مهندسی و مدیریت انرژی

مهندسی و مدیریت انرژی

ایمنی باتری لیتیوم‌یون فرسوده حین فرار حرارتی با استفاده از روش معماری شبکه عصبی بازگشتی حافظه طولانی- کوتاه‌مدت

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه مهندسی مکانیک، دانشکده مهندسی، دانشگاه شهید چمران اهواز، اهواز، ایران
2 گروه مهندسی مکانیک/سیستم‌های انرژی، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران
چکیده
فرار حرارتی یکی از مهم‌ترین چالش‌های ایمنی باتری‌های لیتیوم‌یون فرسوده است و پیش‌بینی دقیق آن نقش مهمی در توسعه سامانه‌های مدیریت باتری و هشدار زودهنگام دارد. در این پژوهش، یک چارچوب یکپارچه مبتنی بر مدل‌سازی چندفیزیکی و شبکه عصبی حافظه بلند-کوتاه‌مدت برای پیش‌بینی دمای فرار حرارتی باتری لیتیوم‌یون 14500 توسعه داده شد. مدل فیزیکی با در نظر گرفتن اثر هم‌زمان پیری تقویمی، رشد لایه فصل مشترک الکترولیتی جامد، رسوب لیتیوم فلزی، دمای کوره و ضریب انتقال حرارت جابه‌جایی ایجاد شد. نتایج نشان داد که افزایش دمای کوره از 418 به 458 کلوین، دمای بیشینه سلول را از 12/422 به 87/587 کلوین افزایش داده و زمان رسیدن به دمای بیشینه را از 2316 به 942 ثانیه کاهش می‌دهد. همچنین، افزایش ضریب انتقال حرارت جابه‌جایی موجب کاهش شدت واکنش‌های گرمازا و بهبود پایداری حرارتی سلول شد. مدل شبکه عصبی حافظه بلند کوتاه‌مدت عملکرد بسیار مطلوبی در پیش‌بینی تاریخچه زمانی دما از خود نشان داد؛ به‌طوری‌که کمترین مقادیر MAE و RMSE به‌ترتیب 0001/0 و 0008/0 و بیشترین مقدار R² و NSE برابر با 9990/0 به دست آمد. نتایج این پژوهش نشان می‌دهد که چارچوب پیشنهادی ابزاری قابل‌اعتماد برای پیش‌بینی فرار حرارتی و توسعه سامانه‌های هوشمند مدیریت باتری است.
کلیدواژه‌ها
موضوعات

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