State Of Health Estimation Research Articles

A Novel Method for Estimating the State of Health of Lithium-Ion Batteries Based on Physics-Informed Neural Network

An accurate state of health (SOH) assessment of lithium-ion batteries is essential for ensuring the reliability and safety of electric vehicles (EVs). Data-driven SOH estimation methods have shown promise but face challenges in generalizing across diverse battery types and variable operating conditions. To address this, this study integrates physical information into data-driven approaches, enabling physically consistent inferences and a rapid adaptation to different battery chemistries and usage scenarios. Specifically, physical features correlated with battery degradation, such as the link between incremental capacity (IC) peaks and SOH, are used as constraints to guide model learning. A fully connected layer within a back-propagation neural network (BPNN) is employed to capture battery aging dynamics effectively. Experimental results on two datasets show that the proposed model outperforms traditional neural networks, reducing the RMSE by at least 1.1% and demonstrating strong generalizability in both single-dataset and transfer learning tasks.

Estimating health state utilities for aromatic L-amino acid decarboxylase deficiency (AADCd) in the United States

BackgroundAADCd is a rare neurometabolic disorder presenting in infancy. Children with AADCd have motor dysfunction and development delays that result in the need for lifelong care; quality of life is greatly impacted. Current characterizations of health-related quality of life and associated health state utilities (HSUs) may be underestimated in AADCd. Accurate characterization of AADCd burden is important when evaluating the benefits of treatment, especially the improvements observed with the recently approved disease-modifying therapy eladocagene exuparvovec. Time-trade-off (TTO) vignette methods may be used to elicit HSUs in AADCd for assessing the value of new treatments. This study aimed to first update previously published health state vignettes, then estimate AADCd HSUs in the United States (US).MethodsExisting vignettes for five AADCd health states were updated based on the review of published literature and clinician/caregiver input. Health states included: “bedridden/no motor function,” “head control,” “sitting unassisted,” “standing with support,” “walking with assistance.” Online composite TTO interviews were conducted 1:1 with adults from the US general public. Participants ranked health states in order of preference using a visual analog scale, then were presented with health state vignettes to elicit utilities using TTO. Mean TTO scores were calculated for each health state, and regression models were used to estimate disutility associated with use of feeding tube.ResultsFollowing revision of the vignettes, 120 participants completed the TTO task (mean age: 47 years; 50% female; 70% White); characteristics were not significantly different from US population norms in terms of age, sex, race or ethnicity. Six participants who appeared to misunderstand the exercise were excluded. Mean (SD) HSUs were: -0.258 (0.534) for bedridden state, -0.155 (0.569) for head control, 0.452 (0.523) for sitting unassisted, 0.775 (0.242) for standing with support, and 0.796 (0.235) for walking with assistance. The need for a feeding tube was associated with a disutility of 0.07.ConclusionsThis study implemented TTO methods to estimate utilities for five health states which reflect the burden and impact of AADCd. The range in values from the most to least severe health state suggests that there is potential for effective treatments to substantially improve quality of life in these patients.

A Joint Prediction of the State of Health and Remaining Useful Life of Lithium-Ion Batteries Based on Gaussian Process Regression and Long Short-Term Memory

To comprehensively evaluate the current and future aging states of lithium-ion batteries, namely their State of Health (SOH) and Remaining Useful Life (RUL), this paper proposes a joint prediction method based on Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) networks. First, health features (HFs) are extracted from partial charging data. Subsequently, these features are fed into the GPR model for SOH estimation, generating SOH predictions. Finally, the estimated SOH values from the initial cycle to the prediction start point (SP) are input into the LSTM network in order to predict the future SOH trajectory, identify the End of Life (EOL), and infer the RUL. Validation on the Oxford Battery Degradation Dataset demonstrates that this method achieves high accuracy in both SOH estimation and RUL prediction. Furthermore, the proposed approach can directly utilize one or more health features without requiring dimensionality reduction or feature fusion. It also enables RUL prediction at the early stages of a battery’s lifecycle, providing an efficient and reliable solution for battery health management. However, this study is based on data from small-capacity batteries and does not yet encompass applications in large-capacity or high-temperature scenarios. Future work will focus on expanding the data scope and validating the model’s performance in real-world systems, driving its application in practical engineering scenarios.

Mapping the Washington Group on Disability Statistics disability measure to the Health Utilities Index Mark 3: Development and validation of a predictive multivariable model in a general population sample.

Statistics Canada routinely collects information on functional health and related concepts. Recently, the Washington Group on Disability Statistics (WG) measure of disability has been introduced to the Canadian Community Health Survey (CCHS). The WG measure is used as a tool for developing internationally comparable data on disability. In alternate cycles of the CCHS, it replaces the Health Utilities Index Mark 3 (HUI3), a generic preference-based measure of health-related quality of life. The HUI3 is used to derive evaluative health measures common in population health and economic evaluations. Since the WG measure is not preference-based, it is unable to derive these measures. To address resulting data gaps, this study empirically maps the health state utility values of the HUI3 score from the WG measure. Empirical mapping used a "head-to-head" subsample of the 2017 CCHS where WG and HUI3 measures were collected from the same respondents aged 40 and over. Empirical mapping used regression models to estimate the statistical relationship between WG and HUI3 measures in addition to health and demographic variables. Out-of-sample predictive performance was assessed through descriptive statistics, mean absolute error, and other measures of predictive accuracy. The preferred estimation strategy resulted in reasonably precise estimates of the HUI3 score corresponding to trends across health and demographic characteristics and reflecting distributional properties of the HUI3 score. Inclusion of different components of the WG measure influenced predictive accuracy. Empirical mapping offers a potential method to estimate health state utility scores from the WG measure and addresses data gaps in health-related quality of life measures in the CCHS when HUI3 is not collected.

Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review

The rising adoption of electric vehicles (EVs) utilizing lithium-ion batteries necessitates a robust understanding of state-of-health (SOH) estimation. The existing literature highlights various SOH estimation models, but a comprehensive comparative analysis is lacking. This paper addresses this gap by conducting an exhaustive review of diverse SOH estimation approaches for EV battery applications, including the direct measurement method, physical-based and data-driven approaches. Results highlight that data-driven methods, particularly those utilizing machine learning techniques, offer superior accuracy and adaptability but often require extensive datasets. In contrast, physical-based approaches provide interpretable insights but are computationally intensive, and direct measurement methods, though simple, lack generalizability. In addition, this paper also systematically reviews the indicators of battery SOH, influential factors affecting battery SOH, and various datasets used for SOH modeling. Future research should focus on integrating multiple modeling methodologies to leverage their combined strengths, enhancing the collection of comprehensive battery lifecycle datasets to support robust model development, and extending the scope of SOH estimation beyond individual cells to encompass entire battery packs.

Investigation of the Suitability of the DTV Method for the Online SoH Estimation of NMC Lithium-Ion Cells in Battery Management Systems

Investigating the temperature behavior of lithium-ion battery cells has become an important part of today’s research and development. The main reason for this is that the temperature profile of a battery cell changes during aging. By using Differential Thermal Voltammetry (DTV), new possibilities are opened up, especially since this diagnostic method is designed to work in operando by only requiring voltage and temperature readings. In this study, a batch of NMC-21700 cells were aged in calendar and cyclic manners. After a specified aging cycle was complete, a check-up measurement was performed. During this time, the cycler collected the electrical measuring values, while a negative temperature coefficient thermistor, which was located on the cell, was used to record the temperature fluctuations. The data were then evaluated by using the DTV analysis technique. By comparing the characteristic points of DTV, correlations between the changing curve characteristics and the capacity loss, and therefore the aging of the respective cell, were established. Based on these results, a simple model suitable for online State of Health (SoH) is derived and validated, showing an estimation accuracy of 1.1%.

A State-of-Health Estimation Method of a Lithium-Ion Power Battery for Swapping Stations Based on a Transformer Framework

Against the backdrop of automobile electrification, an increasing number of battery-swapping stations for electric vehicles have been launched to address the issue of slow battery charging under cold temperature conditions. However, due to the separation of the discharging and charging processes for lithium-ion batteries (LIBs) at swapping stations, and the circulation of batteries across different vehicles and stations, the operating data become fragmented, making it difficult to accurately identify the battery state-of-health (SOH). This study proposes a BiLSTM-Transformer framework that extracts the Constant Voltage Time (CVT) feature using only charging data, enabling the precise estimation of battery capacity degradation. Validation experiments conducted on battery samples under different operating temperatures showed that the model achieved a normalized RMSE of less than 1.6%. In ideal conditions, the normalized RMSE of the estimation reached as low as 0.11%. This model enables SOH estimation without relying on discharge data, contributing to the efficient and safe operation of battery swapping stations.

Machine Learning-Based Lithium Battery State of Health Prediction Research

To address the problem of predicting the state of health (SOH) of lithium-ion batteries, this study develops three models optimized using the particle swarm optimization (PSO) algorithm, including the long short-term memory (LSTM) network, convolutional neural network (CNN), and support vector regression (SVR), for accurate SOH estimation. Key features were extracted by analyzing the temperature, voltage, and current curves of the battery, and health factors with high correlation to SOH were selected as model inputs using the Pearson correlation coefficient. The PSO algorithm was employed to optimize model parameters, resulting in the construction of three predictive models: PSO-LSTM, PSO-CNN, and PSO-SVR. The models were validated using the NASA PCoE battery aging datasets B0005, B0006, and B0007, with prediction accuracy evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2). Results indicate that the optimized models achieved significant improvements in prediction accuracy, with RMSE and MAE reduced by over 0.5%, a minimum reduction of 38% in MAPE, and R2 exceeding 0.8, demonstrating strong fitting capabilities and validating the effectiveness of the PSO strategy. Among the three models, PSO-LSTM exhibited the best predictive performance, achieving a minimum MAE of 0.67%, RMSE of 0.94%, MAPE of 45.82%, and R2 as high as 0.9298 across the three datasets. These findings suggest that the PSO-LSTM model provides a robust reference for accurate SOH prediction of lithium-ion batteries and shows promising potential for practical applications.

State of Health Estimation and Battery Management: A Review of Health Indicators, Models and Machine Learning.

Lithium-ion batteries are a key technology for addressing energy shortages and environmental pollution. Assessing their health is crucial for extending battery life. When estimating health status, it is often necessary to select a representative characteristic quantity known as a health indicator. Most current research focuses on health indicators associated with decreased capacity and increased internal resistance. However, due to the complex degradation mechanisms of lithium-ion batteries, the relationship between these mechanisms and health indicators has not been fully explored. This paper reviews a large number of literature sources. We discuss the application scenarios of different health factors, providing a reference for selecting appropriate health factors for state estimation. Additionally, the paper offers a brief overview of the models and machine learning algorithms used for health state estimation. We also delve into the application of health indicators in the health status assessment of battery management systems and emphasize the importance of integrating health factors with big data platforms for battery status analysis. Furthermore, the paper outlines the prospects for future development in this field.

A Hybrid Deep Learning Method for the Estimation of the State of Health of Lithium‐Ion Batteries

This paper proposes a method for estimating the state of health (SOH) of lithium‐ion batteries (LIBs) using a combination of vision transformer (VIT) and gated recurrent unit (GRU) networks. The new scheme adopts a VIT to extract features from the battery measured data and incorporates a GRU network to mitigate the limitations of the VIT caused by positional encoding. The resulting VIT‐GRU network is designed to comprehensively capture information relevant to the battery SOH. Simulation experiments on the NASA dataset illustrate the notable results achieved by the VIT‐GRU, with prediction root mean square error (RMSE) and mean absolute error (MAE) up to 0.54% and 0.38%, respectively, demonstrating the exceptional performance of the VIT‐GRU network in SOH estimation. Compared to other complex deep learning (DL) methods, the VIT‐GRU significantly outperforms them, according to the RMSE and MAE of the predicted values.

LSTM-based estimation of lithium-ion battery SOH using data characteristics and spatio-temporal attention.

As the primary power source for electric vehicles, the accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for ensuring the reliable operation of the power system. Long Short-Term Memory (LSTM), a special type of recurrent neural network, achieves sequence information estimation through a gating mechanism. However, traditional LSTM-based SOH estimation methods do not account for the fact that the degradation sequence of battery SOH exhibits trend-like nonlinearity and significant dynamic variations between samples. Therefore, this paper proposes an LSTM-based lithium-ion SOH estimation method incorporating data characteristics and spatio-temporal attention. First, considering the trend-like nonlinearity of the degradation sequence, which is initially gradual and then rapid, input features are filtered and divided into trend and non-trend features. Then, to address the significant dynamic variations between samples, especially for capacity regeneration,a spatio-temporal attention mechanism is designed to extract spatio-temporal features from multidimensional non-trend features. Subsequently, an LSTM model is built with trend features, spatio-temporal features, and actual capacity as inputs to estimate capacity. Finally, the model is trained and tested on different datasets. Experimental results demonstrate that the proposed method outperforms traditional methods in terms of SOH estimation accuracy and robustness.

Data enhanced deep transfer learning for health state evaluation of cutting tools

ABSTRACT Health state estimation of cutting tools is a key component for maintaining the reliability and stability of the manufacturing process. Although deep learning (DL) based tool wear indirect monitoring methods have been presented, most of them require a substantial amount of data to avoid high misclassification rates. To this end, this article proposes a novel estimation approach based on data-enhanced deep transfer learning (TL) methodology, which integrates multivariate data enhancements, deep convolution network, and generative adversarial learning for state assessment of the tool wear under small sample size. The proposed method includes three innovative points: 1) a multi-step data enhancement method is employed to augment the wavelet scalogram data as model input; 2) a novel training process is adopted to obtain optimal hyperparameters of TL-AlexNet and TL-VGGNet-16 model; 3) a deep transfer learning model for the enhanced data is presented to adaptively estimate the health state of the cutting tools. The proposed approach only requires a small quantity of data to achieve satisfactory estimation performance compared with other DL methods through experimental validation, and the average prediction accuracy for each stage is greater than 95%, which demonstrates the effectiveness and superiority of the presented model.

DEN-HMM: Deep emission network based hidden Markov model with time-evolving multivariate observations

A Hidden Markov Model (HMM) is a popular statistical modeling technique for system health state estimation, monitoring, and prognosis. However, most existing HMMs adopt some simple parametric probability distribution as the distribution of observations for a given state, and thus, cannot capture the intricate dependency of observations on state and possibly other covariates such as time. To address this, we propose a Deep Emission Network-based Hidden Markov Model (DEN-HMM) to capture the complex evolution of multivariate observations with respect to state and time. We also address the challenging issue of state nondiscrimination in DEN-HMM. To overcome this, we propose a regularized loss function that can prevent certain non-discriminative trivial solutions and enhance the state discriminative capabilities of DEN-HMM. The study further demonstrates extensive numerical studies to show the effectiveness of the proposed DEN-HMM, including a case study on steady-state estimation in ultrasonic cavitation-based dispersion processes.

Advanced State-of-Health Estimation for Lithium-Ion Batteries Using Multi-Feature Fusion and KAN-LSTM Hybrid Model

Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The method is based on fully charged battery characteristics, extracting key parameters such as voltage, temperature, and charging data collected during cycles. Validation was conducted under a temperature range of 10 °C to 30 °C and different charge–discharge current rates. Notably, temperature variations were primarily caused by seasonal changes, enabling the experiments to more realistically simulate the battery’s performance in real-world applications. By enhancing dynamic modeling capabilities and capturing long-term temporal associations, experimental results demonstrate that the method achieves highly accurate SOH estimation under various charging conditions, with low mean absolute error (MAE) and root mean square error (RMSE) values and a coefficient of determination (R2) exceeding 97%, significantly improving prediction accuracy and efficiency.

A lithium-ion battery SOH estimation method based on temporal pattern attention mechanism and CNN-LSTM model

Accurately estimating the state of health (SOH) of lithium-ion batteries is crucial for ensuring the availability and efficiency of the energy storage systems based on lithium-ion batteries. This paper proposes a method based on a temporal pattern attention (TPA) mechanism and a CNN-LSTM model to improve the SOH estimation accuracy. Firstly, the health factors (HFs) related to capacity degradation are selected based on the charge–discharge curves of lithium-ion batteries, and the local outliers in the health factor data are detected through the local outlier factor (LOF) method. The Lagrange interpolation is then applied to correct these outliers to ensure the continuity of the health factor data. Secondly, the effectiveness of the health factor is evaluated by using the Pearson and Spearman correlation analyses, with the valid health factor forming the HFs dataset. Thirdly, the TPA mechanism is integrated into the CNN-LSTM model to form a CNN-LSTM-TPA model to enhance its ability to capture key information. The whale optimization algorithm (WOA) is employed to optimize the model’s hyperparameters. Finally, the proposed method is tested on the NASA and CALCE lithium-ion battery datasets. The experimental results show that on the NASA dataset, the proposed method improves the SOH estimation accuracy by approximately 26.39% to 46.05% compared to the LSTM method; on the CALCE dataset, the accuracy improves by approximately 56.05% to 73.32% compared to the LSTM method, respectively. These experimental results indicate that the proposed method achieves higher accuracy.

SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm

ABSTRACTLithium batteries are increasingly favored for energy storage due to their high energy density, long cycle life, and robust charge and discharge rates. However, safety concerns necessitate the implementation of a battery management system (BMS) to monitor battery status, maintain energy balance, and provide failure warnings to ensure safe operation. This paper proposes an efficient BMS for high‐voltage, high‐current lithium battery energy storage. The approach leverages a multihead‐attention‐enhanced long short‐term memory (LSTM) neural network combined with an adaptive unscented Kalman filter to accurately calculate the battery's state of charge (SOC) and state of health (SOH). To improve accuracy, various factors such as temperature and internal resistance were considered. The algorithm was validated through hardware and simulation experiments, with experimental data compared to estimation results to demonstrate its precision. The findings show strong convergence and tracking capabilities, with SOC estimation presenting a maximum error of 1.5% and SOH estimation a maximum error of under 0.4%. We expect that this approach will allow for a more refined evaluation of SOC and SOH in lithium‐ion batteries, potentially improving Li‐ion battery system management.

State of Health Estimation With Incrementally Integratable Data-Driven Methods in Battery Energy Storage Applications

State of Health Estimation With Incrementally Integratable Data-Driven Methods in Battery Energy Storage Applications

Development of a methodology for MEMS accelerometer health state estimation

Development of a methodology for MEMS accelerometer health state estimation

State of health estimation of lithium-ion battery cell based on optical thermometry with physics-informed machine learning

Effective thermal management and accurate state of health (SOH) estimation of lithium-ion batteries is crucial for ensuring their safety, reliability, and longevity. This study presents three innovative physics-informed machine learning-based SOH estimation techniques trained and demonstrated using experimental temperature data. Temperature distribution measurements were obtained using optical frequency domain reflectometry with optical fibers embedded in a cylindrical lithium-ion battery cell under various SOH. One of the trained model accurately predicted the SOH of a cell within 2% with only a 10-minute measurement. This technique also enables the estimation of SOH for individual cells connected in series or parallel within a battery module or pack simultaneously, thereby reducing the overall SOH estimation uncertainty without the need for disassembly. Furthermore, this not only highlights the necessity of precise thermal management in maintaining battery health but also offers a practical and efficient solution for real-time SOH monitoring in battery systems.

Exploration of Imbalanced Regression in state-of-health estimation of Lithium-ion batteries

The state of health (SOH) estimation for lithium-ion batteries based on deep learning (DL) has made great progress. However, due to different electrochemical compositions of lithium-ion batteries, different ways of conducting experiments and other factors, the degradation process of some batteries shows longer early degradation time and shorter later degradation time, resulting in a long-tailed distribution of degradation data. This leads to the problem of data imbalance in SOH estimation tasks, which affects the accuracy of SOH estimation. This article explores the long-tailed distribution phenomenon in the field of batteries and the corresponding imbalanced regression problem it brings to the estimation of battery SOH. In addition, a method for improving model performance is proposed. Specifically, we use a quadratic interpolation and standardization method to analyze the battery data to ensure the consistency of data features. By discretized analysis of continuous problems, the label distribution smoothing (LDS) method is applied to deep neural networks to analyze and solve this imbalanced regression problem. By convolution processing with the kernel function and label distribution, the weights corresponding to different labels are calculated, which improves the estimation accuracy. We conducted battery aging experiments and verified that the degradation data follows a long-tailed distribution. The effectiveness of the final method was validated on our experimental data and a publicly available dataset.