Abstract
Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods.
Highlights
Among various energy storage solutions, Lithium-ion (Li-ion) batteries are widely regarded as promising candidates for various applications due to their advantages of high energy density and low self-discharge (Peng et al, 2019; Gao et al, 2020)
It can be seen from the above comparison that the application of machine learning to predict remaining useful lifetime (RUL) has the advantages of high accuracy, simple input, and a relatively short training period
According to the analysis of the results in the research literature and publications according to these standards, machine learning is considered to be the most suitable algorithm, with relatively robust and computationally acceptable predictive ability
Summary
Among various energy storage solutions, Lithium-ion (Li-ion) batteries are widely regarded as promising candidates for various applications due to their advantages of high energy density and low self-discharge (Peng et al, 2019; Gao et al, 2020). Wang and Mamo (2018) used the differential evolution (DE) algorithm to obtain the support vector regression (SVR) kernel parameters, and fuse and predict the RUL of Li-ion batteries This method has an error of about 1/99 at the starting point of 80 cycles, which has higher prediction accuracy. Li et al (2019b) combined the empirical mode decomposition algorithm with long-short-term memory (LSTM) and Elman neural network and proposed a new hybrid Kalman-LSTM hybrid model to predict battery RUL. Chinomona et al (2020) proposed a recurrent neural networklong-short-term memory (RNN-LSTM) model to select the best subset, and use a partial charge/discharge data set to predict battery RUL performance. The extracted capacities from six voltage ranges Capacity Capacity Cycle Voltage Current Length (time) of each cycle Terminal voltage Current in the charger The voltage of the charge Temperature Output current Length (time) of each cycle Terminal voltage Current in the charger The voltage of the charge Temperature Output current Intrinsic mode functions
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