Online State Of Health Research Articles

Optimal dispatch approach for second-life batteries considering degradation with online SoH estimation

In light of upcoming electric vehicle (EV) battery retirement issues, second-life batteries (SLBs) have received increasing attention for their ability to extend the life-span of existing batteries and postpone the manufacturing of new batteries. When compared with new batteries, the process by which SLBs degrade demands more attention as they are more vulnerable to external stress and more likely to suffer from physical collapses. Their dispatch approaches, especially in terms of degradation, are essential tools for investors and end-users to investigate their technical and economic viability. In this paper, an optimal dispatch approach considering degradation with online state of health (SoH) estimation is developed, which integrates SoH into the optimization as a time-varying parameter affecting the battery performance. This online SoH estimation model leverages the Kalman filter’s estimation power to achieve higher accuracy by combining short-term estimation and long-term prediction results. Moreover, the heterogeneous characteristics among these retired batteries due to their diverse first-life usage patterns and working conditions are considered by assigning different initial values of SoH and degradation paths. Subsequently, the performance of the proposed approach, an alternative dispatch approach considering degradation with a state of charge (SoC) based model, and a dispatch approach with no degradation consideration were compared in the case study. The results show that the proposed approach can lead to less battery degradation and can save costs with the batteries operating in a complementary way (not charging/discharging uniformly) to improve energy balancing and energy arbitrage.

A Novel Online State of Health Estimation Method for Electric Vehicle Pouch Cells Using Magnetic Field Imaging and Convolution Neural Networks

Lithium-ion batteries (LiBs) are used as the main power source in electric vehicles (EVs). Despite their high energy density and commercial availability, LiBs chronically suffer from non-uniform cell ageing, leading to early capacity fade in the battery packs. In this paper, a non-invasive, online characterisation method based on deep learning models is proposed for cell-level SoH estimation. For an accurate measurement of the state of health (SoH), we need to characterize electrochemical capacity fade scenarios carefully. Then, with the help of real-time monitoring, the control systems can reduce the LiB’s degradation. The proposed method, which is based on convolutional neural networks (CNN), characterises the changes in current density distributions originating from the positive electrodes in different SoH states. For training and classification by the deep learning model, current density images (CDIs) were experimentally acquired in different ageing conditions. The results confirm the efficiency of the proposed approach in online SoH estimation and the prediction of the capacity fade scenarios.

Online state-of-health estimation of lithium-ion battery based on relevance vector machine with dynamic integration

The lithium-ion battery’s state of health (SOH) is one of the essential parameters of the battery management system. An accurate state of health estimation of the battery pack helps to improve the service life of the overall battery pack. Given the poor generalization ability of a single data-driven model during SOH online estimation and the lack of uncertainty expression ability in the estimation results, this paper proposes an online SOH estimation method of lithium-ion battery based on bat algorithm optimized relevance vector machine (BA-RVM) with dynamic integration. Firstly, we perform feature extraction and select equal voltage drop discharge time as an indirect health factor. Secondly, we establish the integration model, take the wavelet kernel relevance vector machine (RVM) as the sub-model, and use the bat algorithm (BA) to optimize its kernel parameters to improve the estimation accuracy of the sub-model. Then we use the online monitoring data to update the weights of the sub-models continuously and dynamically integrate the output of the sub-models to improve the accuracy of SOH online estimation further. Finally, the correctness and effectiveness of the method are verified based on battery data from NASA and compared with other data-driven methods. The experimental results show that compared with the method based on a single data-driven model, this method has higher accuracy and more vital generalization ability, and the estimation results have specific uncertainty expression ability.

Online Estimations of Li-Ion Battery SOC and SOH Applicable to Partial Charge/Discharge

Estimating the state of health (SOH) and state of charge (SOC) of lithium-ion batteries is crucial for increasing the battery lifetime and performance. Many estimation methods are offline and require large datasets for training. The majority of online estimation methods either take too much time or need a full discharge or charge cycle. In this article, a fast online SOH estimation method that can work with partial charge/discharge is introduced. Only two consecutive partial discharge intervals are used to estimate the battery equivalent circuit model parameters and the open-circuit voltage (OCV). By comparing the estimated OCV curve at each interval with a reference or datasheet OCV curve, the battery capacity and, therefore, its SOH and SOC are accurately estimated. It is shown that updating the OCV reference curve based on temperature readings will provide more accurate results. NASA degradation dataset is used to validate the proposed method and the average reported root-mean-square error is below 1% for SOH and 1.07% for SOC.

Fast capacity estimation for lithium-ion battery based on online identification of low-frequency electrochemical impedance spectroscopy and Gaussian process regression

Accurate capacity estimation is critical to improving the safety and reliability of lithium-ion (Li-ion) battery systems. Traditional capacity estimation mainly extracts capacity-related features by passively collecting the battery voltage, current, and temperature signals, which requires high integrity and regularity of charging curves. In this paper, six health indicators (HIs) are extracted through the online identification of Low-frequency electrochemical impedance spectroscopy (LEIS) by step wave, combined with Gaussian process regression (GPR) to achieve a fast capacity estimation for Li-ion batteries. The step wave is injected into the battery system during the charging process through BMS and bi-directional converter cooperation. Compared with square and multi-sine waves, the stress and response of each frequency step wave are equivalent to that of the sine wave. Moreover, HIs are resolved from the actual Warburg impedance angle instead of the empirical angle π/4. Three novel HIs related to the Li-ion diffusion coefficient are proposed: Warburg factor Wd, pseudo-Li-ion diffusion state PLDS and residual signal of empirical mode decomposition PLDSr. The volume of GPR training data is only 34% of the whole frequency band EIS data. The results show that the identified LEIS achieves 0.96 goodness-of-fit (R2) at the minimum sampling frequency of 50 Hz, and the novel HIs significantly improve the state of health (SOH) estimation accuracy with R2 above 0.95, root mean squared error below 1%, and mean absolute percentage error of about 0.9%. This method is an effective way to the active SOH detection for Li-ion battery, which is vital for the online SOH evaluation and early safety warning.

Online Estimating State of Health of Lithium-Ion Batteries Using Hierarchical Extreme Learning Machine

Battery state-of-health (SoH) monitoring is of great importance to ensure the safety and reliability of battery systems. This study proposed an innovative SoH estimation method using hierarchical extreme learning machine (HELM) to improve the estimation robustness and accuracy without the complex parameter model was directly applied to establish the HELM-oriented online SoH estimation framework. First, the increase in mean ohmic resistance was constructed as a novel health indicator (HI) to characterize battery aging. Then, the HI was adopted for offline training to build an HELM model, which captures the underlying correlation between the extracted HI and capacity degradation. Finally, the datasets of four batteries at three different temperatures with dynamic loading profiles were used for validation. The results show that the SoH estimation errors are no more than 1.5%, while the training and estimation datasets are from the same temperature; when the SoH estimation is conducted at different temperatures, the maximum error is only 3.36%. The results indicate that the proposed method had good generalization and reliability for SoH estimation, which is applicable for dynamic scenarios with different temperatures.

State of health estimation of lithium-ion batteries based on the regional frequency

The state of health (SOH) estimation of lithium-ion batteries at low data sampling frequencies has important practical significance in engineering application. This paper proposes a new SOH evaluation method for lithium-ion batteries under the framework of probability density function (PDF). The concepts of regional voltage and regional frequency are introduced, and the SOH models are established based on the experimental cycle data of the nickel-cobalt-aluminium (NCA) battery cell and lithium iron phosphate (LFP) battery module. The results show that the SOH is a simple linear function of the regional frequency in the obtained model, and the fitting R -squared ( R 2 ) of two battery SOH models can reach more than 0.99 with appropriate regional voltage. This method does not directly use the characteristic parameters of PDF and is insensitive to the sampling frequency as low as 1/60 Hz. It is effective for accurate online SOH evaluation in occasions where the sampling frequency cannot be too high, such as large energy storage power station. Moreover, this method is more suitable for module SOH evaluation, which can overcome the poor SOH evaluation caused by the inconsistency of cells in a module. • An improved PDF method for SOH evaluation is proposed based on regional frequency. • The regional frequency method can be used at the sampling frequency as low as 1/60 Hz. • The regional frequency method is suitable for SOH modeling of cells and modules. • A good linear positive correlation between battery SOH and regional frequency.

State of health estimation of cycle aged large format lithium-ion cells based on partial charging

Incremental capacity analysis (ICA) is a widely used method for assessing Li-ion battery state of health (SOH). The ICA method is typically applied to low C-rate charging or discharging, which is seldom feasible in online SOH estimation in real applications. In this paper the applicability of the ICA method to higher C-rates is investigated with C/3, 1C and 2C charging data from cycle aged large format commercial LiFePO4/graphite cells, LiNiMnCoO2/graphite cells, and cells with Li2TiO3 anode. Alongside the ICA method, a less studied integrated voltage (IV) method is investigated with the same data set. The results show that both the ICA method and the IV method can be used for battery SOH estimation utilizing partial charging data. The results for the ICA method indicate an accuracy of 0.9–2.1% (RMSE) with C/3 charging for all studied cells, and corresponding accuracy also for some of the cell types studied with 1C and 2C charging currents. The accuracy of the IV method does not suffer from increasing charging current as heavily as the accuracy of the ICA method, and the SOH estimation error remains below 2.0% (RMSE) with all studied cell types up to 2C charging.

Online state of health monitoring of lithium-ion battery based on model error spectrum for electric vehicle applications

Lithium-ion battery, as the mainstream power source of electric vehicles, suffers the performance degradation in the long-term vehicular usage. Battery health monitoring is quite an important task of the battery management system. This paper presents a novel method of online estimating the battery state of health (SOH) based on the evolution of the model error during the battery aging process. The monitoring model is established by using the test data of fresh batteries. During the process of battery aging, the model precision gradually declines and there is an implicit correlation between the aging degree and the distribution of model error. The model error spectrum (MES) is established based on the kurtosis and skewness of the model error statistics. It is found that there is an exponential relation between battery SOH and the MES. Based on the above work, the online SOH estimation is proposed by obtaining the MES within the historical data in a pre-set time window. The experiments are used to evaluate the presented method. The results indicate that the precise estimation results of battery SOH can be obtained with the error less than 1.07%. It is expected that the presented method is useful in online health monitoring and prognosis applications of the lithium-ion batteries in electric vehicles.

Research on the SOH Prediction Based on the Feature Points of Incremental Capacity Curve

The accurate prediction of the state of health (SOH) is an important basis for ensuring the normal operation of the lithium-ion battery (LIB). The accurate SOH can extend the life-span, ensure safety, and improve the performance of LIBs. The charging voltage curve and incremental capacity (IC) curve of the LIB in different SOH are obtained through experiments. The location parameters of each feature point on IC curve are closely related to battery aging, to characterize the SOH of the LIB with the location of feature points. To solve the difficulty in identifying feature points due to the oscillation in solving IC curves with a traditional numerical analytic method, the piecewise polynomial fitting method is adopted to smooth IC. To discuss the law between the location change of all feature points on the IC curve and the capacity attenuation, a capacity prediction regression model is established after the dimensionality reduction of the coordinate data of feature points on the IC curve with the principal component analysis method. The proposed method can rapidly estimate the online SOH of LIBs during the charging process of electric vehicles and the results show the maximum error is 0.63AH (3.15%).

Determination of Optimal Indicators Based on Statistical Analysis for the State of Health Estimation of a Lithium-Ion Battery

Online state of health (SOH) estimation is essential for lithium-ion batteries in a battery management system. As the conventional SOH indicator, the capacity is challenging to be estimated online. Apart from the capacity, various indicators related to the internal resistance are proposed as indicators for the SOH estimation. However, research gaps still exist in terms of optimal resistance-related indicators, online acquisition of indicators, temperature disturbance elimination, and state of charge (SOC) disturbance elimination. In this study, the equivalent circuit model parameters are identified based on recursive least square method in dynamic working conditions in the life span. Statistical analysis methods including multiple stepwise regression analysis and path analysis are introduced to characterize the sensitivity of the parameters to SOH estimation. Based on the above approach, the coupling relationship between the parameters is comprehensively analyzed. Results indicate that the ohmic resistance R0 and the diffusion capacitance Cd are the most suitable parameters for the SOH indication. Furthermore, R0 and Cd are proved to be exponentially correlated to the ambient temperature, while SOC demonstrates a quadratic trend on them. To eliminate the disturbance caused by the ambient temperature and SOC, a compensating method is further proposed. Finally, a mapping relationship between SOH and the indicators under normal operations is established. SOH can be estimated with the maximum error of 2.301%, which proves the reliability and feasibility of the proposed indicators and estimation method.

Aging performance characterization and state-of-health assessment of retired lithium-ion battery modules

State of health (SOH) is an important index to evaluate the use value of batteries. The rapid SOH evaluation method for battery modules is a more popular topic in the secondary utilization of retired batteries. This paper explores the aging behavior of a 15P4S battery module in the cycle protocol of 2 C-rate and 50% DOD among 30–80% SOC using electrochemical impedance spectroscopy (EIS), charge/discharge (C/D) curve, incremental capacity analysis (ICA) and average Fréchet distance (AFD). It is found that Rs, Rct and Rf all increase with aging and the sum of Rs and Rf can be used as a health factor for off-line SOH evaluation of battery modules. The height of the characteristic peak near 13.45 V on the ICA curves can quickly evaluate the module SOH. The SOH estimation model based on the AFD method achieves the maximum percentage error of 1.518% for lithium-ion battery modules, suggesting the high accuracy of the fast SOH evaluation model. AFD has a better accuracy on online SOH evaluation for modules than ICA.

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

Online state-of-health (SOH) estimation is critical for second-use retired lithium-ion batteries. However, the SOH of retired batteries is highly nonlinear, and the existing degradation trend data are limited. Consequently, achieving accurate and effective SOH estimation remains a challenge. To address the above problem, an online SOH estimation based on a weighted least squares support vector machine (WLS-SVM) is proposed in this paper. In this work, the health features (HFs) are first extracted from the partial charging curves of retired batteries, and the Pearson correlation coefficient is applied to select the important HFs that are strongly correlated to the SOH. These selected HFs are used as the estimation model inputs for characterizing the aging procedure of the retired battery effectively. Then, to enhance the accuracy and robustness of SOH estimation, the standard support vector machine (SVM) is improved by a weighting function and linear equations. Last, the online SOH estimation is conducted by using the test data sets of second-use batteries with different battery materials and under different conditions. The results show that compared with the most popular methods, such as the back-propagation neural network (BPNN)-based method, the Gaussian process regression (GPR)-based method, and the standard SVM-based method, the performance of the WLS-SVM-based method is superior. The root mean square error (RMSE) for the SOH estimation with the WLS-SVM-based method for all the test cells is less than 1.85% at different aging paths and levels, whereas the RMSEs of the BPNN-based method, GPR-based method, and standard SVM-based method are within 3.6%, 5.7%, and 7.6%, respectively. The proposed WLS-SVM-based method can thus provide highly robust and accurate online SOH estimation.

Online state-of-health estimation of lithium-ion battery based on dynamic parameter identification at multi timescale and support vector regression

Accurate real-time State-of-Health (SoH) estimation is important in ensuring battery safety and promoting efficiency. The open circuit voltage (OCV), relative to State-of-Charge (SoC), changes as a battery degrades. In this paper, we propose using an extended Kalman filter-recursive least squares parameter identification method based on the second-order RC equivalent circuit model. SoC estimation, RC model parameter identification and OCV-SoC relationship revision can be conducted simultaneously at three timescales to improve the identification accuracy. Then, by analyzing the relationship between battery SoH and the model parameters, we establish a support vector regression algorithm for online SoH estimation. Finally, the dynamic stress test and the federal urban driving schedule test are adopted and used as practical simulations to verify the feasibility of the method under dynamic conditions. The results show that the proposed method achieves highly accurate estimations and has strong robustness under both static and dynamic conditions.

Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries

Inaccurate state-of-health (SoH) estimation of battery can lead to over-discharge as the actual depth of discharge will be deeper, or a more-than-necessary number of charges as the calculated SoC will be underestimated, depending on whether the inaccuracy in the maximum stored charge is over or under estimated. Both can lead to increased degradation of a battery. Inaccurate SoH can also lead to the continuous use of battery below 80% actual SoH that could lead to catastrophic failures. Therefore, an accurate and rapid on-line SoH estimation method for lithium ion batteries, under different operating conditions such as varying ambient temperatures and discharge rates, is important. This work develops a method for this purpose, and the method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a lithium-ion battery for the estimation of its maximum stored charge capacity, and thus its state of health. The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of lithium ion batteries (LiB) for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.

A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis

Accurate estimation of state of health (SOH) is crucial for battery management system in ensuring the reliability and safety for system operation. For SOH estimation, the model-based methods require sophisticated battery models while the data-driven methods need huge battery data and computation burden. To avoid these drawbacks, a model-free SOH calculation method by fusion of coulomb counting method and differential voltage analysis (DVA) is proposed, realizing rapid online SOH calculation under constant current discharging stage. Firstly, the conventional coulomb counting method is converted to calculate SOH, which needs two state of charge (SOC) and corresponding measurement time to further proceed. Subsequently, DV curves are obtained based on the battery measurable parameters without smoothing or function fitting, then the x-axis of DV curves is replaced by SOC axis to get SOC based DV curves. Using discrete analysis, two SOC feature points are identified from SOC based DV curves, whose corresponding measurement time and mean SOC are used to compute SOH directly. In addition, the SOH calculation accuracy of the proposed method is verified by experimental data. The validation results indicate that this method can provide online accurate SOH calculation under constant current discharging stage with low computation.

Online state of health and aging parameter estimation using a physics-based life model with a particle filter

Degradation of lithium-ion batteries due to undesirable side reactions causes changes in the compounds and morphologies of electrode materials that lead to the loss of lithium ions, loss of active materials, consumption of electrolyte solvents, and increase of internal resistance, eventually resulting in capacity and power fade, thereby reducing battery life. In this paper, we propose an online estimator for the state of health (SOH) and aging parameters using a high-fidelity, reduced-order physics-based life model, which consists of a pseudo-two-dimensional model coupled with a degradation model of two types of side reactions—solid electrolyte interphase (SEI) layer formation and lithium plating—for the negative electrode, and a single particle model for the positive electrode to increase computational efficiency. Simultaneous SOH and aging parameter estimation are achieved by reformulating the model into a control-oriented recursive aging model and with the employment of a particle filter from real-time current and terminal voltage measurements, which enables the prediction of cell failure due to bulk and local aging mechanisms. Validations and battery-in-the-loop tests were conducted to illustrate the performance of the proposed estimation scheme at different temperatures, where the resulting errors of capacity and power fade estimation are within 3% and 4%, respectively.

Real-time state-of-health monitoring of lithium-ion battery with anomaly detection, Levenberg–Marquardt algorithm, and multiphase exponential regression model

The state of health (SOH) of lithium-ion (Li+) battery prediction plays significant roles in battery management and the determination of the durability of the battery in service. This study used segmentation-type anomaly detection, the Levenberg–Marquardt (LM) algorithm, and multiphase exponential regression (MER) model to determine SOH of the Li+ batteries. By determining the changepoint boundaries using the characteristic values such as voltage transition rate (VTR), temperature transition rate (TTR), and charge capacities of the Li+ battery at the changepoint timestamps, we determined the parametric values of the biphasic MER. The characteristic transition rate values, which depend on the transition probabilities of the rolling standard deviations of the measured voltage and temperature, were later utilized with the matching charge capacities to model various training–testing dataset combinations. This helped to estimate the SOH of the battery at different life-cycle phases. This study also developed a technique for real-time estimation of the remaining useful life of the battery by using the MER model parameters, VTR, and TTR which were previously unseen parametric values of the Li+ battery. The result obtained from the proposed model indicates that our technique will be effective for online SOH estimation of Li+ batteries.

Data-Driven Online Health Estimation of Li-Ion Batteries Using A Novel Energy-Based Health Indicator

Li-Ion batteries have been widely applied in power engineering. Aiming at online state of health (SOH) estimation of Li-Ion batteries, this letter develops a data-driven method using a novel energy-based health indicator (HI). The proposed HI is extracted from the discharge process considering that the discharge process is often less controllable than the charge process. Unlike previous works where only voltage sequences are considered, this HI incorporates both voltage sequences and discharge rates. Therefore, the developed HI enables online SOH estimation at different discharge rates from the offline training dataset. An open dataset is used for verification of the proposed method and very high accuracy is reported with an average RMSE of 1.23%.

An online state of health estimation method based on battery management system monitoring data

A state of health (SOH) estimation method that can be achieved online and only requires battery management system (BMS) detection data is proposed in this article. In the State of Health mathematical model proposed in this article, the using time of power battery is treated as an independent variable and SOH is treated as a hidden variable. And the mathematical model just used online process data from BMS. So it would make the SOH estimation method more suitable for actual engineering. Then, the article proposes an interleaved time model parameter update framework to reduce the computational complexity of the algorithm in a single sampling period. In this framework, we propose a fast model parameter identification algorithm that uses nonlinear least squares to initialize a genetic algorithm searched range. Finally, the whole method is verified by using the NASA database. The results prove that the proposed online SOH estimation method has higher SOH estimation accuracy and is more suitable for engineering applications in the field of electric vehicles than the existing SOH estimation methods.