Extended Kalman and center difference Kalman comparative analysis

In view of the significant nonlinear characteristics of lithium batteries and the complexity of the battery use environment, two algorithms were introduced to estimate lithium batteries, the central difference Kalman filter and the extended Kalman filter. Under the same conditions, the central difference Kalman filter algorithm is used to estimate the battery state of charge (SOC), and the results are compared with the results estimated by the extended Kalman filter algorithm. Compared with the extended Kalman filter algorithm, the SOC estimation method of the center-difference Kalman filter shows stronger anti-interference ability, faster convergence speed and higher estimation accuracy, especially suitable for the estimation of battery SOC.

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Published
2025-03-07
DOI
https://doi.org/10.1117/12.3059936
Primary Topic
Target Tracking and Data Fusion in Sensor Networks
Type
article
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article

Extended Kalman and center difference Kalman comparative analysis

Hairong Zou, Jingdong Li
Target Tracking and Data Fusion in Sensor Networks
article

Extended Kalman and center difference Kalman comparative analysis

Hairong Zou, Jingdong Li
article en

Abstract

In view of the significant nonlinear characteristics of lithium batteries and the complexity of the battery use environment, two algorithms were introduced to estimate lithium batteries, the central difference Kalman filter and the extended Kalman filter. Under the same conditions, the central difference Kalman filter algorithm is used to estimate the battery state of charge (SOC), and the results are compared with the results estimated by the extended Kalman filter algorithm. Compared with the extended Kalman filter algorithm, the SOC estimation method of the center-difference Kalman filter shows stronger anti-interference ability, faster convergence speed and higher estimation accuracy, especially suitable for the estimation of battery SOC.

Shanghai Dianji University (CN)
Openalex Percentile: Top 98%
Target Tracking and Data Fusion in Sensor Networks
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