Cost-To-Go-Based Predictive Equivalent Consumption Minimization Strategy for Fuel Cell Vehicles Considering Route Information

The equivalent consumption minimization strategy (ECMS) is a well-established energy management strategy for hybrid vehicles, which is easily real-time implementable and can provide optimal energy management. However, optimality requires knowledge of the optimal equivalence factor, which highly depends on the driving cycle and is therefore unknown in advance. This work proposes a predictive ECMS for fuel cell hybrid vehicles, which derives a map describing the optimal equivalence factor for any vehicle position and battery state of charge from the optimal cost-to-go provided from an offline optimization. The offline optimization is conducted with dynamic programming before departure and considers a long-term driving cycle prediction derived from static route information such as speed limits and altitude. Based on the optimal equivalence factor map, the ECMS implicitly considers the long-term prediction in each instant allowing for continuous adaption to the current situation while driving. The performance of the predictive ECMS is demonstrated in a numerical study based on real-world driving cycles highlighting its robustness against unpredicted changes in traffic conditions.