Connected Eco-Bus: An Innovative Vehicle-Powertrain Eco-Operation - - PowerPoint PPT Presentation

• Fed. funding:

$3.15M Length 36 mo.

Connected Eco-Bus:

An Innovative Vehicle-Powertrain Eco-Operation System for Efficient Plug-in Hybrid Electric Buses

• Developed an innovative vehicle-powertrain eco-operation system for plug-in hybrid electric buses through co-
• ptimization of vehicle dynamics and powertrain controls, achieving 20+% energy efficiency increase
• Developed three key innovative velocity trajectory planning modules: Eco-Approach and Departure at Signalized

Intersections; Eco-Stop and Launch; and Eco-Cruise

• Developed innovative powertrain modules: Efficiency Based Powertrain Controls, Intelligent Energy Management
• Developed a new hardware-in-the-loop development and testing approach called Dyno-in-the-Loop (DiL) testing

Project Key Technical Achievements:

• Created technology deployment strategies for the primary fixed route transit market, expanding to electric transit

markets accelerated by CARB’s Zero Emissions Transit regulations

• Evaluated technology applicability and deployment for heavy-duty-truck market (drayage truck market)
• Coordinating licensing efforts with multiple partners for initial market deployment

Project T2M Achievements: PI: Matthew Barth, University of California-Riverside Partners: Oak Ridge National Lab, US Hybrid

Technical Accomplishments

0s 1s 2s 3s 4s

Simplified model as Cost in the graph Optimal trajectory as driving cycle Key strategy as simplified model

Intersection

• f Interest

Time Location Preceding vehicle Stop line Source node Destination node Gap keeping

Traffic and Road Infrastructure Sensing SPaT Transmission Information Integration Scenario Identification Simplified Powertrain Model Trajectory Planning Powertrain Model in Simulink

Define reachable region Identify target state

Innovative Velocity Trajectory Planning Modules:

• Eco-Approach and Departure at Signalized

Intersections: determines an energy-efficient speed profile based on SpaT information from signalized intersections;

• Eco-Stop and Launch: determines energy-efficient

speed profile for decelerating to and accelerating from bus stops and stop signs

• Eco-Cruise: determines cruising speed profile based
• n look-ahead traffic and terrain conditions

Innovative Powertrain Modules:

• Efficiency Based Powertrain Controls: optimizes both

the engine and motor/generator operation by managing transmission and battery state-of-charge

• Intelligent Energy Management: optimizes the

power split between the internal combustion engine and electric motors for the vehicle speed and power demand profiles

Dynamometer-in-the-Loop Testing Methodology

Tech-to-Market Accomplishments

Multiple Technology Deployment Strategies:

• Initial market is fixed route transit with > 20% savings
• Electric Transit range extension to meet CARB’s Zero Emissions

Transit regulations (electric bus manufacturers)

• Heavy Duty trucking applications for fleet savings (US Hybrid)
• T2M focus on heavy-duty-truck drayage market (US Hybrid, others)

Coordinating licensing efforts with multiple partners:

• UC Riverside I-Corps Technology Transfer
• US Hybrid (project partner)
• Controls company (algorithm licensing)
• AzTech Labs (driver’s aid system)
• Antelope Valley Transit Agency (EV Transit)

Final Efficiency Breakdown Table

NEXTCAR Technology Energy Efficiency Improvement Trip time penalty Ref Notes Transit Bus Eco-Approach and Departure 10.5% – 20.9% (simulation) negligible [2] Varies with congestion levels and CAV penetration 9.6% – 22.9% (real-world DiL*) negligible [1] Eco-Stop and Launch 10.9% - 17.1% (simulation) negligible [7] Numerical simulation was used to evaluate this separately Eco-Cruise 0% - 12.8% (simulation) negligible [7] Numerical simulation was used to evaluate this separately Combined Powertrain Optimization 13.7% – 18.0% (simulation) negligible [2] Varies with congestion levels and CAV penetration 8.5% – 10.5% (real-world DiL and projected from simulation) negligible project QR The results have not yet been published Total Integrated (VD & PT) Energy Benefits 20.2% – 29.4% (simulation) negligible [2] Varies with congestion levels and CAV penetration 19.4% – 32.4% (real-world DiL and projected from simulation) negligible project QR The results have not yet been published *DiL: dynamometer-in-the-loop testing of real-world bus [1]

• G. Wu, D. Brown, Z. Zhao, P. Hao, M. Barth, K. Boriboonsomsin and Z. Gao (2020) “Dyno-in-the-Loop: An Innovative Hardware-

in-the-Loop Development and Testing Platform for Emerging Mobility Technologies. SAE Technical Paper 2020-01-1057, 4/2020. [2]

• F. Ye, P. Hao, G. Wu, D. Esaid, K. Boriboonsomsin, Z. Gao, T. LaClair, and M. Barth (2020) “Deep Learning-based Queue-aware

Eco-Approach and Departure system for Plug-in Hybrid Electric Bus at Signalized Intersections: a Simulation Study”, SAE Technical Paper 2020-01-0584, April 2020. [7]

• P. Hao, K. Boriboonsomsin, G. Wu, Z. Gao, T. LaClair, and M. Barth (2019) “Deeply Integrated Vehicle Dynamic and Powertrain

Operation for Efficient Plug-in Hybrid Electric Bus”, Proceedings of the 98th TRB Annual Meeting, Washington D.C., 1/ 2019.

Key Lessons Learned

• Traditional on-board hybrid electric vehicle control strategies can be greatly enhanced

based on (partial) outside information: knowledge of location and stop locations, traffic information, signal information, past/current/future state of the bus operation (route, etc.). It is non-trivial to balance between the solution optimality and real-time performance.

• Overall energy savings is very corridor, traffic, and route specific: higher speed and/or

more hilly routes with moderate congestion likely have greater energy savings potential.

• Tight integration between vehicle dynamics and powertrain controls is critical, requiring

feedback in both directions: VDPT and PTVD. It is challenging to integrate simulation and dynamometer operation (time synchronization is critical).

• Planning demonstration at ITS World Congress

HD Chassis Dyno and Test Vehicle: PHEB Simulation Environment: VISSIM Powertrain Control: Matlab & Simulink Vehicle Dynamics Optimization: Matlab

(UDP) Socket DriverModel.dll (coded in C++) CAN bus CAN bus Interfacing Tool (UDP) Socket (UDP) Socket Power Dyno PC HDCD Control PC (UDP) Socket

Dynamometer-in-the-Loop is an effective testing method: