High-Fidelity Modeling of Light-Duty Vehicle Emissions and ......High-Fidelity Modeling of...

High-Fidelity Modeling of Light-Duty Vehicle Emissions and

Fuel Economy Using Deep Neural Networks

Farhang Motallebiaraghi, Aaron Rabinowitz, Jacob Holden, Alvis Fong,

Shantanu Jathar, Thomas Bradley, and Zachary D. Asher

Western Michigan University, Colorado State University, NREL

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SAE WCX Digital Summit Paper # 2021-01-0181 2

Acknowledgements

This material is based upon work supported by the

U.S. Department of Energy’s Office of Energy

Efficiency and Renewable Energy (EERE). The

specific organization overseeing this report is the

Vehicle Technologies Office under award number

DE-EE0008468.

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Agenda

Introduction

Methodology

Results

Conclusion

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ConclusionsResultsMethodologyIntroduction ConclusionsResultsMethodologyIntroduction

Transportation Sector: Major greenhouse gas emission source

Transportation Sector…

• accounts for ~ ⅓ of all energy used in the U.S.

• is the largest source of CO2 production.

From Transportation Energy Data Book Edition 38: https://tedb.ornl.gov and https://www.epa.gov/mobile-source-pollution/research-health-effects-exposure-risk-mobile-source-pollution

● Severe climate change.

● ~15,000 premature deaths every year.

https://tedb.ornl.gov

https://www.epa.gov/mobile-source-pollution/research-health-effects-exposure-risk-mobile-source-pollution

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ICE Light Duty Vehicles: Fleet dominators

https://www.epa.gov/greenvehicles/fast-facts-transportation-greenhouse-gas-emissions https://www.eia.gov/outlooks/aeo/aeo2019

• Future reduction in vehicular emissions depends on improvements in energy efficiency

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Gap: High-fidelity emission and fuel economy modeling

Existing emission models:

• MOtor Vehicle Emission Simulator (MOVES)

and EMission FACtor (EMFAC) may

underestimate on-road emissions.

• Previously it is shown that shallow Artificial

Neural Networks (ANNs) have the potential to

predict on-road emissions and better model the

fuel economy.

www.acscm.com/projects/4wd-chassis-dyno-emissions-test-cell-upgrade

a- Annenberg et al., Impacts and mitigation of excess diesel-related NOx emissions in 11 major vehicle markets,2017

Chenna, Shiva Tarun. n.d. “ARTIFICIAL NEURAL NETWORKS FOR FUEL CONSUMPTION AND EMISSIONS MODELING IN LIGHT DUTY VEHICLES.” Colorado State University. Libraries. https://mountainscholar.org/handle/10217/197403.

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Focus: Deep Neural Networks (DNNs) modeling

• Modeling and evaluation of Deep Neural Networks (DNNs) and history-sensitive (recurrent) DNNs for both fuel consumption and tailpipe emissions from a targeted light-duty ICE vehicle.

• In this study we answered how different combination sets of input data can affects on prediction accuracy using different models.

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PEMS (Portable Emission

Measurement System)

Drive Cycle Development and on-road

Data Collection

● NOx (ppmv, g/s)

● CO (%, g/s)

● CO2(%, g/s)

● PM10 (mg/m3, g/s)

● HC (ppmv, g/s)

PEMS AXIONR/S+ Volkswagen Jetta, 2003

CAN

● Velocity (mph)

● Engine speed (RPM)

● Manifold air pressure (kPa)

● Intake air temperature (F)

● Exhaust fuel rate (g/s)

● Intake fuel rate (g/s)

● Fuel consumption (g/s)

GPS

● Latitude (deg)

● Longitude (deg)

● Altitude (deg)

Drive cycle used for training

● Data was collected from 5 drive cycles in Fort Collins in 2018.

● We selected 3 drive cycles for training, 1 for validation and 1 for

testing the models

Drive Cycle Development and on-road Data Collection

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Data Classification

Data Classification

Which combination set of these predictors is more accurate?

● Predictors were categorized into two main categories:

○ Externally Observable Variables (EOV)

○ Internally Observable Variables (IOV)

Predictor Name Predictor Symbol Variable Type

Vehicle Velocity V EOV

Vehicle Acceleration ACC EOV

Time since Start time EOV

Vehicle Altitude Alt EOV

Vehicle Specific Power VSP EOV

Engine Velocity RPM IOV

Intake Air Temperature IAT IOV

Manifold Air Pressure MAP IOV

Fuel Consumption FC IOV

Frey, H.C., Zhang, K., and Rouphail, N.M., “Vehicle-specific emissions modeling based

upon on-road measurements,” Environ. Sci. Technol. 44(9):3594–3600, 2010.

http://paperpile.com/b/QGmBpq/7KbQc

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Data Classification

Data Classification

To select the best combination set sufficient number of iterations were

tested using predictors.

● 10 classes were defined for emission modeling.

● 9 classes were defined for fuel consumption modeling.

Class

Input combination sets

IOV needed

Emission prediction Fuel consumption prediction

C1 V, ACC, time V, ACC, time no

C2 V, ACC, time, VSP V, ACC, time, VSP no

C3 V, ACC, time, Alt V, ACC, time, Alt no

C4 V, ACC, time, VSP, Alt V, ACC, time, VSP, Alt no

C5 V, ACC, time, VSP, Alt, RPM V, ACC, time, VSP, Alt, RPM yes

C6 V, ACC, time, VSP, Alt, RPM, IAT V, ACC, time, VSP, Alt, RPM, IAT yes

C7 V, ACC, time, VSP, Alt, RPM, MAP V, ACC, time, VSP, Alt, RPM, MAP yes

C8 V, ACC, time, VSP, Alt, MAP, IAT V, ACC, time, VSP, Alt, MAP, IAT yes

C9 V, ACC, time, VSP, Alt, RPM, IAT,

MAP

V, ACC, time, VSP, Alt, RPM, IAT,

MAP

yes

C10 V, ACC, time, VSP, Alt, RPM, IAT,

MAP, FC--- yes

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Emissions and Fuel Consumption Modeling

CAN

● Velocity (mph)

● Engine speed (RPM)

● Manifold air pressure (kPa)

● Intake air temperature (F)

● Exhaust fuel rate (g/s)

● Intake fuel rate (g/s)

● Fuel consumption (g/s)

GPS

● Latitude (deg)

● Longitude (deg)

● Altitude (deg)

● Data collected from 5 drive cycles in Fort Collins in 2018.

● 3 for training, 1 for validation and 1 for testing the

models

Emission and fuel consumption

models

Non-Machine

learning

Machine Learning

(ML)

Artificial Neural

Networks (ANN)

Multiple Linear

Regression (MLR)

Deep FFNN Deep CNN

Motor Vehicle Emission

Simulator

Deep LSTM

Artificial Intelligence

Machine Learning

CNNLSTM

FFNN

Artificial Neural Networks

RNN

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Deep FFNN (Feed Forward Neural

Network)

We selected a three-hidden layer FFNN and “sigmoid” activation

function.

General structure of a Feed Forward Neural Network (FFNN)

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Deep LSTM (Long Short-term Memory)

General structure of an LSTM

We selected a three-hidden layer LSTM and “ReLu” activation function.


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Deep CNN (Convolutional Neural

Network)

General structure of an CNN

We selected a two-hidden layer CNN and “ReLu” activation function.


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MLR (multivariable linear regression)

General formulation of an MLRThis model changes the coefficients in the fitting process to

reduce the error indicated in the assessment metrics section.


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• For evaluation of the model’s accuracy, we used Mean Absolute

Error (MAE) criteria parameter:

Assessment Metric

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● Adding IOV predictors increases the accuracy

of the prediction.

● Adding fuel consumption, engine speed and

manifold air Pressure affects the most on the

emission prediction accuracy (C10)

EOV

EOV &IOV

Behavior of adding predictors on emission prediction accuracy

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Behavior of adding predictors on fuel consumption prediction accuracy

• The effect of adding IOV parameters can be

seen the most in class 7 and class 9.

• class 9 (C9) was selected as the primary input

class for fuel consumption prediction.

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● CO2 is predicted the best based on visual

analysis of the traces.

● For better CO prediction more drive cycle

data is required, errors on predictions

happened mostly during cold start phase

(first 500 seconds)

Emissions comparison results using LSTM

Emission comparison

MAE (%)

CO2 NOx HC CO PM10

LSTM 0.04 0.03 0.09 0.06 0.02

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● LSTM was able to predict HC with very

low error after 500 seconds of drive cycle

and NOx for almost complete drive cycle.

● LSTM was not able to have a good fit on

PM10 on the spikes, though the MAE is

the minimum.

Emissions comparison results using LSTM

Emission comparison

MAE (%)

CO2 NOx HC CO PM10

LSTM 0.06 0.03 0.09 0.04 0.02

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MAE (%)

Input classes LSTM CNN FFNN MLR

C9 1.21 11.2 6.02 199.5

Comparison of measured fuel consumption rates to all

models fuel consumption estimations (test) using C9.

Fuel Consumption comparison results using LSTM

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Comparison of measured emission rates to MOVES emission rate estimations

Emission

comparison

Average emission rate (g/mi)

CO2 NOx HC CO PM10

Test dataset 626 1.82 0.34 0.49 0.041

MOVES 532 3.27 0.79 5.23 0.017

LSTM 625.72 1.82 0.34 0.49 0.04

CNN 619.99 1.79 0.33 0.48 0.04

FFNN 555.58 0.91 0.26 0.33 0.03

MLR 1297.39 2.80 0.55 0.10 0.05

Relative Error (RE) rate (%)

MOVES 15 26.7 132.3 967 58.5

LSTM 0.05 0.02 0.09 0.05 0.03

CNN 0.96 1.83 1.57 1.06 0.99

FFNN 11.25 50.24 23.15 32.11 30.20

MLR 307.25 254.1 262.2 120.14 217.15

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• The results show that the deep neural network’s performance consistently

improves when given datasets with more variables (EOV and IOV).

• manifold absolute pressure (MAP), engine speed (RPM), and fuel

consumption are the most beneficial parameters categorized as IOV for

emission prediction.

• LSTM had the best performances for both emission and fuel

consumption prediction. This is because LSTM account for both delayed

effects and recurrent effects for more accurate predictions.

• This model, if developed for a vehicle and integrated within the vehicle

controller, may have value for real time vehicle/engine controls

optimization thereby producing real-time reductions in fuel consumption

and emissions.

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Speaker Information

Thank you• Farhang Motallebiaraghi

• Western Michigan University

• [email protected]

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Image Resources

• https://www.hrw.org/news/2020/04/02/us-car-emissions-rollback-endangers-peoples-health• https://www.vitalstrategies.org/breathing-smoke-city-air-pollution-lung-cancer

• https://www.epa.gov/greenvehicles/fast-facts-transportation-greenhouse-gas-emissions

• https://www.sciencedirect.com/science/article/pii/S0301421520302627?via%3Dihub

• https://www.eia.gov/outlooks/aeo/

https://www.hrw.org/news/2020/04/02/us-car-emissions-rollback-endangers-peoples-health

https://www.vitalstrategies.org/breathing-smoke-city-air-pollution-lung-cancer/

https://www.epa.gov/greenvehicles/fast-facts-transportation-greenhouse-gas-emissions

https://www.sciencedirect.com/science/article/pii/S0301421520302627?via%3Dihub

https://www.eia.gov/outlooks/aeo/

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