This is where AI could help, reckon Xuewei Qi, Matthew Barth and their colleagues at the University of California, Riverside. They are developing a system of energy management which uses a piece of AI that can learn from past experience.
Their algorithm works by breaking the trip down into small segments, each of which might be less than a minute long, as the journey progresses. In each segment the system checks to see if the vehicle has encountered the same driving situations before, using data ranging from traffic information to the vehicle’s speed, location, time of day, the gradient of the road, the battery’s present state of charge and the engine’s rate of fuel consumption. If the situation is similar, it employs the same energy-management strategy that it used previously for the next segment of the journey. For situations that it has not encountered before, the system estimates what the best power control might be and adds the results to its database for future reference. Ultimately, the idea is that the algorithm will also learn from the experiences of its brethren in other cars, by arranging for all such systems to share their data online.