The Load Profile Monitoring System Based on Cloud Computing in Hybrid Electric Vehicle  

Oleh:

Kim, Ki Tae ; An, Young Gi ; Cho, Hwee ; Wang, Dawei ; Park, Keon Young ; Kim, Jun Soo ; Jang, Joong Soon ; Kim, Chong Man

Jenis: Article from Proceeding
Dalam koleksi: 12th ANQ Congress in Singapore, 5-8 Agustus 2014, page 1-8.
Topik: Prognostics; Load Profile Monitoring; Cloud Computing; Big Data; HEV
Fulltext: QP1-2.8-P0174.pdf (1.38MB)

Isi artikel

In Prognostics and Health Monitoring (PHM) applications, the monitoring of life-cycle load data on system plays essential role. However, the load cycle monitoring system usually suffers from the lack of accuracy, since an estimated load profile limited by the number of vehicle cannot represent loads during the system life cycle. The limited load profile causes an inaccurate residual life estimation of battery to an individual system’s life cycle. In order to improve the accuracy of the load profile modeling, it is required to collect and analyze the numerous operation, load, environment profiles in individual vehicles. To resolve these problems, this paper proposes a load profile monitoring system based on cloud computing in battery of hybrid electric vehicle (HEV) and electric vehicle (EV). The proposed system comprises two ideas: i) to monitor a lot of vehicles and their operation parameters, the system uses a distributed data processing architecture for a lot of various load profile parameters and ii) the system proposes a method of displaying and summarizing the load profile for PHM in automotive applications. An implemented system provides large scale data processing from numerous vehicles and a load profile model for electric motors in hybrid electric vehicle

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