Artificial Intelligence based Machine Learning with data, Optimization and Prediction
MOEV AITM uses real time and historical data gathered from EV chargers, fleet telematics, driver preferences and driving history, duty cycle needs, vehicle information, routes, weather, terrain, traffic and various other types of information to perform prediction of energy needs of the EV so as to guide the driver and fleet operator on when to recharge, how to recharge and where to recharge and subsequently to precisely manage and control the charging of the vehicle to optimize around fleet needs of minimum cost, optimize fleet operations and dispatch, maximize EV battery life and achieve maximum utilization of the EV charging infrastructure. MOEV AI™ technology is unique and it uses a combination of machine learning, deep learning, neural networks, regression, and dynamic optimization to achieve these objectives. Our system operates on the Internet Cloud and is offered as a software as a service (SaaS), and functions independent of the charging hardware by supporting the OCPP (Open Charge Point Protocol) standard, is independent of the vehicle telematics by way of using an API (application programming interface), and is independent of the vehicle manufacturer. We support a variety of charging modalities as provided by OEMs including V1G (smart charging), V2G (vehicle to grid), V2B (vehicle to building) and in general V2X (vehicle to anything).