Nonlinear Model Predictive Control for the Energy Management of Fuel Cell Hybrid Electric Vehicles in Real Time

Pereira, Derick Furquim; Lopes, Francisco da Costa; Watanabe, Edson H.

doi:10.1109/tie.2020.2979528

Cited by 111 publications

(35 citation statements)

References 27 publications

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“…Using a nonlinear model predictive control (MPC) Pereira et al [31] developed an energy management system (EMS) for FCEV and modelled the proton exchange membrane of the fuel cell using a recurrent neural network (RNN). Results indicated that the RNN which was trained with the Bayesian regularization algorithm in MATLAB shows accurate results of the FC voltage prediction, while the MPC model was able to minimize hydrogen consumption.…”

Section: Energy Management Of Pev Load Demand On the Distribution Networkmentioning

confidence: 99%

Sustainable Electric Vehicle Transportation

2021

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The future direction of electric vehicle (EV) transportation in relation to the energy demand for charging EVs needs a more sustainable roadmap, compared to the current reliance on the centralised electricity grid system. It is common knowledge that the current state of electricity grids in the biggest economies of the world today suffer a perennial problem of power losses; and were not designed for the uptake and integration of the growing number of large-scale EV charging power demands from the grids. To promote sustainable EV transportation, this study aims to review the current state of research and development around this field. This study is significant to the effect that it accomplishes four major objectives. (1) First, the implication of large-scale EV integration to the electricity grid is assessed by looking at the impact on the distribution network. (2) Secondly, it provides energy management strategies for optimizing plug-in EVs load demand on the electricity distribution network. (3) It provides a clear direction and an overview on sustainable EV charging infrastructure, which is highlighted as one of the key factors that enables the promotion and sustainability of the EV market and transportation sector, re-engineered to support the United Nations Climate Change Agenda. Finally, a conclusion is made with some policy recommendations provided for the promotion of the electric vehicle market and widespread adoption in any economy of the world.

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Section: Energy Management Of Pev Load Demand On the Distribution Networkmentioning

confidence: 99%

Sustainable Electric Vehicle Transportation

2021

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“…Yang et al [12] set up a model of stochastic driving behaviours as probability transition matrix and proposed a stochastic MPC framework based on fast rolling optimization using continuation/generalized minimum algorithm. In [13], a nonlinear MPC EMS is proposed for fuel cell hybrid electric vehicle and recurrent neural network was employed to accurately predict fuel cell nonlinear dynamics. He et al [14] developed a nonlinear MPC EMS with lifetime constraints for fuel cell hybrid electric vehicles by constructing a novel objective function with power slope and temperature.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control-Based Optimal Energy Management for Hybrid Electric Aircraft Considering Aerodynamics-Propulsion Coupling Effects

Zhang

Roumeliotis

Zolotas

2022

IEEE Trans. Transp. Electrific.

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Hybrid electric propulsion systems have been identified as the feasible solutions for regional jets and narrowbody aircraft to reduce block fuel burn, emissions, and operating cost. Different from land-based vehicles, weight is a major consideration for air transport due to weight-related lift-induced drag penalty. Thus, energy onboard balance, variation in aircraft mass during flight and the aerodynamics-propulsion coupling effects should be considered in hybrid electric aircraft energy management strategy (EMS) while satisfying aircraft maximum take-off weight constraints. In this paper, a Nonlinear Model Predictive Control based optimal energy management scheme (MPC-EMS) has been proposed to minimize the block fuel burn during flight. Firstly, the Artificial Neural Network (ANN) model is adopted with back-propagation algorithm to predict turbofan engine performance with high accuracy and computational efficiency, meanwhile gas turbine-electrical powertrain coupling effects are investigated and analysed for typical operating conditions. Then, by combining a point-mass aircraft dynamic model, nonlinear model predictive control with Cross-Entropy Method (CEM) is proposed to obtain optimal energy management based on a fully coupled aerodynamicspropulsion hybrid electric aircraft model. Besides, this state-constrained optimal control problem is re-formulated as a state-unconstrained problem with penalty function to reduce the computational load. Finally, the proposed MPC EMS algorithm is applied to Boeing 737-800 aircraft with mechanically parallel hybrid electric propulsion configuration to minimize the block fuel burn and compared with the optimization results using global Genetic Algorithm (GA) based EMS. The simulation results indicate that the proposed MPC-EMS can significantly reduce the computational time by more than 97% while obtaining similar objectives of block fuel burn and emissions reduction.

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“…The offline methods include numerical techniques such as genetic algorithms [15] and dynamic programming [16][17][18][19][20], as well as analytical techniques such as Pontryagin's Minimum Principle [21,22]. Instead, when implementing online optimization techniques in FCHVs, the systems can be controlled through the Equivalent Consumption Minimization Strategy (ECMS) [23][24][25][26] and Model Predictive Control [27][28][29][30]. Although offline techniques are well-suited for defining the reference ideal solution, they cannot be applied in real time on a vehicle since they need previous information of the whole driving cycle.…”

Section: Introductionmentioning

confidence: 99%

Control Strategy Assessment for Improving PEM Fuel Cell System Efficiency in Fuel Cell Hybrid Vehicles

Luciani

Tonoli

2022

Energies

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Concerns about climate change, air pollution, and the depletion of oil resources have prompted authorities to enforce increasingly strict rules in the automotive sector. There are several benefits to implementing fuel cell hybrid vehicles (FCHV) in the transportation sector, including the ability to assist in reducing greenhouse gas emissions by replacing fossil fuels with hydrogen as energy carriers. This paper examines different control strategies for optimizing the power split between the battery and PEM fuel cell in order to maximize the PEM fuel cell system efficiency and reduce fuel consumption. First, the vehicle and fuel cell system models are described. A forward approach is considered to model the vehicle dynamics, while a semi-empirical and quasi-static model is used for the PEM fuel cell. Then, different rule-based control strategies are analyzed with the aim of maximizing fuel cell system efficiency while ensuring a constant battery state of charge (SOC). The different methods are evaluated while the FCHV is performing both low-load and high-load drive cycles. The hydrogen consumption and the overall fuel cell system efficiency are considered for all testing conditions. The results highlight that in both low-load cycles and high-load cycles, the best control strategies achieve a fuel cell system efficiency equal or greater to 33%, while achieving a fuel consumption 30% less with respect to the baseline control strategy in low-load drive cycles.

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Nonlinear Model Predictive Control for the Energy Management of Fuel Cell Hybrid Electric Vehicles in Real Time

Cited by 111 publications

References 27 publications

Sustainable Electric Vehicle Transportation

Sustainable Electric Vehicle Transportation

Nonlinear Model Predictive Control-Based Optimal Energy Management for Hybrid Electric Aircraft Considering Aerodynamics-Propulsion Coupling Effects

Control Strategy Assessment for Improving PEM Fuel Cell System Efficiency in Fuel Cell Hybrid Vehicles

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