Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization

Abstract

In this article, a multi-objective optimization-oriented energy management strategy is investigated for fuel cell hybrid vehicles on the basis of rule learning. The degradation of fuel cells and lithium-ion batteries are considered as the objective function and translated into the equivalent hydrogen consumption. The optimal fuel cell power sequence and state of charge trajectory, considered as the energy management input, are solved offline via the Pontryagin's minimum principle. The K-means algorithm is employed to hierarchically cluster the optimal data set for preparation of rules extraction, and then the rules are excavated by the improved repeated incremental pruning to production error reduction algorithm and fitted by the quasi-Newton method. The simulation results highlight that the proposed rule learning-based energy management strategy can effectively save hydrogen consumption and prolong fuel cell life with real-time application potential.

Comparison of Two Energy Management Strategies Considering Power System Durability for PEMFC-LIB Hybrid Logistics Vehicle

“…The reason for the low efficiency of the fuel cell system at the low partial loads is that the power consumption of the auxiliary system accounts for a large proportion of the power of the stack. Many studies on the fuel cell degradation model are mainly carried out from the following four kinds of conditions as the starting point: dynamic load change, start-stop, idling, and high-power [27][28][29][30] (kW) and high (kW) represent the low-power and high-power threshold of the fuel cell under idling and high-power conditions, respectively. Referring to the recommended voltage operating range of PEMFC [27], is 5.9 kW and high is 48.2 kW.…”

“…Referring to the recommended voltage operating range of PEMFC [27], is 5.9 kW and high is 48.2 kW. Many studies on the fuel cell degradation model are mainly carried out from the following four kinds of conditions as the starting point: dynamic load change, start-stop, idling, and high-power [27][28][29][30]. The discrete equations are shown as follows:…”

Power Management Strategy of a Parallel Hybrid Three-Wheeler for Fuel and Emission Reduction

“…Note that an alternate approach to calculate battery efficiency is using battery circuit equivalent model data. Similar to [45] open-circuit voltage, internal resistance and output power can be used to better approximate the battery charging-discharging efficiency.…”

Data-driven based eco-driving control for plug-in hybrid electric vehicles

“…Moreover, 2 plug-in HEVs (PHEVs) are also widely adopted due to the operation mode incorporation of EVs and HEVs, which, in turn, complicates the design of control algorithms, including energy allocation scheme among different energy sources (usually internal combustion engine (ICE) and lithium-ion battery pack). On this account, the so-called energy management strategies (EMSs) are intensively investigated to allocate energy distribution and promote energy controlling performance of PHEVs, with the target of fuel economy optimization, battery lifespan extension, and driving experience promotion [3][4][5]. On the other hand, the flourishment of connected and automated technologies allows vehicles to easily access surrounding driving conditions, and thus brings in opportunities for velocity planning and subsequent energy saving, referred to as eco-driving [6][7][8].…”