A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles

For plug-in hybrid electric vehicles, the equivalent consumption minimum strategy is typically regarded as a battery state of charge reference tracking method. Thus, the corresponding control performance is strongly dependent on the quality of state of charge reference generation. This paper propose...

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Veröffentlicht in:Energy (Oxford) Jg. 243; S. 122727
Hauptverfasser: Chen, Zhihang, Liu, Yonggang, Zhang, Yuanjian, Lei, Zhenzhen, Chen, Zheng, Li, Guang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 15.03.2022
Elsevier BV
Schlagworte:
Back propagation
Back propagation networks
batteries
Bayesian analysis
Bayesian regularization neural network
Bayesian theory
Consumption
Dynamic programming
electric power
Electric vehicles
energy
Energy efficiency
Energy management
Equivalence
Equivalent factor online correction
Fuel consumption
Fuel economy
Hybrid electric vehicles
Intelligent equivalent consumption minimum strategy
Neural networks
Optimization
Plug-in hybrid electric vehicles
prediction
Regularization
State of charge
Strategy
Equivalent factor online correction
Intelligent equivalent consumption minimum strategy
Bayesian regularization neural network
Plug-in hybrid electric vehicles
ISSN:0360-5442, 1873-6785
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Zusammenfassung:For plug-in hybrid electric vehicles, the equivalent consumption minimum strategy is typically regarded as a battery state of charge reference tracking method. Thus, the corresponding control performance is strongly dependent on the quality of state of charge reference generation. This paper proposes an intelligent equivalent consumption minimum strategy based on dual neural networks and a novel equivalent factor correction, which can adaptively regulate the equivalent factor to achieve the near-optimal fuel economy without the support of the state of charge reference. The Bayesian regularization neural network is constructed to predict the near-optimal equivalent factor online, while the backpropagation neural network is designed to forecast the engine on/off with the aim of improving the quality of equivalent factor prediction. The corresponding neural network training takes advantage of the global optimality of dynamic programming. Besides, the novel equivalent factor correction can guarantee that the electrical energy is gradually consumed along the trip and the terminal battery state of charge satisfies the preset constraints. A series of virtual simulations under a total of nine driving cycles demonstrates that the proposed method can deliver a competitive fuel economy comparing to the optimal solution derived from the dynamic programming, as well as regulating the battery state of charge to reach the desired terminal value at the end of the trip. •Dual neural networks are constructed to regulate equivalent factor online.•Dynamic algorithm is exploited to optimize equivalent factor globally.•A novel equivalent factor correction is designed to satisfy terminal SOC constrains•Standard and real-world cycles are utilized to prove the control effectiveness.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2021.122727