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Journals / Journal of Materials and Mechatronics: A / 2022 / Cilt: 3 - Sayı: 2

A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries

Kurşun Asit Bataryalar için SOC Tahmini Yaklaşımını İçeren Bir Batarya Yönetim Sistemi Tasarımı

Emre AKARSLAN
Said Mahmut ÇINAR
Pages
300–313
Publication Date
2022-12-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

With the model developed based on Elman Neural Networks, the SOC of battery could be estimated at an error level of less than 1%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Storage is one of the most important issues of the last decades.
📊
Key Findings & Data:One of the most important problems in storage is that as the amount of energy desired to be stored increases, the need for space also increases.
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Conclusion & Impact:With the model developed based on Elman Neural Networks, the SOC of battery could be estimated at an error level of less than 1%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:batarya yönetim sistemi · şarj durumu · elman sinir ağaları

Abstract

Storage is one of the most important issues of the last decades. In particular, storage systems are needed in order to benefit more effectively from renewable energy systems where production cannot be controlled. One of the most important problems in storage is that as the amount of energy desired to be stored increases, the need for space also increases. Therefore, it is of great importance to manage energy effectively in such systems. In this study, a battery management system (BMS) that can be used for lead acid batteries has been designed. This BMS has a measurement and control system based on STM 32 microcontroller and is controlled via an interface prepared in the MATLAB Simulink environment and the test data is imported into the MATLAB Workspace environment. The designed system can also perform battery charge-discharge experiments in accordance with the battery characteristics. Charge-discharge experiments were carried out using the designed system, and a model was developed to determine the state of charge (SOC) of the battery using the data collected during these experiments. With the model developed based on Elman Neural Networks, the SOC of battery could be estimated at an error level of less than 1%.

Özet

Depolama, son dönemin en önemli konularından biridir. Özellikle üretimin kontrol edilemediği yenilenebilir enerji sistemlerinden daha etkin yararlanabilmek için depolama sistemlerine ihtiyaç duyulmaktadır. Depolamada en önemli sorunlardan biri, depolanması istenen enerji miktarı arttıkça, alan ihtiyacının da artmasıdır. Bu nedenle bu tür sistemlerde enerjinin etkin bir şekilde yönetilmesi büyük önem taşımaktadır. Bu çalışmada kurşun asit bataryalar için kullanılabilecek bir batarya yönetim sistemi tasarlanmıştır. Bu pil yönetim sistemi, STM 32 mikro-denetleyici tabanlı bir ölçüm ve kontrol sistemine sahip olup, MATLAB Simulink ortamında hazırlanan bir arayüz üzerinden kontrol edilmekte ve test verileri MATLAB Workspace ortamına aktarılabilmektedir. Tasarlanan sistem ile, batarya özelliklerine uygun olarak batarya şarj-deşarj deneyleri de yapabilmektedir. Tasarlanan sistem kullanılarak şarj-deşarj deneyleri gerçekleştirilmiş ve bu deneyler sırasında toplanan veriler kullanılarak bataryanın şarj durumunu belirlemek için bir model geliştirilmiştir. Elman Sinir Ağları temel alınarak geliştirilen model ile batarya şarj durumu %1'den daha düşük bir hata seviyesinde tahmin edilebilmiştir.

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Cite This Article

AKARSLAN, E., ÇINAR, S. M. (2022). A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries. Journal of Materials and Mechatronics: A, Cilt: 3 - Sayı: 2, 300–313. https://ajindex.com/articles/869302

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