Abstract
This study investigates the impact of battery and fuel cell (FC) degradation on energy management of a FC hybrid electric vehicle. In this respect, an online energy management strategy (EMS) is proposed considering simultaneous online adaptation of battery and FC models. The EMS is based on quadratic programming which is integrated into an online battery and proton exchange membrane FC (PEMFC) parameters identification. Considering the battery and PEMFC states of health, three scenarios have been considered for the EMS purpose, and the performance of the proposed EMS has been examined under two driving cycles. Numerous test scenarios using standard driving cycles reveal that the ageing of battery and PEMFC has a considerable impact on the hydrogen consumption. Moreover, the proposed EMS can successfully tackle the model uncertainties owing to the performance drifts of the power sources at the mentioned scenarios.
Highlights
Global warming, air pollution owing to toxic fumes of combustion engines, and limitation of fossil fuels have motivated automobile industry to exploit alternative energy sources, such as fuel cell (FC) and electro-chemical battery [1, 2]
In the first scenario, which is called QPnew the battery and FC are both in their beginning of life (BOL) and the quadratic programming (QP)-based energy management strategy (EMS) is set up for the online identification performed based on the new characteristics of the power sources
This paper explores the performance variation influence of the Proton exchange membrane FC (PEMFC) and battery pack owing to ageing over the hydrogen economy of a FC hybrid electric vehicle (FCHEV)
Summary
Air pollution owing to toxic fumes of combustion engines, and limitation of fossil fuels have motivated automobile industry to exploit alternative energy sources, such as fuel cell (FC) and electro-chemical battery [1, 2]. Hybrid electric vehicles (HEVs) incorporate more than one energy source to propel the vehicle. FC hybrid electric vehicle (FCHEV) is a kind of HEV that uses the FC as the primary power source and the electric battery as the secondary one [3]. Lithiumion battery, because of its high energy and power density and low self-discharge rate, is the dominant battery technology in the electric vehicles and HEVs [5]. They can be utilised to refine the rule sets of the expert system in the rule-based methods [10]. Optimisation-based EMSs are divided into global and real-time methods. Global strategies determine the optimal policy of a defined cost function over a known driving profile and are not appropriate for real-time applications. Dynamic programming (as an optimal solution) [11, 12] and metaheuristic algorithms, such as genetic algorithm [13] (as a near-optimal solution), have been employed several times as off-line global
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