Distributed Unknown Input and State Estimation for Nonlinear Multi-Agent Systems with Applications to Battery Management

Changqing Liu; Kang Li; Xuan Liu; Youqing Wang

doi:10.17775/CSEEJPES.2020.00530

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Distributed Unknown Input and State Estimation for Nonlinear Multi-Agent Systems with Applications to Battery Management

Regular Papers | 更新时间：2026-10-15

- Distributed Unknown Input and State Estimation for Nonlinear Multi-Agent Systems with Applications to Battery Management
- CSEE Journal of Power and Energy Systems Vol. 10, Issue 6, Pages: 2470-2482(2024)
- 作者机构：
  
  1. College of Information Science and Technology, Beijing University of Chemical Technology,Beijing,China
  2. School of Electronic and Electrical Engineering, University of Leeds,UK
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.00530
  CLC：
- Published：01 November 2024
- 稿件说明：
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Changqing Liu, Kang Li, Xuan Liu, et al. Distributed Unknown Input and State Estimation for Nonlinear Multi-Agent Systems with Applications to Battery Management[J]. CSEE Journal of Power and Energy Systems, 2024, 10(6): 2470-2482.
DOI：

Changqing Liu, Kang Li, Xuan Liu, et al. Distributed Unknown Input and State Estimation for Nonlinear Multi-Agent Systems with Applications to Battery Management[J]. CSEE Journal of Power and Energy Systems, 2024, 10(6): 2470-2482. DOI： 10.17775/CSEEJPES.2020.00530.

摘要

Abstract

This paper proposes a novel filtering algorithm for simultaneous estimation of unknown inputs and states of a class of nonlinear discrete-time heterogeneous multi-agent systems. Based on the Taylor approximation of the nonlinear multi-agent system

a distributed semi-cooperative switch-mode filter is developed to achieve the minimum-variance unbiased (MVU) estimation of the unknown inputs and states. Compared with the conventional decentralized EKF-based unknown input filter

the proposed distributed filter has a more relaxed existence condition of the filter

which makes it more applicable in reality. This new type of filter is then successfully applied to the simultaneous estimation of state of charge (SOC) and temperature of a battery pack for battery management of electric vehicles and grid-tied energy storage systems.

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