilize the system performance. Meanwhile, the cost and durability of FC hinder its commercialization. This paper takes the composite energy storage system composed of FC, battery (BAT) and ultracapacitor (UC) as the research object. In order to improve the system performance, a hierarchical energy management strategy is proposed based on the analysis of the relationship between UC output power and vehicle demand power under dynamic programming (DP) strategy, taking into account the vehicle energy consumption and energy source lifetime. A fuzzy control based on speed prediction and a dynamic state-of-charge (SOC) threshold control optimization algorithm of UC based on working condition recognition are designed to optimize the hierarchical control strategy. In the upper layer strategy, for realizing the online real-time distribution of UC output power, the generalized regression neural network is used to learn the relationship between UC output power and demand power under DP strategy; in the lower layer strategy, the power of the FC and BAT is distributed using an equivalent energy minimization strategy. Based on above strategy, this paper proposes a life balance control strategy for energy sources to maximize the lifetime of the composite energy storage system. Simulation and experimental results show that the proposed strategy has a comparable life-cycle average operating cost to the DP strategy with better life mileage.

Automated summary

This paper proposes a hierarchical energy management strategy for a composite energy storage system composed of fuel cell (FC), battery (BAT), and ultracapacitor (UC) in electric vehicles. The strategy aims to improve system performance and maximize the lifetime of the system. Simulation and experimental results demonstrate that the proposed strategy outperforms traditional dynamic programming strategy in terms of life mileage.