Enhancement of Ev On-Board Diagnostics System into Preliminary Operation Using Estimation Theory

Chun Liang Chen; Chan Wei Hsu

doi:10.4028/www.scientific.net/AMM.391.592

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 391Enhancement of Ev On-Board Diagnostics System into...

Enhancement of Ev On-Board Diagnostics System into Preliminary Operation Using Estimation Theory

Abstract:

This paper presents a recent study to construct an EV preliminary research platform using hybrid communications. Vehicular network, which integrated battery and powertrain information, is applied for performance display and on-line diagnostics. Mobile communication offers one-way data collection for remote surveillance when data transmission is continuously linked with server through mobile communication. Data acquisition is handled by on-board unit via EV CAN network. However, on-line data size, e.g. state of health, is so huge that it isnt easily transmitted when hundreds of vehicles are simultaneously connected for data logging into server. Taking SOH as example, the continuity of curve variation is a challenge in order to be effectively provided for remote health estimation. Hence, estimation theory is applied and embedded into hardware implementation before data reporting. Cubic spine and linear Kalman filter are applied to curve fitting and estimation for data size reduction. The proposed system provides not only information display but also on-board diagnosis; furthermore, the processed data can be devoted to EV preliminary operation in Taiwan.

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Periodical:

Applied Mechanics and Materials (Volume 391)

Pages:

592-595

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.391.592

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Online since:

September 2013

Authors:

Chun Liang Chen, Chan Wei Hsu

Keywords:

CAN, Cubic Spine, Estimation, Kalman Filter (KF)

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