Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health

Piao, Changhao; Li, Zuncheng; Lu, Sheng; Jin, Zhekui; Cho, Chongdu

doi:10.6113/jpe.2016.16.1.217

Cited by 25 publications

(11 citation statements)

References 18 publications

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“…Using stochastic process modeling Recently, there are many studies using the classical machine learning methods (known as shallow learning) to deal with SoH estimation. Some examples of shallow learning approaches include Bayesian predictive model proposed by Hu et al [28], Hidden Markov model by Piao et al [29], Liu et al [30] used Gaussian process regression and Relevancevector machine [31]. The internal computation of those shallow learning are still based on statistics, but their fine-tuning processes are performed step-wise until the final convergence is reached.…”

Section: A Statistical and Shallow Learning Approachesmentioning

confidence: 99%

ARNS: A Data-Driven Approach for SoH Estimation of Lithium-Ion Battery Using Nested Sequence Models With Considering Relaxation Effect

Pham

Dang

et al. 2022

IEEE Access

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In recent years, lithium-ion batteries (LIB) have been used widely in portable electronic devices because of their advantages of durability, stability, high-capacity, low-cost, light-weight and smallscale. It makes LIB also deployed in various complex systems, in which efficient prediction of battery data, especially state-of-health (SoH), becomes crucial to ensure that the systems work stably without risks of power interruptions. With the recent improvement of Artificial Intelligence (AI) technologies, many works have been reported using deep learning (DL) models to investigate this problem, since such models can potentially increase their performance with more training data. This is also our direction in this research, which introduces a novel data-driven approach so-called Autoregression Nested Sequence (ARNS). On one hand, we come up with a nested sequence model to efficiently aggregate channel-wise and cycle-wise information, both of which are closely related to the operations of LIB. On the other hand, we incorporate relaxation effects into the model operations to handle peak prediction. To the best of our knowledge, ARNS is the first sophisticated deep learning model that combines all those features into a whole predictive system. The experimental results obtained using the NASA and CALCE datasets confirm significant improvement of ARNS, especially when dealing with peak periods in different SoH of multiple cycles.

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Section: A Statistical and Shallow Learning Approachesmentioning

confidence: 99%

ARNS: A Data-Driven Approach for SoH Estimation of Lithium-Ion Battery Using Nested Sequence Models With Considering Relaxation Effect

Pham

Dang

et al. 2022

IEEE Access

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“…Hu et al [19] introduced sparse Bayesian predictive modeling with a sample entropy of short voltage sequences to improve the accuracy of the estimation. Piao et al [20] proposed a hidden Markov model to estimate and analyze the SOH of a battery. Liu et al [21] used Gaussian process regression, which combines the covariance functions and mean functions to estimate the SOH.…”

Section: Introductionmentioning

confidence: 99%

Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge

Jung

Roh

2021

Energies

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Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a greater emphasis on machine learning for SOH estimation. Furthermore, data preprocessing has received much attention because it is an important step in determining the efficiency of machine learning methods. In this paper, we propose a new preprocessing method for improving the efficiency of machine learning for SOH estimation. The proposed method consists of the relative state of charge (SOC) and data processing, which transforms time-domain data into SOC-domain data. According to the correlation analysis, SOC-domain data are more correlated with the usable capacity than time-domain data. Furthermore, we compare the estimation results of SOC-based data and time-based data in feedforward neural networks (FNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM). The results show that the SOC-based preprocessing outperforms conventional time-domain data-based techniques. Furthermore, the accuracy of the simplest FNN model with the proposed method is higher than that of the CNN model and the LSTM model with a conventional method when training data are small.

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“…Artificial intelligence, such as deep learning and machine learning, covers all walks of life and extends to music, which is also developing in the direction of intelligence. Computers begin to assist or even replace professional workers to complete music work [1].…”

Section: Introductionmentioning

confidence: 99%

Research on Autoarrangement System of Accompaniment Chords Based on Hidden Markov Model with Machine Learning

Shi

Tsai

2021

Mathematical Problems in Engineering

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Accompaniment production is one of the most important elements in music work, and chord arrangement is the key link of accompaniment production, which usually requires more musical talent and profound music theory knowledge to be competent. In this article, the machine learning model is used to replace manual accompaniment chords’ arrangement, and an automatic computer means is provided to complete and assist accompaniment chords’ arrangement. Also, through music feature extraction, automatic chord label construction, and model construction and training, the whole system finally has the ability of automatic accompaniment chord arrangement for the main melody. Based on the research of automatic chord label construction method and the characteristics of MIDI data format, a chord analysis method based on interval difference is proposed to construct chord labels of the whole track and realize the construction of automatic chord labels. In this study, the hidden Markov model is constructed according to the chord types, in which the input features are the improved theme PCP features proposed in this paper, and the input labels are the label data set constructed by the automated method proposed in this paper. After the training is completed, the PCP features of the theme to be predicted and improved are input to generate the accompaniment chords of the final arrangement. Through PCP features and template-matching model, the system designed in this paper improves the matching accuracy of the generated chords compared with that generated by the traditional method.

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Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health

Cited by 25 publications

References 18 publications

ARNS: A Data-Driven Approach for SoH Estimation of Lithium-Ion Battery Using Nested Sequence Models With Considering Relaxation Effect

ARNS: A Data-Driven Approach for SoH Estimation of Lithium-Ion Battery Using Nested Sequence Models With Considering Relaxation Effect

Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge

Research on Autoarrangement System of Accompaniment Chords Based on Hidden Markov Model with Machine Learning

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