Enabling Prediction for Optimal Fuel Economy Vehicle Control

Abstract

V ehicle control using prediction based optimal energy management has been demonstrated to achieve better fuel economy resulting in economic, environmental, and societal benefits. However, research focusing on prediction derivation for use in optimal energy management is limited despite the existence of hundreds of optimal energy management research papers published in the last decade. In this work, multiple data sources are used as inputs to derive a prediction for use in optimal energy management. Data sources include previous drive cycle information, current vehicle state, the global positioning system, travel time data, and an advanced driver assistance system (ADAS) that can identify vehicles, signs, and traffic lights. To derive the prediction, the data inputs are used in a nonlinear autoregressive artificial neural network with external inputs (NARX). Two real world drive cycles were developed for analysis in the Denver, Colorado region: a city-focused drive cycle that passes through downtown as well as a highway-focused drive cycle that transitions across multiple interstates. A validated model of a 2010 Toyota Prius in Autonomie is used to determine the vehicle control fuel economy improvements that are possible from the NARX prediction. The optimal energy management control strategy is determined using dynamic programming due to its ease of use and that the solution produced is the globally optimal solution. The control strategies compared include the existing 2010 Toyota Prius control strategy as a baseline, the neural network prediction optimal energy management control strategy, and a 100% accurate prediction optimal energy management control strategy. Results show that inclusion of various sensors and signals enables a significant amount of the fuel economy improvement with respect to 100% accurate prediction. The conclusion is that prediction based optimal energy management enabled fuel economy improvements can be realized with currently available sensors and signals.

Towards Improving Vehicle Fuel Economy with ADAS

“…Therefore, the process of perception, planning, and vehicle actuation uses second-by-second feedback with the Optimal EMS computed for 15-second predictions which has been shown in previous research to be an effective prediction window for achieving FE improvements [26]. Plots of the output of this particular algorithm are shown in another research paper from the authors [34]. Figure 12 shows the overview of the planning subsystem model.…”

“…Details of an Optimal EMS derivation and implementation can be found in numerous articles [25,34,35]. An overall system-level viewpoint of an Optimal EMS implementation developed in previous research [27] is shown in Figure 10.…”

Research on the fuel consumption conservation potential of ADAS on passenger cars

“…In 2018, OSU announced two research results, one is of the performance improvement of coasting in N gear [18] ,and the other is about the nonlinear simulation of predictive cruise control [19] . By modifying 2010 Prius, CSU(Colorado State University) inquired several research topics, including the speed prediction by V2V-based technology vehicle to improve HEV fuel economy [20] , ADAS to improve vehicle fuel economy [21] , and improve the accuracy of prediction on fuel economy by control optimize [22] .…”

Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

“…In 2015, the advantages of ANN prediction were shown in [21,22]. In 2017 and 2018, a series of studies [15,17,29,30] experimented with different data streams to optimize prediction with a shallow ANN. In 2019, more modern machine learning techniques were introduced into the field in [31] where reinforcement learning was used along with traffic data to train an ANN to produce optimal controls for a power-split hybrid.…”