Hybrid Powertrain Optimization With Real-Time Traffic Information

Combining hybrid powertrain optimization with traffic information has been researched before, but tradeoffs between optimality, driving-cycle sensitivity and speed of calculation have not been cohesively addressed. Optimizing hybrid powertrain with traffic can be done through iterative methods such as Dynamic Programming (DP), Stochastic-DP and Model Predictive Control, but high computation load limits their online implementation. Equivalent Consumption Minimization Strategy (ECMS) and Adaptive-ECMS were proposed to minimize computation time, but unable to ensure real-time charge-sustaining-operation (CS) in transient traffic environment. Others show relationship between Pontryagin’s Minimum Principles (PMP) and ECMS, but iteratively solve the CS-operation problem offline. This paper proposes combining PMP’s necessary conditions for optimality, with sum-of State-Of-Charge-derivative for CS-operation. A lookup table is generated offline to interpolate linear mass-fuel-rate vs net-power-to-battery slopes to calculate the equivalence ratio for real-time implementation with predicted traffic data. Maximum fuel economy improvements of 7.2% over Rule-Based is achieved within a simulated traffic network.