Impact of Connection and Automation on Electrified Vehicle Energy Consumption

Transcription

1 Impact of Connection and Automation on Electrified Vehicle Energy Consumption SAE 215 Vehicle Electrification and Connected Vehicle Technology Forum December 4, 215 Aymeric Rousseau, Pierre Michel, Dominik Karbowski Argonne National Laboratory

2 Connected & Automated Vehicles Multi-objective optimization of energy, mobility & safety Automated Driving relies on a broad range of onboard sensors Connected Driving Uses Communication with the infrastructure (V2I), other vehicles (V2V) and the cloud CAVs: Connected & Automated Vehicles will use both automation and connectivity Source: Praveen Chandrasekar, Frost&Sullivan, Leveraging ADAS to leapfrog in the Automated Driving space 2

3 Tomorrow s Transportation Will Feature a Rich Combination of Technologies Autonomou s Vehicle 53 Variable Speed Limit Dynamic light sequencing Signal Broadcasting Adaptive Cruise Control Eco-Approach PHEV V2V Platooning Various powertrains Various level of vehicle automation Various ITS technologies Various levels of communication EV HEV V2I Traffic Management Center 3

4 Electric Drive Vehicles Could Benefit from CAVs Technologies from Connectivity and Automation Route Based Control would allow lower fuel consumption by optimizing electric consumption throughout a trip. Opportunity for optimal powertrain design and speed control Knowing location of charging stations, their status as well as the vehicles battery SOC would provide helpful information to drivers (i.e., when should they charge, where is the closest charging point ) Drivers could reserve charging stations in advance (I.e., shopping, restaurants ) or know when one becomes available as soon as a car is charged (i.e., work) 4

5 But Connectivity & Automation Could Also Lower the Energy Savings Potential of xevs A lot of the CAVs technologies focus on improving traffic flow, leading to lower accelerations & decelerations (i.e. EcoSignal). This will improve the efficiency of conventional vehicles much more than that of xevs that benefit from regenerative braking Since xevs benefit from deceleration events to recharge the battery, what will be the impact of having smoother a smaller number of deceleration events or even none of them? 5

6 Argonne Expertise Mobility Assumptions Current: Market Penetration, Current Fleet distribution, fleet VMT distribution, current vehicle technologies Energy Argonne has unique expertise and capabilities, of interest to DOT and DOE for differentiated research. Additional Lab expertise and resources could be leveraged: HPC, optimization, vehicle dynamometer testing, test procedure, sensors, cyber security, infrastructure resilience, grid, urban planning, buildings National Impact Polaris Transportation Current: Simulation Model VMT, microscopic traffic flow Current: High Fidelity Vehicle Average Energy Consumption energy consumption per distance, simplistic models National Impact Current: (VISION) Only vehicle impact evaluated

7 Full Suite of Capabilities Required to Address CAVs Energy Impact Evaluating new vehicle technologies, developing new vehicle controls Developing controls for connected and automated vehicles Analyzing the impact of new infrastructure, control and new forms of transportation Evaluating energy impacts at the national level Single Vehicle Small Network Entire Urban Area Eco-driving Eco-Routing Route-Based Control Connected Intersections V2X ACC, CACC & Platooning Connected Intersections Platooning & Eco-lanes Low-emission zones VMT changes National Level 7

8 At the Vehicle Level, Autonomie is Used to Model Advanced Vehicles Autonomie is a Plug&Play system simulation tool developed by Argonne & licensed by Siemens to more than 175 companies and universities worldwide. Autonomie has been developed in partnership with General Motors under funding from the US Department of Energy One of the main application of the tool is focused on assessing the energy impact of advanced technologies with a particular focus on xevs. The models and control algorithms have been validated using Argonne s dynamometer test data. More than 5 turn-key vehicles and 12 powertrains are currently available. CATARC 8

9 Autonomie Vehicle Models Validated with Test Data Test data from APRF (ANL) Control and Performance Analysis 12 engine operation target 1 Engine torque (Nm) ºC Heat capacity estimation Test data vehicle speed (m/s) engine speed (rad/s) engine torque (Nm) UDDS Test 3 Simu 2 8 Engine(Test) 1 Engine(Simu) 6 Battery(Test) Battery(Simu) Model time Validation (s) Simulation data Model Development (Autonomie) fuel consumption (kg) SOC (%) temperature (C) Test Simu Test 7 Simu 65 6 Test Simu time (s) component Tamb Fan UDDS Radiator Teng_room coolant loop Engine heatercore loop Teng Valve Test Simu Heatercore Engine speed (rad/s) Driver power demand SOC Engine on/off demand Mode decision (Engine on/off) Engine on/off demand mode behaviors Thermal conditions controller Energy Engine management power demand (SOC balancing) Engine torque demand Engine target Engine Motor 2 generating speed demand Motor 2: torque demand Engine speed tracking Motor: Battery power demand torque target Driver power demand generation Engine torque demand Motor 2 torque demand Motor torque demand 9

10 Vehicle Model Validated within Test to Test Uncertainty Fuel consumption (kg) Conv. Test Simulation.8 Test Simulation C 7ºC 21ºC 22 C 35ºC35 C -7 C 7ºC 21ºC 22 C 35ºC35 C Fuel consumption (kg) HEV Fuel consumption (kg) Test PHEV (CS) Simulation Fuel consumption (kg) Test EREV (CS) Simulation 7ºC 21ºC 35ºC 7ºC 21ºC 35ºC -7 C 22 C 35 C -7 C 22 C 35 C 1

11 Vehicle Energy Impact Analysis for Various CAV Scenarios SSSpeed cycles RWDC CAV2 RWDC CAV1 RWDC Speed transformation Selection with Energy Criteria Database of recorded GPS traces Fuel Consumption (l/1km or l/1km equivalent) Autonomie 3 Midsize vehicles Conventional HEV BEV Results Conv. SSSpeed HEV SSSpeed BEV SSSpeed RWDC modif Speed (km/h) 11

12 Ideal CAVs Use Case -> Steady-State Cycles Fuel consumption results obtained with SSSpeed cycles simulations Theoretical representation of the highest connectivity degree No stops and constant speed Fuel Consumption (l/1km or l/1km equivalent) Conv. HEV BEV Speed (km/h) 12

13 Energetic Criteria Used to Select RWDC Source - Chicago Database Database of recorded GPS traces speed include different drivers, different cars Positive Kinetic Energy (PKE) is a good driving style indicator: PPPPPP = vv tt+1 2 vv tt 2 when aa tt > xx Where: vv tt : speed vv mm : mean speed aa tt : acceleration Selection of RWDC with: Distance between 2 and 7 km 2 cycles with the same vv mm per ten km/h PPPPPP close to the average database PPPPPP aaaaaa.95 PPPPPP aaaaaa (vv mm ) < PPPPPP < 1.5 PPPPPP aaaaaa (vv mm ) 18 RWDC Selected Average PKE (Positive Kinetic Energy) All RWDC Selected RWDC Averaged PKE PKE upper limit PKE lower limit Average speed (km/h) 13

14 Connectivity Potential is defined between RWDC and SSSpeed Energy Consumptions Fuel consumption results obtained with selected RWDC simulations Fuel Consumption (l/1km or l/1km equivalent) 1 Conv. SSSpeed 9 HEV SSSpeed BEV SSSpeed 8 RWDC Speed (km/h) 14

15 Connectivity Representation with Stops Removed Modifications of the RWDC speed Distance unchanged Transformations : 1. Stops removal Every other stop removed Speed (km/h) Intial RWDC Modified RWDC Speed (km/h) Intial RWDC Modified RWDC Speed (km/h) Intial RWDC Modified RWDC Distance (km) Distance (km) Distance (km) 15

16 Connectivity Representation with Speed Smoothing Modifications of the RWDC speed Distance unchanged Transformations : 1. Stops removal Every other stop removed 2. Traffic Smoothing 5s moving average Speed (km/h) Intial RWDC Modified RWDC Speed (km/h) Intial RWDC Modified RWDC Speed (km/h) Intial RWDC Modified RWDC Distance (km) Distance (km) Distance (km) 16

17 Connectivity Representation with Acceleration Saturation Modifications of the RWDC speed Distance unchanged Transformations: 1. Stops removal Every other stop removed 2. Traffic Smoothing 5s moving average 3. Acceleration saturation by -1.5 and 1.5 m/s 2 17

18 Connectivity Representation with Speed Transformation Speed (km/h) Modifications of the RWDC speed Distance unchanged Transformations: 1. Stops removal Every other stop removed 2. Traffic Smoothing 5s moving average 3. Acceleration saturation by -1.5 and 1.5 m/s 2 4. Speed point by point transformation Intial RWDC Modified RWDC Distance (km) Speed (km/h) Transformed Speed (km/h) Distance (km) Original Speed (km/h) Intial RWDC Modified RWDC Speed (km/h) Intial RWDC Modified RWDC Distance (km) 18

19 Two Sets of CAV RWDC Defined Modifications of the RWDC speed Distance unchanged Transformations: 1. Stops removal 2. Traffic Smoothing 3. Acceleration saturation 4. Speed point by point transformation 2 CAVs RWDC scenarios: CAVs RWDC 1 Assumptions {1,2,3} No speed point by point transformation 1% PKE decrease Same averaged speed CAVs RWDC 2 Assumptions {1,2,3,4} 12% PKE decrease Average speed increased at low speed 19

20 Connectivity Decreases Fuel Consumption Especially at Low Vehicle Speed Fuel consumption results obtained with selected CAV RWDC simulations Fuel Consumption (l/1km or l/1km equivalent) 1 Conv. SSSpeed 9 HEV SSSpeed BEV SSSpeed 8 RWDC modif Speed (km/h) 2

21 35 to 5 % Potential Fuel Savings at Low Vehicle Speed Potential fuel consumption decrease results obtained with selected CAV RWDC simulations Potential fuel Consumption reduction (%) Fuel Cons. at RWDC speed as reference Conv. HEV split BEV RWDC CAV2 RWDC CAV1 Steady-Speed Speed (km/h) BEVs have biggest potential at low speed 21

22 Virtual Proving Grounds to Quickly Evaluate the Impact of V2V, V2I on the Energy Virtual Proving Ground Environment Model (1) VEH1 VEH N VEH2 Use cases examples: - Eco-Approach & Departure at Signalized Intersections - Eco-Traffic Signal Timing - Eco-Traffic Signal Priority - Connected Eco-Driving - Route based control - Impact on traffic flow Sensors/V2X Models Co-simulation of High Fidelity Vehicle Models Closed loop control critical for energy and speed optimization 22

23 Driving Environments and Vehicle Model Automatic building Traffic Environment UI provides many objects such as road, car, human, sensor, and signal Simulink Model User needs to change if desired Visualization & results No analysis tool provided 23

24 Autonomie Vehicle Models in PreScan Conventional High fidelity vehicle models from Autonomie can replace PreScan vehicle model placeholders within Simulink HEV Electric Vehicle Simulink Model User needs to change if desired 24

25 Adaptive Cruise Control Impact for Multiple Powertrain Configurations Adaptive Cruise Control Car1 follows Manhattan cycle Car2, Car3, and Car4 follow the vehicle ahead of each one Autonomie vehicles are applied. Conv(Car1), HEV(Car2 & Car3), and EV(Car4) Simulation Demo New controller path follower Autonomie Vehicle model 25

26 gotoaccelec_3 -T- [gen_3] [str_3] [main_info_bus] [str] str [main_info_bus] [ess] ess [str_3] gotostr_3 [main_info_bus] [eng] [main_info_bus] [gen] eng gen [main_info_bus] [accelec] accelec [cpl_3] [gen_3] gotogen_3 -T- [main_info_bus] [accmech] [main_info_bus] [tc] accmech tc [main_info_bus] [cpl] cpl [cpl_3] gotocpl_3 [main_info_bus] [gb] gb [main_info_bus] [fd] fd [main_info_bus] [whl] whl [main_info_bus] [chas] chas At the Fleet Level, Large Transportation System Models are Required to Evaluate CAVs Impact Use cases examples: - Eco-Lanes (dedicated freeway, variable speed limits, ECACC ) - Wireless charging (bus lanes) - Low Emissions Zones - Platooning - Smoother braking - Mixed vehicle fleet (i.e. HEVs, BEVs + few CAVs) - Increased VMT due to travel behavior changes - Charging station location Fleet Definition Transportation Simulation Powertrain Simulation Energy consumption of the transportation network 26

27 Integrated Transportation Model ( ) POLARIS is an agent-based transportation system model Decision making is decentralized. Each traveler has its own goals and behaviors. All aspects of activity and travel are represented in a single model Travelers are autonomous and can adopt to current conditions (congestion, mode availability, information available) Not restricted to a limited number of market segments (user groups) The agent based framework is flexible and can accommodate other types of agents (buildings, authorities, smart infrastructure) NETWORK MODEL Physical laws that govern dynamics of traffic flow Newell s model Managed Lanes Controlled intersections (traffic signals) Traveler information systems Traffic management Multimodal travel (Integrated corridor management) 27

28 Integrated Transportation System Model ( ) 28

29 Individual Activity Travel Patterns Allow Accurate Drive Cycle Evaluation 1: AM Arrive at Work 1:55 PM Return to Work 1:15 PM Lunch Drive Trip Passenger Trip 9:15 AM Drop off 9:15 AM Day Care 3:5 PM Pick up 3: PM Recreation Train Trip Activity Locations 9:AM Leave 7:15 HomePM Return 6:3PM Shop In Chicago over 46% of time away from home is not at a work or school location 9: AM Leave Home 4:1PM Return 8: AM Leave Home 4:1 PM Return 8:15 AM On Train 2:45 PM Off Train 29

30 Evaluating the Energy Impact of an Automation Scenario 3 Scenarios: UM: Unmanaged ML: Managed lane for heavy-duty trucks ML+ACC: Managed lane for trucks, and all trucks have adaptive cruise control (ACC) 18 km Stretch of Highway in Chicago 25 on- and off- ramps Powertrain Technologies Each vehicle class has a conventional ICE version (CV) and a hybrid (HEV) version Each vehicle template has a unique combination of components and average mass 3

31 Evaluating the Energy Impact of an Automation Scenario Class 8 hybridization ( mild with ISG) saves approx. 15% in the unmanaged case; for class 6 ( full HEV ), savings are approx. 2%. With managed lanes, the savings are lower, and nonexistent when ACC is used (No regen braking) Average Truck Fuel Consumption With managed lanes, the savings are lower, and nonexistent when ACC is used. This is because braking, and its recuperation, is virtually eliminated. Overall fuel savings for trucks: - managed lanes (ML): 25% - managed lanes + ACC: 4% Fuel Consumption Distribution (all trucks, FS1) 31

32 Conclusion A lot of good work has been performed, but since the focus has been for conventional vehicles at the individual level, additional in-depth analysis needs to be performed to assess the impact on energy. At the vehicle level using CAV-like RWDC, we showed that, Connectivity decreases fuel consumption especially at low speed 35 to 5 % connectivity potential fuel savings at low speed BEVs have biggest potential at low speed Connectivity doesn t modify the hybridization potential System level analysis has to be performed including uncertainty using new set of tools. The potential increase in travel demand could reverse the recent significant gains. Advanced vehicle technologies such as electrified vehicles could minimize the impact of the demand effect through fuel energy diversification. CAVs could lead to an increase in advanced vehicles market penetration 32