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Cluster Computing

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13-02-2018

SOC estimation optimization method based on parameter modified particle Kalman Filter algorithm

Authors: Shouzhen Zhang, Changjun Xie, Chunnian Zeng, Shuhai Quan

Published in: Cluster Computing | Special Issue 3/2019

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Abstract

Traditional Kalman Filter algorithm requires the system noise to be Gaussian distribution, but the power battery operating condition generally can not meet the requirement due to complexity and disturbance by the environment. However, the Particle Filter algorithm can adapt to various forms of system noise. In this work, the calculation process of the standard Particle Filter algorithm is improved based on the engineering characteristics of SOC estimation. In the calculation process, the key parameters including the total number of particles and the effective particle threshold are optimized and verified under FTP75 and NEDC conditions. The systematic error under different conditions is evaluated, based on the vehicle platform computing capacity, the proposed total number of particles is 1000, the effective particle threshold is 0.01. In this case, the SOC estimation accuracy can reach 1–2%, meeting the practical requirements.

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Title: SOC estimation optimization method based on parameter modified particle Kalman Filter algorithm
Authors: Shouzhen Zhang
Changjun Xie
Chunnian Zeng
Shuhai Quan
Publication date: 13-02-2018
Publisher: Springer US
Published in: Cluster Computing / Issue Special Issue 3/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-1784-0

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