Driving cycle recognition for hybrid electric vehicle

Xing, Jie; Han, Xuefeng; Ye, Hui; Chen, Yan; Ye, Huiping

doi:10.1109/itec-ap.2014.6940693

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…As shown in Figure 5, a comprehensive driving cycle which is originated from the six typical driving cycles shown in Table 1 is employed as the test driving cycle, 18 namely, US06_HWY + MANHATTAN + WVUSUB + HWFET + CSHVR + NYCC. The I-LVQ and LVQ algorithm 21 are used to train the DCR model to verify the effectiveness of the model training algorithm. Each 200-s time length of the comprehensive driving cycle is considered the recognition target.…”

Section: Typementioning

confidence: 99%

“…The LVQ neural network is a class of algorithms for pattern recognition and has been widely used in DCR. 21 However, the LVQ neural network cannot easily obtain ideal recognition accuracy and low computational complexity when the sample space is relatively large. 24 Therefore, an I-LVQ neural network is considered and applied to recognize driving cycle online.…”

Section: Dcr Model Based On the I-lvq Neural Networkmentioning

confidence: 99%

Section: Dcr Model Based On the I-lvq Neural Networkmentioning

confidence: 99%

“…Besides, the learning vector quantization (LVQ) neural network was introduced into the driving cycle recognition (DCR). Xing et al 21 formulated an LVQ-based DCR method to recognize the real driving cycle, but the training samples and characteristic parameters are insufficient to guarantee recognition accuracy. He et al 18 applied the principal component analysis and micro-trip extraction to the LVQ model to improve the adaptability of the model.…”

Section: Introductionmentioning

confidence: 99%

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A strategy of electrical energy management for internal combustion engine vehicle based on driving cycle recognition and electrical load perception

Wang

Zhang

et al. 2018

Advances in Mechanical Engineering

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Electrical energy consumption is an important component of energy consumption for internal combustion engine vehicle, which directly affects the fuel economy. A bus-based electrical energy management system is built, and an electrical energy management strategy based on driving cycle recognition and electrical load perception is presented to achieve the refined management of vehicle energy. Six typical driving cycles are selected to establish an improved learning vector quantization neural network model for driving cycle recognition. The corresponding model training algorithm is designed by utilizing a similar driving cycle classification and the gradient optimization so that the better recognition accuracy and the less computation intensity can be obtained. An online recognition mechanism based on sliding time window is devised, and the optimal time window length is determined. To minimize fuel consumption, a dynamic optimal regulation strategy for the output power of the alternator and battery, which considers driving cycle recognition and electrical load perception, is proposed. Experimental results show that the strategy can effectually improve the vehicle fuel economy according to the driving cycle and the electrical load change and decrease the fuel consumption per 100 miles of vehicle.

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Section: Typementioning

confidence: 99%

Section: Dcr Model Based On the I-lvq Neural Networkmentioning

confidence: 99%

Section: Dcr Model Based On the I-lvq Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A strategy of electrical energy management for internal combustion engine vehicle based on driving cycle recognition and electrical load perception

Wang

Zhang

et al. 2018

Advances in Mechanical Engineering

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show abstract

“…The current energy management strategies of HEV are mainly divided into rule-based energy management strategy, 2,3 instantaneous optimized energy management strategy, 4,5 global optimization energy management strategy [6][7][8] and adaptive driving condition energy management strategy. 9,10 The first three energy strategies distribute power between the engine and the motor only based on the current vehicle operating conditions or analysis of driving conditions. Daniel et al 2 proposed a rule-based energy management strategy combining neural network and fuzzy control, which determine the start point and the duration of the engine to drive the generator for charging according to the engine power and battery SOC, and the charging current, respectively.…”

Section: Introductionmentioning

confidence: 99%

Equivalent consumption minimization strategy of hybrid electric vehicle considering the impact of driving style

Gong

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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The impact of driving style on the fuel economy is normally unconsidered in the development process of the equivalent consumption minimization strategy for hybrid electric vehicles. This article established a real vehicle energy management strategy based on the combination of driving style recognition and optimized equivalent consumption minimization strategy. In detail, the driving style recognition algorithm is obtained by introducing the driving style identification coefficient containing driving condition type influence factor and combining the coefficient with the genetic optimization K-means driving condition recognition algorithm. The equivalent consumption minimization strategy is optimized by genetic algorithm to optimize the battery SOC penalty function and charge–discharge coefficient. Comparing the proposed energy management strategy with the traditional equivalent consumption minimization strategy in a stochastic driving condition, the simulated result shows that the fuel consumption rate is reduced by 8.49% by making the operating points of the engine closer to the engine best efficiency curve, and making battery SOC changes more smooth and maintaining it in a reasonable area. It means that the proposed energy management strategy achieves higher fuel computation efficiency and better power distribution between the integrated starter generator and the engine.

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Energy Management Strategy of Hybrid Electric Vehicles Based on Driving Condition Prediction

et al. 2021

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Driving cycle recognition for hybrid electric vehicle

Cited by 6 publications

References 3 publications

A strategy of electrical energy management for internal combustion engine vehicle based on driving cycle recognition and electrical load perception

A strategy of electrical energy management for internal combustion engine vehicle based on driving cycle recognition and electrical load perception

Equivalent consumption minimization strategy of hybrid electric vehicle considering the impact of driving style

Energy Management Strategy of Hybrid Electric Vehicles Based on Driving Condition Prediction

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