Online Adaptive Model Identification and State of Charge Estimation for Vehicle-Level Battery Packs
Artikel i vetenskaplig tidskrift, 2024

Accurate state of charge (SOC) estimation of traction batteries plays a crucial role in energy and safety management for electric vehicles. Existing studies focus primarily on cell battery SOC estimation. However, numerical instability and divergence problems might occur for a large-size lithium-ion battery pack consisting of many cells. This paper proposes a high-performance online model identification and SOC estimation method based on an adaptive square root unscented Kalman filter (ASRUKF) and an improved forgetting factor recursive least squares (IFFRLS) for vehicle-level traction battery packs. The model parameters are identified online through the IFFRLS, where the conventional method might encounter numerical stability problems. By updating the square root of the covariance matrix, the divergence problem in the traditional unscented Kalman filter is solved in the ASRUKF algorithm, where the positive semi-definiteness of the covariance matrix is guaranteed. Combined with the adaptive noise covariance matched filtering algorithm and real-time compensation of system error, the proposed method solves the problem of ever-degrading estimation accuracy in the presence of time-varying noise with unknown statistical characteristics. Using a 66.2-kWh vehicle battery pack, we experimentally verified that the proposed algorithm could achieve high estimation accuracy with guaranteed numerical stability. The maximum error of SOC estimation can be bounded by 1%, and the root-mean-square error is as low as 0.47% under real-world vehicle operating conditions.

Computational modeling

Adaptive square root unscented Kalman filter

Equivalent circuit model

State of charge

Forgetting factor recursive least squares

Transportation

Covariance matrices

State of charge

Estimation

Batteries

Integrated circuit modeling

Författare

Xiaohua Wu

Xihua University

Junhao Shu

Xihua University

Zhanfeng Fan

Chengdu University of Technology

Jianbo Xie

WM Motor Technology Group Company Limited

Yang Li

Chalmers, Elektroteknik, System- och reglerteknik

Jibin Yang

Xihua University

Zhongwei Deng

University of Electronic Science and Technology of China

IEEE Transactions on Transportation Electrification

2332-7782 (eISSN)

Vol. 10 1 596-607

Ämneskategorier

Reglerteknik

Signalbehandling

DOI

10.1109/TTE.2023.3274548

Mer information

Senast uppdaterat

2024-04-04