[PPT] - Consideration Liuwang Kang, Haiying Shen, and Ankur Sarker PowerPoint Presentation

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Velocity Optimization of Pure Electric Vehicles with Traffic Dynamics Consideration

Liuwang Kang, Haiying Shen, and Ankur Sarker

Department of Computer Science, University of Virginia

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Introduction
System Design
Performance Evaluation
Conclusion

Outline

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Introduction

Factors impeding wide electric vehicle application

 Short driving range

100 200 300 400 500 600 700 Tradtional vehicle Pure EV Driving range (Mile) Vehicle type

Driving range per battery charge or full fuel fill

60%

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Introduction

Factors impeding wide electric vehicle application

 Short driving range  Limited battery cycle life

Battery cycle life of lithium-ion battery

0.5 0.6 0.7 0.8 0.9 1 1.1 300 600 900 1200 1500 1800 Capacity (Ah) Battery cycle life (times) 100 200 300 400 500 600 700 Tradtional vehicle Pure EV Driving range (Mile) Vehicle type

Driving range per battery charge or full fuel fill

60%

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Introduction

Solution: Velocity optimization

Consider constraints such as vehicle acceleration, speed limit, stop sign and traffic light on the road

https://t3.ftcdn.net/jpg/01/51/49/66/500_F_151496666_8VitGP5svgi3vOOZz3NpeytN53jz3sh2.jpg

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Introduction

Solution: Velocity optimization

Consider constraints such as vehicle acceleration, speed limit, stop sign and traffic light on the road Optimize the velocity profile to reduce total energy consumption

https://t3.ftcdn.net/jpg/01/51/49/66/500_F_151496666_8VitGP5svgi3vOOZz3NpeytN53jz3sh2.jpg

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Introduction

Solution: Velocity optimization

Consider constraints such as vehicle acceleration, speed limit, stop sign and traffic light on the road

Energy consumption reduced by 20%

Optimize the velocity profile to reduce total energy consumption

https://t3.ftcdn.net/jpg/01/51/49/66/500_F_151496666_8VitGP5svgi3vOOZz3NpeytN53jz3sh2.jpg

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Introduction

Challenges of current velocity optimization methods

How to estimate waiting vehicles in the traffic signal areas

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Introduction

Challenges of current velocity optimization methods

How to estimate waiting vehicles in the traffic signal areas How to apply waiting vehicle information into velocity optimization

Waiting vehicles

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Introduction

Our method: DP-based velocity optimization system

 Propose vehicle movement (VM) model

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Introduction

Our method: DP-based velocity optimization system

 Propose vehicle movement (VM) model

Queue length

 Build queue length model

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Introduction

Our method: DP-based velocity optimization system

 Propose vehicle movement (VM) model

Queue length Storage Computing Voptimized

 Build queue length model  Apply vehicle queue length into DP (Dynamic Programming) algorithm

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System Design

Overview Waiting vehicles in traffic signal areas Traffic volume VM model Arrival vehicle rate Leaving vehicle rate Queue length model

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System Design

Overview Constraints Waiting vehicles in traffic signal areas DP-based velocity optimization Traffic volume VM model Arrival vehicle rate Leaving vehicle rate Acceleration Stop sign Speed limit Queue length model

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System Design

Overview Constraints Waiting vehicles in traffic signal areas DP-based velocity optimization Traffic volume VM model Optimized velocity profile Arrival vehicle rate Leaving vehicle rate Acceleration Stop sign Speed limit Queue length model

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System Design

Energy consumption model of pure EVs

Driving force:

2

1 sin cos 2

drive f d

dv F m A C v mg mg dt        

Driving force of pure EV

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System Design

Energy consumption model of pure EVs

Driving force:

2

1 sin cos 2

drive f d

dv F m A C v mg mg dt        

1 2

E UQ  𝑉 - Battery pack voltage; 𝑅 - Charge consumption; 𝜃1- Battery transforming efficiency; 𝜃2- Powertrain working efficiency;

Energy generated by the battery pack:

Driving force of pure EV

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System Design

Energy consumption model of pure EVs

Driving force:

2

1 sin cos 2

drive f d

dv F m A C v mg mg dt        

1 2

E UQ 

1 2 drive

F v U    𝑉 - Battery pack voltage; 𝑅 - Charge consumption; 𝜃1- Battery transforming efficiency; 𝜃2- Powertrain working efficiency;

Energy generated by the battery pack: Energy consumption per time:

Driving force of pure EV

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System Design

Traffic dynamics in traffic signal areas

Queue length model is built to estimate waiting vehicle numbers in traffic signal areas:

Vout Vin … 1 2 n Queue length= nd n+1 d

Vehicle arrival rate Vin Vehicle leaving rate Vout

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System Design

Traffic dynamics in traffic signal areas

Arrival vehicle rate Vin : estimated based on real-time traffic volume

Arrival and leaving vehicle rates

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System Design

Traffic dynamics in traffic signal areas

Arrival vehicle rate Vin : estimated based on real-time traffic volume Vehicle leaving rate Vout : estimated with vehicle movement model

Arrival and leaving vehicle rates

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System Design

Traffic dynamics in traffic signal areas

Arrival vehicle rate Vin : estimated based on real-time traffic volume Vehicle leaving rate Vout : estimated with vehicle movement model

Arrival and leaving vehicle rates Waiting vehicle numbers in one traffic light period of US-25 highway

Queue length Lq: calculated with Vin and Vout

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System Design

Traffic dynamics in traffic signal areas

Arrival vehicle rate Vin : estimated based on real-time traffic volume Vehicle leaving rate Vout : estimated with vehicle movement model

Arrival and leaving vehicle rates Waiting vehicle numbers in one traffic light period of US-25 highway

Queue length Lq: calculated with Vin and Vout

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Experiment

Simulation settings

1. Vehicle parameters in energy consumption model

Parameters 𝒏 𝑩𝒈 𝑫𝒆 𝝂 𝜽𝟐 𝜽𝟑 Values 1300 kg 1.97 m2 0.33 0.018 0.9 0.97

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Experiment

Simulation settings

1. Vehicle parameters in energy consumption model

 Total 4050 m long  One stop sign  Two traffic signals  speed limit - 65 mile/hour

2. Experiment road segment on US-25 highway

Parameters 𝒏 𝑩𝒈 𝑫𝒆 𝝂 𝜽𝟐 𝜽𝟑 Values 1300 kg 1.97 m2 0.33 0.018 0.9 0.97

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Experiment

Simulation settings

1. Vehicle parameters in energy consumption model

 Total 4050 m long  One stop sign  Two traffic signals  speed limit - 65 mile/hour

2. Experiment road segment on US-25 highway

Parameters 𝒏 𝑩𝒈 𝑫𝒆 𝝂 𝜽𝟐 𝜽𝟑 Values 1300 kg 1.97 m2 0.33 0.018 0.9 0.97

3. Velocity optimization results are verified in SUMO environment

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Experiment

Velocity optimization

Metric: Total energy consumption during the trip

Consumed energy comparisons

Observation: Reduces by 8.4% energy compared with current method in the experiment Reason: Enables EVs to immediately pass through traffic lights without meeting waiting vehicles

Velocity optimization comparisons

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Conclusion

1. We proposed a velocity optimization system for EVs with

considering queue length in traffic signal areas

2. We conducted velocity optimization simulation study with

SUMO to verify our method

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Conclusion

1. We proposed a velocity optimization system for EVs with

considering queue length in traffic signal areas

2. We conducted velocity optimization simulation study with

SUMO to verify our method

Future work

1. Consider the effect of road gradient on the proposed

system

2. More practical experiments in different traffic conditions

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Thank you! Questions & Comments?

Ankur Sarker as4mz@Virginia.edu Ph.D. Candidate Pervasive Communication Laboratory University of Virginia