title: ÐÐ½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐ½Ð¸Ð¶ÐµÐ½Ð¸Ñ Ð´ÐµÐ³ÑÐ°Ð´Ð°ÑÐ¸Ð¸ Li-ion Ð°ÐºÐºÑÐ¼ÑÐ»ÑÑÐ¾ÑÐ¾Ð² Ð´Ð»Ñ ÑÐ¾Ð»Ð½ÐµÑÐ½ÑÑ ÑÐ»ÐµÐºÑÑÐ¸ÑÐµÑÐºÐ¸Ñ ÑÑÑÐ°Ð½Ð¾Ð²Ð¾Ðº
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Analysis of methods for reducing the degradation of Li-ion batteries for solar electrical installations
Creators: name: Ð§Ð¶Ñ Ð¦Ð·Ð¸Ð½ÑÑÐ°Ð¾
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¼Ð°ÑÑÐ¸ÑÐ½ÑÐ¹ Ð³Ð»ÑÐ±Ð¸Ð½Ð½ÑÐ¹ Ð³Ð°ÑÑÑÐ¾Ð²ÑÐºÐ¸Ð¹ Ð¿ÑÐ¾ÑÐµÑÑ
ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ðµ Ð·Ð´Ð¾ÑÐ¾Ð²ÑÑ
ÐºÐ¾Ð½ÐµÑ Ð¶Ð¸Ð·Ð½Ð¸
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð· Ð¸ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ°
deep learning
matrix deep gaussian process
health status
end of life
prognosis and diagnosis

description: Ð ÑÑÐ¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð³Ð°ÑÑÑÐ¾Ð²Ð° Ð¿ÑÐ¾ÑÐµÑÑÐ° Ð´Ð»Ñ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð° Ð»Ð¸ÑÐ¸Ð¹-Ð¸Ð¾Ð½Ð½ÑÑ Ð°ÐºÐºÑÐ¼ÑÐ»ÑÑÐ¾ÑÐ¾Ð². ÐÐ»ÑÐ±Ð¾ÐºÐ°Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ Ð´Ð°Ð½Ð½ÑÐµ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð² ÑÐ°ÑÑÐ¸ÑÐ½Ð¾Ð³Ð¾ Ð·Ð°ÑÑÐ´Ð° Ð¸ ÑÐ°Ð·ÑÑÐ´Ð° Ð² ÑÐ¾ÑÐ¼Ðµ Ð½Ð°Ð¿ÑÑÐ¶ÐµÐ½Ð¸Ñ, ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÑ Ð¸ ÑÐ¾ÐºÐ° Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÐµÐ¼ÐºÐ¾ÑÑÐ¸, ÑÐµÐ¼ ÑÐ°Ð¼ÑÐ¼ ÑÑÑÑÐ°Ð½ÑÑ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾ÑÑÑ Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð²ÑÐ¾Ð´Ð½ÑÑ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸Ðº. ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼ ÑÐ°ÐºÐ¶Ðµ Ð¾ÑÐµÐ½Ð¸Ð²Ð°ÐµÑ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÑÑ ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ñ Ð¼ÐµÐ¶Ð´Ñ Ð¼Ð¾ÑÐ½Ð¾ÑÑÑÑ Ð¾Ñ Ð¿ÐµÑÐ²Ð¾Ð³Ð¾ ÑÐ¸ÐºÐ»Ð° Ð¸ Ð¿ÑÐ¾ÑÐµÐ´ÑÐ¸Ð¼ Ð²ÑÐµÐ¼ÐµÐ½ÐµÐ¼, ÑÑÐ¾Ð±Ñ Ð¼Ð¾Ð¶Ð½Ð¾ Ð±ÑÐ»Ð¾ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°ÑÑ ÐºÐ¾Ð½ÐµÑ ÑÑÐ¾ÐºÐ° ÑÐ»ÑÐ¶Ð±Ñ. ÐÑÑÐ¾Ð´Ð½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ ÑÐºÑÑÑÐ¾Ð³Ð¾ ÑÐ»Ð¾Ñ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÑÐµÑÐ½Ð¾ ÑÐ²ÑÐ·Ð°Ð½Ñ Ñ Ð²Ð½ÑÑÑÐµÐ½Ð½Ð¸Ð¼ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸ÐµÐ¼ Ð±Ð°ÑÐ°ÑÐµÐ¸, Ð¿Ð¾ÑÑÐ¾Ð¼Ñ Ð¼ÐµÑÐ°Ð½Ð¸Ð·Ð¼ Ð´ÐµÐ³ÑÐ°Ð´Ð°ÑÐ¸Ð¸ Ð±Ð°ÑÐ°ÑÐµÐ¸ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐ¾Ð²Ð°Ð½. ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ Ð¾ ÑÑÐ°ÑÐµÐ½Ð¸Ð¸ Ð±Ð°ÑÐ°ÑÐµÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐ¸ÐºÐ»Ð¸ÑÐµÑÐºÐ¸ Ð¼ÐµÐ½ÑÑÑÑÑ Ð¿ÑÐ¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÑÐ»Ð¾Ð²Ð¸ÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ, Ð´Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÐµÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°. ÐÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·ÑÐ²Ð°ÐµÑ ÐµÐ¼ÐºÐ¾ÑÑÑ Ð¸ ÑÑÐ¾Ðº ÑÐ»ÑÐ¶Ð±Ñ ÑÐ°ÐºÐ¸Ð¼ Ð¾Ð±ÑÐ°Ð·Ð¾Ð¼, ÑÑÐ¾ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÑÑÑÐ¸Ð¹ ÐºÐ¾ÑÑÑÐ¸ÑÐ¸ÐµÐ½Ñ Ð±Ð¾Ð»ÑÑÐµ 0,9, Ð° ÑÑÐµÐ´Ð½ÑÑ Ð°Ð±ÑÐ¾Ð»ÑÑÐ½Ð°Ñ Ð¾ÑÐ¸Ð±ÐºÐ° Ð¼ÐµÐ½ÑÑÐµ 0,1, ÑÑÐ¾ ÑÐºÐ°Ð·ÑÐ²Ð°ÐµÑ Ð½Ð° Ð²ÑÑÐ¾ÐºÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð°.
description: This work proposes a deep Gaussian process algorithm for monitoring lithium-ion batteries. The deep structure of the proposed algorithm allows the use of partial charge and discharge time series data in the form of voltage, temperature, and cur-rent to estimate capacitance, thereby eliminating the need to isolate input character-istics. The algorithm can also estimate the statistical correlation between the power of the first cycle and the elapsed time, thereby predicting the end of the service life. The output of the hidden layer of the algorithm is closely related to the internal state of the battery, so the mechanism of battery degradation can be diagnosed. Using battery aging data sets that change periodically under different usage conditions proves the effectiveness of the algorithm. The proposed algorithm predicts capacity and service life in a way that the factor is greater than 0.9 and the average absolute error is less than 0.1, which shows that the method has high prediction accuracy.