Improved sliding mode control of mine filling slurry concentration based on preview information

Tang, Wenming; Gao, Haiyan; Xu, Tianpeng

doi:10.1080/21642583.2022.2123061

Cited by 1 publication

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…eq (17) After that, according to Equation (10) for U d and Equation (11) for the error e d , the dynamics of e d can be expressed as:…”

Section: Conventional Slipform Observermentioning

confidence: 99%

“…In addition, there are studies analyzing Kalman filter, traceless Kalman filter, and sliding mode observer‐based algorithms [9, 10]. The Kalman filter algorithm linearizes the non‐linear system function via a first‐order Taylor expansion, resulting in significant linearization errors and potential filter instability [11]. The traceless Kalman filter algorithm employs a traceless transform to manage the non‐linear transmission of the mean and covariance, which is advantageous in terms of estimation precision and robustness, but suffers from the issue of computational overload [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SOC estimation and fault identification strategy of energy storage battery PACK: Based on adaptive sliding mode observer

Xueyi,

Tinglong

2024

The Journal of Engineering

View full text Add to dashboard Cite

Accurate state of charge (SOC) estimation and fault identification and localization are crucial in the field of battery system management. This article proposes an innovative method based on sliding mode observation theory for SOC estimation and short‐circuit fault location. The core of this new method is the design of an adaptive sliding mode observer, which reduces jitter by introducing adaptive switching gain, establishes an internal loop of gain and error, and improves the performance of SOC estimation. In addition, recursive least squares method was used to identify the key parameters of the model. Secondly, based on obtaining the SOC of each battery cell in series with the energy storage PACK, the specificity of the faulty battery cell in SOC change trend is utilized to identify and locate the short‐circuit fault of the energy storage PACK. The simulation and test results show that the designed adaptive sliding mode observer can significantly improve the estimation accuracy of SOC and has better stability. Compared to the commonly used Kalman estimation and BP neural network estimation methods, the designed method has improved accuracy by 5.53% and 3.42%, respectively. In addition, based on the accurate identification of SOC, the short‐circuit fault diagnosis results of the battery PACK have a high accuracy, confirming the feasibility and effectiveness of the designed strategy that includes SOC estimation and short‐circuit fault identification and positioning, and has broad application prospects.

show abstract

“…eq (17) After that, according to Equation (10) for U d and Equation (11) for the error e d , the dynamics of e d can be expressed as:…”

Section: Conventional Slipform Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SOC estimation and fault identification strategy of energy storage battery PACK: Based on adaptive sliding mode observer

Xueyi,

Tinglong

2024

The Journal of Engineering

View full text Add to dashboard Cite

show abstract

Improved sliding mode control of mine filling slurry concentration based on preview information

Cited by 1 publication

References 23 publications

SOC estimation and fault identification strategy of energy storage battery PACK: Based on adaptive sliding mode observer

SOC estimation and fault identification strategy of energy storage battery PACK: Based on adaptive sliding mode observer

Contact Info

Product

Resources

About