A fuzzy neural network energy management strategy for parallel hybrid electric vehicle

Zhang, Xiumei; Yang, Liu; Jia, Zhang; Dai, Wei; Liu, Ziyu

doi:10.1109/icmic.2017.8321666

Cited by 10 publications

(10 citation statements)

References 4 publications

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“…ANNs can be used to save the time consumed for optimization. They are suitable for improvement of instantaneous optimization strategies [19]. There are a variety of power source output couplings in HEVs with new topologies.…”

Section: Cspm-hev Operating Mode Analysismentioning

confidence: 99%

“…In the BP neural network controller of this paper, the input and output variables such as vehicle power, speed, and battery SOC vary greatly in magnitude, and they need to be normalized before training, that is, all the data were converted into (0, 1), to eliminate the impact of different orders of magnitude on the network. The linear function method was used here, as shown in the following equation: (19) In the equation, x i represents the sample; x max and x min represent the sample maximum and minimum values respectively; x i is the standardized sample.…”

Section: Bp Neural Network Trainingmentioning

confidence: 99%

“…The CSPM has two mechanical ports and two electrical ports, which realize efficient energy transmission, so the CSPM has broad application prospects in wind power generation, ship propulsion systems and hybrid electric vehicles [1]. A correlative research study demonstrates that a suitable Energy Management Strategy (EMS) can reduce HEV fuel consumption [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], and it is also beneficial to emissions reduction. In [2], dynamic programming was investigated for reducing the fuel consumption of vehicles when the future driving conditions are acquired.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor

Mao

Zhao

et al. 2018

Energies

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A dynamic optimization energy management strategy called Hybrid Electric Vehicle Based on Compound Structured Permanent-Magnet Motor (CSPM-HEV) is investigated in this paper. CSPM-HEV has obvious advantages in power density, heat dissipation efficiency, torque performance and energy transmission efficiency. This paper describes the topology and working principle of the CSPM-HEV, and analyzes its operating mode and corresponding energy flow laws. On this basis, the relationship about the power loss of the vehicle, the CSPM transmission ratio iCSPM and the CSPM-HEV power distribution coefficient f1 were derived. According to the optimal combination of (iCSPM, f1), the engine power and speed which minimize the power loss of the vehicle, were calculated, thus realizing the instantaneous optimal control of the vehicle. In addition, in order to improve the instantaneously optimized control processing speed, a neural network controller was established. The drive axle demand power, speed and battery State of Charge (SOC), were taken as input variables. Then, the engine power and speed were taken as output variables. The simulation results show that the average speed of the instantaneous optimization strategy after BP neural network optimization is increased by 98.1%, the control effect is significant, and it has high application value.

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Section: Cspm-hev Operating Mode Analysismentioning

confidence: 99%

Section: Bp Neural Network Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor

Mao

Zhao

et al. 2018

Energies

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show abstract

“…Recent studies showed that driving cycles have a significant impact on the fuel consumption and emission performance of HEVs. Therefore, many researchers carried out studies on driving cycle recognition (DCR) technology and incorporated it into EMSs [13][14][15][16][17][18][19][20][21][22][23]. Fotouhi et al [14] summarized the application of traffic information and driving data in the field of automobile energy conservation and environmental protection.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers such as Montazeri et al used intelligent control methods such as clustering to study the identification of traffic conditions and driving segments, and they studied the most effective energy-saving control methods of HEV through driving pattern recognition [20][21][22]. Zhang et al studied a fuzzy neural network energy management strategy for PHEV based on the adaptive neuro-fuzzy inference system (ANFIS) optimization algorithm on the ADVISOR software platform [23]. Based on the above recent researches, the traditional rule-based energy management strategy is not ideal.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition

Zhang

2020

Applied Sciences

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Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles.

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Research on Driving Control Strategy of Electric Racing Car Based on Pattern Recognition

Huang

D’Apolito

et al. 2022

Lecture Notes in Electrical Engineering

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A fuzzy neural network energy management strategy for parallel hybrid electric vehicle

Cited by 10 publications

References 4 publications

A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor

A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor

A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition

Research on Driving Control Strategy of Electric Racing Car Based on Pattern Recognition

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