PROJECT TITLE :
An Energy Management Controller to Optimally Trade Off Fuel Economy and Drivability for Hybrid Vehicles
Hybrid vehicle fuel economy performance is extremely sensitive to the energy management strategy used to regulate power flow among the various energy sources and sinks. Optimal non-causal solutions are straightforward to work out if the drive cycle is thought a priori. It's very difficult to style causal controllers that yield sensible fuel economy for a range of possible driver behavior. Extra challenges return in the shape of constraints on powertrain activity, like shifting and starting the engine, that are commonly called “drivability” metrics and will adversely have an effect on fuel economy. In this paper, drivability restrictions are included in a shortest path stochastic dynamic programming (SP-SDP) formulation of the important-time energy management problem for a prototype vehicle, where the drive cycle is modeled as a stationary, finite-state Markov chain. When the SP-SDP controllers are evaluated with a high-fidelity vehicle simulator over commonplace government drive cycles, and compared to a baseline industrial controller, they're shown to enhance fuel economy a lot of than 11percent for equivalent levels of drivability. Furthermore, the explicit tradeoff between fuel economy and drivability is quantified for the SP-SDP controllers.
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