Fuzzy Logic Control Strategy Based on Genetic Algorithm Optimization for PHEV

Zhang, Yu; Meng, Dawei; Zhou, Ming; Lu, Dan

doi:10.14257/astl.2014.53.77

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…An energy management technique that utilises fuzzy logic and GA optimisation is suggested in Ref. [44]. The GA is utilised for optimising the FLC's membership function.…”

Section: Energy Management Strategies For Hevsmentioning

confidence: 99%

Energy management strategies of hybrid electric vehicles: A comparative review

Azim Mohseni,

Bayati,

Ebel

2023

IET Smart Grid

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With increasing environmental problems, including air pollution, rising greenhouse gases and global warming, the need to use clean energy resources seems essential. Therefore, the usage of clean and renewable energy sources is suggested as a suitable solution to overcome the mentioned concerns. The transportation section contributes to a huge percentage of energy consumption. Hybrid electric vehicles (HEVs), by combining several energy resources, are considered as a crucial solution to decrease fossil fuel consumption and improve the environmental challenges. The existence of an alternative energy resource and the internal combustion engine together provides optimal power distribution among them to maximise power usage and minimise fuel consumption. Energy management strategy (EMS) is an essential challenge in HEV's design procedure to deal with the power distribution in multiple power source systems to improve the performance of the HEVs. A review of various EMSs for HEVs, followed by an analysis of each type, including its benefits and drawbacks, is presented by the authors. In addition to this, the major challenges in EMSs for HEVs are described and a comprehensive review is presented for strategies addressing these issues.

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“…An energy management technique that utilises fuzzy logic and GA optimisation is suggested in Ref. [44]. The GA is utilised for optimising the FLC's membership function.…”

Section: Energy Management Strategies For Hevsmentioning

confidence: 99%

Energy management strategies of hybrid electric vehicles: A comparative review

Azim Mohseni,

Bayati,

Ebel

2023

IET Smart Grid

View full text Add to dashboard Cite

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“…The genetic algorithm has the advantages of strong robustness, minimal conditions required for optimisation, and easy implementation. Thus, it is widely used in various fields [28,29]. The optimisation steps of the genetic algorithm in this study are as follows:…”

Section: Algorithm Optimisationmentioning

confidence: 99%

Intelligent Vehicle Path Tracking Control Method Based on Curvature Optimisation

Gao

Zhang

et al. 2023

Sensors

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In this study, an intelligent vehicle (IV) path tracking control method based on curvature optimisation is proposed to reduce the comprehensive performance conflict of the system. This system conflict is caused by the mutual restriction between the path tracking accuracy and the body stability during the movement of the intelligent automobile. First, the working principle of the new IV path tracking control algorithm is briefly introduced. Then, a three-degrees-of-freedom vehicle dynamics model and a preview error model considering vehicle roll are established. In addition, a path tracking control method based on curvature optimisation is designed to solve the deterioration of vehicle stability even when the path tracking accuracy of the IV is improved. Finally, the effectiveness of the IV path tracking control system is validated through simulations and the Hardware in the Loop (HIL) test with various conditions forms. Results clearly show that the optimisation amplitude of the IV lateral deviation is up to 84.10%, and the stability is improved by approximately 2% under the vx = 10 m/s and ρ = 0.15 m−1 condition; the optimisation amplitude of the lateral deviation is up to 66.80%, and the stability is improved by approximately 4% under the vx = 10 m/s and ρ = 0.2 m−1 condition; the body stability is improved by 20–30% under the vx = 15 m/s and ρ = 0.15 m−1 condition, and the boundary conditions of body stability are triggered. The curvature optimisation controller can effectively improve the tracking accuracy of the fuzzy sliding mode controller. The body stability constraint can also ensure the smooth running of the vehicle in the optimisation process.

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“…In [33], an energy management strategy is proposed based on fuzzy logic and genetic algorithm optimization. The membership function of a fuzzy logic controller is optimized using the genetic algorithm.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook

et al. 2020

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Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

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Fuzzy Logic Control Strategy Based on Genetic Algorithm Optimization for PHEV

Cited by 5 publications

References 6 publications

Energy management strategies of hybrid electric vehicles: A comparative review

Energy management strategies of hybrid electric vehicles: A comparative review

Intelligent Vehicle Path Tracking Control Method Based on Curvature Optimisation

Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook

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