Early prediction of remaining discharge time for lithium-ion batteries considering parameter correlation between discharge stages

• Autorzy: Yu Jinsong , Yang Jie , Tang Diyin , Dai Jing

Identyfikatory

DOI

10.17531/ ein.2019.1.10

Warianty tytułu

PL

Wczesne przewidywanie czasu pozostałego do rozładowania baterii litowo-jonowej z uwzględnieniem korelacji parametrów z różnych etapów procesu rozładowania

• Języki publikacji: EN

Abstrakty

EN

In this paper, we propose a method for making early predictions of remaining discharge time (RDT) that considers information about future battery discharge process. Instead of analyzing the entire degradation process of a battery, as in the existing literature, we obtain the information about future battery condition by decomposing the discharge model into three stages, according to level of voltage loss. Correlation between model parameters at the first and last stages of discharge process allows the values of model parameters in the future to be used to predict the value of parameters at early stages of discharge. The particle swarm optimization (PSO) and particle filter (PF) algorithms are employed to update parameters when new voltage data is available. A case study demonstrates that the proposed approach predicts RDT more accurately than the benchmark PF-based prediction method, regardless of the degradation period of the battery.

PL

W pracy zaproponowano metodę wczesnego przewidywania czasu pozostałego do rozładowania baterii (RDT), która uwzględnia informacje na temat przyszłego procesu jej rozładowywania. Zamiast analizować cały proces degradacji baterii, jak to ma miejsce w literaturze przedmiotu, wykorzystano informacje o przyszłym stanie baterii uzyskane na drodze podziału modelu procesu rozładowania na trzy etapy, według poziomu utraty napięcia. Korelacje między parametrami modelu uzyskanymi na pierwszym i ostatnim etapie procesu rozładowania baterii umożliwiają wykorzystanie przyszłych wartości parametrów do przewidywania wartości parametrów we wczesnych etapach rozładowania. Do aktualizacji parametrów zgodnie z napływającymi nowymi danymi napięciowymi wykorzystano algorytm optymalizacji rojem cząstek (PSO) i algorytm filtra cząsteczkowego (PF). Studium przypadku pokazuje, że proponowane podejście pozwala bardziej precyzyjnie prognozować RDT niż metoda prognozowania oparta na PF, niezależnie od okresu degradacji baterii.

Słowa kluczowe

EN

lithium-ion battery; parameter correlation; particle swarm optimization (PSO); particle filter; remaining discharge time prognostics

PL

bateria litowo-jonowa; korelacja parametrów; optymalizacja rojem cząstek; filtr cząsteczkowy; prognozowanie czasu do rozładowania

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2019
• Tom: Vol. 21, no. 1
• Strony: 81--89
• Opis fizyczny: Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Yu Jinsong

• School of Automation Science and Electrical Engineering Beihang University No.37 Xueyuan Road, Haidian District, Beijing 100191, China Collaborative Innovation Center of Advanced Aero-Engine, Beijing 100191, China

autor

Yang Jie

• School of Automation Science and Electrical Engineering Beihang University No.37 Xueyuan Road, Haidian District, Beijing 100191, China

autor

Tang Diyin

• School of Automation Science and Electrical Engineering Beihang University No.37 Xueyuan Road, Haidian District, Beijing 100191, China

autor

Dai Jing

• China Academy of Launch Vehicle Technology R&D center NO.1 Nan Da Hong Men Road, Feng Tai District, Beijing 100191, China

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).