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摘要
En 中文
State-of-charge (SOC) estimation plays a crucial role in battery management, and filtering methods based on equivalent circuit models (ECMs) are popular. In ECMs, the measured variable is typically the terminal voltage, which has a nonlinear relationship with SOC but linear relationships with other state variables. Therefore, the measurement model has highly nonlinear and linear terms simultaneously. To address the system’s nonlinearity, many classical nonlinear filtering methods that estimate the state of nonlinear dynamic systems from noisy measurements have been applied. However, most of these methods are linear-structured filters based on the linear minimum mean-square-error (LMMSE) criterion, which does not match well with nonlinear problems. Recently proposed uncorrelated conversion (UC) filtering is a nonlinear-structured filter that breaks through the limitations of the linear framework, making better use of measurement information and improving estimation performance for highly nonlinear systems. However, the presence of linear terms in the measurement model means that directly applying UC does not significantly enhance performance. To address this issue, this article proposes a variational Bayesian filtering method for battery systems, which iteratively processes linear and nonlinear terms. For the linear term, the optimal posterior estimate is obtained, while for the nonlinear terms, UC is applied to further improve information utilization efficiency. Evaluations based on public datasets and experiments conducted in this article show that the proposed method reduces SOC absolute estimation error from 1.2% with existing methods to 0.09%, representing a significant performance improvement.
Keyword:
Nonlinear filtering
state-of-charge (SOC) estimation
uncorrelated conversion (UC)
variational Bayesian estimation
期刊
I
IF:
8.3
论文数:
2.9K
被引数:
1.6W
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