Research on a multi-objective hierarchical prediction energy management strategy for range extended fuel cell vehicles

Abstract

In this paper, a multi-objective hierarchical prediction energy management strategy is proposed to achieve optimal fuel cell life economy and energy consumption economy for a range extended fuel cell vehicle.First, a global state of charge rapid planning method is proposed based only on the expected driving distance.Then, the vehicle speed information in the prediction horizon is estimated by a vehicle speed prediction module based on the back propagation neural network. According to the predicted speed and state of charge reference, a novel fusion algorithm that combines the direct configuration method and sequential quadratic programming is proposed to achieve optimal fuel cell life economy and energy consumption economy in the prediction horizon. Simulation results validate that the proposed strategy can effectively reduce the operating costs compared with that of the charge depletion-charge sustaining strategy and the equivalent consumption minimization strategy, thereby proving the feasibility of the proposed strategy.

Energy Efficiency and Fuel Economy of a Fuel Cell/Renewable Energy Sources Hybrid Power System with the Load-Following Control of the Fueling Regulators

“…Additionally, under 7 kW load, the fuel economy obtained is still high, being of about 8% (=100•12.16/152.3) and 9% (=100•14.46/150) using the Air-LFW and Fuel-LFW strategies. The experimental and simulation studies [69][70][71] validate the aforementioned fuel savings by comparison with a baseline strategy, reporting a fuel economy in the same range, as follows: a fuel economy of 12.36% (=100•(4.47−3.9782)/3.9782) using a hierarchical energy management strategy [69]; a lower fuel economy of 6.25% (=100•(64.91−61.09)/61.09) using a Kriging-based bi-objective constrained optimization strategy as reported in [70]; a fuel economy of 8.6% and 13.5% compared with those based on the charge-depletion-charge-sustaining strategy and equivalent consumption minimization strategy have been reported for a multi-objective hierarchical prediction energy management strategy [71] when the drive cycle is unknown.…”

Architectures of Planetary Hybrid Powertrain System: Review, Classification and Comparison

“…It is urgent to develop alternative fuels and energy-save technology [11][12][13][14][15]. Thus, the automotive makers and researchers have made great effort to explore efficient and sustainable solutions, most of which are focused on vehicle hybridization/electrification [16][17][18][19][20][21][22].…”

Multi-Objective Optimization for Plug-In 4WD Hybrid Electric Vehicle Powertrain

“…Parameter optimization of PHEVs is related to the energy management strategy, and energy management strategies need to be developed before parameter optimization. At present, the research on the energy management strategy for PHEVs mainly focuses on the development of the advanced optimization algorithm, such as the algorithm based on the minimum equivalent fuel consumption [26][27][28][29], the dynamic programming algorithm [30][31][32], stochastic dynamic programming [33], the algorithm based on convex optimization [2,34] and the model predictive control algorithm [35][36][37][38]. Although the above-mentioned optimization algorithm can obtain the local or global optimum, it is difficult to apply to real vehicle control for hardly knowing the driving cycles beforehand or the large amount of calculation.…”