dr. thomas h. bradley, advisor zachary d. asher, phd candidate...solution: optimal control at every...

Enabling Predictive Energy Management in VehiclesFINAL DEFENSE

Zachary D. Asher, PhD Candidate Dr. Thomas H. Bradley, Advisor

SummaryResearch Question 3Research Question 2Research Question 1Introduction

Enabling Predictive Energy Management in Vehicles

• Introduction: Existing Research Gaps◦ Dissertation Chapter 1

▪ Asher et. al. (2017) SAE Technical Paper▪ Asher et. al. (Submitted for Review 2017)

Jrnl. Renew. Sust. Energy Rev.

• Introduction: Research Questions◦ Dissertation Chapter 2

• Research Question 1: Prediction Errors◦ Dissertation Chapter 3

▪ Asher et. al. (2016) SAE Technical Paper▪ Asher et. al. (2017) IEEE Trans. Ctrl. Syst.

Tech.

2

Agenda• Research Question 2: Perception Fidelity and

Scope◦ Dissertation Chapters 4 & 5

▪ Asher et. al. (Pending Toyota Release) IEEE Trans. Ctrl. Syst. Tech.

▪ Asher et. al. (Pending Toyota Release) IEEE Trans. Ctrl. Syst. Tech.

• Research Question 3: Prediction and Computational Effort

◦ Dissertation Chapter 6▪ Asher et. al. (2018) SAE Technical Paper

• Summary: Overall Conclusions◦ Dissertation Chapter 7



3

University Faculty• Dr. Tom Bradley

• Dr. Edwin Chong

• Dr. Peter Young

• Dr. Jianguo Zhao

• Dr. Jason Quinn

• Dr. Shantanu Jathar

• Dr. Sudeep Pasricha

• Dr. Andrew Frank

• Dr. Scott Samuelsen

• Dr. Fanoush Banai-Kashani

• Dr. Steve Tragesser

• Dr. Ilya Kolmanovski

• Dr. Regan Zane

Graduate Students• David Trinko

• David Baker

• Jordan Tunnell

• Evan Sproul

• Carlos Quiroz

• Van Wifvat

• Tom Cummings

• Robert Fitzgerald

• Jamison Bair

• Vippin Kumar Kukkula

• Gabe Di Domenico

• Matt Knopf

• Matt Reese

Industry/Gov./Lab Collaborators• Haraldo Stefanon

• Josh Payne

• Dr. Ben Geller

• Dr. Ken Butts

• Mike Huang

• Abril Galang

• Anthony Navarro

• Haneet Mahajan

• Joe Olsen

• Nawa Baral

Acknowledgements


Improving Fuel Economy• Addresses economic, environment, human health

problems

• U.S. regulation: 49.7 mpg vehicle average by 2025• Worldwide pledge to eliminate greenhouse gas emissions

Autonomous Vehicles• Addresses driver and passenger safety

• Possible due to driver assistance technology advancements

• Numerous companies heavily invested in development

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Fagnant, Kockelman. 2015. Transp. Res. Part A: Policy and PracticeInternational Energy Agency. 2016. Key World Energy Statistics

Paris Declaration on Electro-Mobility and Climate Change: Call to Action, 2017Fulton et. al. 2017 “Three Revolutions in Urban Transportation”


Combining these trends…• Development may be

mutually reinforcing

• Explore synergistic benefit


Predictive Energy Management

PerceptionCompute

Worldview

PlanningCompute Optimal

Energy ControlComponent Limitations

Misprediction

Vehicle Plant

Proprietary

Running Control


5

Autonomous Vehicle Technologies

Asher, Wifvat, Samuelsen, Frank, Bradley. “Review of Research Gaps to Optimal Fuel Economy Vehicle Control” Journal of Renewable and Sustainable Energy Reviews. In Review.



PerceptionCompute

Worldview



Misprediction

Vehicle Plant

Proprietary

Running Control


6

Autonomous Vehicle Technologies Mathematical Optimization


Minimum



PerceptionCompute

Worldview



Misprediction

Vehicle Plant

Proprietary

Running Control


7

Autonomous Vehicle Technologies Mathematical Optimization Vehicle Results


Minimum



PerceptionCompute

Worldview



Misprediction

Vehicle Plant

Proprietary

Running Control


8


Note:• The vehicle is not autonomous

◦ The vehicle velocity determined by human driver• Choose architecture that allows a large fuel economy improvement

◦ Hybrid electric vehicle (engine propulsion or battery propulsion)




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PerceptionCompute

Worldview



Misprediction

Vehicle Plant

Proprietary

Running Control

Perception Subsystem: High Technology Readiness• Modern vehicles have significant driver assistance technology

◦ Bengler et. al. 2014. “Three Decades of Driver Assistance Systems: Review and Future Perspectives.”• Success of autonomous vehicle competitions

◦ Thrun, Sebastian. 2010. “Toward Robotic Cars.”




10

PerceptionCompute

Worldview



Misprediction

Vehicle Plant

Proprietary

Running Control

Planning Subsystem: High Technology Readiness• 300+ published research papers demonstrating fuel economy improvements with improved energy

management◦ Zhang et. al. 2015. “A Comprehensive Analysis of Energy Management Strategies for Hybrid Electric Vehicles

Based on Bibliometrics.”• They all follow a formula

◦ Choose a vehicle model and drive cycle -> optimization scheme -> show a fuel economy improvement




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PerceptionCompute

Worldview



Misprediction

Vehicle Plant

Proprietary

Running Control

Planning with Misprediction: Low Integration Readiness• Limited research addressing mispredictions

◦ O’Keefe et. al. 2006 “Dynamic programming … to investigate energy management .. for a plug-in HEV”◦ Fu et. al. 2011 “Real-time energy management and sensitivity study for HEVs”◦ He et. al. 2012 “An energy optimization strategy for … plug-in HEVs “◦ Opila et. al. 2014 “Real-World robustness for HEV … energy management strategies …”◦ Mohd Zulkefli et. al. 2016 “Real-Time … strategy for connected HEVs”

None use real-world mispredictions → informs research question 1B Sauser, et. al. 2006 “From TRL to SRL: The concept of systems readiness levels”




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PerceptionCompute

Worldview



Misprediction

Vehicle Plant

Proprietary

Running Control

Perception with Planning: Low Integration Readiness• Limited research using perception and planning subsystems

◦ Bender et. al. 2013 “Drive cycle prediction and energy management … for HHVs”◦ Mohd Zulkefli et. al. 2014 “HEV optimization with trajectory prediction ….”◦ Sun et. al. 2015 “….traffic feedback data enabled energy management in plug-in HEVs”◦ Sun et. al. 2017 “Investigating adaptive-ECMS with velocity forecast ability for HEVs”

All use future technology → informs research questions 2 & 3

B Sauser, et. al. 2006 “From TRL to SRL: The concept of systems readiness levels”



Research Question 1: What are the effects of different types of prediction errors on the fuel economy results enabled by predictive energy management?

Hypothesis 1: Certain misprediction types will result in FE improvements being maintained while other misprediction types will result in a FE loss

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Proprietary

PerceptionCompute

Worldview



Misprediction

Vehicle PlantRunning Control




Methods: Drive Cycle Development• The “expected" drive cycle• Drive cycles for predictions errors

◦ Added stops◦ Route changes◦ Speed changes from traffic◦ Compounded mispredictions

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Methods: Custom Vehicle Model• Simulated 2010 Toyota Prius

◦ Vehicle force balance◦ Propulsion from engine or battery◦ Battery charging from engine and braking◦ Gearing and operation constraints◦ Brake specific fuel consumption map

• Validated against real world performance• Input: vehicle velocity and time (drive cycle)• Output: fuel economy

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Hybrid vehicle applied formMathematical form

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Methods: Optimal Solution Derivation• Dynamic

programming◦ Dynamic equation

◦ Cost equation (to be minimized)

◦ Constraints



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Hybrid vehicle applied form2010 Toyota Prius applied form




◦ Constraints


Solution: Optimal Control at Every Feasible State


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◦ Constraints

2010 Toyota Prius applied form



Results: Why Fuel Economy is Improved• Elimination of inefficient engine power

◦ Vehicle can be powered by engine or battery

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Results: Drive Cycle Mispredictions

• “Perfect Prediction”◦ We are predicting the actual drive cycle being

driven◦ A useful comparison case → Max achievable fuel

economy (ceiling)

• “Prediction of Expected Drive Cycle” ◦ We always assume we are driving the black drive

cycle◦ The actual drive might be different◦ Misprediction

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Perfect Prediction Prediction of Expected Drive Cycle



Results: Drive Cycle Mispredictions• Predict the expected drive cycle,

drive with unpredicted stops

• Predict the expected drive cycle, drive with unpredicted traffic

◦ Fuel economy improvements are maintained

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Results: Drive Cycle Mispredictions• Predict the expected drive cycle,

drive with an unpredicted route change

• Predict the expected drive cycle, drive with unpredicted stops, traffic, slowdowns, etc.

◦ Fuel economy improvements are not maintained

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Research Question 1: What are the effects of different types of prediction errors on the fuel economy results enabled by predictive energy management?

Answer: Mispredicted stops, traffic levels, (and vehicle parameters) result in FE improvements being maintained while mispredicted route changes and compounded mispredictions result in a FE loss

Real-world mispredictions challenge the use of stochastic prediction errors for future studies

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Proprietary

PerceptionCompute

Worldview



Misprediction



4. Asher, Baker, Bradley. “Prediction Error Applied to … Optimal Fuel Economy”IEEE Trans. on Control Sys. Tech. (2017)



Proprietary

PerceptionCompute

Worldview



Misprediction


Research Question 2: What level of prediction fidelity and scope is required to realize a fuel economy improvement through predictive energy management and potentially eliminate the need for real-time computation?

Hypothesis 2: Implementing a general prediction solution for the acceleration portions of a drive cycle will result in FE improvements without the need for perfect prediction


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Methods: Perception Technique• Assume acceleration event prediction

Methods: Dataset Development• Extract accelerations from 384 drive cycles• Final dataset of 7,708 acceleration events

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Prediction!



Methods: Acceleration Event Dataset Organization• Start velocity, end velocity

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Lots of low velocity accelerations

Few high velocity accelerations



Methods: Acceleration Event Dataset Organization• Recall from previous study: “Prediction of Expected Drive Cycle”

◦ Need an “expected” prediction▪ Most common duration

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Methods: Drive Cycle Development• Environmental Protection Agency: Official Testing Cycles

◦ UDDS (City Driving)◦ HWFET (Highway Driving)◦ US06 (Aggressive Driving)◦ NYCC (Dense City Driving)

• Custom: Driven Cycles◦ Fort Collins City Cycle◦ Fort Collins Highway Cycle◦ Denver City Cycle◦ Denver Highway Cycle

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Methods: Vehicle Model• Simulated 2010 Toyota Prius

◦ Input: vehicle velocity and time (drive cycle)◦ Output: fuel economy

Methods: Optimal Solution Derivation • Dynamic Programming

◦ Maximize fuel economy by controlling engine power▪ Solution can be input into the vehicle model

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•

30



Results: Acceleration event organization scheme• Start velocity,

end velocity

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Results: Acceleration event organization scheme• Start velocity,

end velocity

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Largest fuel economy gain

Lot of low velocity acceleration data

points

Few high velocity acceleration data points

Worst fuel economy loss


Can also change the number of categories


Results: Acceleration event organization scheme discretization• Start velocity,

end velocity

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Can also change the number of categories



end velocity

34




end velocity

35

Fuel economy improvements not robust

Too many categories,overfit data

Significant and robust fuel economy improvement




end velocity

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Significant and robust fuel economy improvement

Predicting only the acceleration event category

Approximate starting speedApproximate ending speed



Results: Drive Cycles

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Acceleration category prediction works well for city driving

Max. FE improvement (ceiling)

Max. FE from acceleration prediction Predicting only approx. starting/ending velocity



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Highway cycles don’t have many accelerations

Time (sec)


Max. FE from acceleration prediction Predicting only approx. starting/ending velocity



39


Time (sec)

Nearly Equivalent



40


Close

Time (sec)



41


Worse

Time (sec)

Dataset lacks aggressive high speed accelerations



Proprietary

PerceptionCompute

Worldview



Misprediction


Research Question 2: What level of prediction fidelity and scope is required to realize a fuel economy improvement through predictive energy management and potentially eliminate the need for real-time computation?

Answer: Acceleration event prediction provides a means for improving fuel economy which is a limited perception and limited computational power technique

Challenges the use of real-time derivations that sacrifice optimality


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Asher, Trinko, Payne, Geller, Bradley. “Improved Fuel Economy through Acceleration Event Prediction Part 1: Optimal Control” (2018) Pending Toyota Release.

Asher, Trinko, Payne, Geller, Bradley. “Improved Fuel Economy through Acceleration Event Prediction Part 2: Drive Cycle Application.” (2018) Pending Toyota Release.



Research Question 3: What prediction and computational effort is required to realize a fuel economy improvement when using current technology integrated with predictive energy management?

• Hypothesis 3: Fuel economy improvements can be realized using only current vehicle technology

43


Proprietary

PerceptionCompute

Worldview



Misprediction




44

Proprietary

PerceptionCompute

Worldview



Misprediction

Vehicle PlantRunning ControlInfrastructure Data

GPS Location, Current Velocity(every 1 second)

Methods: Perception Subsystem Overivew

Driver Assistance Detection Data



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Proprietary

PerceptionNeural

Network



Misprediction



Methods: Perception Subsystem Overivew

*Baker, Asher, Bradley (2017) “Investigation of Vehicle Speed Prediction (for fuel economy).”

Infrastructure Data




Methods: Data Collection• Driver assistance detection data

◦ Camera physically mounted in vehicle◦ New detection objectives

▪ Traffic light detection, state▪ Vehicle in front speed change▪ Turn lane detection

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= Traffic Light Detected



47

•



Methods: Drive Cycles

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4 x Denver city 4 x Denver highway



Methods: Perception Model• Artificial neural network (time series)

◦ Inspired by biological neural networks◦ Became mainstream in 1980s◦ Powerful tool for large datasets

▪ Does not require a user to guide learning

▪ Features learned automatically by the training algorithm

Adjusts weights and biases

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InputOutput



Methods: Perception Model Development• Set-up (determining weights and biases)

◦ 3 of 4 drive cycles used

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Neural Network Set-Up

PerceptionNeural

NetworkGPS Location,Current Velocity

Infrastructure Data


Compared to Actual Velocity(Weights and Biases Adjusted)

15 secVelocity

Prediction

• Application (fixed weights and biases)◦ 1 of 4 drive cycles used

Neural Network Application

PerceptionNeural

NetworkGPS Location,Current Velocity

Infrastructure Data

Driver Assistance Detection Data 15 sec

VelocityPrediction

Vel

ocity

(mph

)

Distance (miles)

Distance (miles)

Vel

ocity

(mph

)



Methods: Perception Model Development• Reported result is an average of all permutations

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Distance (miles)

Vel

ocity

(mph

)

City Drive Cycle 1

City Drive Cycle 2

City Drive Cycle 3

City Drive Cycle 4

◦ Test 1▪ Set-up: cycles 1, 2, 3▪ Application: cycle 4




◦ City fuel economy result = average(Test 1, 2, 3, 4)◦ Note: This is also done for the highway drive cycles


Methods: Vehicle Model• Simulated 2010 Toyota Prius

◦ Input: vehicle velocity and time◦ Output: fuel economy

Methods: Optimal Solution Derivation • Dynamic Programming

◦ Maximize fuel economy by controlling engine power▪ Solution can be input into the vehicle model

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Results: Test 1, Repeated City and Highway Driving

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Perfect full drive cycle prediction

Perfect 15 second prediction

Prediction using only GPS data

Prediction using GPS, detection dataPrediction using GPS, detection, travel time data


Max. FE from any 15 sec. prediction

Actual prediction from these perception models




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Perfect 15 second prediction

Prediction using only GPS data


Prediction using GPS, detection dataPrediction using GPS, detection, travel time data

For city driving, just GPS and detection data works best

For highway driving, GPS, detection, and travel time data

works best



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For city driving, just GPS and detection data works best

For highway driving, GPS, detection, and travel time data

works best


City driving has more velocity variability



Research Question 3: What prediction and computational effort is required to realize a fuel economy improvement when using current technology integrated with predictive energy management?

Answer: Currently available technology with an artificial neural network perception model provides a means for improving fuel economy through predictive energy management

Challenges the reliance on future technologies to make accurate predictions

56


Proprietary

PerceptionCompute

Worldview



Misprediction


Asher, Tunnell, Baker, Fitzgerald, Banaei-Kashani, Pasricha, Bradley, “Enabling Prediction for Optimal Fuel Economy Vehicle Control”. SAE Technical Paper (2018).


Fuel economy improvements are needed ⇨ leverage autonomous technology• (RQ1) Mispredicted stops and traffic levels result in FE improvements being maintained while mispredicted

route changes and compounded mispredictions result in a FE loss

▪ (RQ2) Acceleration event prediction provides a means for improving fuel economy which is a limited perception and limited computational power technique

• (RQ3) Currently available technology with an artificial neural network perception model provides a means for improving fuel economy through predictive energy management

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Asher. “Enabling Predictive Energy Management in Vehicles”. PhD Dissertation (2018)


Industry collaboration and outcomes• 3 year Toyota funded project• Delivered numerous reports tailored to

Toyota’s process• Acceleration event prediction is now in

practice at Toyota

Paving the way for future research• Challenges the continued use of:

◦ Stochastic prediction errors◦ Real-time control derivations◦ Future vehicle technology reliance

• Developed systems-level model guides future studies



Future Research

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PerceptionCompute

Worldview



Misprediction


Model Predictive

Control

Stochastic Optimization

Eco-Driving

Real World Tech. Perception Inputs

Fuel Cell Vehicles

Semi-Trucks

Perception Algorithm Development

Eco-Routing


• Add vehicle to infrastructure communication (V2I)◦ Traffic light status and changing times

• Add vehicle to vehicle communication (V2V)◦ Other vehicle locations and states


Future Research

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NNSet-up

NNApplied

For new routes, fuel economy improvements can be realized quickly


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Dissertation Research Publications1. Asher, Wifvat, Samuelsen, Frank, Bradley. “Review of Research Gaps to Optimal Fuel Economy Vehicle Control” Journal of Renewable and Sustainable Energy Reviews. In Review.2. Asher, Wifvat, Navarro, Samuelsen, Bradley. “… Two Research Gaps Preventing … Optimal Energy Management," SAE Technical Paper (2017).3. Asher, Baker, Bradley. “Prediction Error Applied to … Optimal Fuel Economy”IEEE Trans. on Control Sys. Tech. (2017)4. Asher, Cummings, Bradley. "The Effect of … Route Type Identification … on Fuel Economy," SAE Technical Paper. (2016)5. Asher, Trinko, Payne, Geller, Bradley. “Improved Fuel Economy through Acceleration Event Prediction Part 1: Optimal Control.” IEEE Trans. On Transportation Electrification. (2018) Pending Toyota

Release.6. Asher, Trinko, Payne, Geller, Bradley. “Improved Fuel Economy through Acceleration Event Prediction Part 2: Drive Cycle Application.” IEEE Trans. On Transportation Electrification. (2018) Pending

Toyota Release.7. Asher, Tunnell, Baker, Fitzgerald, Banaei-Kashani, Pasricha, Bradley, “Enabling Prediction for Optimal Fuel Economy Vehicle Control”. SAE Technical Paper (2018).

8. Asher, Trinko, Bradley. “Increasing the Fuel Economy of Connected and Autonomous Lithium Ion Electrified Vehicles”. Chapter 6. Behavior of Lithium-Ion Batteries in Electric Vehicles. Springer International Publishing (2018).

9. Cummings, Asher, Bradley. “… Trip Preview Prediction (applied to) Fuel Economy.” E-COSM Conf. Proc. (2015)10. Trinko, Asher, Bradley. “Application of Pre-Computed Acceleration Event Control to Improve Fuel Economy…”SAE Technical Paper (2018).11. Baker, Asher, Bradley “Investigation of Vehicle Speed Prediction (for fuel economy).” SAE Technical Paper (2017).12. Tunnell, Asher, Pasricha, Bradley. “Towards Improving Vehicle Fuel Economy with ADAS”SAE Technical Paper (2018).13. Baker, Asher, Bradley. “V2V Communication Based Real-World Velocity Predictions for Improved HEV Fuel Economy” SAE Technical Paper (2018).14. Sproul, Asher, Trinko, Bradley, Quinn. " Class 8 Line-Haul Truck Electrification: Economic Analysis of In-Motion Wireless Power Transfer Compared to Long-Range Batteries“ IEEE Transportation

Electrification Conference (2018). Draft Accepted.15. Asher, Galang, Briggs, Johnston, Bradley, Jathar. “Economic and Efficient Hybrid Electric Vehicle Fuel Economy and Emissions Modeling Using an Artificial Neural Network” SAE Technical Paper

(2018).16. Quiroz-Arita, Asher, Baral, Bradley. "Vehicle Electrification in Chile: A Life Cycle Assessment and Techno-economic Analysis Using Data Generated by Autonomie Vehicle Modeling Software" SAE

Technical Paper (2018). 17. Navarro, Joerdening, Khalil, Asher. "Development of an Autonomous Vehicle Control Strategy Using a Single Camera and Deep Neural Networks" SAE Technical Paper (2018).18. Trinko, Went, Peyfuss, Asher, Sproul, Quinn, Bradley. “Green Zone Control of Hybrid Electric Vehicles" IEEE Transportation Electrification Conference (2018). Draft Accepted.19. Baral, Asher, Bradley. “Economic and Environmental Impacts of Semi-Truck Hybridization on Feedstock Supply Logistics for Near-term Cellulosic Biorefineries”. Anticipated April 2018 submission.

Additional Publications


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Predictive Energy Management in VehiclesTHE FUTURE OF FUEL ECONOMY


Methods: Perception Model• Test 1: Currently technology, repeated route

• Test 2: Current and near future technology, new route

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Perception

Artificial Neural Net. Planning

Compute Optimal Energy Control

Component Limitations

Misprediction


PerceptionArtificial

Neural Net.



Misprediction




*10. Baker, Asher, Bradley “Investigation of Vehicle Speed Prediction (for fuel economy).” SAE Technical Paper (2017).

ADAS Detection Data

Travel Time Data

ADAS Detection Data

Travel Time Data

Other Vehicle Data

Traffic Light Status Data



63

Test 2 Results: Currently and Near Future Technology, New Route


Perfect prediction to next traffic light

First drive cycle section

Second drive cycle section

Third drive cycle section

Trained Implemented



64

Trained

Implemented









65

TrainedPerfect full drive cycle prediction






Implemented

For new routes, fuel economy improvements can be implemented quickly




Methods: Perception Model Development• Generating results: Denver city Test 1

66

Set-up Application




67

Set-up Application




68

Set-up Application




69

Set-up Application

dr. thomas h. bradley, advisor zachary d. asher, phd candidate...solution: optimal control at every...

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