Hybrid Vehicle fuel economy performance is highly sensitive to the ;;Energy Management;; strategy used to regulate power flow among the various energy sources and sinks. Optimal solutions are easy to specify if the drive cycle is known a priori. It is very challenging to compute controllers that yield good fuel economy for a class of drive cycles representative of typical driver behavior. Additional challenges come in the form of constraints on powertrain activity, like shifting and starting the engine, which are commonly called ;;drivability;; metrics. These constraints can adversely affect fuel economy. In this dissertation, drivability restrictions are included in a Shortest Path Stochastic Dynamic Programming (SPSDP) formulation of the energy management problem to directly address this tradeoff and generate optimal, causal controllers. The controllers are evaluated on Ford Motor Company;;s highly accurate proprietary vehicle model and compared to a controller developed by Ford for a prototype vehicle. The SPSDP-based controllers improve fuel economy morethan 15% compared to the industrial controller on government test cycles. In addition, theSPSDP-based controllers can directly quantify tradeoffs between fuel economy and drivability. Hundreds of thousands of simulations are conducted using real-world drive cycles to evaluate performance and robustness in the real world, demonstrating 10% improvement compared to the baseline. Finally, the controllers are tested in a real vehicle.
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Incorporating Drivability Metrics into Optimal Energy Management Strategiesfor Hybrid Vehicles.
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