Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles

Phan, Duong; Bab–Hadiashar, Alireza; Hoseinnezhad, Reza; Jazar, Reza N.; Date, Abhijit; Jamali, Ali; Pham, Dinh Ba; Khayyam, Hamid

doi:10.3390/en13071745

Cited by 13 publications

(4 citation statements)

References 31 publications

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“…The optimization problems are resolved by Pontryagin's minimum principle. Phan et al [12] study the EMS of a CAV, in an effort to improve its powertrain efficacy. The developed EMS including 2 NF schemes for producing an optimum torque of the engine.…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

Hamza¹,

Alajmi²,

Hassine³

et al. 2022

Computers, Materials &Amp; Continua

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Recent advancements of the intelligent transportation system (ITS) provide an effective way of improving the overall efficiency of the energy management strategy (EMSs) for autonomous vehicles (AVs). The use of AVs possesses many advantages such as congestion control, accident prevention, and etc. However, energy management and traffic flow prediction (TFP) still remains a challenging problem in AVs. The complexity and uncertainties of driving situations adequately affect the outcome of the designed EMSs. In this view, this paper presents novel sustainable energy management with traffic flow prediction strategy (SEM-TPS) for AVs. The SEM-TPS technique applies type II fuzzy logic system (T2FLS) energy management scheme to accomplish the desired engine torque based on distinct parameters. In addition, the membership functions of the T2FLS scheme are chosen optimally using the barnacles mating optimizer (BMO). For accurate TFP, the bidirectional gated recurrent neural network (Bi-GRNN) model is used in AVs. A comprehensive experimental validation process is performed and the results are inspected with respect to several evaluation metrics. The experimental outcomes highlighted the supreme performance of the SEM-TPS technique over the recent state of art approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

Hamza¹,

Alajmi²,

Hassine³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Combinando dois diferentes conceitos de inteligência computacional, utilizamse as forças individuais da lógica Fuzzy e redes neurais artificiais em um sistema híbrido de estrutura homogênea (Sremac et al (2019)). Devido a essa caracteristica, o sistema de identificação neurofuzzy possui aplicação em diversas áreas, como exemplo Phan et al (2020). O esquema de inferência pode ser representado pela estrutura em rede a seguir: O sistema fuzzy desenvolvido nesse trabalho, baseia-se em:…”

Section: Identificação Neurofuzzyunclassified

Sistema Fuzzy para análise dos Níveis de desenvolvimento emocional e funcional baseado no modelo DIR/Floortime

Montel¹,

Pires²,

Pires

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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The diagnostics of neurodevelopment disorders is a very complicated process, because do not exist an exam, physical feature, or fixed methodology for your fulfillment. The diagnostics is realized through the specific features presented by the patient (normally children). In this context, the DIR method introduced by Greenspan, show an analysis about the stage of development that every child must pass to has a healthy development, listing them into levels and group known as Stage of Development. The diagnostic can be accomplished by the co-relations between the stages. However, to every stage, there are a lot of variables to be analyzed, that cause troubles and delayed the process, that must be accurate and fast, to result in a good treatment. In this paper are proposed a fuzzy system, to analyze the stage of development, through variables obtained by the psychologist professional. The results prove the analyze capability of the fuzzy system and have a strong potential to be applicated in the diagnostic of neurodevelopmental disorders. Resumo: O diagnóstico de transtornos do neurodesenvolvimento é um processo extremamente complicado, pois não existe um exame, característica física ou metodologia fixa para a sua realização. O diagnóstico é realizado através das características específicas apresentadas por cada paciente (normalmente crianças). Nesse contexto, o método DIR introduzido por Greenspan, apresenta uma análise acerca dos estágios do desenvolvimento que toda criança deve passar para ter um desenvolvimento saudável, elencando-os em diferentes níveis e grupos conhecidos como Níveis de Desenvolvimentos emocional e Funcional (NDEF). O diagnóstico pode ser realizado através da correlação entre os Níveis de Desenvolvimento Emocional e Funcional. Entretanto, para cada estágio, existe uma série de variáveis a se analisar, dificultando e atrasando o processo, que deve ser preciso e rápido para resultar em um bom tratamento. Nesse artigo, é proposto um sistema de identificação neurofuzzy, para a análise dos Níveis de Desenvolvimento Emocional e Funcional, através de variáveis obtidas pelo profissional psicólogo. O sistema foi testado usando um conjunto de dados gerados aleatoriamente. Os resultados comprovam a capacidade de análise do sistema fuzzy e um forte potencial para a aplicação no diagnóstico dos transtornos do neurodesenvolvimento.

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“…Many studies have opted for optimal solutions based on artificial intelligence techniques facilitated by the development of computer technology, such as genetic algorithms [10], Fuzzy Logic [11,12], Neural Networks [13,14], decoupling control, and others. They are used in several areas such as controlling electrical DC motors [15,16], automatic and robot manipulation systems [17], controlling temperature performance [18], and controlling systems in agriculture [19].…”

Section: Introductionmentioning

confidence: 99%

Optimal Neural Network PID Approach for Building Thermal Management

et al. 2023

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The process of thermal control and regulation in buildings is considered complex. Its complexity lies in the various internal and external physical phenomena impacting its control, and also in the increasingly important requirements of occupant comfort, energy optimization and efficiency, and optimization of measuring and monitoring equipment. Recently, the establishment of technical rules for optimal building thermal control has gained interest in academia and industry. These rules have focused mainly on three aspects: the use of renewable energy, optimal management, and the use of equipment and materials allowing the optimization of energy. However, optimal control has not been addressed enough. In this article, we present a PID controller based on a Neural Network approach for thermal building management and control. The proposed approach is based on two processes: an optimal identification process dedicated to the thermal building behavior prediction impacted by variable and invariable elements, measured and unmeasured factors, and a control process to ensure the desired performance with optimal energy control. The results obtained show the advantages of the adopted system in terms of energy optimization, with an important energy gain of 8% to 11%, along with better regulation and control performance, and in terms of occupant comfort with minimal temperature variations.

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Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles

Cited by 13 publications

References 31 publications

Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

Sistema Fuzzy para análise dos Níveis de desenvolvimento emocional e funcional baseado no modelo DIR/Floortime

Optimal Neural Network PID Approach for Building Thermal Management

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