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.

Monitoring Electric Vehicles on The Go / Aguiari, Davide; Chou, Ka Seng; Tse, Rita; Pau, Giovanni. - ELETTRONICO. - (2022), pp. 885-888. (Intervento presentato al convegno 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) tenutosi a Las Vegas, NV, USA nel 8-11 Jan. 2022) [10.1109/CCNC49033.2022.9700713].

Monitoring Electric Vehicles on The Go

Aguiari, Davide;Pau, Giovanni
2022

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.
2022
2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)
885
888
Monitoring Electric Vehicles on The Go / Aguiari, Davide; Chou, Ka Seng; Tse, Rita; Pau, Giovanni. - ELETTRONICO. - (2022), pp. 885-888. (Intervento presentato al convegno 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) tenutosi a Las Vegas, NV, USA nel 8-11 Jan. 2022) [10.1109/CCNC49033.2022.9700713].
Aguiari, Davide; Chou, Ka Seng; Tse, Rita; Pau, Giovanni
File in questo prodotto:
File Dimensione Formato  
Aguiari-Monitoring_Electric_Vehicles_on_The_Go.pdf

accesso aperto

Tipo: Postprint
Licenza: Licenza per accesso libero gratuito
Dimensione 1.76 MB
Formato Adobe PDF
1.76 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/875565
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? ND
social impact