Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts

Shao, Yuehjen E.; Lin, Shun‐Tian

doi:10.3390/math7100959

Cited by 24 publications

(23 citation statements)

References 42 publications

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“…TDNN overcomes this issue by having a past memory with tapped delay lines. TDNN exhibits dynamic memory and employs multiple layers under a necessary interconnection between units which confirms the capability to address non-linear and complex decisions [27]. TDNN has intelligent self-learning skills and robust computation abilities, and hence it is suitable for SOC estimation.…”

Section: Soc Modeling With Time Delay Network Algorithmmentioning

confidence: 82%

State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach

et al. 2020

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The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences.

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Section: Soc Modeling With Time Delay Network Algorithmmentioning

confidence: 82%

State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach

et al. 2020

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“…In this study, RStudio [41] was used to generate the MIMO process data vectors. Additionally, because the previous studies used the ratio of 7:3 for training and testing data vectors [19,20], we followed their suggestion. Therefore, a ratio of 7:3 for training and testing data vectors is used for all cases.…”

Section: Mccps For An Mimo Processmentioning

confidence: 99%

“…While most studies have focused on the use of ANN and SVM classifiers for CCP recognition, extreme learning machine (ELM) techniques have been used to identify MCCPs for a process [17,18]. The multivariate adaptive regression splines (MARS) scheme was also applied to the recognition of MCCPs for an SPC and engineering process control (EPC) process [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…In fact, correlation broadly exists in continuous and chemical processes [4,24,25]. The integration of EPC and SPC mechanisms is commonly used to overcome this independence difficulty for SPC applications [18,20,26,27]. Although the approach is able to overcome the correlation problems for SPC applications, the use of EPC may conceal the effects of underlying process disturbances.…”

Section: Introductionmentioning

confidence: 99%

“…However, the study did not utilize EPC actions for CCP recognition. The authors also reported that the first-order integrated moving average (IMA(1, 1)) component was used as the noise in the SPC-EPC system [19,20]. Although they considered EPC actions in the CCP recognition scheme, they only considered the univariate system of an SPC-EPC.…”

Section: Introductionmentioning

confidence: 99%

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Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

Shao

2020

Mathematics

Self Cite

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A statistical process control (SPC) chart is one of the most important techniques for monitoring a process. Typically, a certain root cause or a disturbance in a process would result in the presence of a systematic control chart pattern (CCP). Consequently, the effective recognition of CCPs has received considerable attention in recent years for their potential use in improving process quality. However, most studies have focused on the recognition of CCPs for SPC applications alone. Specifically, even though numerous studies have addressed the increased use of the SPC and engineering process control (EPC) mechanisms, very little research has discussed the recognition of CCPs for multiple-input multiple-output (MIMO) systems. It is much more difficult to recognize the CCPs of an MIMO system since two or more disturbances are simultaneously involved in the process. The purpose of this study is thus to propose several machine learning (ML) classifiers to overcome the difficulties in recognizing CCPs in MIMO systems. Because of their efficient and fast algorithms and effective classification performance, the considered ML classifiers include an artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and multivariate adaptive regression splines (MARS). Furthermore, one problem may arise due to the existence of embedded mixture CCPs (MCCPs) in MIMO systems. In contrast to using typical process outputs alone in a classifier, this study employs both process outputs and EPC compensation to ensure the effectiveness of CCP recognition. Experimental results reveal that the proposed classifiers are able to effectively recognize MCCPs for MIMO systems.

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A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Cui

Wang

et al. 2021

Intl J of Energy Research

204

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Implementing carbon neutrality and emission peak policies requires a highlevel electric vehicle field. Lithium-ion batteries have been considered an essential component of electric vehicle power batteries. Effective state of charge (SOC) estimation for lithium-ion batteries is a critical problem that needs to be addressed at present. With the feature extraction and fitting capability, the neural network can achieve accurate SOC estimation without considering the internal electrochemical state of the battery. This article overviews the definition of SOC and the relationship with battery aging state. Then, by examining recent literature on estimating the SOC of Lithium-ion batteries using neural network methods, the methods are classified into three categories: feed-forward neural network method, deep learning method, and hybrid method. The progress of neural network methods in SOC estimation applications is systematically reviewed, including principles, advantages, disadvantages, current status, and estimation errors. Possible recommendations for next-generation intelligent battery management systems and SOC estimation are also presented. This review's highlighted insights will inspire researchers in the battery field and point the way to developing electric vehicles.

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Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts

Cited by 24 publications

References 42 publications

State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach

State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach

Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

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