Enabling predictive energy management in vehicles
Date
2018
Authors
Asher, Zachary D., author
Bradley, Thomas H., advisor
Chong, Edwin, committee member
Young, Peter, committee member
Zhao, Jianguo, committee member
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Abstract
Widespread automobile usage provides economic and societal benefits but combustion engine powered automobiles have significant economic, environmental, and human health costs. Recent research has shown that these costs can be reduced by increasing fuel economy through optimal energy management. A globally optimal energy management strategy requires perfect prediction of an entire drive cycle but can improve fuel economy by up to 30\%. This dissertation focuses on bridging the gap between this important research finding and implementation of predictive energy management in modern vehicles. A primary research focus is to investigate the tradeoffs between information sensing, computation power requirements for prediction, and prediction effort when implementing predictive energy management in vehicles. These tradeoffs are specifically addressed by first exploring the resulting fuel economy from different types of prediction errors, then investigating the level of prediction fidelity, scope, and real-time computation that is required to realize a fuel economy improvement, and lastly investigating a large computational effort scenario using only modern technology to make predictions. All of these studies are implemented in simulation using high fidelity and physically validated vehicle models. Results show that fuel economy improvements using predictive optimal energy management are feasible despite prediction errors, in a low computational cost scenario, and with only modern technology to make predictions. It is anticipated that these research findings can inform new control strategies to improve vehicle fuel economy and alleviate the economic, environmental, and human health costs for the modern vehicle fleet.
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Subject
dynamic programming
hybrid electric vehicle
optimal control
fuel economy
artificial neural network
misprediction