تخمین وضعیت شارژ باتری لیتیوم با استفاده از فیلتر کالمن مکعبی تطبیقی فازی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشیار گروه الکترونیک دانشگاه بیرجند

2 دانشجوی کارشناسی ارشد گروه الکترونیک دانشگاه بیرجند

چکیده

تخمین وضعیت شارژ باتری(SOC) در باتری‌های لیتیوم یون برای اطمینان از عملکرد ایمنی و جلوگیری از شارژ و دشارژ بیش از حد از اهمیت بالایی برخوردار است. با وجود اهمیت بسیار زیاد پارامتر SOC، این پارامتر به طور مستقیم از پایانه‌های باتری قابل اندازه‌گیری نیست. بنابراین نیاز به تخمین آن وجود دارد. تاکنون روش‌های مختلفی برای تخمین وضعیت شارژ باتری‌های لیتیوم یون معرفی شده است. در این مقاله شناسایی مدل باتری و الگوریتم تخمین SOC بر اساس فیلتر کالمن مکعبی تطبیقی فازی (FACKF) برای باتری‌های لیتیوم یون در وسایل نقلیه الکتریکی ارائه شده است. در این روش، ابتدا باتری لیتیوم یون توسط مدار معادل RC مرتبه دوم مدل شده است. سپس روش فیلتر کالمن مکعبی برای تخمین پارامترهای باتری و وضعت شارژ باتری استفاده شده است. یکی از ملزومات فیلتر کالمن مکعبی اطلاع داشتن از ماتریسهای کواریانس نویز اندازه‌گیری و پروسه است. با وجود این، این ماتریس‌ها عموما در عمل نامعلوم می‌باشند. درصورت انتخاب نادقیق ماتریسهای Q و R عملکرد فیلتر تحت تاثیر قرار گرفته و دقت تخمین وضعیت شارژ کاهش و حتی امکان واگرایی وجود دارد. برای رفع این مشکل در این مقاله یک سیستم فازی برای نظارت بر عملکرد فیلتر کالمن مکعبی طراحی شده است. سیستم فازی ماتریسهای R و Q به گونه ای تنظیم می نماید که فیلتر دارای عملکرد بهینه باشد. برای ارزیابی عملکرد روش پیشنهادی، این روش با روش های کلاسیک مقایسه شده است. نتایج نشان دهنده عملکرد موثر روش پیشنهادی در مقایسه با سایر روش‌ها است.

کلیدواژه‌ها


عنوان مقاله [English]

State-of-Charge Estimation for Lithium-Ion Batteries using Fuzzy Adaptive Cubature Kalman Filter

نویسندگان [English]

  • ramazan havangi 1
  • Samaneh Hemati 2
1 University of Birjand
2 University of Birjand
چکیده [English]

The state of charge (SOC) estimation of battery in the lithium-ion batteries is of great importance for ensuring its safe operation and preventing it from over-charging or over-discharging. Despite the high importance of the SOC parameter, this parameter cannot be directly measured from battery terminals. So it needs to be estimated. So far, various methods have been introduced for the state of charge estimation of lithium-ion batteries. This paper presents the identification of the battery model and the SOC estimation algorithm for lithium-ion batteries in electric vehicles based on an Fuzzy Adaptive Cubature Kalman Filter (FACKF). In this method, firstly the lithium-ion battery is modeled by a second order RC circuit and then, the Cubature Kalman Filter method is used to estimate the battery parameters and the state of battery charge. One of the requirements of the Cubature Kalman filter is to know the measurement of covariance matrices and process. However, these matrices are generally unknown in practice. In the case of inaccurate selection of Q and R matrices, the performance of the filter is affected and the accuracy of the state of charge estimation is reduced and even divergence may occurs. To solve this problem, in this paper a fuzzy system is designed to monitor the performance of a Cubature Kalman filter. The fuzzy system of R and Q matrices adjusts the filter to have optimal performance. To evaluate the performance of the proposed method, this method is compared with classical methods. The results show the effective performance of the proposed method in comparison with other methods.

کلیدواژه‌ها [English]

  • Lithium ion battery
  • state of charge estimation
  • Extended Kalman Filter
  • Fuzzy Adaptive Cubature Kalman filter
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