A machine learning approach to predict future power demand in real-time for a battery operated car

Abstract

For any battery-employed system, it is essential for the battery management system to correctly predict the present operational condition of the battery. The fail safe operation of a safety critical system like battery operated car or any other lifesaving systems are heavily depend upon earlier prediction of battery life. SOC or State-of-Charge estimation is one of the well-known method to predict the runtime of a battery. Various approaches are adapted by automotive society to correctly predict the runtime or the SOC of a battery like Kalman filter, UKF and many others. This paper proposes a new approach, the method of regression to predict the future power demand of a car while running on the road. The aim is to identify that, the battery will support the run of the car in next 10 seconds or not. The runtime prediction of a battery, not only depends upon the starting SOC but also depends upon other factors like battery health and road profile imposed. To overcome this type of difficulties the self-corrective regression model is proposed and implemented. Experiments performed on different road profiles, validate demanded power by the car in up-coming 10 seconds of its run. The major problem of SoC estimation is to determine initial SoC of a battery. Extensive experiments needed to calculate the initial SoC and which may also vary with the life of the battery. The novelty of this work shows, the method to predict the future power demand by updating its model parameters and without any initial SoC calculation. Model parameters are updated by the introducing new current and voltage sample in the model.