Regenerative Braking Control Strategy for Electric Commercial Bus under Indian Road Condition Using Artificial Neural Network

The main focus of this paper is to create a more efficient regenerative braking control strategy for electric commercial buses operating under Indian road conditions. The strategy uses Artificial Neural Networks (ANNs) to optimize regenerative braking process. Regenerative braking helps to recover energy that would otherwise be lost during braking and convert it back into usable power for the vehicle. The challenge is to design a system that works effectively on the diverse and often challenging road conditions found in India, such as varying gradients, traffic patterns, and road surface types. This study begins by collecting data (which includes vehicle speed, traffic condition, etc.) from real-world driving conditions and aims to train an Artificial Neural Network (ANN) using a large set of driving data which is collected under various conditions to predict the most efficient regenerative braking settings for different driving scenarios. This research brings a new approach to the application of regenerative braking in electric buses by using Artificial Neural Networks. Previous works in this area mostly focused on passenger vehicles or did not take into account the unique challenges posed by Indian road conditions, such as heavy traffic and frequent elevation changes. This study addresses those challenges directly by focusing on electric buses, which are a growing segment of the public transportation sector in India.