State Of Health Estimation Method Research Articles

Modeling and health feature extraction method for lithium-ion batteries state of health estimation by distribution of relaxation times

Accurate state of health (SOH) estimation of lithium batteries (LIBs) depends on the accuracy of LIBs modeling and the correlation of extracted health features (HFs). Aiming at the problems of a priori assumptions for existing battery modeling and poor correlation of HFs, this paper proposes a SOH estimation method for LIBs based on distribution of relaxation times (DRT). Firstly, we deduce and establish the developed DRT-based impedance model from battery electrochemical impedance spectroscopy (EIS), and the model parameters are calculated by EIS split-frequency domain method based on the principle of parsimony. Then, we analyze the variation rules of the peak and valley information in the distribution function of relaxation times curve with the capacity decay, and we extract multiple features strongly correlated with SOH as HFs. The extracted HFs are subjected to Weighted Principal Component Analysis (WPCA) to obtain indirect health features (IHFs), which achieve the estimation of SOH for LIBs. Finally, the effectiveness of the proposed modeling and health feature extraction method is validated by SOH estimation using the public dataset from Nature subseries. The results indicated that the EIS fitting accuracy R2 of the developed model is the best and the RMSE of SOH estimation is within 0.873 %.

State of Health Estimation for Lithium-Ion Battery Based on Sample Transfer Learning under Current Pulse Test

Considering the diversity of battery data under dynamic test conditions, the stability of battery working data is affected due to the diversity of charge and discharge rates, variability of operating temperature, and randomness of the current state of charge, and the data types are multi-sourced, which increases the difficulty of estimating battery SOH based on data-driven methods. In this paper, a lithium-ion battery state of health estimation method with sample transfer learning under dynamic test conditions is proposed. Through the Tradaboost.R2 method, the weight of the source domain sample data is adjusted to complete the update of the sample data distribution. At the same time, considering the division methods of the six auxiliary and the source domain data set, aging features from different state of charge ranges are selected. It is verified that while the aging feature dimension and the demand for target domain label data are reduced, the estimation accuracy of the lithium-ion battery state of health is not affected by the initial value of the state of charge. By considering the mean absolute error, mean square error and root mean square error, the estimated error results do not exceed 1.2% on the experiment battery data, which highlights the advantages of the proposed methods.

State of health estimation method based on real data of electric vehicles using federated learning

State of health estimation method based on real data of electric vehicles using federated learning

State-of-health estimation for lithium-ion batteries based on GWO–VMD-transformer neural network

State-of-health (SOH) estimation of lithium-ion batteries stands as a fundamental metric within the battery management system. It reflects the current level of battery aging and is important for early warning of battery failure to avoid unsafe battery behavior. Therefore, accurate SOH estimation can ensure safe and reliable battery operation. In this paper, the capacity data of the discharge phase are used as the input of the SOH estimation model, and a gray wolf optimization (GWO)–variable mode decomposition (VMD)-transformer-based SOH estimation method for lithium-ion batteries is proposed in a data-driven framework. First, the GWO algorithm is adopted to optimize VMD to decompose the original battery capacity degradation sequence into a series of intrinsic mode functions (IMFs). Then, the transformer is used to separately predict each of these IMFs. Finally, the predicted values of each IMF are integrated to obtain the final prediction of the battery capacity degradation sequence. The model undergoes testing across various datasets, and comparative evaluations are conducted against other data-driven prediction models. The experimental findings underscore the superior SOH estimation performance of the proposed method, along with its robustness when confronted with diverse types of lithium-ion batteries, spanning distinct operational conditions and different aging degrees.

A self-attention knowledge domain adaptation network for commercial lithium-ion batteries state-of-health estimation under shallow cycles

Accurate state-of-health (SOH) estimation is crucial for the safety, efficiency, and reliability of battery-powered systems. Conventional SOH estimation methods are designed for the full state-of-charge (SOC) range of 0%–100%, assuming uniform distributions. Yet, batteries in real-world applications often operate under partial SOC ranges and experience shallow-cycle conditions, which result in distinct and unlabeled degradation profiles that make SOH estimation more complex. To tackle this issue, we propose a novel method—self-attention knowledge domain adaptation network (SKDAN)—which utilizes a self-attention distillation module combined with a multi-kernel maximum mean discrepancy framework to effectively navigate between these disparate domains. This strategy successfully extracts domain-specific features from charging curves, facilitating the transfer of knowledge from well-labeled full cycles to unlabeled shallow cycles. The effectiveness of the SKDAN method is validated through extensive testing on the CALCE and SNL battery datasets, demonstrating its robust capability to estimate SOH accurately across different SOC ranges, temperatures, and discharge rates. With a root-mean-square error (RMSE) of less than 2%, the SKDAN method outperforms existing transfer learning techniques for various SOC ranges. Additionally, it exhibits exceptional SOH estimation performance when applied to batteries from different manufacturers and operating under varied conditions. This study represents the first instance of accurately estimating battery SOH under shallow-cycle conditions without relying on full-cycle characteristic tests.

Online State of Health Estimation for Series-Connected LiFePO₄ Battery Pack Based on Differential Voltage and Inconsistency Analysis

Low-complexity and accurate state of health estimation of series-connected batteries has always been a difficult problem to solve in a well-designed battery management system. Lithium iron phosphate battery has been widely used as energy storage carrier due to its better safety and longer cycle life. In this paper, we proposed an online state of health estimation method for lithium iron phosphate battery pack based on differential voltage and inconsistency analysis. According to the aging mechanism of lithium iron phosphate battery, the region capacity in differential voltage curve is extracted as health feature to establish an aging model. Furthermore, all four inconsistency cases that affect pack capacity are analyzed, and the calculation formulas are concluded relying only on three representative battery modules, thereby reducing the computational burden. The pack capacity is estimated to verify the feasibility of the proposed method at different aging cycles. The estimation errors of the state of health of eight in-pack battery modules are less than 2%, and further the maximum error for the battery pack is only 2.27% in all aging cycles. In addition, the maximum error of the state of health for the other battery pack is only slightly over 1% at different inconsistencies.

State of Health Estimation for Lithium-Ion Batteries Under Arbitrary Usage Using Data-Driven Multimodel Fusion

Accurately estimating the state of health (SoH) of batteries is indispensable for the safety, reliability, and optimal energy and power management of electric vehicles. However, from a data-driven perspective, complications, such as dynamic vehicle operating conditions, stochastic user behaviors, and cell-to-cell variations, make the estimation task challenging. This work develops a data-driven multi-model fusion method for SoH estimation under arbitrary usage profiles. All possible operating conditions are categorized into six scenarios. For each scenario, an appropriate feature set is extracted to indicate the SoH. Based on the obtained features, four machine learning algorithms are applied individually to train SoH estimation models using time-series data. In addition to the estimates at the current time step, a histogram data-based and online adaptive model is taken from previous work for predicting the next-step SoH. Then, a Kalman filter is applied to systematically fuse the results of all the estimation and prediction models. Experimental data collected from different types of batteries operated under diverse profiles verify the effectiveness and practicability of the developed method, as well as its superiority over individual models.

Lithium-ion battery state of health estimation method based on variational quantum algorithm optimized stacking strategy

Accurate state-of-health (SOH) estimation is critical for the performance and safety of lithium-ion batteries. An innovative method for SOH estimation is proposed by employing a variational quantum algorithm to optimize a stacking integrated learning strategy. The strategy effectively combines multiple model advantages, enhancing the estimation accuracy and generalizability. Using this method, eight sets of health factors are extracted, focusing on the relationship between battery capacity degradation and electrothermal parameters. A stacking integrated learning framework is developed by utilizing diverse primary learners to effectively capture the dynamic changes in health factors. A ridge regression meta-learner is incorporated to address overfitting problems found in primary learners. A significant innovation is the integration of a variational quantum circuit module as the primary learner. This module plays a crucial role in optimizing the hyperparameters for the analysis of complex and high-dimensional battery data. The effectiveness of the method is validated using four different types of batteries, showing a 77.4% improvement in prediction accuracy compared with traditional methods, with the SOH estimation error maintained within a tight margin of 0.67%. The mean absolute error, mean absolute percentage error, and root mean square error with maximum reduction rates are 76.7%, 77.4%, and 62.7%, respectively. The maximum increase in the R-squared coefficient is 5.3%. This study demonstrates the potential of variational quantum algorithms in enhancing the SOH estimation accuracy and opens new possibilities for the advanced health status management of lithium-ion batteries.

Non-Invasive Method-Based Estimation of Battery State-of-Health with Dynamical Response Characteristics of Load Surges

Battery state-of-health (SOH) estimation is an effective approach to evaluate battery reliability and reduce maintenance costs for battery-based backup power supply systems. This paper proposes a novel SOH estimation method for batteries, which only uses the response characteristics of load surges and is, therefore, non-destructive to the estimated battery and its system. The discrete wavelet transform (DWT) method based on multi-resolution analysis (MRA) is used for wavelet energy features extraction, and the fuzzy cerebellar model neural network (FCMNN) is introduced to design the battery SOH estimator. The response voltage signals to load surges are used in the training and detection process of the FCMNN. Compared to conventional methods, the proposed method only exploits characteristics of online response signals to the inrush currents rather than injecting interference signals into the battery. The effectiveness of the proposed method is validated by detailed simulation analysis and experiments.

State of health estimation of lithium-ion battery based on CNN–WNN–WLSTM

Accurate and stable estimation of the state of health (SOH), which is one of the critical indicators to characterize the ability of lithium-ion (Li-ion) batteries to store and release energy, is critical in the stable driving of electric vehicles. In this paper, a novel SOH estimation method based on the aging factors of battery, which combines convolutional neural network (CNN), wavelet neural network (WNN), and wavelet long short-term memory (WLSTM) named CNN–WNN–WLSTM, is designed. The proposed CNN–WNN–WLSTM estimation scheme inherits both the fast convergence and robust stability of the WNN, as well as the ability of long short-term memory neural network (LSTM) to extract the time series features of the data; moreover, using CNN can make the proposed algorithm extract the data features from the original battery data automatically, and the WNN–WLSTM is then adopted to produce the final SOH estimation by exploiting the features from the CNN. To further speed and achieve global optimization, the RMSprop optimizer, instead of the usually used Adagrad optimizer, is chosen as the solver of the CNN–WNN–WLSTM network. Experimental results on data set from the NASA Ames Prognostics Center of Excellence show that the proposed algorithm can be commendably used for Li-ion battery health management by quantitative comparison with other commonly used machine learning methods, such as back-propagation neural network, WNN, LSTM, WLSTM, convolutional neural network–long short-term memory neural network (CNN–LSTM), and Gaussian process regression.

A Transfer Learning-Based Method for Personalized State of Health Estimation of Lithium-Ion Batteries.

State of health (SOH) estimation of lithium-ion batteries (LIBs) is of critical importance for battery management systems (BMSs) of electronic devices. An accurate SOH estimation is still a challenging problem limited by diverse usage conditions between training and testing LIBs. To tackle this problem, this article proposes a transfer learning-based method for personalized SOH estimation of a new battery. More specifically, a convolutional neural network (CNN) combined with an improved domain adaptation method is used to construct an SOH estimation model, where the CNN is used to automatically extract features from raw charging voltage trajectories, while the domain adaptation method named maximum mean discrepancy (MMD) is adopted to reduce the distribution difference between training and testing battery data. This article extends MMD from classification tasks to regression tasks, which can therefore be used for SOH estimation. Three different datasets with different charging policies, discharging policies, and ambient temperatures are used to validate the effectiveness and generalizability of the proposed method. The superiority of the proposed SOH estimation method is demonstrated through the comparison with direct model training using state-of-the-art machine learning methods and several other domain adaptation approaches. The results show that the proposed transfer learning-based method has wide generalizability as well as a positive precision improvement.

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 Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression

Battery aging is a complex phenomenon, and precise state of health (SoH) monitoring is essential for effective battery management. This paper presents a data-driven method for SoH estimation based on support vector regression (SVR), utilizing features built from both full and partial discharge capacity curves, as well as battery temperature data. It provides an in-depth discussion of the novel features constructed from different voltage intervals. Moreover, three combinations of features were analyzed, demonstrating how their efficacy changes across different voltage ranges. Successful results were obtained using the full discharge capacity curves, built from the full interval of 2 to 3.4 V and achieving a mean R2 value of 0.962 for the test set, thus showcasing the adequacy of the selected SVR strategy. Finally, the features constructed from the full voltage range were compared with ones built from 10 small voltage ranges. Similar success was observed, evidenced by a mean R2 value ranging between 0.939 and 0.973 across different voltage ranges. This indicates the practical applicability of the developed models in real-world scenarios. The tuning and evaluation of the proposed models were carried out using a substantial dataset created by Toyota, consisting of 124 lithium iron phosphate batteries.

A neural-driven stochastic degradation model for state-of-health estimation of lithium-ion battery

Due to usage for a long time, lithium-ion battery degradation issues may endanger security operations for service equipment. As batteries usually operate under uncertain usage profiles and experience random aging processes, accurately estimating the battery state of health (SOH) can be challenging. Most existing SOH estimation methods are either empirical knowledge-based with complicated parameters or serve as a black box neural network-based without stochastic nature. To address this limitation, a neural-driven stochastic degradation model for battery health estimation is proposed to ensure the security and reliability of the operation. Firstly, the parameters of a stochastic process are estimated by martingale methodology to describe battery degradation characteristics. Then the optimized sequence-to-sequence (Seq2Seq) neural network is utilized to develop a mapping model in order to update drift coefficient online. Concurrently, the martingale estimating function is used to obtain the diffusion coefficient function. Finally, the transition probability density function of SOH estimation is derived. Comparative experiments are carried out on different battery datasets to show the effectiveness of the proposed model against traditional Wiener process-based models and existing methods in SOH estimation. The model proposed provides an effective solution for battery SOH estimation of energy storage equipment.

Open access dataset, code library and benchmarking deep learning approaches for state-of-health estimation of lithium-ion batteries

Great progress has been made in deep learning (DL) based state-of-health (SOH) estimation of lithium-ion batteries, which helps to provide recommendations for predictive maintenance and replacement of lithium-ion batteries. However, despite the abundance of articles, few open-source codes are publicly available. While there are several public datasets, they tend to be more oriented toward simulating laboratory environments rather than real-world usage scenarios. Moreover, they solely provide raw data without any corresponding preprocessing codes, resulting in inconsistencies in preprocessing methods across different papers. These reasons lead to unfair comparisons and ineffective improvements. In response to these problems, this paper publishes a large-scale lithium-ion battery run-to-failure dataset, consisting of 55 batteries, and provides a unified data preprocessing method. Besides, we comprehensively evaluate 5 well-known DL-based models to provide benchmark research. To be specific, first, the existing DL-based SOH estimation methods are reviewed in detail. Second, we provide a comprehensive evaluation of DL-based models on 2 large-scale datasets, including 100 batteries, with 3 input types and 3 normalization methods. Third, we make the complete evaluation codes and dataset publicly available for better comparison and model improvement. Fourth, we discuss future DL-based SOH estimation, including unsupervised learning, transfer learning, interpretability, and physics-informed machine learning. We emphasize the importance of open-source code, provide baseline estimation errors (error upper bounds), and discuss existing issues in this field. The code library is available at: https://github.com/wang-fujin/SOHbenchmark.

SOH estimation method for lithium-ion batteries under low temperature conditions with nonlinear correction

The burgeoning growth of green energy in the transportation sector has resulted in increased expectations for battery longevity and safety. However, the capacity of lithium-ion batteries (LIBs) decreases with each successive charge and discharge cycle. And under harsh operating conditions, the capacity decay can exhibit strong nonlinearity. To enable effective battery management under such complex conditions, it is crucial to possess precise understanding of the state of health (SOH) of LIB. In this study, low-temperature aging experiments were designed to obtain capacity attenuation data of LIBs. Then the nonlinear component in capacity decay is identified and transformed into model nonlinear correction. Subsequently, we developed a long short-term memory (LSTM) neural network and extracted incremental capacity (IC) features based on experimentally collected data to train the model. The findings indicate that by training with only one cell aging dataset, the model can estimate the SOH for other samples. Furthermore, we introduced nonlinear correction features with an attention mechanism (AM) and analyzed the improvement of prediction accuracy through feature optimization and network adjustment to achieve SOH estimation with a maximum root mean square error (RMSE) of 0.92 %. This approach provides a potential solution for predicting SOH of LIBs at low temperature.

Adaptation of Deep Network in Transfer Learning for Estimating State of Health in Electric Vehicles during Operation

Battery state of health (SOH) is a significant metric for evaluating battery life and predicting battery safety. Currently, SOH research is largely based on laboratory data, with a dearth of research on electric vehicle (EV) operating data. Due to the difficulty in obtaining complete charge data under EV operating conditions, this study presents a SOH estimation method utilizing deep network adaptation. First, a data-driven approach is employed to extract voltage, current, state of charge (SOC), and incremental capacity (IC) data features. To compensate for the lack of aging information in the EV operation data domain, transfer learning is employed to construct the SOH estimation model. Additionally, to resolve inconsistent data distribution between the source laboratory battery data domain and the target EV operation data domain, an adaptive layer is added to the network, and adaptation of deep network (ADN) is utilized to enhance the model’s performance. Finally, the model is validated using electric bus operational data. Results indicate that this model’s average Mean Absolute Error (MAE) is less than 3.0%, and, compared to support vector machine (SVM) regression and Gaussian Process Regression (GPR) algorithms, the MAE is reduced by 27.7% and 38.4%, respectively.

State-of-health estimation method for fast-charging lithium-ion batteries based on stacking ensemble sparse Gaussian process regression

Gaussian process regression (GPR) is extensively employed in lithium-ion battery state-of-health (SOH) estimation, which ensures the safe, reliable operation of electric vehicles (EVs). However, a single GPR can produce performance discrepancies across different fast-charge batteries, as well as high time consumption. Therefore, we propose an SOH estimation method for fast-charging batteries based on stacking ensemble sparse Gaussian process regression (SGPR). First, health factors are extracted in partial discharge fragments to reflect battery degradation. Then, SGPRs based on the fully independent training condition (FITC) are developed with different kernel functions as level-1 learners, and a genetic algorithm (GA) is used to optimize the parameters of the kernel function. Further, the level-2 learner integrates the features produced by the level-1 learner based on cross validation. Finally, the accuracy, robustness, and reliability of the proposed method were evaluated under various fast-charging experiments. The results show that the mean absolute error (MAE) and root mean square error (RMSE) of SOH estimation were within 1.0852% and 1.2123%, respectively, and that the average relative time consumption was reduced by 85.68% compared with stacking ensemble GPR. Thus, the proposed method has broad application prospects in processing vast datasets from numerous batteries in monitoring platforms or cloud data centers.

A new SOH estimation method for Lithium-ion batteries based on model-data-fusion

A new method for the estimation of the state-of-health (SOH) of lithium-ion batteries (LIBs) is proposed. The approach combines a LIB equivalent circuit model (ECM) and a deep learning network. Firstly, correlation analysis is performed between the LIB data and SOH and suitable portions are selected as health features (HFs). Simultaneously, a fractional-order RC ECM of the LIB is derived and a hybrid fractional particle swarm optimization with crisscross learning (FPSO-CL) strategy is used to identify the model parameters. Secondly, correlation analysis between the model parameters and SOH is conducted and those that best represent the battery health are selected as additional HFs. Thirdly, an improved vision transformer network (VIT) is designed by including a dimension transformation layer, multilayer perceptron and a trainable regression token. Finally, the VIT is trained with all determined HFs, yielding a compete framework for predicting the SOH of LIBs. Experimental verification is carried out on real LIBs data and the results show that the proposed scheme can achieve higher prediction accuracy than other alternative methods.

Power Batteries State of Health Estimation of Pure Electric Vehicles for Charging Process

Abstract Under different usage scenarios of various electric vehicles (EVs), it becomes difficult to estimate the battery state of health (SOH) quickly and accurately. This article proposes an SOH estimation method based on EVs’ charging process history data. First, data processing processes for practical application scenarios are established. Then the health indicators (HIs) that directly or indirectly reflect the driver's charging behavior in the charging process are used as the model's input, and the ensemble empirical mode decomposition (EEMD) is introduced to remove the noise brought by capacity regeneration. Subsequently, the maximum information coefficient (MIC)—principal component analysis (PCA) algorithm is employed to extract significant HIs. Eventually, the global optimal nonlinear degradation relationship between HIs and capacity is learned based on Bayesian-optimized Gaussian process regression (BO-GPR). Approximate battery degradation models for practical application scenarios are obtained. This article validates the proposed method from three perspectives: models, vehicles, and regions. The results show that the method has better prediction accuracy and generalization capability and lower computational cost, which provides a solution for future online health state prediction based on a large amount of real-time operational data.