Auto-Adaptive Filtering-Based Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles

Abstract

The global need to solve pollution problems has conducted automotive engineers to promote the development and the use of electric vehicle technologies. This paper focuses on the fuel cell hybrid electric vehicle which uses a proton exchange membrane fuel cell as a main source associated to hybrid storage device: lithium ion battery and ultracapacitors. A common interest in such technology is to spread out the energy flow between its different sources in order to satisfy the power demand for any requested mission. However, the challenging task stills the optimization of this split to reduce hydrogen consumption and respect, at the same time, the system limitations such as admissible limits of storage system capacities and battery current variation. An adaptive filtering-based energy management strategy is proposed in this paper to ensure an optimum distribution of the energy between the sources taking into account dynamic and energetic constraints of each device. For more performance, a fuzzy logic system is used to adapt the frequency of separation with the system state evolution. A sliding mode control is applied to control electric characteristics (voltage and currents) in the considered hybrid power supply. Simulation results, obtained under MATLAB R /SimPowerSystems R for four driving cycles are presented. The proposed strategy achieved good performances by respecting the ultracapacitors state of charge while preserving the battery lifetime under various driving missions.

A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles

“…The SOC of the SC ( ) is defined as the ratio of the voltage of the SC ( ) to its maximum voltage of SC ( _ ). The is calculated using [37]: Table 1 presents the main parameters of the SC model used in this paper.…”

“…The SOC of the SC (SOC sc ) is defined as the ratio of the voltage of the SC (V sc ) to its maximum voltage of SC (V sc_max ). The SOC sc is calculated using [37]: The battery is the main ESS for an EV, which is connected directly to a DC bus in a semi-active HESS. The charging and discharging of the battery considerably depends on various parameters, such as temperature (T) and the SOC of the battery (SOC bat ) [35].…”

“…Figure 9 presents the membership function of two inputs and the output variable of the FLC. The linguistic variables are very low (VL), low (L), medium (M), and high (H) for f c and SOC sc fuzzy variables and negative (N), positive (P), and zero (Z) are the linguistic variables for the input variable I L [37]. In defuzzification, to transform the fuzzy outcome to a crisp output, a centroid defuzzification and min-max fuzzy inference were used [62].…”

“…Hamid et al [36] studied an adaptive intelligent EMS for a HEV using FL, but required high memory for the optimization. Jamila et al [37] used an adaptive FLC-based filtering strategy, but for controlling the SC, they used a sliding mode controller, which is a tedious and complicated task for the optimum sliding surface [38]. References [39,40] used the filtering method for optimizing the sizing of the electric system without discussing the fluctuation in SC voltage and other battery parameters under a high transient load current.…”

Combination of LiCs and EDLCs with Batteries: A New Paradigm of Hybrid Energy Storage for Application in EVs

“…There are some interesting research works that have been carried out with the combination of the battery and SCs or the battery and HC with renewable energy sources such as photovoltaic (PV) systems, fuel cells, and wind turbines. Some of these studies can be found in [30][31][32][33][34][35][36]. The dearth of research involving the use of LiCs cannot be overemphasized, particularly in energy storage hybridisation.…”