Search Results

This study investigates using driver prediction to anticipate energy usage over a 160-meter look-ahead distance for a series, plug-in, hybrid-electric vehicle to improve conventional thermostatic powertrain control. Driver prediction algorithms utilize a hidden Markov model to predict route and a regression tree to predict speed over the route. Anticipated energy consumption is calculated by integrating force vectors over the look-ahead distance using the predicted incline slope and vehicle speed. Thermostatic powertrain control is improved by supplementing energy produced by the series generator with regenerative braking during events where anticipated energy consumption is negative, typically associated with declines or decelerations.

Means to extend the range of cryogen (liquid nitrogen or liquid air) powered automobiles via burning a small amount of fossil fuel (gasoline or liquid methane) have been investigated. By utilizing both an ambient air-heat exchanger to vaporize the cryogen and a fossil fuel-fired superheater to elevate the temperature of the gaseous product, the range of the vehicle can be three times that of an ambient-heated propulsion system while not exceeding current ultra-low emission standards. Internal and external combustion power cycles using either liquid air or nitrogen as the working fluid were found to be more fuel efficient than an internal combustion engine operating on the standard Otto cycle. The fuel-cryogen operating expense for the proposed hybrid propulsion systems was found to be higher than that of the conventional automobile; however, the performance calculations were very conservative.