Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Journal Article · · Journal of Dynamic Systems, Measurement, and Control
DOI:https://doi.org/10.1115/1.4052819· OSTI ID:1980679
 [1];  [1];  [2];  [2];  [3];  [1]
  1. Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI 48109
  2. Ford Motor Company, Dearborn, MI 48124
  3. Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109

Abstract Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Research Organization:
Univ. of Michigan, Ann Arbor, MI (United States)
Sponsoring Organization:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
DOE Contract Number:
AR0000797
OSTI ID:
1980679
Journal Information:
Journal of Dynamic Systems, Measurement, and Control, Vol. 144, Issue 1; ISSN 0022-0434
Publisher:
ASME
Country of Publication:
United States
Language:
English

References (24)

Energy saving potentials of connected and automated vehicles journal October 2018
Control of connected and automated vehicles: State of the art and future challenges journal January 2018
Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles journal August 2020
Comparative Evaluation of Microscopic Car-Following Behavior journal September 2005
Traffic Prediction Using Multivariate Nonparametric Regression journal March 2003
Long short-term memory neural network for traffic speed prediction using remote microwave sensor data journal May 2015
Short-term speed predictions exploiting big data on large urban road networks journal December 2016
Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting journal October 2005
Forecasting of Short-Term Freeway Volume with v-Support Vector Machines
  • Zhang, Yunlong; Xie, Yuanchang
  • Transportation Research Record: Journal of the Transportation Research Board, Vol. 2024, Issue 1 https://doi.org/10.3141/2024-11
journal January 2007
Intelligent Trip Modeling for the Prediction of an Origin–Destination Traveling Speed Profile journal June 2014
Vehicle Speed Prediction by Two-Level Data Driven Models in Vehicular Networks journal July 2017
Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management journal May 2014
MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle journal May 2012
Integrated cabin heating and powertrain thermal energy management for a connected hybrid electric vehicle journal February 2021
Eco-Trajectory Planning with Consideration of Queue along Congested Corridor for Hybrid Electric Vehicles journal January 2019
Short Term Prediction of a Vehicle's Velocity Trajectory Using ITS
  • Moser, Dominik; Waschl, Harald; Schmied, Roman
  • SAE International Journal of Passenger Cars - Electronic and Electrical Systems, Vol. 8, Issue 2 https://doi.org/10.4271/2015-01-0295
journal April 2015
Data-driven approach for short-term power demand prediction of fuel cell hybrid vehicles journal October 2020
Naturalistic Data-Driven Predictive Energy Management for Plug-In Hybrid Electric Vehicles journal June 2021
Real-Time Energy Management Strategy Based on Velocity Forecasts Using V2V and V2I Communications journal February 2017
A Bayesian Network Approach to Traffic Flow Forecasting journal March 2006
Model predictive control for drift counteraction of stochastic constrained linear systems journal January 2021
Thermal impact on the control and the efficiency of the 2010 Toyota Prius hybrid electric vehicle journal April 2015
Plant and Controller Optimization for Power and Energy Systems With Model Predictive Control journal April 2021
Kernel estimation of a distribution function journal January 1985