Hierarchical Control Strategy of Predictive Energy Management for Hybrid Commercial Vehicle Based on ADAS Map

Considering the change of vehicle future power demand in the process of energy distribution can improve the fuel saving effect of hybrid system. However, current studies are mostly based on historical information to predict the future power demand, where it is difficult to guarantee the accuracy of prediction. To tackle this problem, this paper combines hybrid energy management with predictive cruise control, proposing a hierarchical control strategy of predictive energy management (PEM) that includes two layers of algorithms for speed planning and energy distribution. In the interest of decreasing the energy consumed by power components and ensuring transportation timeliness, the upper-level introduces a predictive cruise control algorithm while considering vehicle weight and road slope, planning the future vehicle speed during long-distance driving. The lower-level calculates the future power demand based on the results of speed planning, and a dynamic programming method is utilized to determine the global optimal power distribution rules for the current road and driving condition with the goal of optimal engine fuel consumption. The comparison of simulation and vehicle test results indicates that under the various high-speed cruising conditions with little change in speed range and road slope, the predictive energy management strategy has a significant improvement in fuel saving compared with the rule-based energy management strategy.