Abstract
Currently, the most popular health indicator used to assess the degradation of lithium-ion batteries (LIBs) is the state-of-health (SOH). This indicator is necessary to ensure the safety, degradation management, and good operation of the battery, for example, the correct estimate of the state-of-charge (SOC). In this paper, a new health indicator is proposed as an alternative to the use of the SOH because it has a high correlation and similarity with the SOH and has the advantage that it can be calculated and/or estimated very easily. The new health indicator, named “Degradation Speed Ratio” (DSR) is calculated with variables directly measured (voltage and time), and it is not necessary to spend any time on the total charging cycle, therefore reducing waiting times about 84%. In addition, due to its high correlation with capacity, it is a significant marker of battery end-of-life (EOL). In this study, the obtained DSR and a Gaussian process regression (GPR) model were used to estimate the lost capacity and to compare it with existing models in the literature. The accuracy achieved using the DSR indicator as input is very high. Similarly, the results of a multilayer perceptron neural network (MLPNN) model are shown using the new indicator (DSR) as input to estimate the degradation. The sensitivity and precision of this NN model with unknown data are also very high.
Highlights
This paper proposes a new health indicator (HI) based on charging voltage profiles to solve some of the problems mentioned before and to cover some of the gaps detected in the state-of-the-art methods
It is not needed to wait for the full charge and discharge battery cycle using this method, the time used to measure the predictive variables is reduced to 500 s, the time spent to calculate the Degradation Speed Ratio’’ (DSR) in the range [3.8-3.9V] in the BOL of the lithium-ion batteries (LIBs)
Gaussian process regression (GPR) In this sub-section, to test the new DSR indicator, it was used as an input of two GPR models to predict the SOH and to compare the proposed method with other studies
Summary
There are more and more devices using Lithium-ion cells in our lifestyle: laptops, electric vehicles (EV), Battery Energy Storage Systems (BESS) [1]. Once this range has been chosen, and due to the fact that the voltages and the time are always available, the DSR can be calculated It is not needed to wait for the full charge and discharge battery cycle using this method, the time used to measure the predictive variables is reduced to 500 s, the time spent to calculate the DSR in the range [3.8-3.9V] in the BOL of the LIB. DSR only requires a small charging ‘‘voltage segment’’, which simplifies the method and makes it different from existing ones, because none (of the current methods proposed in scientific literature) uses the slopes calculated in a ‘‘voltage segment’’ of the charging curve profiles to analyze its variation and relationship with battery degradation
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