Optimal Torque Based Fuzzy Logic Control Strategy of Parallel Hybrid Electric Vehicle

Zhu, Yang; Zhao, Zhiguo; Yu, Zhuoping; Yin, Minglu

doi:10.1109/iwisa.2009.5072990

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…On the contrary, the penalty function should decrease significantly in the direction of less than 1 when the SOC approaches the preset high limit. Zhu QL 22 selected a fourth-order function as the penalty function and obtained a satisfactory result. Therefore, a fourth-order function is also selected as the penalty function in this study, which is shown in equation ( 7)…”

Section: Optimized Ecms Based On Genetic Algorithmmentioning

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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Section: Optimized Ecms Based On Genetic Algorithmmentioning

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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“…All the above papers didn't considered the distribution of demand torque between the power sources. Qinglin Zhu and Hong Shu has proposed Equivalent Consumption Minimization Stratety (ECMS) to distribute the demand torque based cars and achieved good fuel efficiency [4] [5]. So this article will apply ECMS to hybrid loader and propose the Instantaneous Optimal Control Strategy(IOCS).The integrated instantaneous fuel consumption (IIFC) is considered as the objective function in IOCS, and the key to get IIFC is to calculate the instantaneous equivalent fuel consumption of the motor.…”

Section: Introductionmentioning

confidence: 99%

The Instantaneous Optimal Control Strategy of Parallel Hybrid Loader

Zeng

Bai

Wang

et al. 2013

AMM

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In this paper, the instantaneous optimal control strategy of parallel hybrid loader is presented. The aim is to achieve the real time optimal allocation of internal combustion engine (ICE) torque and motor torque in any driving cycle for loader. Thus, all combinations of the ICE torque and the motor torque is determined in any demand torque. Then integrated instantaneous fuel consumption (IIFC) is calculated as a target function, by establishing the equivalent relationship between the electric energy consumption of battery and the fuel consumption, which is converted to the electric energy. When the minimum integrated instantaneous fuel consumption is found, the instantaneous optimal allocation of ICE torque and the motor torque is achieved. Finally in order to verify the functionality of the control strategy, the vehicle and the control algorithm co-simulation model is built on AMESim and Matlab/Simulink platforms. The simulation results show that the strategy is able to improve the fuel economy by more than 10% while ensuring the vehicle power performance.

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