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 3RQWU\DJLQ¶s Minimum Principles (PMP) and ECMS, but iteratively solve the CS-operation problem offline. This paper proposes combining PMP¶V 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 netpower- 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.