The Effect of Hill Planning and Route Type Identification Prediction Signal Quality on Hybrid Vehicle Fuel Economy 2016-01-1240
Previous research has demonstrated an increase in Fuel Economy (FE) using an optimal controller based on limited foreknowledge using methods such as Engine Equivalent Minimization Strategy (ECMS) and Stochastic Dynamic Programming (SDP) with stochastic error in the prediction signal considerations. This study seeks to quantify the sensitivity of prediction-derived vehicle FE improvements to prediction signal quality assuming optimal control. In this research, a hill pattern and route type identification scenario control subjected to varying prediction signal quality is selected for in depth study. This paper describes the development of a baseline Toyota Prius Hybrid Vehicle (HV) simulation models, real world drive cycles and real-world disturbances, and an optimal controller incorporating a prediction of vehicle power requirements. Each of the two scenario’s potential for FE improvements are analyzed by comparing the absolute and relative performance of the scenario control policies to the baseline vehicle control and a comparison of the results to previous research is made in order to discuss the current understanding of the sensitivity of prediction-derived vehicle FE improvements to prediction signal quality.
Citation: Asher, Z., Cummings, T., and Bradley, T., "The Effect of Hill Planning and Route Type Identification Prediction Signal Quality on Hybrid Vehicle Fuel Economy," SAE Technical Paper 2016-01-1240, 2016, https://doi.org/10.4271/2016-01-1240. Download Citation
Zachary D. Asher, Thomas Cummings, Thomas H. Bradley