The University of Washington EcoCAR team proposes to continue work on the Year 1 predictive generator control strategy to improve energy efficiency of thermostatic control of a series generator. The majority of the work will lie in developing the implementation details such as Google Maps queries, on-board map and training data storage, far-improved predictive control, and real-time predictions and tracking. Hardware requirements will be developed and minimized in the process through lighter weight algorithms to reduce price per prediction controller and increase marketability.
The goal for Year 2 is to take the prototyped code developed in Matlab / Simulink from Year 1, finish prototyping, and implement similar prediction and optimization algorithms on the S32V processor board or hardware of the team’s choosing. The targeted impact is to reduce petroleum energy usage of thermostatic generator control of a series hybrid operating in charge sustaining mode.