Incorporating Drivability Metrics into Optimal Energy Management Strategies for Hybrid Vechilcles

Hybrid Vehicle fuel economy and drivability performance are very sensitive to the "energy Management" controller that regulates power flow among the various energy sources and sinks. Many methods have been proposed for designing such controllers. Most analytical studies evaluate closed-loop performance on government test cycles, Moreover, there are few results that compare stochastic optimal control algorithms to the controllers employed in today’s production hybrids. This talk studies controllers designed using Shortest Path Stochastic Dynamic Programming (SPSDP). The controllers are evaluated on Ford Motor Company’s highly accurate proprietary vehicle model over large numbers of real-world drive cycles, and compared to a controller developed by Ford for a prototype vehicle. Results show the SPSDP based controllers yield 10% better performance than the Ford controller on real-world driving data, with even more improvement on a government test cycle in addition, the SPSDP based controllers can directly quantify tradeoffs between fuel economy and drivability. Preliminary results of hardware testing in the prototype vehicle will also be presented.