Lithium Battery State-of-Health Estimation via Differential Thermal Voltammetry With Gaussian Process Regression

Wang, Zhenpo; Yuan, Changgui; Li, Xiaoyu

doi:10.1109/tte.2020.3028784

Cited by 126 publications

(21 citation statements)

References 39 publications

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“…Wang et al. acquires the DT curve through differentiating the measured temperature to voltage during the galvanostatic charge, which is called DTV and calculated according to ( Equation 6 ) ( Wang et al., 2021 ). On the other hand, the DT curve is roughly inferred using finite difference over

sampling time, defined as DTt and expressed in ( Equation 7 ).…”

Section: Study Of Feature Extractionmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

et al. 2021

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Summary Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.

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sampling time, defined as DTt and expressed in ( Equation 7 ).…”

Section: Study Of Feature Extractionmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

et al. 2021

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show abstract

“…Batarya sağlık durumunu belirlemek için literatürdeki yöntemler üç grup altında toplanabilir; doğrudan ölçüm yöntemi, model tabanlı yöntem ve veri tabanlı yöntemler [14][15][16]. Doğrudan ölçüm yöntemini kullanarak batarya sağlığını belirlemek için literatürde çalışmalar yapılmıştır [17][18][19][20]. Doğrudan ölçüm yönteminde bataryanın tamamen şarj ve deşarj esnasında batarya kapasitesi ölçülür [15].…”

Section: Introductionunclassified

Batarya sağlık durumunun makine öğrenmesi ile kestirimi

ÇAVUŞ

SANCAKTAR

2022

NÖHÜ Müh. Bilim. Derg.

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Bu çalışmada batarya sağlık durumunun belirlenmesi için makine öğrenmesi yöntemi kullanılmıştır. Bu amaçla bataryanın deşarj olması esnasında elde edilen akım, kapasite azalması, gerilim gibi değerler kullanılmıştır. Literatürdeki diğer yöntemlerden farklı olarak, deşarj gerilim grafiğindeki diz-dirsek noktaları belirlenerek gerilimdeki değişimler daha ayrıntılı olarak dikkate alınmıştır. Belirlenen giriş verileri kullanılarak batarya sağlık durumunun belirlenebilmesi için k-En Yakın Komşu yöntemi ve Rastgele Orman Regresyon yöntemi olmak üzere iki farklı makine öğrenmesi algoritması oluşturulmuştur. Gerçekleştirilen sağlık durumu belirleme yazılımı için PYHTON dili kullanılmıştır. Batarya sağlık durumunun belirlenmesi için kullanılan yöntemlerin başarısı iki farklı senaryo ile değerlendirilmiştir. İlk senaryo tüm batarya verilerinin karışık olarak değerlendirilip, tüm bataryalara ait verilerden oluşan eğitim ve test verilerinin oluşturulması ile gerçekleştirilmiştir. Diğer senaryo ise elde bulunan 12 bataryadan 11’ini eğitim verisini kalan 1 bataryanın ise test verisini oluşturduğu durumdur. Burada 12 bataryanın her biri ayrı ayrı test verisi olarak değerlendirilmiştir.

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“…combined GPR with other regression methods and proposed a multi-time-scale framework for predicting the SOH and RUL of Lithium ion batteries 30 ; Wang et al . used a modified kernel GPR algorithm to simulate the degradation process of batteries 31 ; Hu et al . designed a dual GPR model for the SOH and RUL prognosis of battery packs 32 .…”

Section: Introductionmentioning

confidence: 99%

High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression

Ren

Cai

2021

Sci Rep

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State of health (SOH) prediction of supercapacitors aims to provide reliable lifetime control and avoid system failure. Gaussian process regression (GPR) has emerged for SOH prediction because of its capability of capturing nonlinear relationships between features, and tracking SOH attenuations effectively. However, traditional GPR methods based on explicit functions require multiple screenings of optimal mean and covariance functions, which results in data scarcity and increased time consumption. In this study, we propose a GPR-implicit function learning, which is a prior knowledge algorithm for calculating mean and covariance functions from a preliminary data set instead of screening. After introducing the implicit function, the average root mean square error (Average RMSE) is 0.0056 F and the average mean absolute percent error (Average MAPE) is 0.6%, where only the first 5% of the data are trained to predict the remaining 95% of the cycles, thereby decreasing the error by more than three times than previous studies. Furthermore, less cycles (i.e., 1%) are trained while still obtaining low prediction errors (i.e., Average RMSE is 0.0094 F and Average MAPE is 1.01%). This work highlights the strength of GPR-implicit function model for SOH prediction of energy storage devices with high precision and limited property data.

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Lithium Battery State-of-Health Estimation via Differential Thermal Voltammetry With Gaussian Process Regression

Cited by 126 publications

References 39 publications

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Batarya sağlık durumunun makine öğrenmesi ile kestirimi

High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression

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