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 Develop an innovative vehicle-powertrain eco-operation system for plug-in hybrid electric buses through co-optimization of vehicle dynamics and powertrain controls, achieving 20+% energy efficiency increase

Project Goal

• Integrated control strategies have been designed and implemented for PHEB.
• Simulation shows up to 24% energy improvements for its specific target corridor.
• Actual bus is being exhaustively tested using a Dyno-in-the-Loop (DiL) approach.

Current Technical Status PI: Matthew Barth, University of California-Riverside Partners: Oak Ridge National Lab, US Hybrid

Team Information

Matthew Barth: faculty, electrical and computer engineering Kanok Boriboonsomsin: research faculty, transportation engineering Guoyuan Wu: research faculty, mechanical engineering Peng Hao: research faculty, transportation engineering Mike Todd: development engineer, environmental engineering Fei Ye: Ph.D. student, electrical and computer engineering Ziran Wang: Ph.D. student, mechanical engineering Zhiming Gao: R&D Staff, hybrid powertrain simulation & analysis Tim LaClair: R&D Staff, hybrid powertrain testing & analysis Abas Goodarzi: president; hybrid powertrain design, manufacturer & integration Christophe Salgues: on-board vehicle controls Transit Partner: Riverside Transit Agency

2

Technical Accomplishments

Vehicle: A unique plug-in hybrid bus platform has been built by US Hybrid. Integrated Eco-Operation System: Vehicle dynamics control (in traffic) has been optimally integrated with powertrain control to maximize

• verall energy efficiency.

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

Technical Accomplishments

Scenario 3 Scenario 2 Scenario 4 Scenario 1 Speed Distance Accelerating Cruising Analysis Boundary

Intersection

• f Interest

Cruising

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

Traffic and Road Infrastructure Sensing SPaT Transmission Information Integration Scenario Identification

Define reachable region Identify target state

Technical Accomplishments

Integrated traffic and road information

0s 1s 2s 3s 4s

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

Simplified Powertrain Model Trajectory Planning Powertrain Model in Simulink

Identified Target State

Technical Accomplishments

Dynamometer-in-the-Loop (DiL) Testing: High fidelity simulation drives the bus on the dyno while actual bus capabilities are fed back to govern the bus in simulation.

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

Updated Efficiency Breakdown Table

Strategy Description Savings Source Vehicle Dynamics (VD) Control Eco-Approach and Departure Determines energy-efficient speed profile based on Signal Phase and Timing information 5% - 20% Traffic simulation & field studies Eco-Stop and Launch Determines energy-efficient speed profile for decelerating to and accelerating from bus stops and stop signs 3% - 17% Numerical simulation Eco-Cruise Determines cruising speed profile based on look-ahead traffic and terrain conditions Up to 10% Numerical simulation Integrated VD Combined vehicle dynamics control strategies on target corridor 8% - 14%; Traffic simulation; Powertrain (PT) Control Efficiency-Based PT Control Optimizes both the engine and motor/generator operation by managing transmission and battery state-of-charge 13 - 15% Simulation Intelligent Energy Management Optimizes power split between ICE and electric motor for the vehicle speed and power demand profiles 3 - 8% Simulation Integrated VD&PT Control Integration of above strategies with VD&PT co-optimization

• n target corridor

18% - 24% 16% (DiL) Simulation DiL

Efficiency Improvements Due to Control Strategies:

Efficiency Improvements on Target Corridor

Traffic Level Duration Distance Powertrain control strategy % Savings V/C = 0.0 2.4 hours 36 km Charge/Discharge Dominant Control 18.7 V/C = ~ 0.2 2.4 hours 36 km Charge/Discharge Dominant Control 20.6 V/C = ~ 0.4 2.4 hours 36 km Charge/Discharge Dominant Control 19.9 V/C = ~ 0.6 2.4 hours 36 km Charge/Discharge Dominant Control 21.7 V/C = ~ 0.8 2.4 hours 36 km Charge/Discharge Dominant Control 24.0

Target “Innovation Corridor”: University Avenue between UCR and downtown City of Riverside

Tech-to-Market Strategy

• Participating and following Phase I and Phase II NSF I-Corps through UC

Riverside’s Office of Technology Partnerships

• Managing IP and licensing through UC Riverside’s Office of Technology

Commercialization

• Initial testing, evaluation, and deployment of Driver’s Aid with transition to

OEM integrated longitudinal powertrain and vehicle dynamics control.

• Development expanding beyond fixed route transit to include heavy duty

goods movement and medium duty delivery applications

• Extensive Dyno in Loop (DiL) evaluation to advance technology

development

Techno-Economic-Analysis Activities

• Comparative analysis between driver’s aid (Level 1) to automated

longitudinal control (Level 2+)  off-the-shelf HMI vs. Integrated System

• Identification of deployment requirements: infrastructure, on-board

vehicle requirements, regulatory, liability, standards, IP

• Estimation of production process costs
• Estimation of scalability
• Cost-performance model

www.truck.man.eu

Key Lessons Learned

• Traditional on-board control strategies can be greatly enhanced based on (partial)
• utside 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.

• 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).

• Eco-Approach & Departure: Deep Learning-based calculation is computationally efficient.

Time Location Stop line Source node Destination node Time Location Stop line Source node Destination node Time Location Preceding vehicle Stop line Source node Destination node Gap keeping No Constraints Constraints from signal and stop line Constraints from preceding vehicle

Key Lessons Learned (Bus-Specific)

• Overall energy savings is very corridor and route specific: our target corridor is mild

(in terms of travel speed and terrain) to achieve large savings; higher speed and/or more hilly routes would have greater energy savings potential.

• Bus stop locations along the corridor are important for efficiency; near-side and far-

side stops hinder efficiency; mid-block placement enhance efficiency.

• Dwell time management can be a key efficiency contributor (including skipping bus

stops with no passenger boarding/alighting).

• Passenger weight is not as large of an efficiency issue as we originally thought for
• ur target corridor.

Impacts of new California Zero Emission Bus (ZEB) Rule:

• All California transit buses must be zero emissions by 2040.
• Urban routes most likely can be served by pure battery electric buses.
• Longer (and hilly) routes can use plug-in hybrid electric buses.

Current Challenges

• Complete signal-level control of the vehicle dynamics and powertrain is critical for

implementing predictive operation.

• Roadway type (e.g., freeway, arterial) and infrastructure (e.g., signal spacing)

significantly affect bus operations and associated efficiencies; each corridor must be specifically optimized.

• Availability of infrastructure information is critical: some test corridors are fully

instrumented; other corridors have partial information; most corridors have very little information; it is tedious to code complete information by hand.

• Real-time operation of complex VD and PT controls: tradeoff between optimality

and real-time performance; (note DL-based approach works well).

• It is hard to quantify the total energy efficiency of a plug-in hybrid electric bus,

including both CNG consumption rate and battery energy flow.

• Managing vehicle operations in extremely hot and cold environments (minimum

engine operation, after-treatment off/on, rapid transmission shifting, passenger comfort).