Lithium-ion Battery Health Research Articles

Estimation of Lithium-Ion Battery Health in Electric Bicycles Using Internal Resistance Measurement Method

This study evaluates the performance of a 36 Volt 10 Ah battery in an electric bicycle with a 350-Watt Brushless DC (BLDC) motor as an environmentally friendly alternative to overcome the negative impacts of motorized vehicle use in Indonesia. In addition, this study measured the State of Health battery’s value of internal resistance, which is different from other studies that use capacity fading. With a focus on maximum travel distance and travel time, experiments were conducted without load and with a 70kg load. The no-load test was conducted only once, resulting in a travel time of 600 minutes and a distance of 330.1 Km. Although the battery was not discharged, the results were not in line with expectations, so the no-load test was only conducted once. In the 70kg load test, six trials were conducted with variable measurements of distance, battery voltage, and battery resistance. Results showed variations in distance between 50.7 km and 53.1 km, and travel time between 151 and 160 minutes. The battery voltage varied from 31.316 Volts to 31.850 Volts. The resistance in the battery also showed an increase of about 0.0001 ohms from 0.1132 ohms to 0.1139 ohms. Overall, the results from the study showed that as time and usage progressed, the battery voltage and internal resistance values tended to increase, while the distance and travel time tended to decrease. The internal resistance measurement method proved to be effective in assessing battery health as the State of Health value decreased throughout the experiment.

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.

Uneven Usage Battery State of Health Estimation via Fractional-Order Equivalent Circuit Model and AutoML Fusion

To accurately predict the State of Health (SOH) of lithium-ion batteries under the continuously changing charging and discharging conditions in practical applications, this study proposes a hybrid modeling approach that integrates a Fractional Order Equivalent Circuit Model (F-ECM) with the AutoGluon automatic machine learning framework. By leveraging Electrochemical Impedance Spectroscopy (EIS) to capture battery frequency response characteristics, F-ECM accurately fits EIS data to extract detailed internal state parameters. The integration of AutoGluon automates the machine learning process, enhancing the precision of SOH predictions. Through testing and analysis on real battery datasets, this method has demonstrated superior prediction precision and computational efficiency compared to existing mainstream modeling approaches. Specifically, the hybrid method achieved a Root Mean Square Error (RMSE) of 2.12% and a Mean Absolute Error (MAE) of 1.67%. This study presents a highly accurate, interpretable, and adaptable predictive framework for lithium-ion battery health assessment, offering valuable insights for battery health management system development.

Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries

Rapid technological changes and disruptive innovations have resulted in a significant shift in people’s behavior and requirements. Electronic gadgets, including smartphones, notebooks, and other devices, are indispensable to everyday routines. Consequently, the demand for high-capacity batteries has surged, which has enabled extended device autonomy. An alternative approach to address this demand is battery swapping, which can potentially extend the battery life of electronic devices. Although battery sharing in electric vehicles has been well studied, smartphone applications still need to be explored. Crucially, assessing the batteries’ state of health (SoH) presents a challenge, necessitating consensus on the best estimation methods to develop effective battery swap strategies. This paper proposes a model for estimating the SoH curve of lithium-ion batteries using the state of charge curve. The model was designed for smartphone battery swap applications utilizing Gated Recurrent Unit (GRU) neural networks. To validate the model, a system was developed to conduct destructive tests on batteries and study their behavior over their lifetimes. The results demonstrated the high precision of the model in estimating the SoH of batteries under various charge and discharge parameters. The proposed approach exhibits low computational complexity, low cost, and easily measurable input parameters, making it an attractive solution for smartphone battery swap applications.

Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods

Monitoring the health of lithium-ion batteries’ internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typically involves aging many cells and destructively analyzing them throughout the aging test, limiting the scope of quantifiable degradation to the test conditions and duration. Fortunately, recent advances in physics-informed machine learning (PIML) for modeling and predicting the battery state of health demonstrate the feasibility of building models to predict the long-term degradation of a lithium-ion battery cell’s major components using only short-term aging test data by leveraging physics. In this paper, we present four approaches for building physics-informed machine learning models and comprehensively compare them, considering accuracy, complexity, ease-of-implementation, and their ability to extrapolate to untested conditions. We delve into the details of each physics-informed machine learning method, providing insights specific to implementing them on small battery aging datasets. Our study utilizes long-term cycle aging data from 24 implantable-grade lithium-ion cells subjected to varying temperatures and C-rates over four years. This paper aims to facilitate the selection of an appropriate physics-informed machine learning method for predicting long-term degradation in lithium-ion batteries, using short-term aging data while also providing insights about when to choose which method for general predictive purposes.

Comparison-Transfer Learning Based State-of-Health Estimation for Lithium-Ion Battery

Abstract Rapid and accurate estimation of the state of health of lithium-ion batteries is of great significance. This paper aims to address two issues faced when applying deep learning methods to estimate the health status of lithium-ion batteries: high data quality requirements and poor model generalizability. This paper proposes a comparison-transfer learning approach with cyclic synchronization to estimate the state of health of lithium-ion batteries. First, a cyclic synchronization method based on the Bezier curve fitting algorithm is introduced to synchronize the data obtained at different charge–discharge cycles of the lithium-ion battery, facilitating input to the model. Second, a comparison-transfer network using the Pearson correlation coefficient is proposed to transfer knowledge from the source dataset to predict the target dataset under different environmental temperatures. By training a pre-trained model on the source dataset and utilizing the correlation coefficient to analyze the similarity between the source and target datasets, the accumulated knowledge in the source dataset can be effectively utilized to enhance prediction performance on the target dataset. In the experiments, the proposed method is validated using the lithium-ion battery aging public datasets. The experimental results demonstrate that the proposed approach achieves superior prediction performance in the case of small-sample sizes, exhibiting higher accuracy and stability compared to traditional deep learning methods.

Improved lithium-ion battery health prediction with data-based approach

The accurate modeling and forecasting of Battery State of Health (SOH) is crucial for ensuring reliable performance and longevity of lithium-ion batteries. This article introduces a data-driven approach for SOH prediction using Gaussian Process Regression (GPR), selected for its ability to model complex data relationships and capture prediction uncertainty without relying on future load information. Recognizing the effect of reversible capacity recoveries on prediction accuracy, this work employs the ISEA-RWTH 48 Li-ion Cells dataset, deliberately devoid of such recoveries prior to training the GPR model. The GPR model was evaluated and compared with Support Vector Regression (SVR) using the publicly available dataset. First and second End of Life (EOL) scenarios were considered, relevant to primary and secondary battery applications. The results demonstrated the GPR model's superior performance. Particularly, mid-life and late-life predictions displayed better accuracy with GPR, showcasing higher R2 values and lower MAPE values (e.g., mid-life prediction: GPR's average R2 = 0.99, SVR's = 0.9789; GPR's average MAPE = 0.1916, SVR's = 1.3028). Moreover, GPR exhibited the ability to quantify uncertainty in capacity degradation and forecast first and second EOL instances effectively (e.g., mid-life predictions had 1.7 cycle error at 1st EOL and 8.9 cycle error at 2nd EOL). The research also offers valuable insights into the application of machine learning methods for predicting the health degradation of lithium-ion batteries.

A method for estimating the state of health of lithium-ion batteries based on physics-informed neural network

Data-driven methods have been widely used to estimate the State of health (SOH) of Lithium-Ion batteries (LIBs). However, these methods lack interpretability. In response to this issue, this article proposes a method called Physics-informed neural network (PIFNN) to enhance the interpretability of predictions made by a feedforward neural network (FNN). First, the features are extracted from incremental capacity (IC) curves and differential temperature curves, which can characterize battery aging from different perspectives. Specifically, the peaks of the IC curves (P-IC) reflect the electrochemical reactions that occur during the charge-discharge processes of LIBs. The decline of the P-IC is related to the loss of active materials in LIBs, which is a major cause of the decrease of the SOH. This article converts the monotonous relationship between the P-IC and the SOH into physical constraints to guide the “learning process” of the model. In the prediction process, a physics-constrained secondary “training” is applied to the FNN predictions to further enhance interpretability and improve prediction accuracy. The feasibility of the proposed method is validated using the Oxford and NASA battery datasets. The results indicate that PIFNN effectively improves prediction accuracy and reduces errors to below 1.5 %.

Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm

Conducting online estimation studies of the SOH of lithium-ion batteries is indispensable for extending the cycle life of energy storage batteries. Data-driven methods are efficient, accurate, and do not depend on accurate battery models, which is an important direction for battery state estimation research. However, the relationships between variables in lithium-ion battery datasets are mostly nonlinear, and a single data-driven algorithm is susceptible to a weak generalization ability affected by the dataset itself. Meanwhile, most of the related studies on battery health estimation are offline estimation, and the inability for online estimation is also a problem to be solved. In this study, an integrated learning method based on a stacking algorithm is proposed. In this study, the end voltage and discharge temperature were selected as the characteristics based on the sample data of NASA batteries, and the B0005 battery was used as the training set. After training on the dataset and parameter optimization using a Bayesian algorithm, the trained model was used to predict the SOH of B0007 and B0018 models. After comparative analysis, it was found that the prediction results obtained based on the proposed model not only have high accuracy and a short running time, but also have a strong generalization ability, which has a great potential to achieve online estimation.

Estimation of SOH of Lithium-Ion Batteries Based on PSO-Bi GRU-Attention Network

The State of Health (SOH) of lithium-ion batteries is a critical parameter that characterizes their actual lifespan, and its accurate assessment ensures the safe and reliable operation of batteries. However, in practical applications, SOH cannot be directly measured. To further improve the accuracy of SOH estimation for lithium-ion batteries, this study employs the Particle Swarm Optimization (PSO) algorithm to search for the optimal hyperparameters of the Bidirectional Gated Recurrent Unit (Bi GRU) neural network, enabling the prediction of time series information. Additionally, Attention Mechanism (AM) is integrated to allocate weights to the prediction results, resulting in the SOH prediction for lithium-ion batteries. The propose model is validated using the B0005 battery from the NASA lithium battery dataset. Experimental results demonstrate that, compared to the Bi GRU-Attention and Bi GRU models, the propose model reduces the Root Mean Square Error (RMSE) by 52.34% and 66.88%, respectively.

A Deep Learning Method for the Health State Prediction of Lithium-Ion Batteries Based on LUT-Memory and Quantization

The precise determination of the state of health (SOH) of lithium-ion batteries is critical in the domain of battery management systems. The proposed model in this research paper emulates any deep learning or machine learning model by utilizing a Look Up Table (LUT) memory to store all activation inputs and their corresponding outputs. The operation that follows the completion of training is referred to as the LUT memory preparation procedure. This method’s lookup process supplants the inference process entirely and simply. This is achieved by discretizing the input data and features before binarizing them. The term for the aforementioned operation is the LUT inference method. This procedure was evaluated in this study using two distinct neural network architectures: a bidirectional long short-term memory (LSTM) architecture and a standard fully connected neural network (FCNN). It is anticipated that considerably greater efficiency and velocity will be achieved during the inference procedure when the pre-trained deep neural network architecture is inferred directly. The principal aim of this research is to construct a lookup table that effectively establishes correlations between the SOH of lithium-ion batteries and ensures a degree of imprecision that is tolerable. According to the results obtained from the NASA PCoE lithium-ion battery dataset, the proposed methodology exhibits a performance that is largely comparable to that of the initial machine learning models. Utilizing the error assessment metrics RMSE, MAE, and (MAPE), the accuracy of the SOH prediction has been quantitatively evaluated. The indicators mentioned above demonstrate a significant degree of accuracy when predicting SOH.

Data-driven autoencoder neural network for onboard BMS Lithium-ion battery degradation prediction

An autoencoder based neural network architecture, CD-Net, is proposed to predict Lithium-ion battery capacity degradation as a function of operation time as part of a battery management system. CD-Net's generalization performance on various LIB cell chemistry is tested. The incorporation of cell chemistry in CD-Net leads to an improvement in the overall battery capacity prediction accuracy of >2 % for LiNiMnCoO2 cells, >5 % for LiNiCoAlO2 cells, and >12 % for LiFePO4 cells when compared to the similar ML models that do not incorporate cell chemistry information. A comparison of onboard battery health prediction using CD-Net against support vector regression, Bayesian regression, and Gaussian process regression-based approaches shows that CD-Net has higher computational efficiency with <2 % of relative remaining useful life (RUL) prediction error in a no-cell chemistry information setting. In summary, our work presents a chemistry-independent neural network model tailored specifically for onboard BMS applications, showcasing notable predictive capabilities in the context of Lithium-ion battery health assessment.

Research on State of Health Estimation of Lithium Batteries Based on Electrochemical Impedance Spectroscopy and CNN-VIT Models

Abstract With the development of electric vehicles, the demand for lithium-ion batteries has been increasing annually. Accurately estimating the state of health (SOH) of lithium-ion batteries is crucial for their efficient and reliable use. Most of the existing research on SOH estimation is based on parameters such as current, voltage, and temperature, which are prone to fluctuations. Estimating the SOH of lithium-ion batteries based on electrochemical impedance spectroscopy (EIS) and data-driven approaches has been proven effective. In this paper, we explore a novel SOH estimation model for lithium batteries based on EIS and Convolutional Neural Network (CNN)-Vision Transformer (VIT). The EIS data are treated as a grayscale image, eliminating the need for manual feature extraction and simultaneously capturing both local and global features in the data. To validate the effectiveness of the proposed model, a series of simulation experiments are conducted, comparing it with various traditional machine learning models in terms of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The simulation results demonstrate that the proposed model performs best overall in the testing dataset at three different temperatures. This confirms that the model can accurately and stably estimate the SOH of lithium-ion batteries without requiring manual feature extraction and knowledge of battery aging temperature.

Data-Driven Transfer-Stacking-Based State of Health Estimation for Lithium-Ion Batteries

State of health (SOH) of lithium-ion batteries plays a vital role in the safe and reliable operation of electric vehicles. However, most of the existing SOH estimation methods still require a large number of battery aging data, while the established model usually lacks generalization. Here, we build a data-driven transfer learning model to obtain more generality on the SOH estimation. Firstly, potential health features are extracted from battery charging data and then pruned via the importance function. Secondly, support vector regression (SVR) is employed to establish source models with different battery data, which takes selected features as the input and capacity as the output. Thirdly, the transfer-stacking (TS) method is utilized to combine all source models. A TS-SVR method for SOH estimation is then established only using the first 30 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> target battery data after solving the optimization problem of assigning weight to each source model. Finally, the proposed algorithm is verified by three different battery datasets and shows better estimation performance than the comparative algorithms. It is proved that the proposed method uses only a small amount of target battery data, while together with the source battery data, can achieve an accurate SOH estimation during its life cycle.

State of charge and state of health estimation of a lithium-ion battery for electric vehicles: A review

This article, based on the state of charge (SoC) and state of health (SoH) of lithium-ion batteries, highlights the importance of these parameters for battery performance and durability. It explores advanced simulation methods, including intelligent algorithms. These methods are presented as essential tools for estimating and anticipating SoC and SoH, thus offering proactive management. It highlights the integration of these approaches into Li-ion battery management to support the ongoing development of electrical technologies and improve the sustainability of energy storage applications. This paper presents a description of the different SoC and SoH estimation methods. In addition to the use of the Kalman filter to establish a comparison between real and estimated SoC and the determination of SoH.

Analyzing the Health of Lithium-ion Batteries through Heat Distribution and Thermal Modeling

Analyzing the Health of Lithium-ion Batteries through Heat Distribution and Thermal Modeling

Method for Estimating the State of Health of Lithium-ion Batteries Based on Differential Thermal Voltammetry and Sparrow Search Algorithm-Elman Neural Network

Method for Estimating the State of Health of Lithium-ion Batteries Based on Differential Thermal Voltammetry and Sparrow Search Algorithm-Elman Neural Network

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

A Physics-Informed Hybrid Data-Driven Approach with Generative Electrode-Level Features for Lithium-Ion Battery Health Prognostics

A Physics-Informed Hybrid Data-Driven Approach with Generative Electrode-Level Features for Lithium-Ion Battery Health Prognostics

Using tens of seconds of relaxation voltage to estimate open circuit voltage and state of health of lithium ion batteries

Relaxation voltage (RV) of a battery is informative since it not only approximates open circuit voltage (OCV) as time evolves, but it is also related to the battery's state of charge (SOC) and state of health (SOH). Given that RV is easy to obtain by simply stopping a battery's operation, it is an excellent data source to estimate battery states. Without using complete RV history whose acquisition is time-consuming and hinders further applications, this study uses Gaussian process regression model with the input of only a small portion of RV to rapidly and simultaneously estimate the OCV and SOH of a battery. Various input lengths are tested, showing that using only 30-s RV data, the mean absolute error (MAE) for predicting OCV is 2.99 mV, and that for estimating SOH is 2.76%. As soon as the voltage difference is also treated as the model input, we find that the MAE for the SOH estimation is further declined to about 1.83%. Compared to previous methods which either estimate single battery state or require minutes of RV data for estimation, the current model is able to perform multiple battery estimation using only first tens of seconds of data.