Multi-objective optimization of HEV transmission system parameters based on immune genetic algorithm

Tan, Guangxing; Lin, Cong; Bai, Yinshan; Chen, Zan

doi:10.1109/iccps.2015.7454193

Cited by 3 publications

(1 citation statement)

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“…However, non‐gradient algorithms, such as the GA used in the paper, can solve the optimisation problem of HEV system well [13]. GA is a global optimisation algorithm with good robustness and adaptability derived from simulating the survival and multiplication of living things in the ecological environment [14, 15]. In the paper, the GA is used to optimise the rule base to get the minimum fuel consumption of HEV [16].…”

Section: Ga Optimisationmentioning

confidence: 99%

Research on double fuzzy control strategy for parallel hybrid electric vehicle based on GA and DP optimisation

Luo

Jiang

et al. 2018

IET Electrical Systems in Transportation

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In this study, a double fuzzy control strategy for the parallel hybrid electric vehicle (HEV) is proposed, then based on the genetic algorithm (GA) to get better simulation results, and the results are verified by dynamic programming (DP) optimisation. First, the energy management strategy is established by fuzzy control theory. On this basis, considering braking energy recovery, this study designs a double fuzzy vehicle energy control strategy. A simulation analysis of the above two control strategies is carried out in urban dynamometer driving schedule, and the comparison with the work efficiency of the engine and fuel economy performance, respectively, is made; the simulation results show that the double fuzzy control strategy can effectively improve the HEV performance. In order to make rule base more accurate, this study also uses a GA to optimise the fuzzy control rules of the fuzzy controller. Then the DP is used to optimise the energy control strategy and obtain optimal results. The results verified that the design of fuzzy controllers is correct, and the optimised fuzzy control strategy by GA can improve the work efficiency of the engine and fuel consumption.

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Section: Ga Optimisationmentioning

confidence: 99%

Research on double fuzzy control strategy for parallel hybrid electric vehicle based on GA and DP optimisation

Luo

Jiang

et al. 2018

IET Electrical Systems in Transportation

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Driving state evaluation of intelligent connected vehicles based on centralized multi-source vehicle road collaborative information fusion

Qiu-hong

Yang

Chai

et al. 2022

Environ Dev Sustain

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When the intelligent vehicle is driving on the road, the driving state of the vehicle needs to be monitored and evaluated in real time, such as whether the distance from the vehicle or obstacle in front of it is often less than or close to the safe driving distance, whether it often deviates from the lane at high speed, whether the temperature of the power battery is often higher or lower, etc. The intelligent vehicle has rich environment perception systems and communication systems. A large amount of effective driving state information is obtained through these systems, then the information is fused by intelligent algorithm, which is very useful to evaluate the driving state. By using the developed Intelligent Connected Vehicle (ICV) information collection system platform, including vision, radar, environmental perception, positioning, and V2X communication system for obtaining roadside information, etc. various driving state parameters of the vehicle during the current driving process of the intelligent vehicle are obtained in real time when the vehicle is driving, then transmitted to the monitoring platform, and the intelligent immune algorithm is applied to establish model on monitoring platform. In the model, the state space representation of related problems is designed. The user establishes the autologous library according to the typical vehicle driving state in the monitoring road driving characteristics, which is used as the known antigen. The parameters of the real-time driving state of the vehicle are used as the unknown antigen, and the immune algorithm model is applied to get the evaluation of the current vehicle driving state and feedback the evaluation results to the vehicle and give early warning. In this paper, the model is verified by experiments, and the data are analyzed. In the experiment, the evaluation results of the monitoring platform are consistent with the state of the driving vehicle, which shows the feasibility of this model for the evaluation of the driving state of the ICV.

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Power-Oriented Gearbox Modeling and Gearshift Strategy Optimization Using Dynamic Programming

Tebaldi

Villani

Rizzoni

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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Multi-objective optimization of HEV transmission system parameters based on immune genetic algorithm

Cited by 3 publications

References 0 publications

Research on double fuzzy control strategy for parallel hybrid electric vehicle based on GA and DP optimisation

Research on double fuzzy control strategy for parallel hybrid electric vehicle based on GA and DP optimisation

Driving state evaluation of intelligent connected vehicles based on centralized multi-source vehicle road collaborative information fusion

Power-Oriented Gearbox Modeling and Gearshift Strategy Optimization Using Dynamic Programming

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