Stochastic Optimal Control for Advanced Propulsion Systems

Andreas  Malikopoulos, Oak Ridge National Laboratory

Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) have shown great potential for enhancing fuel economy and reducing emissions compared to vehicles powered only by internal combustion engines (conventional vehicles). The main advantage of these powertrain configurations is the existence of two individual subsystems, thermal (internal combustion engine) and electrical (motor, generator, and battery), that can power the vehicle either separately or in combination. The power management control algorithm in HEVs and PHEVs determines how to split the power demanded by the driver between the thermal and electrical subsystem so that maximum fuel economy and minimum pollutant emissions can be achieved. These vehicles have sophisticated electronic control units, particularly to control each subsystem with respect to a balance between fuel economy and emissions. These control units are designed for specific driving conditions and testing. Each individual driving style, however, is different and rarely meets those driving conditions of testing for which HEVs and PHEVs have been optimized.

The talk presents the theoretical framework and algorithms for making these vehicles into realizing continuously their most efficient operating point for any different driving style. We draw from stochastic control theory research in a wide range of areas including game theory, agent modeling and learning, and develop the theory and algorithms to address this problem. The long-term potential benefits of this approach are substantial. True fuel economy of HEVs and PHEVs will be increased while meeting emission standard regulations with respect to any different driving style.

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