Design and performance analysis of braking system in an electric vehicle using adaptive neural networks

Title

Design and performance analysis of braking system in an electric vehicle using adaptive neural networks

Creator

Indu K.; Aswatha Kumar M.

Description

Research article emphasizes on the impact of braking concepts considering regenerative braking system and energy consumption aspects in electric vehicles through a new perspective. The electric vehicle system is modeled and simulated using the MATLAB/Simulink software. A dataset is developed using the virtual simulation environment created by co-simulation using the MATLAB/Simulink and the IPG Carmaker software. This dataset is also used in a neural network model based on adaptive neuro fuzzy logic and the system performance is analyzed. Parameters considered for training the neural network are the brake pedal displacement, braking change rate and the need for brake application. The highlight of this study is the focus on a front wheel driven electric vehicle, which uses a standard drive cycle input to validate the model. The significant parameters evaluated in this study include the braking effects, kinetic energy, regenerative braking torque, battery state of the charge and the motor torque. The torque generation and its intended braking force requirements based on the acceleration, deceleration and braking conditions are the notable observations. The regenerative capability of this proposed system design is also illustrated along with the surface plots based on the training dataset. Investigation and analysis reveal that, the battery state of charge could be revived throughout the drive with a steady and stable increase. Transitions of motor torques between tractive and regenerative phases are also illustrated and explained for clarity and brevity. 2023 Elsevier Ltd

Source

Sustainable Energy, Grids and Networks, Vol-36

Date

2023-01-01

Publisher

Elsevier Ltd

Subject

Adaptive; Control system; Electric vehicle; Electronic control unit; Fuzzy systems; Neural networks; Regenerative braking

Coverage

Indu K., ECE, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Aswatha Kumar M., ECE, CHRIST (Deemed to be University), Bengaluru, Karnataka, India

Rights

Restricted Access

Relation

ISSN: 23524677

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1016/j.segan.2023.101215

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