An equivalent consumption minimization strategy (ECMS) is one of the most powerful and practical ways to improve the fuel efficiency of hybrid electric vehicles (HEVs). In an ECMS, it is important to determine the optimal equivalent factor to reach a global optimal solution. The optimal equivalent factor is determined by driving conditions. Previous studies have used an adaptive ECMS (A-ECMS) to determine the appropriate equivalent factor according to changing driving conditions. An A-ECMS adjusts the equivalent factor by controlling the battery’s state of charge (SOC) to follow a reference SOC trajectory. It is therefore critical to identify a reference SOC trajectory that reflects real-world driving conditions. These conditions, which are composed of the HEV’s nonlinear dynamics and complex constraints, can be formulated into a nonlinear optimal control problem (NOCP). Here, we propose applying nonlinear programming (NLP) to an A-ECMS. The NLP-based ECMS algorithm can be divided into two parts: the use of an NLP to solve an NOCP to obtain the reference SOC trajectory and the application of an NLP solution (the result of the first part) to an A-ECMS. Simulation results demonstrate that the proposed NLP-based ECMS closely resembles a global optimal solution for dynamic programming in a relatively brief calculation time.

Intelligent Transportation System (ITS) is actively studied as the sensor and communication technology in the vehicle develops. The Intelligent Transportation System collects, processes, and provides information on the location, speed, and acceleration of the vehicles in the intersection. This paper proposes a fuel optimal route decision algorithm. The algorithm estimates traffic condition using information of vehicles acquired from several ITS intersections and determines the route that minimizes fuel consumption by reflecting the estimated traffic condition. Simplified fuel consumption models and road information (speed limit, average speed, etc.) are used to estimate the amount of fuel consumed when passing through the road. Dynamic Programming (DP) is used to determine the route that fuel consumption can be minimized. This algorithm has been verified in an intersection traffic model that reflects the actual traffic environment (Korea Daegu Technopolis) and the corresponding traffic model is modeled using AIMSUN.