Monitoring Electric Vehicles on The Go

Davide Aguiari; Ka Seng Chou; Rita Tse; Giovanni Pau

doi:10.1109/CCNC49033.2022.9700713

Monitoring Electric Vehicles on The Go

Davide Aguiari
, Ka Seng Chou
, Rita Tse
, Giovanni Pau

Faculty of Applied Sciences

Research output: Contribution to journal › Conference article › peer-review

15 Citations (Scopus)

Abstract

Electric vehicles (EV) feature detailed monitoring and control over the CAN bus. Some of this data is made available to users on the On-Board Diagnostic version II (OBDII) bus thus providing an opportunity for large scale high-frequency data collection. This paper introduces a connected monitoring system for OBDII equipped vehicles. The system comprises a low cost hardware design and monitoring algorithms designed to optimize the number of variables collected and their collection frequency. The algorithm aims at collecting a high quantity of Battery Management System (BMS) data in electric vehicles together with power-usage data to enable short and long term estimation for battery state of health (SOH) and state of charge (SOC). The proposed system has been implemented and tested on a Nissan Leaf and lead to the acquisition of 1.7 million records over 120 hours of driving.

Original language	English
Pages (from-to)	885-888
Number of pages	4
Journal	Proceedings - IEEE Consumer Communications and Networking Conference, CCNC
DOIs	https://doi.org/10.1109/CCNC49033.2022.9700713
Publication status	Published - 2022
Event	19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 - Virtual, Online, United States Duration: 8 Jan 2022 → 11 Jan 2022

Keywords

4G
Electric Vehicles
OBDII
SOC
SOH

Access to Document

10.1109/CCNC49033.2022.9700713

Cite this

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title = "Monitoring Electric Vehicles on The Go",

abstract = "Electric vehicles (EV) feature detailed monitoring and control over the CAN bus. Some of this data is made available to users on the On-Board Diagnostic version II (OBDII) bus thus providing an opportunity for large scale high-frequency data collection. This paper introduces a connected monitoring system for OBDII equipped vehicles. The system comprises a low cost hardware design and monitoring algorithms designed to optimize the number of variables collected and their collection frequency. The algorithm aims at collecting a high quantity of Battery Management System (BMS) data in electric vehicles together with power-usage data to enable short and long term estimation for battery state of health (SOH) and state of charge (SOC). The proposed system has been implemented and tested on a Nissan Leaf and lead to the acquisition of 1.7 million records over 120 hours of driving.",

keywords = "4G, Electric Vehicles, OBDII, SOC, SOH",

author = "Davide Aguiari and Chou, \{Ka Seng\} and Rita Tse and Giovanni Pau",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 ; Conference date: 08-01-2022 Through 11-01-2022",

year = "2022",

doi = "10.1109/CCNC49033.2022.9700713",

language = "English",

pages = "885--888",

journal = "Proceedings - IEEE Consumer Communications and Networking Conference, CCNC",

issn = "2331-9860",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - Monitoring Electric Vehicles on The Go

AU - Aguiari, Davide

AU - Chou, Ka Seng

AU - Tse, Rita

AU - Pau, Giovanni

PY - 2022

Y1 - 2022

N2 - Electric vehicles (EV) feature detailed monitoring and control over the CAN bus. Some of this data is made available to users on the On-Board Diagnostic version II (OBDII) bus thus providing an opportunity for large scale high-frequency data collection. This paper introduces a connected monitoring system for OBDII equipped vehicles. The system comprises a low cost hardware design and monitoring algorithms designed to optimize the number of variables collected and their collection frequency. The algorithm aims at collecting a high quantity of Battery Management System (BMS) data in electric vehicles together with power-usage data to enable short and long term estimation for battery state of health (SOH) and state of charge (SOC). The proposed system has been implemented and tested on a Nissan Leaf and lead to the acquisition of 1.7 million records over 120 hours of driving.

AB - Electric vehicles (EV) feature detailed monitoring and control over the CAN bus. Some of this data is made available to users on the On-Board Diagnostic version II (OBDII) bus thus providing an opportunity for large scale high-frequency data collection. This paper introduces a connected monitoring system for OBDII equipped vehicles. The system comprises a low cost hardware design and monitoring algorithms designed to optimize the number of variables collected and their collection frequency. The algorithm aims at collecting a high quantity of Battery Management System (BMS) data in electric vehicles together with power-usage data to enable short and long term estimation for battery state of health (SOH) and state of charge (SOC). The proposed system has been implemented and tested on a Nissan Leaf and lead to the acquisition of 1.7 million records over 120 hours of driving.

KW - 4G

KW - Electric Vehicles

KW - OBDII

KW - SOC

KW - SOH

UR - https://www.scopus.com/pages/publications/85135734345

U2 - 10.1109/CCNC49033.2022.9700713

DO - 10.1109/CCNC49033.2022.9700713

M3 - Conference article

AN - SCOPUS:85135734345

SN - 2331-9860

SP - 885

EP - 888

JO - Proceedings - IEEE Consumer Communications and Networking Conference, CCNC

JF - Proceedings - IEEE Consumer Communications and Networking Conference, CCNC

T2 - 19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

Y2 - 8 January 2022 through 11 January 2022

ER -

Monitoring Electric Vehicles on The Go

Abstract

Keywords

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