A novel method for SoC estimation of lithium-ion batteries based on previous covariance matrices and variable ECM parameters

Korkmaz, Mehmet

doi:10.1007/s00202-022-01692-4

Cited by 3 publications

(4 citation statements)

References 37 publications

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“…Integrating the ANNs with additional adaptive methods can improve the estimation accuracy and boost the whole process's speed. The modified technique can be linked with any member of the Kalman filter family (Dao et al, 2021; Sassi et al, 2019), attached to ECMs (Korkmaz, 2023), utilized by the EIS for training the network (Y.‐F. Luo & Lu, 2022), or joined with more than one technique (Awadallah & Venkatesh, 2016).…”

Section: Soc Estimation Methodsmentioning

confidence: 99%

“…The modified technique can be linked with any member of the Kalman filter family (Dao et al, 2021;Sassi et al, 2019), attached to ECMs (Korkmaz, 2023), utilized by the EIS for training the network (Y.-F. Luo & Lu, 2022), or joined with more than one technique (Awadallah & Venkatesh, 2016). In most of these ways, the estimated SoC can be employed as one of the ANN's inputs while the battery voltage becomes the output to be compared with the actual voltage, and the difference is utilized to adjust the estimate of the SoC or in the updating process, as in Figure 16.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

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A review of battery state of charge estimation and management systems: Models and future prospective

Hussein,

Aghmadi,

Abdelrahman

et al. 2024

WIREs Energy & Environment

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Batteries are considered critical elements in most applications nowadays due to their power and energy density features. However, uncontrolled charging and discharging will negatively affect their functions and might result in a catastrophic failure of their applications. Hence, a battery management system (BMS) is mandated for their proper operation. One of the critical elements of any BMS is the state of charge (SoC) estimation process, which highly determines the needed action to maintain the battery's health and efficiency. Several methods were used to estimate the Lithium‐ion batteries (LIBs) SoC, depending on the LIBs model or any other suitable technique. This article provides a critical review of the existing SoC estimation approaches and the main LIB models with their pros and cons, their possibility to integrate with each other's for precise estimation results, and the applicability of these techniques in electric vehicles and utility applications with the commonly used standards and codes in these sectors. Moreover, this study will also explore a future framework for integrating digital twins (DTs) with BMSs for improved and advanced management. Based on this comprehensive review, it can be concluded that merging the model‐based estimation techniques with the data‐driven approaches with their promising development to determine the dynamic patterns inside the battery can efficiently achieve precise estimation results while reducing the complexity of these models. This integration will align also with the integration of digital twin technology to provide complete and accurate supervision for the BMSs.This article is categorized under: Emerging Technologies > Energy Storage Emerging Technologies > Digitalization Energy and Power Systems > Distributed Generation

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Section: Soc Estimation Methodsmentioning

confidence: 99%

Section: Neural Network (Nn)mentioning

confidence: 99%

A review of battery state of charge estimation and management systems: Models and future prospective

Hussein,

Aghmadi,

Abdelrahman

et al. 2024

WIREs Energy & Environment

View full text Add to dashboard Cite

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“…Literatürle paralel olmak adına bu çalışmada da bu türde bir model kullanılmıştır. Bu modele ait detaylar çalışmalarında bulunabilir [4,14,15].…”

Section: B Batarya Modeliunclassified

“…Bu değişken şarj durum göstergesi (State of Charge -SoC) olarak adlandırılır ve bataryadaki ilgili an için kalan enerjinin bataryanın nominal kapasitesine oranıyla tanımlanır (Denk. ( 1)) [4,5].…”

Section: Introductionunclassified

Kalman Filtresi Parametre Değişimlerinin Batarya SoC Tahmini Üzerine Etkileri

Korkmaz

2023

ICAENS

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Küresel ısınma ve iklim değişkenliğinin başat aktörlerinden birisi fosil yakıtların tüketilmesidir. Özellikle ulaşım endüstrisi yüksek miktarda petrol ürün gereksinimlerinden dolayı bu duruma önemli ölçüde sebep olmaktadır. Bu noktada elektrikli araçlar bir alternatif olarak düşünülmektedir. Gelişen batarya teknolojisi, özellikle de Lityum iyon (Li-ion) bataryaların geliştirilmesi ile fosil yakıtlı araçlardan elektrikli araçlara geçiş mümkün olabilmektedir. Li-ion bataryaların yüksek enerji yoğunluğu, düşükboşta deşarj etkinliği, hafıza etkisinin olmaması, hızlı şarja imkan vermeleri, uzun çevrim ömrü gibi özellikleri sayesinde elektrikli araç teknolojisinde tercih sebebi olmaktadır. Bu avantajlarının yanı sıra elektrikli araç endüstrisinde bu türde bataryaların kullanılmasıyla ortaya çıkan bazı problemlerde bulunmaktadır. Bunların en başında kalan enerji miktarını gösteren şarj durum gösterge (SoC) değerinin bulunmasıdır. Bu değerin doğru bir şekilde elde edilmesi hem batarya yönetim sistemi (BMS) için önemli bir koşul hem de kullanıcının sürüş konforu açısından vazgeçilmez bir unsurdur. SoC değerinin doğru bir şekilde elde edilmesi için kimyasal, analitik, algoritmik birçok yöntem kullanılmaktadır. Kalman filtresi tabanlı yöntemler ise SoC değeri hesabında akla gelen ilk metotlardandır. Yinelemeli yapıları sayesinde SoC değerinin gerçek değere yakın bir şekilde tahmini mümkün olmaktadır. Ancak bu filtrelerde gürültü değerlerinin SoC değişimi üzerine etkileri olabilmektedir. Bu çalışmada bu türde bir etkinin nasıl olacağı irdelenmiştir. Gürültü değerinin pozitif veya negatif yönde belirli aralıklarda değişimi için SoC ve terminal gerilim değerlerinin değişimi ele alınarak sonuçlar Turnigy Graphene 5Ah bataryası için değerlendirilmiştir.

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Design and implementation of an inductor based cell balancing circuit with reduced switches for Lithium-ion batteries

Venkatasatish,

Dhanamjayulu

2024

Sci Rep

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Electrical Vehicles (EVs) will eventually lead to reduced availability of fossil fuels and increased asset demand. The efficiency and range of electrically powered vehicles are influenced by the battery. The chemical structure of lithium-ion (LIB) batteries is particularly vulnerable to overcharging and deep discharge, which may damage the battery, reduce its life, and even cause dangerous things. Hence an efficient management system known as a battery management system (BMS) is needed to balance, protect, and manage the energy of the battery pack. Cell balancing is the most important of the three in terms of the longevity of the battery structure. Cells in a battery pack are imbalanced during charging and discharging due to the design parameters of cells in a battery pack which results in battery degradation and an increase in temperature. Hence efficient cell balancing techniques are needed to balance the battery pack to improve the safety level and life. Hence, the paper proposed a novel 2-layer multi-inductor active cell balancing (2 L MI-ACB) and single-layer multi-inductor active cell balancing with a state of charge-based controller. In the MATLAB/SimScape environment, the inductor-based balancing method for 52 V battery systems is implemented based on the comparison, and the results are explained. The model is tested with OPAL-RT 5700 real-time HIL Simulator and compared with simulation results to show its effectiveness.

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A novel method for SoC estimation of lithium-ion batteries based on previous covariance matrices and variable ECM parameters

Cited by 3 publications

References 37 publications

A review of battery state of charge estimation and management systems: Models and future prospective

A review of battery state of charge estimation and management systems: Models and future prospective

Kalman Filtresi Parametre Değişimlerinin Batarya SoC Tahmini Üzerine Etkileri

Design and implementation of an inductor based cell balancing circuit with reduced switches for Lithium-ion batteries

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