[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Wysocki et al., 2019 - Google Patents

Heavy duty vehicle fuel consumption modeling using artificial neural networks

Wysocki et al., 2019

View PDF
Document ID
9503421864624833764
Author
Wysocki O
Deka L
Elizondo D
Publication year
Publication venue
2019 25th International Conference on Automation and Computing (ICAC)

External Links

Snippet

In this paper an artificial neural network (ANN) approach to modelling fuel consumption of heavy duty vehicles is presented. The proposed method uses easy accessible data collected via CAN bus of the truck. As a benchmark a conventional method, which is based …
Continue reading at mostwiedzy.pl (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR
    • G01M15/00—Testing of engines
    • G01M15/04—Testing of internal-combustion engines, e.g. diagnostic testing of piston engines
    • G01M15/042—Testing of internal-combustion engines, e.g. diagnostic testing of piston engines by monitoring a single specific parameter not covered by groups G01M15/06 - G01M15/12
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0808—Diagnosing performance data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/26—Investigating or analysing materials by specific methods not covered by the preceding groups oils; viscous liquids; paints; inks
    • G01N33/28—Oils, i.e. hydrocarbon liquids
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/80—Technologies aiming to reduce green house gasses emissions common to all road transportation technologies

Similar Documents

Publication Publication Date Title
Duarte et al. Analysis of fuel consumption and pollutant emissions of regulated and alternative driving cycles based on real-world measurements
CN103091112B (en) Method and device of car emission fault detection and diagnosis based on fuzzy reasoning and self-learning
Ntziachristos et al. In-use vs. type-approval fuel consumption of current passenger cars in Europe
Barth et al. Recent validation efforts for a comprehensive modal emissions model
Barth et al. Modal emissions modeling: A physical approach
Montazeri-Gh et al. Development of car drive cycle for simulation of emissions and fuel economy
CN106536315A (en) System for assessing and/or optimising the operating behaviour of a vehicle
López-Martínez et al. Modelling the fuel consumption and pollutant emissions of the urban bus fleet of the city of Madrid
US20200240346A1 (en) Calculation of exhaust emissions of a motor vehicle
Duer et al. Training of an artificial neural network in the diagnostic system of a technical object
Wysocki et al. Heavy duty vehicle fuel consumption modeling using artificial neural networks
Khakimov et al. Strategic assessment aspect of vehicles’ technical condition influence upon the ecosystem in regions
CN203083829U (en) Automobile fault diagnosis system based on tail gas discharge detection adopting simple driving mode
Rymaniak Comparison of the combustion engine operating parameters and the ecological indicators of an urban bus in dynamic type approval tests and in actual operating conditions
Della Ragione et al. The influence of road gradient in an integrated approach of real driving cycles and emissions factors model
Martin et al. Can UK passenger vehicles be designed to meet 2020 emissions targets? A novel methodology to forecast fuel consumption with uncertainty analysis
Li et al. Development of local emissions rate model for light-duty gasoline vehicles: Beijing field data and patterns of emissions rates in EPA simulator
CN111896264B (en) Method and device for generating test working condition of range extender engine and electronic equipment
Mądziel Modelling CO2 Emissions from Vehicles Fuelled with Compressed Natural Gas Based on On-Road and Chassis Dynamometer Tests
Abouel-Seoud et al. Real world evaluation and control of vehicle engine exhaust air pollution using adaptive neural fuzzy inference system
Hajmohammadi et al. Data-driven models for microscopic vehicle emissions
Sevkli et al. Comparison of data mining and mathematical models for estimating fuel consumption of passenger vehicles
Zheng et al. Development of a Practical Method to Estimate the Eco‐Level of Driver Performance
Abas et al. Simulation of fuel economy for Malaysian urban driving
Rosero et al. Developing Fuel Efficiency and CO2 Emission Maps of a Vehicle Engine Based on the On-Board Diagnostic (OBD) Approach