State Of Health Estimation Method Research Articles

A Relative State of Health Estimation Method Based on Wavelet Analysis for Lithium-Ion Battery Cells

The state of health (SOH) is essential for the safety and reliability of lithium-ion batteries in different applications. Moreover, the inconsistency of battery cells in battery packs is inevitable. To determine the SOH difference is even more challenging without the calculation of the SOH of each battery cell. To solve the problem, this article proposes a novel relative SOH (rSOH) estimation method based on wavelet analysis, and the SOH differences for series-connected lithium-ion battery cells in the battery pack can be obtained. It should be noted that no battery model is needed in this method. The more accessible battery voltage of each battery cell is sufficient to obtain the SOH differences, without SOH estimation for each battery cell. With the wavelet analysis, more detailed information of the battery cells is obtained. The rSOH is then introduced as the index of the SOH difference, and the estimation procedure is further analyzed. Finally, experimental validations are conducted by different aged battery cells. The different state of charge conditions and different driving cycles are applied to show the robustness of the proposed method. The experimental results show that the rSOH estimation error is limited within 5%, equivalently with less than 1% SOH estimation error, thus proving the effectiveness of the proposed rSOH estimation method.

An Incremental Voltage Difference Based Technique for Online State of Health Estimation of Li-ion Batteries

Accurate state of health (SOH) estimation of rechargeable batteries is important for the safe and reliable operation of electric vehicles (EVs), smart phones, and other battery operated systems. We propose a novel method for accurate SOH estimation which does not necessarily need full charging data. Using only partial charging data during normal usage, 10 derived voltage values ({v}_{sei}) are collected. The initial {v}_{sei} point is fixed and then for every 1.5% increase in the Coulomb counting, other points are selected. The difference between the {v}_{sei} values (Delta {v}_{sei}) and the average temperature during the charging form the feature vector at different SOH levels. The training data set is prepared by extrapolating the charging voltage curves for the complete SOH range using initial 400 cycles of data. The trained artificial neural network (ANN) based on the feature vector and SOH values can be used in any battery management system (BMS) with a time complexity of only O({n}^{4}). Less than 1% mean absolute error (MAE) for the test cases has been achieved. The proposed method has a moderate training data requirement and does not need any knowledge of previous SOH, state of charge (SOC) vs. OCV relationship, and absolute SOC value.

Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage

The state of charge (SOC) and state of health (SOH) are two crucial indicators needed for a proper and safe operation of the battery. Coulomb counting is one of the most adopted and straightforward methods to calculate the SOC. Although it can be implemented for all kinds of applications, its accuracy is strongly dependent on the operation conditions. In this work, the behavior of the batteries at different current and temperature conditions is analyzed in order to adjust the charge measurement according to the battery efficiency at the specific operating conditions. The open-circuit voltage (OCV) is used to reset the SOC estimation and prevent the error accumulation. Furthermore, the SOH is estimated by evaluating the accumulated charge between two different SOC using a recursive least squares (RLS) method. The SOC and SOH estimations are verified through an extensive test in which the battery is subjected to a dynamic load profile at different temperatures.

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.

State-of-Health Estimation Based on Differential Temperature for Lithium Ion Batteries

State-of-health (SOH) estimation is necessary for lithium ion batteries due to ineluctable battery ageing. Existing SOH estimation methods mainly focus on voltage characteristics without considering temperature variation in the process of health degradation. In this article, we propose a novel SOH estimation method based on battery surface temperature. The differential temperature curves during constant charging are analyzed and found to be strongly related to SOH. Part of the differential temperature curves in a voltage range is adopted to establish a relationship with SOH using support vector regression. The influence of battery discrepancy, voltage range, and sampling step are systematically discussed and the best combination of voltage range and sampling step is determined using leave-one-out validation. The proposed method is then validated and compared with an incremental capacity analysis (ICA)-based SOH estimation method using the Oxford and NASA datasets, which were collected from different cells under different conditions, respectively. The results show that the proposed method is capable of estimating SOH with the root-mean-square error less than 3.62% and 2.49%, respectively. In addition, the proposed method can improve the overall SOH estimation accuracy and robustness by combining with the ICA-based method with little computational burden.

A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs

An accurate lithium-ion battery state of health (SOH) estimate is a key factor in guaranteeing the reliability of electronic equipment. This paper proposes a new method that is based on an indirect enhanced health indicator (HI) and uses support vector regression (SVR) to estimate SOH values. First, three original features that can describe the dynamic changes of the battery charging and discharging processes are extracted. Considering the coupling relationship between pairs of the original health indicators, we use the differential evolution (DE) algorithm to optimize their corresponding feature parameters and combine them to form an enhanced health indicator. Second, this paper modifies the kernel function of the SVR model to describe the trend of SOH as the number of cycles increases, with simultaneous hyperparameters optimization via DE algorithm. Third, the proposed model and other published methods are compared in terms of accuracy on the same NASA datasets. We also evaluated the generalization performance of the model in dynamic discharging experiments. The simulation results demonstrate that the proposed method can provide more accurate SOH estimation values.

Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction

SOH (state of health) estimation is important for battery management. Since the electrochemical reaction inside LIBS (lithium-ion battery system) is extremely complex and the external working environment is uncertain, it is difficult to achieve accurate determination of SOH. To improve the accuracy of SOH estimation, we propose a SOH estimation method for lithium-ion battery based on XGBoost algorithm with accuracy correction. We extract several features, including average voltage, voltage difference, current difference, and temperature difference, to describe the aging process of batteries. Due to the higher prediction accuracy and generalization ability of ensemble learning algorithm, the XGBoost model is established to estimate the SOH of lithium-ion battery. Then, the estimation values are corrected by Markov chain. Compared with the methods by XGBoost, random forest, k-nearest neighbor algorithm (KNN), SVM, linear regression, our proposed method shows an accuracy improvement by 10% to 20%. Additionally, the errors of our method are also superior to the others in terms of the average absolute error, root mean square error, and root mean square error.

SOH Estimation and SOC Recalibration of Lithium-Ion Battery with Incremental Capacity Analysis & Cubic Smoothing Spline

Conventional state of health (SOH) estimation often requires capacity measurement from battery’s full charge or discharge profile between fully charged state and cut-off state. Incremental capacity analysis can improve estimation efficiency by extracting features to estimate SOH or recalibrate state of charge estimation without using full profile. While direct numerical derivatives often do not show smooth result due to measurement noise, this paper utilizes robust cubic smoothing spline method on producing incremental capacity curve, which is superior over typical filters that require tuning on window size usually by trial&error because smoothing parameters in the proposed method can be determined by cross validation. Comparison through simulated data shows that the proposed method maintains good fidelity on data and feature of interest with low RMSE values under derivative form. This paper also proposes a peak height ratio feature for SOH estimation. While a linear relationship is noted between SOH and peak height ratio, estimation of SOH from peak height ratio is demonstrated using linear regression. A more generalized version of SOH estimation method is also demonstrated using multiple linear regression with covariates of both peak height ratio and the height of peak associated with “last phase-transition of Li ions intercalation during charging.”

动力锂电池健康状态评估方法综述

动力锂电池健康状态评估方法综述

Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance

Real-time state-of-health (SoH) estimation is often difficult to obtain due to the unavailability of capacity measurements in real-time monitoring. The equivalent internal resistance (EIR), which is easily obtained and closely related to battery deterioration, is studied as a possible solution for achieving real-time and reliable SoH estimation for lithium-ion batteries. A novel real-time SoH estimation method based on the EIR is introduced for lithium-ion batteries. First, an experimental study of the relationship between the EIR and battery degradation is implemented, and this study is used to develop an empirical description of battery degradation using the EIR vector. Second, a fast extraction method for identifying the EIR in real time is proposed by leveraging the relationship between the EIR vector and state of charge (SoC). Third, a support vector regression (SVR)-based method for real-time SoH estimation is introduced by characterizing the hidden relationship between the EIR vector and battery SoH. The proposed method is demonstrated using laboratory test data. The results show that the proposed method can predict the battery SoH in real time with good accuracy and robustness.

State-of-Health Estimation of Lithium-Ion Batteries Using Incremental Capacity Analysis Based on Voltage–Capacity Model

State of health (SOH) is critical to evaluate the life expectancy of lithium-ion battery (LIB), thus should be estimated accurately in practical applications. This article proposes a computationally efficient model-based method for SOH estimation of LIB. A revised Lorentzian function-based voltage–capacity (VC) (RL-VC) model is exploited to accurately capture the voltage plateaus of LIB which reflect the material-level phase transition phenomenon. A full set of new features of interest (FOIs) is extracted by simply fitting the RL-VC model leveraging data collected from the constant-current charging process. Correlation analysis is then performed for the captured FOIs, based on which linear models are calibrated to estimate the battery SOH. The proposed method is validated with experimental data from different battery chemistries. The results show that the extracted FOIs have high linearities with the battery capacity, suggesting a good potential for SOH estimation and better feasibility over traditionally used methods. The proposed method shows a high accuracy for battery SOH estimation and an expected robust performance against the initial aging status and practical cycling condition.

State of Health Estimation for Lithium-Ion Battery Using Empirical Degradation and Error Compensation Models

State of health (SOH) estimation is always an important factor in ensuring the reliability and safety of lithium-ion batteries. In view of the shortcomings of the existing SOH estimation methods, such as non-universal, the estimation of different batteries is limited, and the accuracy is insufficient. A fusion estimation method that depends on an empirical degradation model and a data-driven method is proposed. First, we construct an empirical degradation model of lithium-ion battery SOH with charge-discharge cycles. Four working condition characteristics are extracted from the actual charging and discharging process of batteries. Then, with these features as inputs, the prediction error of the empirical degradation model is taken as the output, and training the error compensation model becomes dependent on the data-driven method. The actual working condition characteristics of the tested lithium ion battery are substituted into the training error compensation model, and the model output is fed back to the prediction results of the empirical degradation model. A high-precision estimation of lithium-ion battery SOH is thereby achieved. Finally, the proposed method is verified based on the NASA lithium-ion battery data set. The results show that the fusion method is applicable to different lithium-ion batteries of the same type, and the mean absolute percentage error of SOH estimation is approximately 2%, indicating that the proposed method exhibits good estimation performance and applicability.

A review on state of health estimation for lithium ion batteries in photovoltaic systems

Abstract Enhancing state of health (SOH) estimation accuracy and robustness for battery systems in photovoltaic (PV) systems is a feasible way to improve system performance and economics. Nevertheless, the SOH is not directly measurable and affected by a number of factors, therefore its estimation is challenging. Plenty of SOH estimation methods have been proposed for different applications, but little evaluation and discussion have been made for the SOH estimation for battery management systems in PV systems. In this paper, SOH estimation methods are categorised according to the signals that are used to extract the health indicator. Most methods are based on voltage characteristics while other signals such as temperature, ultrasound and force are also promising for SOH estimation. For each method, the basic theory, advantages and drawbacks are introduced and discussed. Then, a thorough comparison among the existing methods is conducted to provide readers with a comprehensive understanding of the development of the SOH estimation. Finally, key issues and suggestions on the SOH estimation are discussed to give novel insights to researchers and engineers.

A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain

The state of health (SOH) of lithium-ion batteries (LIBs) is a critical parameter of the battery management system. Because of the complex internal electrochemical properties of LIBs and uncertain external working environment, it is difficult to achieve an accurate SOH determination. In this paper, we have proposed a novel SOH estimation method by using a prior knowledge-based neural network (PKNN) and the Markov chain for a single LIB. First, we extract multiple features to capture the battery aging process. Due to its effective fitting ability for complex nonlinear problems, the neural network with a prior knowledge-based optimization strategy is adopted for the battery SOH prediction. The Markov chain, with the advantageous prediction performance for the long-term system, is established to modify the PKNN estimation results based on the prediction error. Experimental results show that the maximum estimation error of the SOH is reduced to less than 1.7% by adopting the proposed method. By comparing with the group method of data handling and the back-propagation neural network in conjunction with the Levenberg-Marquardt algorithm, the proposed estimation method obtains the highest SOH accuracy.

Modeling and state of health estimation of nickel–metal hydride battery using an EPSO-based fuzzy c-regression model

The prognostic and health management of the batteries continued to attract interest from automobile manufacturers as the key for lowering life-cycle costs, reducing unexpected power outages, and one of the most important and efficient ways for energy storage for electric vehicle applications. Indeed, an effective battery health monitoring depends on accurate estimation of state of health (SOH). However, the SOH cannot be directly measured by sensors in the battery management system. Moreover, the SOH estimation based on a standard resistor–capacitor (RC) battery model is not so accurate because a RC model is obtained with some approximations and without taking into account more detailed knowledge about the chemical reactions happening inside the battery. In this paper, a combined battery modeling and SOH estimation method over the lifespan of a nickel–metal hydride (Ni–MH) battery is proposed. First, a fuzzy c-regression model based on Euclidean particle swarm optimization is applied to modeling a Ni–MH battery. Second, the SOH monitoring is determined according to the discharge rate of the battery model. The performance of the proposed method has been analyzed through the modeling and the estimation of the SOH using a real data set of the Ni–MH battery.

Electrode-Specific State of Health Diagnostics for Lithium Ion Batteries Using Cell Voltage and Expansion

Li-ion batteries degrade over time. Traditionally, capacity and resistance of the cell have been used as the state of health (SOH) indicators. However, these parameters cannot provide detail information about the degradation mechanisms. Thus, recently, there have been many efforts on developing diagnostics algorithm that can identify the degradation mechanisms. There exist many degradation mechanisms for Li-ion batteries such as SEI layer growth or lithium plating consuming lithium, structure disordering and metal ion dissolution making active material unavailable for insertion and extraction of lithium. Identifying these mechanisms is challenging due to complex inter-correlations between them. Instead, it is possible to categorize these mechanisms into two degradation modes: loss of lithium inventory (LLI) and loss of active material (LAM) at each electrode. On the electrode level, capacity and utilization window of the individual electrode are proposed as electrode-specific SOH parameters that can be related to the degradation modes. The differential voltage analysis (DVA) is one of the common approaches for estimating these electrode parameters. In this method, the phase transition of the electrode material is used as a fingerprint of each electrode in the cell’s differential voltage (dV/dQ) curve using a low C-rate constant current pseudo-OCV data [1,2]. Another interesting type of battery response is the mechanical response measured as a cell expansion. Batteries expand during charge and contract while discharge in repeatable patterns. Hence, similar to the voltage analysis, the expansion of a cell can be used for identifying the electrode parameters [3]. In this study, electrode-specific parameters (electrode capacity and utilization window) were estimated for a 5 Ah graphite/NMC pouch cell (University of Michigan Battery Lab) during accelerated aging cycle testing at the elevated temperature. A fixture was designed in order to measure the mechanical response of the cell as shown in Fig. 1(e) such that the top and bottom plates were fixed in place while the middle plate was free moving. The expansion was measured using a displacement sensor (Keyence) mounted on the top plate, and a battery cycler (Biologic BCS-815) was used for measuring the voltage. The cell was placed inside a climate chamber at 45˚C. The aging cycle consists of 2C constant current charge and discharge cycles. Diagnostic tests were conducted at intervals corresponding to an expected 5% loss in capacity for a cell at room temperature. The cell was cycled at C/20 to get the pseudo-OCV. Furthermore, in order to measure the potential of graphite and NMC electrodes, coin cells were built. The Li/NMC coin cell was cycled at C/50 between 2.8 and 4.35 V. The Li/graphite coin cell was cycled at C/50 between 0.005 and 1.0 V. The lattice expansion data was taken from literature for graphite and NMC. The terminal voltage, differential voltage, and expansion data of the cell is shown in Fig.1(a), (b), and (d). A model is developed based on [3] to estimate the electrode-specific SOH parameters as the cell ages and to quantify the degradation modes (i.e., LLI and LAM at each electrode). The cell OCV and expansion models share the same electrode SOH parameters and the estimation is done by finding the best fit for the voltage, differential voltage, and expansion curves. The results show a very good match between the data and model for all three different data curves. The estimated parameters then are provided to the equations describing the degradation modes for quantification (see Fig.1(c)). It is found that LAMNE and LLI are the main degradation modes for this chemistry of NMC/graphite cell under the accelerated aging cycling at the elevated temperature accounting for the cell capacity fade (SOH in Fig. 1(c)). Lastly, it was found that cell expansion measurement gives much higher confidence for estimating electrode SOH parameters [3]. Since the differential voltage is a derivative of the measured voltage, which is sensitive to the measurement noise and can lead to incorrect estimation. Furthermore, the addition of expansion makes this electrode SOH parameter estimation feasible when using a partial range of OCV data. This is important for automotive applications where battery packs rarely discharge fully. [1] Suhak Lee et al., "Comparison of Individual-Electrode State of Health Estimation Methods for Lithium Ion Battery," ASME 2018 Dynamic Systems and Control Conference, 2018. [2] Hannah M. Dahn et al., "User-friendly differential voltage analysis freeware for the analysis of degradation mechanisms in Li-ion batteries," Journal of The Electrochemical Society 159, 2012. [3] Peyman Mohtat et al., “Towards better estimability of electrode-specific state of health: Decoding the cell expansion,” Journal of Power Sources 427, 2019. Figure 1

VRLA battery state of health estimation based on charging time

Battery state of health (SoH) is an important parameter of the battery’s ability to store and deliver electrical energy. Various methods have been so far developed to calculate the battery SoH, such as through the calculation of battery impedance or battery capacity using Kalman Filter, Fuzzy theory, Probabilistic Neural Network, adaptive hybrid battery model, and Double Unscented Kalman Filtering (D-UKF) algorithm. This paper proposes an approach to estimate the value of battery SoH based on the charging time measurement. The results of observation and measurements showed that a new and used batteries would indicate different charging times. Unhealthy battery tends to have faster charging and discharging time. The undertaken analysis has been focused on finding out the relationship between the battery SoH and the charging time range. To validate the results of this proposed approach, the use of battery capacity method has been considered as comparison. It can be concluded that there is a strong correlation between the two discussed SoH estimation methods, confirming that the proposed method is feasible as an alternative SoH estimation method to the widely known battery capacity method. The correlation between the charging-disharging times of healthy and unhealthy batteries is very prospective to develop a battery charger in the future with a prime advantage of not requiring any sensor for the data acquisition.

A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model

The state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable and accurate voltage relaxation model is established based on the linear relationship between time coefficient and open circuit time for a Li1(NiCoAl)1O2-Li1(NiCoMn)1O2/graphite battery. The accuracy and effectiveness of the model is verified under different states of charge and states of health. Through systematic experiments under different states of charge and states of health, it is found that the model parameters monotonically increase with the aging of the battery. Three different capacity estimation methods are proposed based on the relationship between model parameters and residual capacity, namely the α-based, β-based, and parameter–fusion methods. The validation of the three methods is verified with high accuracy. The results indicate that the capacity estimation error under most of the aging states is less than 1%. The largest error drops from 3% under the α-based method to 1.8% under the parameter–fusion method.

A novel State of Health estimation method for Lithium-ion battery in electric vehicles

To ensure safe operation of lithium-ion battery, precise estimation of its states like state of health (SOH) is indispensable. This paper proposes a novel SOH estimation method for lithium-ion battery in electric vehicle applications. When it’s time to update battery capacity, the estimator extracts historical current and voltage data of last driving event and estimates open circuit voltage (OCV) based on affine projection algorithm. By means of the estimated OCV and the three-dimensional response surface OCV model, the SOH estimator gives the estimation result of the battery capacity. Experiments verify that the proposed estimation method is accurate and robust under different working conditions and different aging states.

Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries

In order to provide an accurate State-Of-Health (SOH) estimation, a novel estimation method is proposed in this paper. In this work, some battery SOH relate features are selected theoretically, proved and then re-screened mathematically. These features can reflect the battery degeneration from different aspects. Also, a new training set design idea is proposed for Least Squares Support Vector Machine algorithm, thereby a model that is suitable for lithium-ion Battery SOH estimation under multi-working conditions can be built. Several lithium-ion battery degeneration testing datasets from National Aeronautics and Space Administration Ames Prognostics Center of Excellence are used to validate the proposed method. Results demonstrate both the superiority of the proposed method and its potential applicability as an effective SOH estimation method for embedded Battery Management System.