Lithium-ion Charging Research Articles

Real-time and non-contact estimation of state of charge for lithium-ion battery using laser ultrasonics

Lithium-ion batteries (LIBs) are widely used in portable electronics, electric vehicles, and stationary storage systems due to their high power and high energy. Monitoring the status of LIBs, particularly the state of charge (SOC), is crucial to ensuring battery performance and safety. Non-destructive ultrasonic approaches are recently emerging as a promising method for estimating the battery state and improving safety. However, current battery state estimation methods require contact and the use of a couplant, limiting their practicality and real-time applications. This study presents a non-contact laser ultrasonic system for monitoring the battery state during charge/discharge cycles. The experimental setup utilizes a line laser source to generate ultrasonic waves and an interferometer to detect the wave signal in a pitch-catch mode. Various ultrasonic features, including time-of-flight (TOF) and signal amplitude (SA), are analyzed in the time and frequency domains, respectively, at different degradation rates. The results show significant changes in the ultrasonic features in response to battery SOC and cycling degradation. The proposed non-contact ultrasonic method has the capability to determine the internal status of LIBs, enabling real-time monitoring of battery safety.

Electrochemical-Thermal-Mechanical Coupling Analysis of Lithium-Ion Batteries under Fast Charging

Thermal runaway and volume expansion caused by temperature rise under fast charging of lithium-ion batteries (LIBs) are the major reasons of battery explosion and cycle performance degradation. In this work, considering the radiation heat transfer on the battery surface, an electrochemical-thermal-mechanical coupling model of cylindrical LIBs under fast charging (state of charge (SOC) ≤80%) is developed in order to investigate the distributions of the temperature and stress. Then, choosing 18650 LIBs as the object, the charge efficiency, temperature, and stress distributions are compared between fast charging and galvanostatic operation under SOC≤80%, respectively. Finally, the influences of the thickness, particle radius, the maximum lithium-ion concentration of the cathode, and initial electrolyte concentration on the temperature, radial stress, and hoop stress of LIB during charge are explored, respectively. Numerical results show that fast charging improves 20.8% of the charge efficiency. Increasing the cathodic thickness and decreasing the cathodic maximum lithium-ion concentration or initial electrolyte concentration can reduce the temperature of LIB during the charge. The results of this work will provide some reference value for the design of LIBs under fast charging.

A Novel Solver for an Electrochemical–Thermal Ageing Model of a Lithium-Ion Battery

To estimate the state of health, charge, power, and safety (SoX) of lithium-ion batteries (LiBs) in real time, battery management systems (BMSs) need accurate and efficient battery models. The full-order partial two-dimensional (P2D) model is a common physics-based cell-level LiB model that faces challenges for real-time BMS implementation due to the complexity of its numerical solver. In this paper, we propose a method to discretise the P2D model equations using the Finite Volume and Verlet Integration Methods to significantly reduce the computational complexity of the solver. Our proposed iterative solver uses novel convergence criteria and physics-based initial guesses to provide high fidelity for discretised P2D equations. We also include both the kinetic-limited and diffusion-limited models for Solid Electrolyte Interface (SEI) growth into an iterative P2D solver. With these SEI models, we can estimate the capacity fade in real time once the model is tuned to the cell–voltage curve. The results are validated using three different operation scenarios, including the 1C discharge/charge cycle, multiple-C-rate discharges, and the Lawrence Livermore National Laboratory dynamic stress test. The proposed solver shows at least a 4.5 times improvement in performance with less than 1% error when compared to commercial solvers.

Surface coating engineering of prelithiation cathode additives for lithium-ion batteries

The active lithium ions loss during the initial charge and discharge process of lithium ion batteries seriously hampers its increasement of energy density. Pre-lithiation, involving the pre-storage of active lithium ions prior to cycling, emerges as a promising and effective strategy to offset this loss. Li6CoO4 has been identified as a candidate capable of releasing adequate lithium ions to compensate for such loss. However, its poor air stability renders it susceptible to side reactions in the atmosphere, leading to the formation of residual lithium and consequently affecting its electrochemical performance. In this study, we propose application of a lithium aluminate (LiAlO2) coated onto the surface of lithium cobalt oxide (Li6CoO4) to mitigate the presence of residual lithium. Meanwhile, with decreasing of residual lithium, the rate capability is also enhanced. The research results demonstrate that samples treated with this coating layer exhibit an enhanced energy density in the full cell, indicating the efficacy of this approach in optimizing the electrochemical performance of prelithiation additives.

Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge

This study explores the challenges and advances in the estimation of the state of charge (SOC) of lithium-ion batteries (LIBs), which are crucial to optimizing their performance and lifespan. This review focuses on four main techniques of SOC estimation: experimental measurement, modeling approach, data-driven approach, and joint estimation approach, highlighting the limitations and potential inaccuracies of each method. This study suggests a combined approach, incorporating correction parameters and closed-loop feedback, to improve measurement accuracy. It introduces a multi-physics model that considers temperature, charging rate, and aging effects and proposes the integration of models and algorithms for optimal estimation of SOC. This research emphasizes the importance of considering temperature and aging factors in data-driven approaches. It suggests that the fusion of different methods could lead to more accurate SOC predictions, an important area for future research.

Research on the heating effect evaluation of the electromagnetic induction heating system at low temperature based on the electrochemical-thermal coupling model

The extremely fast electromagnetic induction heating system (EIHS) was recently introduced to improve the poor charge and discharge performance of lithium-ion batteries (LiBs) at low temperature. In this work, aimed to investigate the heating effect of EIHS, the accurate electrochemical-thermal coupling model, validated against the charging capacity calibration with various C-rates and HPPC tests at different environments, was developed in depth to reflect the electrochemical and heat transfer behaviors of the LiB exposed to external cool air during the electromagnetic induction heating process. When the copper coil was subjected to a sinusoidal alternating current (SAC) excitation at 10 A, 3 kHz, the LiB can be warmed up to 293.15 K from 243.15 K within 742 s with an even heat generation inside the LiB. The impact of the SAC parameters on the needed heating time was illustrated with time maps, a series of contours suggested that the heating velocity increase with the increase of both the amplitude and frequency. As compared with the low-temperature state, the internal resistance had a more than 3-fold decrease, which can improve the discharging voltage platform and terminal voltage output, thus increasing the pulse power and usable capacity. Neither noticeable capacity retention difference before and after the heating cycles nor obvious capacity loss discrepancy between the normal charge-discharge and heating cycle tests was found at the end of the 200 heating cycles, so the EIHS can achieve a good tradeoff among temperature rising effect, working behaviors and LiB cycle lifespan, which had a considerable potential to enhance the applicability of EVs (electric vehicles) in frigid weather. Moreover, a better heat transfer condition, higher material thermal conductivity and lower specific heat capacity play an important positive role on the improvement of temperature rising performance.

Online capacity estimation of lithium-ion batteries based on convolutional self-attention

ABSTRACT It is of great significance to improve the safety, efficiency, and economy of lithium-ion batteries by improving the capacity estimation accuracy of lithium-ion batteries. In this paper, feature extraction and correlation analysis are carried out on the data of lithium-ion battery charging process, and the voltage curve of constant current charging stage is extracted. The difference characteristics between each cycle are used to describe the battery capacity, and these statistical characteristics are proved to be highly correlated with the battery capacity. Furthermore, an online estimation model of battery capacity based on convolution self-attention is established, and the above characteristics of constant current charging process and the battery capacity of the latest cycle are fused as input vectors of the model to realize online estimation of battery capacity. Finally, an open data set is used to verify the model experiment. The results show that the prediction error of MAE is 0.17% and that of RMSE is 0.22%.

Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data

Accurately estimating the state of charge (SOC) of lithium-ion batteries by the battery management system (BMS) is crucial for efficient energy management and power distribution control in electric vehicles (EVs). By far, data-driven methods have been extensively used in estimating the SOC of lithium-ion batteries to efficiently operate EVs with limited resources. However, the learning ability of these methods needs further enhancement. Therefore, this paper aims to analyze the SOC performance of an extended-input long short-term memory (ELSTM) model. The inputs of the ELSTM are improved with identified battery parameters based on an adaptive multi-timescale identification method with frequency difference decomposition. Second, an adaptive singular value decomposition-transformed unscented Kalman filter (ASVDUKF) is proposed to integrate all unknown variables and guarantee the positive definiteness of the error covariance matrix into a vector to form a multi-fusion model that robustly outputs the denoised and optimized SOCs based on the estimations of the ELSTM. The SOC results demonstrate that the ELSTM outperforms the LSTM, which uses conventional input data. Following that, the ELSTM-ASVDUKF model ensures high accuracy and stability with optimal mean absolute error, root-mean-square error, and mean absolute percentage error of 0.0939%, 0.1074%, and 0.1257%, respectively. The proposed model is validated using various full-cycle current rate and temperature data from two different batteries, establishing it as a promising solution for future development.

A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning

With the advancement of machine-learning and deep-learning technologies, the estimation of the state of charge (SOC) of lithium-ion batteries is gradually shifting from traditional methodologies to a new generation of digital and AI-driven data-centric approaches. This paper provides a comprehensive review of the three main steps involved in various machine-learning-based SOC estimation methods. It delves into the aspects of data collection and preparation, model selection and training, as well as model evaluation and optimization, offering a thorough analysis, synthesis, and summary. The aim is to lower the research barrier for professionals in the field and contribute to the advancement of intelligent SOC estimation in the battery domain.

A novel nonlinearity-aware adaptive observer for estimating surface concentration and state of charge of lithium-ion batteries

Physically based electrochemical models can provide internal state information of batteries, exhibiting significant potential for application in battery management systems across various lithium-ion battery products. However, the complex structure and limited observability of electrochemical models make their integration into battery management systems challenging. In this paper, a novel nonlinearity-aware adaptive (i.e., NAA) observer based on a simplified electrochemical model is proposed for estimating the electrode surface concentration and state of charge. The proposed NAA observer can identify the nonlinearity of the electrochemical model and switches to the most suitable observer structure automatically. Experimental tests on a cylindrical battery were conducted to observe the performance of the observer. The results demonstrate rapid convergence and high accuracy of all estimated states under an initial state error of 20%. Furthermore, to thoroughly validate the performance of the proposed observer, a comparison was made with an interconnected sliding mode observer based on electrochemical models, considering initial state uncertainty, sensor noise, and computation time.

An in-situ cross-linked network PMMA-based gel polymer electrolyte with excellent lithium storage performance

Gel polymer electrolytes (GPEs) effectively combine the advantages of high ionic conductivity and reduce the risk of leakage associated with liquid. In this study, a chemically cross-linked gel polymer electrolyte was prepared by in-situ polymerization using polymethyl methacrylate (PMMA) as a matrix and neopentyl glycol diacrylate (NPGDA) as cross-linking agent. The cross-linked structure of the GPE was preliminarily investigated, as well as the influence of the degree of cross-linking on its physical properties. The GPE exhibited a superior conductivity of 1.391 mS cm−1 at 25 °C. Herein, the Li|GPE|LiNi0.8Co0.1Mn0.1O2 cell has an excellent capacity retention rate of 80.7% after 150 cycles at 0.5 C in addition to a high discharge specific capacity of 203 mAh g−1. The structure of the cathode material is shielded from the production of byproducts during the charging and discharging of lithium-ion batteries by the cross-linked PMMA GPE.

A novel multi-factor fuzzy membership function - adaptive extended Kalman filter algorithm for the state of charge and energy joint estimation of electric-vehicle lithium-ion batteries

In view of the unmeasurable state parameters of electric-vehicle lithium-ion batteries, this paper investigates a novel multi-factor fuzzy membership function - adaptive extended Kalman filter (MFMF-AEKF) algorithm for the online joint estimation of the state of charge and energy. Strong nonlinear characteristics of model parameters are characterized by considering multiple processing factors of electrochemical and diffusion effects for lithium-ion batteries and constructing an optimized multifactor coupling model. In the proposed MFMF-AEKF method, multi-space-scale factors are introduced to realize the numerical analysis of the multi-factor coupled model parameters and state estimation under dynamic working conditions of electric-vehicle lithium-ion batteries. The proposed MFMF-AEKF algorithm estimates the state of charge (SOC) with the overall best mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and maximum error (ME) values of 1.822 %, 4.322 %, 1.947 %, and 2.954 %, respectively, under challenging working conditions. And The MAE, MAPE, RMSE, and ME values for the state of energy (SOE) are 0.617 %, 1.711 %, 0.695 %, and 1.011 %, respectively. Both state estimation results are better than the traditional method. The proposed MFMF-AEKF algorithm has higher estimation accuracy which provides a feasible estimation algorithm for the joint SOC and SOE of lithium-ion batteries.

Interfacial carbon tailoring of porous SiOx/carbon nanotube hybrids towards high-rate lithium storage

Fast charging Lithium-ion batteries (LIBs) is highly required with the massive development of the electric vehicle market. Integrating silicon with carbon nanotubes (CNTs) has shown great promise for constructing high-rate anodes of LIBs. However, current reported silicon axially coated CNTs electrodes fail to provide a robust conductive connection within the interfacial layer, causing unsatisfactory rate performance. In this paper, a series of novel coaxial hollow nanocables of SiOx/C coated CNTs composite were presented based on a simple sol–gel method and subsequent calcination. Due to the uniform composition of carbon and SiOx at sub-nanometer scale in the coating layer, a strong 3D conductive network is formed between the internal carbon nanotubes and the neighboring electrode particles. When utilized as LIBs anodes, such novel hybrids manifest high reversible capacity (511 mA h g−1 remained after 500 cycles at 0.5 A g−1), high-rate capability (232 mA h g−1 at 5 A g−1) and ultra-long high-current cycling stability (396 mA h g−1 remained after 1000 cycles at 1.0 A g−1). The structural characterization and electrochemical dynamics analysis show that the synergistic effect of abundant mesoporous channels in the coating layer and strong carbon 3D conductive network makes this unique composite structure exhibit excellent electrochemical performance. This work sheds novel light on the wisely design of advanced Si-based anodes with enhanced fast charging performance.

Optimizing Average Electric Power During the Charging of Lithium-Ion Batteries Through the Taguchi Method

Optimizing Average Electric Power During the Charging of Lithium-Ion Batteries Through the Taguchi Method

An adaptive fractional-order extended Kalman filtering approach for estimating state of charge of lithium-ion batteries

This paper proposes an adaptive fractional-order extended Kalman filter (AFEKF) approach for estimating state of charge (SOC) of a lithium-ion battery. Firstly, the fractional-order model (FOM) with a constant phase element module is established to describe the fractional-order characteristics inside a lithium-ion battery. Then, the augmented state equation including SOC is built by using the augmented vector approach. To avoid the calculation of the coefficients of the measurement equation, a linear adaptive integer-order Kalman filter is adopted. The AFEKF approach with the Sage-Husa filter is proposed to update the unknown parameters and unknown noises on the basis of augmented state equations. Finally, the comparison experiment between AFEKF approach and adaptive integer-order extended Kalman filter (AIEKF) approach is designed. Besides, the applicability of the AFEKF approach under different working conditions is also tested. The experimental tests indicate that the SOC estimation accuracy of the AFEKF approach is higher than that of the AIEKF approach, and the AFEKF approach can also be applicable in complex environments.

Second harmonic generation for estimating state of charge of lithium-ion batteries

This study applied the nonlinear ultrasonic method, second harmonic generation, to precisely estimate the state of charge (SoC) in lithium-ion batteries. The second harmonic of the longitudinal wave is generated on a pouch cell battery at 5 MHz with a through-transmission setup. The relative nonlinear parameter β′ is determined by analyzing the amplitudes at the fundamental and second harmonic frequencies. To enhance the nonlinear parameter's measurement accuracy, multiple excitation amplitudes are employed. Two separate charge/discharge tests (four-cycle and eight-cycle) are conducted on the battery at a rate of C/10. The nonlinear parameter is measured periodically during the charge/discharge process, and temperature compensation is applied to the measurement. The correlation curves between the nonlinear parameter and the actual SoC align well for the four-cycle and eight-cycle tests, and a robust linear relationship is observed for both correlation curves. A linear model and a second-order polynomial model are applied to fit the correlation using all data points from both tests. The two models are employed to validate the SoC prediction on a second battery by using a four-cycle test. The results indicate that both models can predict the SoC with an accuracy of approximately 3%, whereas the polynomial model demonstrates smaller errors in the regions near 0% and 100% SoC. Therefore, the nonlinear parameter β′, measured through the second harmonic generation, can effectively predict lithium-ion battery SoC with an accuracy of less than 3%.

Carbon-coated Li4Ti5O12 nanoflakes for ultra-fast charging of lithium-ion batteries

Improving the high-rate performance of lithium-ion batteries is one of the pressing needs of the current electronic device and electric vehicle markets. In this work, a carbon-coated nanosheet-structure lithium titanate (CC-LTO) was synthesized by a relatively simple solvothermal reaction and calcination process, which can achieve ultrafast charging and discharging performance and ultralong cycle stability in lithium-ion battery systems. Ultrathin nanosheet structure greatly shortens the diffusion distance of lithium ions, while the uniform carbon cladding improves the material's electrical conductivity, so the CC-LTO exhibits excellent rate performance. The CC-LTO has a high specific capacity of 153.9 mAh g−1 and 137.9 mAh g−1 at high rate of 80C and 160C, respectively. Meanwhile, CC-LTO also has very high cycling stability, with a capacity retention rate of 77.2% after 10,000 cycles at 10C. Therefore, CC-LTO has a very broad application prospect in the field of lithium-ion battery and commercialization market.

An Accurate Co-Estimation of Core Temperature and State of Charge for Lithium-Ion Batteries With Electrothermal Model

An Accurate Co-Estimation of Core Temperature and State of Charge for Lithium-Ion Batteries With Electrothermal Model

An Ultrasonic Reflected Wave-Based Method for Estimating State of Charge of Hard-Shell Lithium-Ion Batteries

An Ultrasonic Reflected Wave-Based Method for Estimating State of Charge of Hard-Shell Lithium-Ion Batteries

Multiphysics-Constrained Fast Charging of Lithium-Ion Battery With Active Set Predictive Control

Multiphysics-Constrained Fast Charging of Lithium-Ion Battery With Active Set Predictive Control