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A Connected Controls and Optimization System for Vehicle Dynamics and Powertrain Operation on a Light-Duty Plug-in Multi-Mode Hybrid Electric Vehicle
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on April 14, 2020 by SAE International in United States
This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND) and in-situ vehicle parameter characterization. Tools at the powertrain level include PHEV mode blending, predictive drive-unit state control, and non-linear model predictive control powertrain torque split management. These tools were developed with the capability of being implemented in a real-time vehicle control system. As a result, many of the developed technologies have been demonstrated in real-time using a fleet of four instrumented Chevrolet Volts which are equipped with on-board sensors, rapid prototyping embedded controllers, and V2X communication devices. This paper provides an overview of each tool developed, its implementation, energy reduction in isolation, and the net energy reduction of various tool combinations. A breakdown of the energy savings and range extension possible for the connected vehicle control and optimization tool set is provided which shows energy reduction benefits ranging from 5 to 50% and range extension upwards of 8%, dependent on the driving and traffic scenarios and initial vehicle state of charge.
- Joseph Oncken - Michigan Technological University
- Joshua Orlando - Michigan Technological University
- Pradeep K. Bhat - Michigan Technological University
- Brandon Narodzonek - Michigan Technological University
- Christopher Morgan - Michigan Technological University
- Darrell Robinette - Michigan Technological University
- Bo Chen - Michigan Technological University
- Jeffrey Naber - Michigan Technological University