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

Technical Accomplishments Innovative Velocity Trajectory Planning Modules: • Eco-Approach and Departure at Signalized Intersections: determines an energy-efficient speed profile based on SpaT information from signalized Traffic and Road intersections; Infrastructure Sensing • Eco-Stop and Launch: determines energy-efficient SPaT Transmission speed profile for decelerating to and accelerating from bus stops and stop signs Analysis Boundary Location Destination node • Eco-Cruise: determines cruising speed profile based Speed Stop line Scenario 2 Scenario 3 Scenario Information Gap keeping Preceding vehicle on look-ahead traffic and terrain conditions Scenario 1 Scenario 4 Identification Define reachable Integration Distance region Intersection of Interest Accelerating Cruising Cruising Time Innovative Powertrain Modules: Source node Identify target state • Efficiency Based Powertrain Controls: optimizes both PHEB operation mode selection Input: Wheel spd/trq demand, SOC control data, & bus data/CEED data 3s the engine and motor/generator operation by Calculate PHEB tractive power, wheel torque and speed, optimal 2s gears Braking Parking Propelling, Braking, or Parking 1s Simplified Propelling SOC monitoring Braking mode Engine charging mode at parking at SOC<SOC parking chg Trajectory managing transmission and battery state-of-charge YES NO 4s Powertrain SOC>SOC ub 0s DDC CDC DDC or CDC Simplified model as PEV mode (or motor Planning propelling mode) NO NO 𝜐 𝑥ℎ𝑚 > 𝜐 𝑓𝑜𝑕−𝑥ℎ𝑚 , 𝑛𝑏𝑦 𝜐 𝑥ℎ𝑚 > 𝜐 𝑓𝑜𝑕−𝑥ℎ𝑚 , 𝑛𝑏𝑦 • Intelligent Energy Management: optimizes the Model Cost in the graph NO YES YES 𝜃 𝑓𝑜𝑕−𝑞𝑥𝑢 > 𝜃 𝑛𝑝𝑢−𝑞𝑥𝑢 Engine & motor propelling mode Engine & motor propelling mode Engine propelling & charging mode YES Engine propelling mode Motor propelling mode power split between the internal combustion engine Filter and modulate the operating states of engine, motor and transmission as functions of engine/motor operation envelop, minimum engine on/off time, minimum wheel power demand activating engine on etc. Outputs: engine/motor load demand and electric motors for the vehicle speed and power Key strategy as Optimal trajectory simplified model as driving cycle demand profiles Powertrain Model in Simulink 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 Energy Efficiency Trip time NEXTCAR Technology Ref Notes Improvement penalty 10.5% – 20.9% (simulation) negligible [2] Varies with congestion levels and CAV penetration Transit Bus Eco-Approach 9.6% – 22.9% (real-world DiL*) negligible [1] and Departure Numerical simulation was used to 10.9% - 17.1% (simulation) negligible [7] Eco-Stop and Launch evaluate this separately Numerical simulation was used to 0% - 12.8% (simulation) negligible [7] Eco-Cruise evaluate this separately 13.7% – 18.0% (simulation) negligible [2] Varies with congestion levels and CAV penetration Combined Powertrain 8.5% – 10.5% (real-world DiL negligible project The results have not yet been Optimization and projected from simulation) QR published 20.2% – 29.4% (simulation) negligible [2] Varies with congestion levels and CAV penetration Total Integrated (VD & PT) 19.4% – 32.4% (real-world DiL negligible project The results have not yet been Energy Benefits and projected from simulation) QR 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 HDCD Control PC Power Dyno PC (UDP) Socket (UDP) Socket Dynamometer-in-the-Loop is an CAN bus DriverModel.dll (coded in C++) effective testing method: CAN bus Interfacing Tool (UDP) Socket (UDP) Socket Powertrain Control: Matlab & Simulink Vehicle Dynamics Optimization: Matlab