Mode Hybrid Electric Vehicle PI: Jeffrey D. Naber, MTU Co-PI(s): - - PowerPoint PPT Presentation

NEXTCAR: Connected and Automated Control for Vehicle Dynamics and Powertrain Operation on a Light-Duty Multi- Mode Hybrid Electric Vehicle

PI: Jeffrey D. Naber, MTU Co-PI(s): Chris Morgan, Bo Chen, Darrell Robinette, Mahdi Shahbakhti, Kuilin Zhang Maribeth Yabes, Jason Liu, General Motors

• 20% energy reduction in real world driving

scenarios

• 6% increase in EV range

Project Goal

• Fed. Funding:

$2.8M Length 36 mo.

• VD&PT model + MPC algorithms complete
• Fully instrumented 5+3 vehicle fleet
• Real world driving with repeated test over MTU cycle
• Cloud communication for co-operative architecture
• 12-14% propulsion system only energy savings on a

23 mile drive cycle, 24% with additional technologies

Current Technical Status

Technical Accomplishments

• 3 control and 5 instrumented vehicles with

LiDAR, GPS Sensing, DSRC, on board telemetry CACC, and OEM Control system interface

• 100% of traffic lights on MTU Drive Cycle

equipped with MDOT RSU’s

Technical Accomplishments

• Developed 11 propulsion system and CAV

energy efficiency improvements.

• 13 different real world drive cycles for

analysis of Urban/Highway, congestion, eco- routing, driver variance, etc.

• Full MPC capability with cloud based V2X

Commerce, MI

14.7

21.4

42% Savings

Updated Efficiency Breakdown Table

Validation/Testing/Demonstration Plan Technology Area Description Task # Individual Energy Reduction Analytical/Simulation Experimental 1 Propulsion System Drive Unit Mode Optimization 3

2-9% 4-7% (95% CI) In Vehicle Algorithm: 5%

Requires GM 2 PHEV Blended Mode 4

1-9% 1-9% upwards of 12% (95% CI)

3 Velocity Profile Optimal velocity planning 3

2-5% 1-7% (95% CI)

In Progress - Target Q10

4 CACC Cooperative drive profiling 2 & 4

2-14% 7% 1-6% (CI 95%)

5 EcoAND Eco optimized stopping and going 2 & 4

2-10%

Target Q10

2-8.6% (CI TBD)

6 EcoRouting Energy based route selection 2 &4

2-12% 0-31% (92% CI) 2-42% (CI TBD)

7 Spd HARM Coordinating platoon speeds 2 & 4

2-13% 2-9% 1-7% (CI TBD)

Additional Technologies In Development

8 Propulsion Thermal Management PT Thermal Modeling & Control 1

TBD

Target Q9 Requires GM (Some Experimental Confirmations are Possible)

9 Intelligent HVAC Cabin HVAC Modeling & Control 1

3-5% 0-28% (CI 92%)

Requires GM (Some Experimental Confirmations are Possible)

Total Combined Measured Reduction Target Q11 Target Q11

By Specific Maneuver: PHEV savings along entire 23 mile MTU Drive Cycle:

PT Tools HVAC Improvement Eco A&D Speed Harm Eco Routing +2%* 21%

*Assumes likelihood of EcoRoute Option

Tech-to-Market Strategy

• Approach to market
• 6 Technology disclosures have been made to General Motors as

commercialization partner who has preferential rights to technology under a mutually executed Participation Agreement.

• Review procedures for General Motors in place as they consider

technology disclosures for proprietary protection and commercial adoption.

• Several other OEM, Tier 1 suppliers interested T2M/TTO event in May

at ACM to highlight

• Anticipated first markets
• General Motors vehicles, application, research and development, and

strategic partners/joint ventures.

• Approach to manufacture
• General Motors, General Motors’ suppliers, strategic partners.

Tech-to-Market Strategy

• Outreach activity to strategic automotive industry professionals

scheduled at the American Center for Mobility

• This Activity intended to serve as a follow up from general public

awareness outreach demonstrated in Texas during Spring 2018

Key Lessons Learned

Improvement in propulsion system modelling routine through combined dynamic and reduced order techniques Driver aid technology difficult to implement prior to more autonomous vehicle capability established. Real time accuracy of map and grade information critical for application of CAV technologies Energy savings potential for PHEV’s in Propulsion and Cabin Thermal Management Model predictive propulsion system control has energy benefits, but correctly implemented also drivability and performance benefits as well. A true “apples to apples” approach is required when realizing efficiency improvements in a light duty vehicle application, and should be considered for an entire drive cycle, not a single driving maneuver.

Current Challenges

Real-time, coupled traffic simulation via cloud to the CAV fleet Developing methodology for “fair reporting” and integration of short term technology applications and eco-routing into real-world energy benefits Effective and efficient methods for determining impact of lane position, CAV penetration rate, and congestion into simulation results Limited opportunities for testing due to weather (304” / 25 feet of snow)