Project Goal An integrated control system that improves fuel - - PowerPoint PPT Presentation

Fuel Economy Optimization with Dynamic Skip Fire in a Connected and Automated Vehicle

Giorgio Rizzoni, The Ohio State University An integrated control system that improves fuel economy 20% on light-duty vehicles by combining look-ahead technology, mild hybridization, and advanced cylinder deactivation.

Project Goal

• Fed. funding:

$5.0M Length 36 mo.

• All Year 1 milestones complete
• Simulation shows significant FE reduction;

test phase beginning

Current Technical Status

Giorgio Rizzoni Marcello Canova Levent Guvenc Abhishek Gupta Peter Olin John Kirwan Mark Wilcutts Walter Kosiak Josh Every

Technical accomplishments

• Demonstration vehicle hardware integration complete and calibrated, including
• 48V, DSF, and engine controls
• ACC, Camera, Radar, V2X, and eHorizon equipment
• Powertrain simulator validated; traffic integration in progress
• Zero-traffic simulations show optimization algorithms to yield up to 23% fuel savings at

equivalent trip travel times

• eDSF Torque Smoothing: evaluated in simulation; hardware evaluation underway
• Drive scenarios and vehicle test plan defined

Conventional Driving Optimized driving Mixed Route

Tech-to-Market Strategy

Approach to market

• Key H/W technologies well-known in the

industry, projected to grow 10X by 2025

• Control software will be licensed for use
• n existing OEM modules

Anticipated first markets

• Light-duty vehicles are the primary market
• Electrification and automation of these

vehicles expected to see explosive growth

• 48V in production now; DSF in production

in 2019 Approach to manufacture

• Key H/W components are already

production-level

• Delphi has the in-house expertise to write

production-level code to implement the controls developed for this project

Value Proposition

Key Lessons Learned

Communication is important to ensure successful collaboration

• Nearly every aspect of the project impacts another, so frequent and strong

communication is important

• Proper communication among team members is key to integrate multi-layer controls

for the P/T and CAV systems Start with simplicity and gradually add complexity

• Need to implement controls in vehicle requires design methods that privilege

simplicity and ease of calibration

• Integration of multiple control algorithms grows complexity very quickly, making

computation time a major constraint

• Start with only the critical information, and track computation time as additional

complexity is added The test plan is non-trivial

• Fuel economy testing of automated vehicles that do not follow prescribed velocity

profiles requires definition of new test procedures

• Each team should spend time thinking deeply about how to obtain accurate and

repeatable results

Current Challenges

Integration of CAV information

• Vast amount of information provided by CAV technologies will present complexity to the control structure
• Addition of drivability and emissions considerations further complicates the control

Mitigation Strategy

• Develop the controls in stages, start with the core powertrain controls, and work outward
• Identify and prioritize the incoming information with the largest impact on FE

Vehicle test repeatability

• FE corresponding to a driving scenario not perfectly repeatable, depends on driving style (for baseline

case) and traffic/traffic lights Mitigation Strategy

• Run many tests of a driving scenario to address statistical variation
• Augment the test results with simulation to improve confidence and accuracy

Challenges with sharing of IP and other confidential information

• Powertrain optimization requires closely interfacing with engine and electric machine controls in the ECU
• Some information is considered sensitive and confidential, and sharing is limited

Mitigation Strategy

• Collaboration and communication

Desirable Partnerships

Hill/Grade Data

• Most OSU testing will be conducted at the Transportation Research Center, Inc, in

central Ohio, a relatively flat facility

• To fully optimize the powertrain operation, OSU would like to test in or run

simulations of more hilly terrain

• Please let OSU know if you expect to do testing in hilly terrain and are willing to

share your facilities or provide grade data that can be used in simulations Connected and Autonomous Transportation Simulator (CATS) Development

• OSU has developed a python-based simulator called CATS for this project
• Please let OSU know if you would like to simulate in this simulation environment so

that the output of the simulator can be validated across various use cases

Connected and Automated Transportation Simulator (CATS)

• Simulate V2V and V2I communication;

written in Python

• Easy integration with modern machine

learning packages

• Simulate pedestrians, blind spots, animals,

rash drivers, etc.

Tools used in Year-1

Aspect Tool Transportation & Traffic model/simulations CATS (Connected and Autonomous Transportation Simulator) Machine Learning CATS (Python) Vehicle dynamic model/simulations CarSim, Matlab/Simulink Powertrain model/simulations Matlab/Simulink High fidelity map OpenStreetMap Real-time implementation strategy dSpace Microautobox