An adaptive neural PID controller for torque control of hybrid electric vehicle

Zhang, Yun; Yu, Xiumin; Bi, Mingshuang; Xu, Sina

doi:10.1109/iccse.2011.6028782

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…De acordo com (ZHANG et al, 2011), a Equac ¸ão 5 define a func ¸ão de transferência do controlador PID.…”

Section: Controle Proporcional Integral Derivativounclassified

Avaliação de Redes Neurais Artificiais para Substituir o Controle PID

Ritter,

Vidal,

Sereno

2024

Anais Do(a) Anais Da Jornada Científica Do IFRJ Campus Volta Redonda

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Resumo: Com o avanc ¸o das tecnologias eletroeletrônicas a utilizac ¸ão das Redes Neurais Artificiais (RNA) tem-se difundido e ampliando sua utilizac ¸ão paras diversos tipos de aplicac ¸ões. Logo novos métodos que substituam o controle Proporcional Integral Derivativo (PID), de forma eficiente, passam a ser de grande valia e as RNA's são uma opc ¸ão. Para este estudo escolheu-se a rede feedforward de múltiplas camadas com backpropagation. E após testar diversas possibilidades, utilizou-se o algoritmo Levenberg-Marquard para treinamento e a func ¸ão de transferência purelin na camada de entrada, e também a purelin na camada de saída. Sendo que a estrutura da rede neural ficou com 3 elementos de entrada, uma camada de entrada com 2 neurônios, nenhuma camada oculta e a camada de saída com 1 neurônio. Com a melhor configurac ¸ão da rede neural determinada foi possível obter um grau de dependência entre a variável manipulada real e estimada de valor 1. E ao utilizarmos esta rede para prever a variável manipulada do motor, em um período fora da faixa conhecida, obteve-se um erro quadrático médio de 0.042.

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“…De acordo com (ZHANG et al, 2011), a Equac ¸ão 5 define a func ¸ão de transferência do controlador PID.…”

Section: Controle Proporcional Integral Derivativounclassified

Avaliação de Redes Neurais Artificiais para Substituir o Controle PID

Ritter,

Vidal,

Sereno

2024

Anais Do(a) Anais Da Jornada Científica Do IFRJ Campus Volta Redonda

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“…Terdapat beberapa permasalahan yang ada pada HEV, diantaranya adalah upaya yang dilakukan agar motor DC dapat bekerja secara optimal dalam hal membantu ICE mencapai kecepatan yang diinginkan. Salah satu cara untuk mengatasi permasalahan tersebut adalah strategi pengaturan torsi pada HEV [2], [4], [5]. Strategi pengaturan traksi dibuat agar dapat diperkiran berpakah nilai arus yang terukur pada HEV saat terjadi akselerasi.…”

Section: Pendahuluanunclassified

Pengaturan Traksi Berbasis Neuro-Fuzzy pada Simulator Hybrid Electric Vehicle (HEV)

Annisa

2020

jeeccis

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The electric car is a solution designed as a zero-emission vehicle which is an alternative to reducing air pollution. There are various types of electric cars, this research focuses more on Hybrid Electric Vehicle (HEV). HEV is a vehicle that has at least two different energy sources. The most common combination today is the Internal Combution Engine (ICE) and an electric battery. HEV uses ICE with a smaller capacity than conventional vehicles, this results in more efficient fuel use. In HEV, there are several problems, including the response from ICE which is less than optimal when there is an increase in speed. ICE as the prime mover has a smaller capacity than conventional vehicles because it is assisted by the performance of the DC motor. When ICE is unable to maintain speed, the DC motor will help provide power. Therefore, it is necessary to regulate traction on a DC motor to help ICE achieve the desired speed, especially when there is an increase in speed. This study uses a neuro-fuzzy control method which has the advantage of being able to adapt to changes in parameters in the system. HEV itself requires a fast response, therefore, a predictive controller is used in order to predict the future torque value

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“…ICE and electric motors are combination of commonly used energy sources [2]. The development of HEV aims to improve efficiency of fuel consumption, reduction of vehicle exhaust emissions, and improvement of HEV speed and acceleration response [3]. HEV combines two torque generators, where the prime mover torque is provided by ICE and secondary torque is provided by the BLDCM.…”

Section: Introductionmentioning

confidence: 99%

Design of Torque Control Strategy for Hybrid Electric Vehicle (HEV) with Maximum Work of Self Commutation Brushless DC Motor using Fuzzy-PI

Hais

Rameli

Effendie

2018

IJPS

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Hybrid Electric Vehicle (HEV) is vehicle with least two energy sources, such as Internal Combustion Engine (ICE) and Brushless DC Motor (BLDCM). BLDCM provide additional torque, to purpose of HEV can reach the set point speed according to the reference model. Commutation of BLDCM still complicated, because between the rotational speed of the motor and the speed of the rotary field on the stator should be kept synchronized. Self Commutation used to maintain synchronization between rotation of the rotor and rotary field velocity on BLDCM stator. In addition, this research also applies torque control strategy by using fuzzy-PI controller. Vehicle performance still follows the reference curve with steady state error of 0.1506 km/h and RMSE relative response speed HEV <2%.

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An adaptive neural PID controller for torque control of hybrid electric vehicle

Cited by 8 publications

References 8 publications

Avaliação de Redes Neurais Artificiais para Substituir o Controle PID

Avaliação de Redes Neurais Artificiais para Substituir o Controle PID

Pengaturan Traksi Berbasis Neuro-Fuzzy pada Simulator Hybrid Electric Vehicle (HEV)

Design of Torque Control Strategy for Hybrid Electric Vehicle (HEV) with Maximum Work of Self Commutation Brushless DC Motor using Fuzzy-PI

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