Intelligent power management in SHEV based on roadway type and traffic congestion levels

This paper presents a machine learning approach to train an intelligent power controller for a series hybrid electric vehicle. The proposed machine learning approach exploits the best efficiency of the components associated with the roadway type and traffic congestion level to reduce the overall fuel consumption. [Given certain non changeable parameters such as the generator efficiency, the battery parameters, and the engine efficiency, the optimal system point can be calculated]. The algorithm itself will be able to exploit the road conditions at a given time, but only an average value of the road conditions. It is the goal of this paper to further refine the standard best efficiency control schemes by utilizing the road type prediction and dynamically controlling the engine/generator power to best match not only the best efficiency calculations but also an optimal prediction of the road conditions, not just the average.