Adaptive ECMS With Gear Shift Control by Grey Wolf Optimization Algorithm and Neural Network for Plug-In Hybrid Electric Buses

The plug-in hybrid electric bus (PHEB) is an important means of public transportation. For PHEB, this article proposes an energy management strategy that considers both gear shift control and power splitting. For gear shift control, a large number of gear-switching data under different working condi...

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Vydáno v:	IEEE transactions on industrial electronics (1982) Ročník 71; číslo 1; s. 667 - 677
Hlavní autoři:	Sun, Xiaodong, Jin, Zhijia, Xue, Mingzhou, Tian, Xiang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptive control Algorithms Batteries Driving cycle division Dynamic programming Energy consumption Energy management Engines Equivalence Fuel consumption Fuel economy gear shift control Gears grey wolf optimizer algorithm Hybrid electric vehicles neural network Neural networks Optimization Optimization algorithms plug-in hybrid electric bus (PHEB) power split control Public transportation Switching Torque
ISSN:	0278-0046, 1557-9948
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Abstract	The plug-in hybrid electric bus (PHEB) is an important means of public transportation. For PHEB, this article proposes an energy management strategy that considers both gear shift control and power splitting. For gear shift control, a large number of gear-switching data under different working conditions are calculated by using the dynamic programming algorithm. The neural network is trained by these data in the offline part so that it can provide the appropriate gear-switching signal in time in the online model. For power-split control, this article mainly selects an improved equivalent fuel consumption minimization strategy (ECMS) and uses the grey wolf optimization algorithm to iteratively solve the optimal equivalent factor. The proposed control strategy has not only been verified under various operating conditions but also verified by relevant experiments on the hardware-in-the-loop platform. The results show that the proposed strategy has better fuel economy than ECMS and the rule-based strategy under the provided mixed operating conditions. Compared with the dynamic programming algorithm, the fuel consumption is only increased by 3.23%, but the problem of the curse of dimensionality is avoided.
AbstractList	The plug-in hybrid electric bus (PHEB) is an important means of public transportation. For PHEB, this article proposes an energy management strategy that considers both gear shift control and power splitting. For gear shift control, a large number of gear-switching data under different working conditions are calculated by using the dynamic programming algorithm. The neural network is trained by these data in the offline part so that it can provide the appropriate gear-switching signal in time in the online model. For power-split control, this article mainly selects an improved equivalent fuel consumption minimization strategy (ECMS) and uses the grey wolf optimization algorithm to iteratively solve the optimal equivalent factor. The proposed control strategy has not only been verified under various operating conditions but also verified by relevant experiments on the hardware-in-the-loop platform. The results show that the proposed strategy has better fuel economy than ECMS and the rule-based strategy under the provided mixed operating conditions. Compared with the dynamic programming algorithm, the fuel consumption is only increased by 3.23%, but the problem of the curse of dimensionality is avoided.
Author	Xue, Mingzhou Tian, Xiang Jin, Zhijia Sun, Xiaodong
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SubjectTerms	Adaptive control Algorithms Batteries Driving cycle division Dynamic programming Energy consumption Energy management Engines Equivalence Fuel consumption Fuel economy gear shift control Gears grey wolf optimizer algorithm Hybrid electric vehicles neural network Neural networks Optimization Optimization algorithms plug-in hybrid electric bus (PHEB) power split control Public transportation Switching Torque
Title	Adaptive ECMS With Gear Shift Control by Grey Wolf Optimization Algorithm and Neural Network for Plug-In Hybrid Electric Buses
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