InfoRich VD&PT ControlsChen-Fang Chang, GM

Raj Rajkumar, CMU

Jeff Gonder, NREL

• Develop and demonstrate novel vehicle dynamics and

powertrain (VD&PT) control technologies that incorporate

the preview information from connectivity and on-board

sensors to reduce ≥20% fuel consumption under real-

world driving scenarios

Project Goal

Fed. funding: $4.2M

Length 36 mo.

• iREAD – an integrated infoRich Eco-Autonomous Driving

system for eco-driving (-approach, -departure, -cruise, -

routing) applications

• In-depth simulation (scenarios, test factors, etc.) on-going

• First demo vehicle build to be completed in April

Current Technical Status

Technical Accomplishments – 1/2

• iREAD (infoRich Eco-Autonomous

Driving) system

Multi-Layer energy consumption

estimation and optimization

Real-time implementable algorithm

*Comprehensive Modal Emission Model

iREAD system Route Planner Behavior Planning Actuator Controls

Tradeoff • Time

• Distance

• Fuel

• Safety

• Progress

• Comfort

• Fuel

• Speed tracking error

• Controls efforts

Fuel Cost Model Road segment’s LUT

(Average Effective

Speed; Grade; Turns)

Vehicle Level LUT

(Vehicle Speed;

Acceleration; Grade)

Enhanced CMEM*

(Engine RPM, Torque,

Gear State, etc.)

Optimization Horizon Entire Mission Long Horizon (e.g.,

200m-400m)

Short Horizon (e.g., less

than 60m)

Optimization Algorithm Dijkstra PCB MPC

Technical Accomplishments – 2/2

• In-depth simulation (scenarios, test factors, etc.) on-going

• HIL/VIL system integration with chassis dyno controller in progress

• First demo vehicle build to be completed in April

Virtual Traffic Environment Autonomous Driving Module

HIL/VIL real-time test platform

Updated Efficiency Breakdown Table

Applications

ORIGINAL CURRENT

Eco-Cruise

5-15%

(Route,

Speed and

Grade

Specific)

~5%

Eco-Departure 3-5% ~1%

Eco-Routing

7-20%

(Route,

Speed and

Grade

Specific)

~7%

Energy Contribution

/ Reduction

Eco-Approach 8-10% ~7%

Tech-to-Market Strategy – 1/2

• A key part of GM’s corporate strategy is Leading in Autonomous Vehicles.

• GM plans to build the world's most advanced self-driving vehicles that can

achieve pricing for ride sharing of less than $1.00 per passenger mile, and

this will unlock a Total Addressable Market worth trillions of miles and dollars.

Tech-to-Market Strategy – 2/2

• Fuel economy gains from GM’s InfoRich project will reduce vehicle operating

costs and help to drive ride sharing costs below $1.00 per passenger mile.

• The project will also help address some higher lever needs of AV customers.

Self-Driving Hierarchy of Needs (GM on 11-30-17)

Key Lessons Learned

• Reaching legal agreements takes time!

• Co-simulation (traffic, vehicle, powertrain) requires integration of different

software tools on different operating systems. Modularized

components/models and common IOs are preferred.

• Vehicle dynamic control plays a more significant role than powertrain control in

energy saving.

“Current” Challenges

• Vehicle build behind schedule! –

• Prediction of vehicle driver behavior (self or fellow) – study impacts through

simulation

• Verifiable energy consumption benefits – use synthetic drive routes derived

from large scale real-world driving data

• Need to examine electrical/computational loads and perform trade-off study for

production implementation

• Safety assurance during the development process – follow GM DVUL safety

process

Thank You 謝謝

감사합니다 ありがとうございました

धन्यवाद