کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7732697 1497947 2015 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Identification of the battery state-of-health parameter from input-output pairs of time series data
ترجمه فارسی عنوان
شناسایی پارامتر وضعیت باتری از جفت ورودی-خروجی داده های سری زمانی
کلمات کلیدی
سیستم های باتری، وضعیت سلامت، دینامیک نمادین، تقسیم بر اساس موجک، استخراج ویژگی،
موضوعات مرتبط
مهندسی و علوم پایه شیمی الکتروشیمی
چکیده انگلیسی
As a paradigm of dynamic data-driven application systems (DDDAS), this paper addresses real-time identification of the State of Health (SOH) parameter over the life span of a battery that is subjected to approximately repeated cycles of discharging/recharging current. In the proposed method, finite-length data of interest are selected via wavelet-based segmentation from the time series of synchronized input-output (i.e., current-voltage) pairs in the respective two-dimensional space. Then, symbol strings are generated by partitioning the selected segments of the input-output time series to construct a special class of probabilistic finite state automata (PFSA), called D-Markov machines. Pertinent features of the statistics of battery dynamics are extracted as the state emission matrices of these PFSA. This real-time method of SOH parameter identification relies on the divergence between extracted features. The underlying concept has been validated on (approximately periodic) experimental data, generated from a commercial-scale lead-acid battery. It is demonstrated by real-time analysis of the acquired current-voltage data on in-situ computational platforms that the proposed method is capable of distinguishing battery current-voltage dynamics at different aging stages, as an alternative to computation-intensive and electrochemistry-dependent analysis via physics-based modeling.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Power Sources - Volume 285, 1 July 2015, Pages 235-246
نویسندگان
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