State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

Characterization and quantification of multi-field coupling in lithium-ion batteries under mechanical constraints

The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.

Internal short circuit evaluation and corresponding failure mode analysis for lithium-ion batteries

Internal short circuit(ISC)is the major failure problem for the safe application of lithium-ion batteries,especially for the batteries with high energy density.However,how to quantify the hazard aroused by the ISC,and what kinds of ISC will lead to thermal runaway are still unclear.This paper investigates the thermal-electrical coupled behaviors of ISC,using batteries with Li(Ni_(1/3)CO_(1/3)Mn_(1/3))O_(2) cathode and composite separator.The electrochemical impedance spectroscopy of customized battery that has no LiPF6 salt is utilized to standardize the resistance of ISC.Furthermore,this paper compares the thermal-electrical coupled behaviors of the above four types of ISC at different states-of-charge.There is an area expansion phenomenon for the aluminum-anode type of ISC.The expansion effect of the failure area directly links to the melting and collapse of separator,and plays an important role in further evolution of thermal runaway.This work provides guidance to the development of the ISC models,detection algorithms,and correlated countermeasures.

Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance

Machine learning (ML) is a rapidly growing tool even in the lithium-ion battery (LIB) research field. To utilize this tool, more and more datasets have been published. However, applicability of a ML model to different information sources or various LIB cell types has not been well studied. In this paper, an unsupervised learning model called variational autoencoder (VAE) is evaluated with three datasets of charge-discharge cycles with different conditions. The model was first trained with a publicly available dataset of commercial cylindrical cells, and then evaluated with our private datasets of commercial pouch and hand-made coin cells. These cells used different chemistry and were tested with different cycle testers under different purposes, which induces various characteristics to each dataset. We report that researchers can recognise these characteristics with VAE to plan a proper data preprocessing. We also discuss about interpretability of a ML model.

An improved model combining machine learning and Kalman filtering architecture for state of charge estimation of lithium-ion batteries

Accurate state of charge(SOC)estimation of lithium-ion batteries is a fundamental prerequisite for ensuring the normal and safe operation of electric vehicles,and it is also a key technology component in battery management systems.In recent years,lithium-ion battery SOC estimation methods based on data-driven approaches have gained significant popularity.However,these methods commonly face the issue of poor model generalization and limited robustness.To address such issues,this study proposes a closed-loop SOC estimation method based on simulated annealing-optimized support vector regression(SA-SVR)combined with minimum error entropy based extended Kalman filter(MEE-EKF)algorithm.Firstly,a probability-based SA algorithm is employed to optimize the internal parameters of the SVR,thereby enhancing the precision of original SOC estimation.Secondly,utilizing the framework of the Kalman filter,the optimized SVR results are incorporated as the measurement equation and further processed through the MEE-EKF,while the ampere-hour integral physical model serves as the state equation,effectively attenuating the measurement noise,enhancing the estimation accuracy,and improving generalization ability.The proposed method is validated through battery testing experiments conducted under three typical operating conditions and one complex and random operating condition with wide temperature variations under only one condition training.The results demonstrate that the proposed method achieves a mean absolute error below 0.60%and a root mean square error below 0.73%across all operating conditions,showcasing a significant improvement in estimation accuracy compared to the benchmark algorithms.The high precision and generalization capability of the proposed method are evident,ensuring accurate SOC estimation for electric vehicles.

Automatic method for the estimation of li-ion degradation test sample sizesrequired to understand cell-to-cell variability

The testing of battery cells is a long and expensive process, and hence understanding how large a test set needsto be is very useful. This work proposes an automated methodology to estimate the smallest sample size ofcells required to capture the cell-to-cell variability seen in a larger population. We define cell-to-cell variationbased on the slopes of a linear regression model applied to capacity fade curves. Our methodology determinesa sample size which estimates this variability within user specified requirements on precision and confidence.The sample size is found using the distributional properties of the slopes under a normality assumption, andan implementation of the approach is available on GitHub.For the five datasets in the study, we find that a sample size of 8–10 cells (at a prespecified precision andconfidence) captures the cell-to-cell variability of the larger datasets. We show that prior testing knowledge canbe leveraged with machine learning models to operationally optimise the design of new cell-testing, leadingup to a 75% reduction in experimental costs.