Diesel engine pollutant prediction and remote vehicle monitoring Part II: Remote vehicle smoke emission prediction - End-user application

E. Berry, P. Kukla, F. Gu, A. D. Ball

Research output: Contribution to journalReview article

1 Citation (Scopus)

Abstract

The ability to infer smoke emission levels from routinely monitored engine operating parameters, without the need for specialist or cumbersome instrumentation, would bring with it the possibility of mapping quantitative particulate emission for an engine in a vehicle operating under normal on-road conditions. This paper (Part II) follows on from its companion paper (Part I) in which a method of predicting vehicle emission characteristics using a neural network (NN) model was described. The NN was trained on various engine and vehicle parameter data and optimised to produce a model to predict diesel engine smoke emission levels with sufficient accuracy. This paper (Part II) describes how the NN model can be used to predict remotely vehicle emission characteristics in real-time using the General Packet Radio Service (GPRS) network with the Internet and mapping the emissions on a city map at street level. The real-time smoke emission prediction methodology is predominantly directed at fleet operators. The paper describes the equipment and process used and demonstrates what impact this can have on overall fleet emissions monitoring, identifying key benefits to the fleet operator. Important spin-off factors resulting from the system are also discussed.

Original languageEnglish
Pages (from-to)31-35
Number of pages5
JournalInternational Journal of COMADEM
Volume7
Issue number4
Publication statusPublished - 1 Oct 2004
Externally publishedYes

Fingerprint

Smoke
Diesel engines
Vehicle Emissions
Engines
Neural networks
Monitoring
Particulate emissions
Mathematical operators
Internet
Prediction
Diesel engine
End users

Cite this

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title = "Diesel engine pollutant prediction and remote vehicle monitoring Part II: Remote vehicle smoke emission prediction - End-user application",
abstract = "The ability to infer smoke emission levels from routinely monitored engine operating parameters, without the need for specialist or cumbersome instrumentation, would bring with it the possibility of mapping quantitative particulate emission for an engine in a vehicle operating under normal on-road conditions. This paper (Part II) follows on from its companion paper (Part I) in which a method of predicting vehicle emission characteristics using a neural network (NN) model was described. The NN was trained on various engine and vehicle parameter data and optimised to produce a model to predict diesel engine smoke emission levels with sufficient accuracy. This paper (Part II) describes how the NN model can be used to predict remotely vehicle emission characteristics in real-time using the General Packet Radio Service (GPRS) network with the Internet and mapping the emissions on a city map at street level. The real-time smoke emission prediction methodology is predominantly directed at fleet operators. The paper describes the equipment and process used and demonstrates what impact this can have on overall fleet emissions monitoring, identifying key benefits to the fleet operator. Important spin-off factors resulting from the system are also discussed.",
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T2 - Remote vehicle smoke emission prediction - End-user application

AU - Berry, E.

AU - Kukla, P.

AU - Gu, F.

AU - Ball, A. D.

PY - 2004/10/1

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N2 - The ability to infer smoke emission levels from routinely monitored engine operating parameters, without the need for specialist or cumbersome instrumentation, would bring with it the possibility of mapping quantitative particulate emission for an engine in a vehicle operating under normal on-road conditions. This paper (Part II) follows on from its companion paper (Part I) in which a method of predicting vehicle emission characteristics using a neural network (NN) model was described. The NN was trained on various engine and vehicle parameter data and optimised to produce a model to predict diesel engine smoke emission levels with sufficient accuracy. This paper (Part II) describes how the NN model can be used to predict remotely vehicle emission characteristics in real-time using the General Packet Radio Service (GPRS) network with the Internet and mapping the emissions on a city map at street level. The real-time smoke emission prediction methodology is predominantly directed at fleet operators. The paper describes the equipment and process used and demonstrates what impact this can have on overall fleet emissions monitoring, identifying key benefits to the fleet operator. Important spin-off factors resulting from the system are also discussed.

AB - The ability to infer smoke emission levels from routinely monitored engine operating parameters, without the need for specialist or cumbersome instrumentation, would bring with it the possibility of mapping quantitative particulate emission for an engine in a vehicle operating under normal on-road conditions. This paper (Part II) follows on from its companion paper (Part I) in which a method of predicting vehicle emission characteristics using a neural network (NN) model was described. The NN was trained on various engine and vehicle parameter data and optimised to produce a model to predict diesel engine smoke emission levels with sufficient accuracy. This paper (Part II) describes how the NN model can be used to predict remotely vehicle emission characteristics in real-time using the General Packet Radio Service (GPRS) network with the Internet and mapping the emissions on a city map at street level. The real-time smoke emission prediction methodology is predominantly directed at fleet operators. The paper describes the equipment and process used and demonstrates what impact this can have on overall fleet emissions monitoring, identifying key benefits to the fleet operator. Important spin-off factors resulting from the system are also discussed.

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