Abstract detail

163 / 2021-03-31 16:25:49
Dynamic force reconstruction of a battery pack based on a Bayesian algorithm
Force reconstruction,Bayesian algorithm,Uncertain structure,Error propagation
Special Sessions > Vehicle noise, vibration and harshness: Challenge and Solution Ⅰ
Abstract Accepted
Ruixue Liu / Tsinghua University
Zhichao Hou / Tsinghua University
Kun Qian / Tsinghua University
Peibao Wu / Tsinghua University
Dynamic input forces of the power battery pack on an EV are usually required for structural integrity and reliability. Force reconstruction based on accessible acceleration signals is of great significance, since forces are often difficult, if is not impractical, to be directly measured in engineering. Problems occurs to force reconstruction, such as ill-posedness, uncertainties in structure parameters or measured responses, especially with a complex structure. In this paper, Bayesian algorithm is adopted to reconstruct in time history the multiply input forces of the power battery pack on an EV, whose vibrational modes are complex ones. The ill-posed problem during matrix inverse computation is addressed by an inherent regularization. Model uncertainties and introduced hyper-parameters are determined by Monte Carlo Markov Chain process. Error propagation induced by uncertain structural parameters and measurement noise is analyzed according to Bayesian inference. The error bounds and distribution of unknown input forces are finally obtained by delivering corresponding posterior probability density functions. The reconstructed forces have good consistency with the directly measured axial forces based on customized bolt force sensors.

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Important Dates

Abstract Submission Deadline:

 31st March 2021 15th April 2021

Extended Deadline: 1st Aug. 2022

 

Abstract Acceptance:

30th April  2021 Rollover

 

Full Paper Submission Deadline:

30th June 2021  14th July 2021

Extended Deadline: 15th Aug. 2022 

 

Notification of Acceptance:

15th August 2021 1st Sept. 2021

1st Sept. 2022

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