Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles

Li, Tianyu; Liu, Huiying; Wang, Hui; Yao, Yongming

doi:10.1016/j.energy.2020.117327

Cited by 60 publications

(15 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To determine the transfer relations f a , f v , and

f_{\overset{false¯}{v}},

which have high nonlinearity characteristics, they are constructed by adopting the BP neural network in this article. BPNN can establish a prediction model using historical driving cycle data and it has strong fault tolerance and computational ability 38 . A common BPNN with three layers including the input layer, a single hidden layer, and output layer is utilized to construct f a , f v , and

f_{\overset{false¯}{v}}

, respectively, as is presented in Figure 2.…”

Section: Improved Velocity Prediction Methods Based On Bpnnmentioning

confidence: 99%

“…BPNN can establish a prediction model using historical driving cycle data and it has strong fault tolerance and computational ability. 38 A common BPNN with three layers including the input layer, a single hidden layer, and output layer is utilized to construct f a , f v , and f v , respectively, as is presented in Figure 2. In the figure, u is the weight from the input layer to the hidden layer, and w is the weight from the hidden layer to the output layer.…”

Section: Construction Of Bpnnmentioning

confidence: 99%

See 1 more Smart Citation

Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle

Lin

Wang

2020

Int J Energy Res

View full text Add to dashboard Cite

Summary The stochastic driving cycles pose a challenge to the actual implementation of the control strategy for fuel cell electric vehicles (FCEVs). Considering the problem, this research proposes a predictive control strategy based on velocity prediction. Firstly, a novel velocity prediction method is developed, which considers the prediction error of back propagation neural network (BPNN)‐based method. Then, it is incorporated into the predictive control strategy for a plug‐in FCEV. Finally, simulation studies are conducted to verify the effectiveness and superiority of the proposed predictive control strategy. Simulation results show that the proposed velocity prediction method can adaptive to different driving cycles with high accuracy. In another case, with the velocity prediction used in the predictive control strategy, hydrogen consumption reduces by 17.07% when compared with the traditional rule‐based strategy. All the results indicate that the designed velocity prediction approach made it possible to forecast vehicle velocity with relatively high precision and promising to promote the energy management strategy (EMS) to reduce the hydrogen consumption of the plug‐in FCEV.

show abstract

“…To determine the transfer relations f a , f v , and

f_{\overset{false¯}{v}},

f_{\overset{false¯}{v}}

, respectively, as is presented in Figure 2.…”

Section: Improved Velocity Prediction Methods Based On Bpnnmentioning

confidence: 99%

Section: Construction Of Bpnnmentioning

confidence: 99%

Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle

Lin

Wang

2020

Int J Energy Res

View full text Add to dashboard Cite

show abstract

“…Bünning et al, [25] developed a model predictive control method for room temperature control in buildings. Li et al, [26] explored a hierarchical model predictive control-based energy management strategy for fuel cell hybrid construction vehicles. Liu et al, [27] presented a novel finite control-set model predictive control (FCS-MPC) strategy for solving the well-known challenges in predictive control regulated NNPC.…”

Section: A Recent Advancements In Crane Spreader and Cargo Stabilizationmentioning

confidence: 99%

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

2021

View full text Add to dashboard Cite

Smart control systems are mostly applied in industry to control the movements of heavy machinery while optimizing overall operational efficiency. Major shipping companies use large quay cranes to load and unload containers from ships and still rely on the experience of on-site operators to perform transportation control procedures using joysticks and visual contact methods. This paper presents the research results of an EU-funded project for the Klaipeda container terminal to develop a novel container transportation security and cargo safety assurance method and system. It was concluded that many risks arise during the container handling procedures performed by the quay cranes and operators. To minimize these risks, the authors proposed controlling the sway of the spreader using a model predictive control method which applies a multi-layer perceptron (MLP) neural network (NN). The paper analyzes current neural network architectures and case studies and provides the engineering community with a unique case study which applies real operation statistical data. Several key training algorithms were tested, and the initial results suggest that the Levenberg-Marquardt (LM) algorithm and variable learning rate backpropagation perform better than methods which use the multi-layer perceptron neural network structure.

show abstract

“…29 We have previously proposed EMSs for fuel cell HEVs and found that the above algorithms achieve a good control effect. 30,31 EMS should be shaped by the characteristics and performance requirements of the HUAV. Another objective of this study is to develop appropriate EMS for HUVAs and evaluate different EMSs for long endurance.…”

Section: Introductionmentioning

confidence: 99%

Energy management and system design for fuel cell hybrid unmanned aerial vehicles

Liu

Yao

Wang

et al. 2022

Energy Science & Engineering

Self Cite

View full text Add to dashboard Cite

The growth in the use of unmanned aerial vehicles (UAVs) has created an increasing demand for energy‐efficient and green power systems. In this paper, we have evaluated energy management strategies (EMSs) and system optimization design methodologies for fuel cell/battery‐powered hybrid UAVs (HUAVs). EMSs aimed at the optimization of flight endurance and fuel cell durability were proposed based on fuzzy logic, dynamic programming, equivalent consumption minimization, and Pontryagin's minimum principle (PMP). System optimization design methodologies, including static design and synergistic sizing optimization design, were also devised. The synergistic sizing optimization was based on multiobjective optimization, while optimization of the EMS used a non‐dominated sorting genetic algorithm. The effectiveness of the proposed EMSs and optimization design were then validated by simulation. Results showed that the proposed EMSs have both long flight time and good fuel cell durability, with the improved PMP prolonging the fight endurance by 4.64% and reducing the mean current of the fuel cell by 16.1% compared with fuzzy logic. Substantial improvements were obtained by using sizing optimization, and parameter sensitivity was addressed. The findings of this study can aid in the future development of fuel cell‐powered UAVs.

show abstract

Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles

Cited by 60 publications

References 30 publications

Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle

Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

Energy management and system design for fuel cell hybrid unmanned aerial vehicles

Contact Info

Product

Resources

About