System and method for electric vehicle operational optimization

A method comprising:

• obtaining, by a processor, input data associated with an electric vehicle from a plurality of data sources, wherein the input data comprises sensor data, battery data, vehicle health data, route data, and vehicle speed data, distance data, environmental data, and traffic data;
• predicting, by the processor, a set of health parameters and performance parameters associated with the electric vehicle based on the obtained input data using a trained machine learning model;
• generating, by the processor, computer simulated instances of the electric vehicle based on the predicted set of health parameters and the performance parameters, wherein the computer simulated instances emulate a behavior and a performance of the electric vehicle;
• validating, by the processor, the predicted set of health parameters and the performance parameters associated with the electric vehicle by simulating the generated computer simulated instances of the electric vehicle in a virtual environment using the trained machine learning model;
• determining, by the processor, a behavior status, a performance status, and a health status of the electric vehicle based on results of the validation;
• determining, by the processor, at least one abnormality associated with the electric vehicle based on the determined behavior status, the performance status, and the health status of the electric vehicle;
• determining, by the processor, at least one action to be performed for rectifying the determined at least one abnormality using the trained machine learning (ML) model, the at least one action comprises at least one recommendation on optimal operational parameters, battery charging stations, a travel route, and a drive mode of the electric vehicle;
• and
• controlling, by the processor, an operation of the electric vehicle by performing the determined at least one action at the electric vehicle.

A non-transitory computer readable medium comprising a processor-executable instructions that cause a processor to:

• obtain input data associated with an electric vehicle from a plurality of data sources, wherein the input data comprises sensor data, battery data, vehicle health data, route data, and vehicle speed data, distance data, environmental data, and traffic data;
• predict a set of health parameters and performance parameters associated with the electric vehicle based on the obtained input data using a trained machine learning model;
• generate computer simulated instances of the electric vehicle based on the predicted set of health parameters and the performance parameters, wherein the computer simulated instances emulate a behavior and a performance of the electric vehicle;
• validate the predicted set of health parameters and the performance parameters associated with the electric vehicle by simulating the generated computer simulated instances of the electric vehicle in a virtual environment using the trained machine learning model;
• determine a behavior status, a performance status and a health status of the electric vehicle based on results of the validation;
• determine at least one abnormality associated with the electric vehicle based on the determined behavior status, the performance status, and the health status of the electric vehicle;
• determine at least one action to be performed for rectifying the determined at least one abnormality using the trained machine learning (ML) model, the at least one action comprises at least one recommendation on optimal operational parameters, battery charging stations, a travel route, and a drive mode of the electric vehicle;
• and
• control an operation of the electric vehicle by performing the determined at least one action at the electric vehicle.