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Battery equivalent circuit model: a). scheme of the battery ECM based model, b). state space representation of the model.
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A battery management system (BMS) ensures performance, safety and longevity of a battery energy storage system in an embedded environment. One important task for a BMS is to estimate the state of charge (SoC) and state of health (SoH) of a battery. The correlation between battery open circuit voltage (OCV) and SoC is an important reference for stat...
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... adopted battery ECM is illustrated in Fig. 1(a). An OCV-SoC relationship often established via coulomb counting and OCV measurement. An important point to note is the improvement of this relationship by declaring two independent battery SoC in- dicators: 1) Coulomb counting based SoC C ; 2) OCV observation based SoC V . Some definitions are declared by the ...
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... first, SoH C is initialized to be 1 for all SoC V regions, which means SoC C and SoC V are initially identical. Through time, SoC C and SoC V will be computed independently as show in Fig. ...
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... consists of three internal resis- tance parameters. As shown in Fig. 1, the model presents battery terminal voltage U BATT as the sum of four voltage components ...
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... discretized state space model is presented in Fig. 1(b). Battery OCV-SoC curve will change due to battery aging [39]. By including the variation of OCV-SoC curve, battery estimator accuracy can be improved. Typical method of updating a battery OCV-SoC curve requires resting a battery cell at different levels of charge [26]. This is time consuming, and difficult to implement in on-line ...
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... EKF observer employed in this paper, different from other designs [9,13,40], was designed to take a 'detour' estimating a 'secondary' variable SoC V , and back calculates the more intuitive battery state SoC C using Equation (4). Writing the state space model of Fig. 1(b) in the following ...
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... of the OCV curve optimization method via PVA, the algorithm updated the OCV-SoC correlation implicitly, leading to the improvement of overall SoC estimation accuracy by more than 1% in the case of the training data. At a high SoC region, where the estimation error is more significant, the PVA algorithm reduced estimation error by more than 3%. Fig. 10 shows the results of the capacity and OCV-SoC correlation optimization for Cell 1. The typical LiFePO 4 battery OCV-SoC cor- relation was initialized with a curve obtained by an off-line test method, as plotted in Fig. 10(a). After simulation, the estimator utilized the training data to update the SoH C function as shown in Fig. 10(b). ...
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... a high SoC region, where the estimation error is more significant, the PVA algorithm reduced estimation error by more than 3%. Fig. 10 shows the results of the capacity and OCV-SoC correlation optimization for Cell 1. The typical LiFePO 4 battery OCV-SoC cor- relation was initialized with a curve obtained by an off-line test method, as plotted in Fig. 10(a). After simulation, the estimator utilized the training data to update the SoH C function as shown in Fig. 10(b). Because the OCV(SoC V ) function was defined to remain unchanged, the more intuitive OCV(SoC C ) function was updated implicitly, with its variation compared to initial values plotted in Fig. 10(c). This approach corrects ...
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... more than 3%. Fig. 10 shows the results of the capacity and OCV-SoC correlation optimization for Cell 1. The typical LiFePO 4 battery OCV-SoC cor- relation was initialized with a curve obtained by an off-line test method, as plotted in Fig. 10(a). After simulation, the estimator utilized the training data to update the SoH C function as shown in Fig. 10(b). Because the OCV(SoC V ) function was defined to remain unchanged, the more intuitive OCV(SoC C ) function was updated implicitly, with its variation compared to initial values plotted in Fig. 10(c). This approach corrects the OCV curve with a very small adjustment, which happens to be beneficial because the OCV curve variation is very ...
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... off-line test method, as plotted in Fig. 10(a). After simulation, the estimator utilized the training data to update the SoH C function as shown in Fig. 10(b). Because the OCV(SoC V ) function was defined to remain unchanged, the more intuitive OCV(SoC C ) function was updated implicitly, with its variation compared to initial values plotted in Fig. 10(c). This approach corrects the OCV curve with a very small adjustment, which happens to be beneficial because the OCV curve variation is very subtle over the course of battery degradation. Typically updating of the battery available capacity requires data of the entire battery operation range. In the proposed method, how- ever, the ...
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... the function SoH C (SoC V ) is a localized representation of bat- tery capacity degradation. It can be updated using piecewise data without cycling through the entire battery operation range. The same simulation was performed on the LiNiCoMnO Cell 2. The battery was continuously cycled with a charge depleting US06 driving profile for 450 cycles. Fig. 11 shows the results of internal resistance optimization. After 1 cycle, the battery had resistance values close to 0.01U. After 450 cycles, the internal resistances of the battery were significantly degraded. The values of R O and R S increased by 100%, and the value of R L increased more than 200%. The estimator effectively tracked the ...
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... had resistance values close to 0.01U. After 450 cycles, the internal resistances of the battery were significantly degraded. The values of R O and R S increased by 100%, and the value of R L increased more than 200%. The estimator effectively tracked the internal resistance and restrained the average voltage simulation error within 0.05 V. Fig. 12 shows the results of SoH C (SoC V ) optimization. After 450 cy- cles, the battery capacity was degraded about 15%. Fig. 12(a) shows battery OCV(SoC V ) to be degraded from its original condition. By adjusting the SoH C (SoC V ) function, the error of SoC estimation was constrained within 5%. Furthermore, the OCV curve updating further ...
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... The values of R O and R S increased by 100%, and the value of R L increased more than 200%. The estimator effectively tracked the internal resistance and restrained the average voltage simulation error within 0.05 V. Fig. 12 shows the results of SoH C (SoC V ) optimization. After 450 cy- cles, the battery capacity was degraded about 15%. Fig. 12(a) shows battery OCV(SoC V ) to be degraded from its original condition. By adjusting the SoH C (SoC V ) function, the error of SoC estimation was constrained within 5%. Furthermore, the OCV curve updating further improved the accuracy of SoC estimation by another 0.5% (in the case of LiNiCoMnO battery the OCV function variation was ...
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... OCV(SoC V ) to be degraded from its original condition. By adjusting the SoH C (SoC V ) function, the error of SoC estimation was constrained within 5%. Furthermore, the OCV curve updating further improved the accuracy of SoC estimation by another 0.5% (in the case of LiNiCoMnO battery the OCV function variation was tested to be very small). Fig. 13 shows the results of the capacity and OCV-SoC correlation optimization for Cell 2. The typical LiNiCoMnO battery OCV-SoC correlation was initialized as Fig. 13(a). Comparing to Fig. 10, after the accelerated aging experiment the overall SoH value of a battery degraded, as shown in Fig. 13(b). Also, the vari- ation of the OCV curve was ...
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... the OCV curve updating further improved the accuracy of SoC estimation by another 0.5% (in the case of LiNiCoMnO battery the OCV function variation was tested to be very small). Fig. 13 shows the results of the capacity and OCV-SoC correlation optimization for Cell 2. The typical LiNiCoMnO battery OCV-SoC correlation was initialized as Fig. 13(a). Comparing to Fig. 10, after the accelerated aging experiment the overall SoH value of a battery degraded, as shown in Fig. 13(b). Also, the vari- ation of the OCV curve was very mild. The resultant OCV curve is optimized to be almost unchanged in shape, as shown in Fig. ...
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... updating further improved the accuracy of SoC estimation by another 0.5% (in the case of LiNiCoMnO battery the OCV function variation was tested to be very small). Fig. 13 shows the results of the capacity and OCV-SoC correlation optimization for Cell 2. The typical LiNiCoMnO battery OCV-SoC correlation was initialized as Fig. 13(a). Comparing to Fig. 10, after the accelerated aging experiment the overall SoH value of a battery degraded, as shown in Fig. 13(b). Also, the vari- ation of the OCV curve was very mild. The resultant OCV curve is optimized to be almost unchanged in shape, as shown in Fig. ...
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... the OCV function variation was tested to be very small). Fig. 13 shows the results of the capacity and OCV-SoC correlation optimization for Cell 2. The typical LiNiCoMnO battery OCV-SoC correlation was initialized as Fig. 13(a). Comparing to Fig. 10, after the accelerated aging experiment the overall SoH value of a battery degraded, as shown in Fig. 13(b). Also, the vari- ation of the OCV curve was very mild. The resultant OCV curve is optimized to be almost unchanged in shape, as shown in Fig. ...
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... The typical LiNiCoMnO battery OCV-SoC correlation was initialized as Fig. 13(a). Comparing to Fig. 10, after the accelerated aging experiment the overall SoH value of a battery degraded, as shown in Fig. 13(b). Also, the vari- ation of the OCV curve was very mild. The resultant OCV curve is optimized to be almost unchanged in shape, as shown in Fig. ...
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Citations
... Currently, the value of the SOC is mostly defined in terms of the remaining battery charge, i.e., the value of the SOC is the percentage of the remaining battery charge to its rated charge under a specific discharge multiplication condition [6], and its expression can be described as: ...
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... On the other hand, in [42], the authors linked the OCV-SOC curves at the different aging levels to the one of the fresh cell by adding an opportune value. In [43], for another LFP battery, the authors used the SOH information for correcting the OCV-SOC relationship considering aging. ...
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... where k is the index of the battery cell, Q M k is the maximum capacity when the cell is fully charged, and SOC k is the state of charge of the cell. Among various SOC estimation methods [29], [30], [31], [32], [33], [34], [35], [36], [37], this article adopts the SOC-OCV method obtained in the ambient temperature of 25 • C. Under the different temperature condition, other SOC estimation methods with higher accuracy can be applied and then the effect of temperature on OCV can be considered. ...
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... Estimating SoC and SoH is crucial for the battery management system in VRFB systems. [84] SoC provides information about the remaining power of the battery during charge and discharge cycles, enabling users to know when to charge and prevent overcharging. SoH represents the degree of battery capacity decay and provides users with an estimate of the remaining battery life as a percentage. ...
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... Two techniques are commonly used for measuring the OCV curve. The first is "stepwise OCV", which consists of charging up to a certain SOC and then measuring the voltage after a suitable relaxation time [30,33]. The second is "low-current OCV", which consists of charging the battery with a very low current (i.e., <0.1 C) and considering the battery voltage in a state of quasi-equilibrium. ...
... Two techniques are commonly used for measuring the OCV curve. The first is wise OCV", which consists of charging up to a certain SOC and then measuring th age after a suitable relaxation time [30,33]. The second is "low-current OCV", whic sists of charging the battery with a very low current (i.e., <0.1 C) and considering t tery voltage in a state of quasi-equilibrium. ...
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... The former is ensured by the battery management system (BMS), which predicts battery parameters such as the authors corrected the SOC estimation with the value of the actual battery capacity and stated that the OCV curves were the same with this correction, despite different aging conditions. A similar procedure was employed in [30], where the SOC was defined as a function of the SOH and then used inside the OCV-SOC relation. The changes in the OCV-SOC curve and the consequent variations in the incremental capacity of a LiB were analyzed in [25], in which an extended Kalman filter was used for the parameter estimation. ...
... In [29], the authors corrected the SOC estimation with the value of the actual battery capacity and stated that the OCV curves were the same with this correction, despite different aging conditions. A similar procedure was employed in [30], where the SOC was defined as a function of the SOH and then used inside the OCV-SOC relation. The changes in the OCV-SOC curve and the consequent variations in the incremental capacity of a LiB were analyzed in [25], in which an extended Kalman filter was used for the parameter estimation. ...
In recent years, lithium-ion batteries (LiBs) have gained a lot of importance due to the increasing use of renewable energy sources and electric vehicles. To ensure that batteries work properly and limit their degradation, the battery management system needs accurate battery models capable of precisely predicting their parameters. Among them, the state of charge (SOC) estimation is one of the most important, as it enables the prediction of the battery’s available energy and prevents it from operating beyond its safety limits. A common method for SOC estimation involves utilizing the relationship between the state of charge and the open circuit voltage (OCV). On the other hand, the latter changes with battery aging. In a previous work, the authors studied a simple function to model the OCV curve, which was expressed as a function of the absolute state of discharge, q, instead of SOC. They also analyzed how the parameters of such a curve changed with the cycle aging. In the present work, a similar analysis was carried out considering the calendar aging effect. Three different LiB cells were stored at three different SOC levels (low, medium, and high levels) for around 1000 days, and an analysis of the change in the OCV-q curve model parameters with the calendar aging was performed.
... Generally, the SOC estimation comprises the following techniques: coulomb counting technique (Jeong et al., 2014), open circuit voltage (OCV) technique (Tong et al., 2015), impedance spectroscopy technique (Qahouq and Xia, 2017), diverse intelligent technique (Hu et al., 2018) and Kalman filter (KF) technique (Wang et al., 2020). For the coulomb counting technique, the SOC of battery is estimated by integral approach, the coulomb-counting technique is the most commonly used approach for SOC estimation by integral approach due to its simplicity and ease of implementation . ...
Due to excellent power and energy density, low self-discharge and long life, lithium-ion battery plays an important role in many fields. Directed against the complexity of above noises and the strong sensitivity of the common Kalman filter algorithm to noises, the state of charge estimation of lithium-ion battery based on extended Kalman filter algorithm is investigated in this paper. Based on the second-order resistor-capacitance equivalent circuit model, the battery model parameters are identified using the MATLAB/Simulink software. A battery parameter test platform is built to test the charge-discharge efficiency, open-circuit voltage and state of charge relationship curve, internal resistance and capacitance of the individual battery are tested. The simulation and experimental results of terminal voltage for lithium-ion battery is compared to verify the effectiveness of this method. In addition, the general applicability of state of charge estimation algorithm for the battery pack is explored. The ampere-hour integral method combined with the battery modeling is used to estimate the state of charge of lithium-ion battery. The comparison of extended Kalman filter algorithm between experimental results and simulation estimated results is obtained to verify the accuracy. The extended Kalman filter algorithm proposed in this study not only establishes the theoretical basis for the condition monitoring but also provides the safe guarantee for the engineering application of lithium-ion battery.
... Typical methods for the evaluation of battery status include the direct measurement method, physical model method, and data analysis method. In direct measurement methods, an open-circuit voltage is applied to calculate the battery capacity [16], and the battery impedance can be measured by electrochemical impedance spectroscopy [17]. Alternatively, in the physical model methods, the battery degradation behaviors are described by an equivalent model developed from the electrochemical mechanism or equivalent circuit [18][19][20]. ...
... Energies 2023, 16, 3096 ...
This paper aims to establish a predictive model for battery lifetime using data analysis. The procedure of model establishment is illustrated in detail, including the data pre-processing, modeling, and prediction. The characteristics of lithium-ion batteries are introduced. In this study, data analysis is performed with MATLAB, and the open-source battery data are provided by NASA. The addressed models include the decision tree, nonlinear autoregression, recurrent neural network, and long short-term memory network. In the part of model training, the root-mean-square error, integral of the squared error, and integral of the absolute error are considered for the cost functions. Based on the defined health indicator, the remaining useful life of lithium-ion batteries can be predicted. The confidence interval can be used to describe the level of confidence for each prediction. According to the test results, the long short-term memory network provides the best performance among all addressed models.
... The model parameters are updated during aging by minimizing the difference between an estimated and measured model output, which is usually the terminal voltage. The obtained model parameters either directly include the SOH or indirectly allow its calculation [1][2][3][4][5][6][7][8][9]. 2. Feature correlation-based: the correlation between a measurable feature and the SOH is established via lab experiments. ...
... No parametrization of an aging model or correlation of a feature with SOH is necessary. The algorithm is independent of SOC estimation and the updated OCV curve, which is obtained as an additional output, can be used for other BMS tasks that depend on an accurate OCV curve such as model-based SOC estimation [1,2,6,25,41]. ...
The open circuit voltage (OCV) curve of a lithium-ion cell can be described as the difference between the half-cell open circuit potential curves of both electrodes. Fitting a reconstructed OCV curve to the OCV curve of an aged cell allows identification of degradation modes. In this study, we show that this method can also be applied to partial charging curves of a commercial cell with silicon–graphite and NMC-811 as electrode materials. Both the degradation modes and the remaining cell capacity can be determined from the reconstructed OCV curve. For the investigated cell, accurate OCV reconstruction and degradation mode estimation can be obtained from C/30 partial charging curves if the state of charge (SOC) window between 20% and 70% is available. We show that the method is also applicable to charging curves at higher current rates if the additional overpotential is considered by subtracting a constant voltage offset. Capacity estimation with an accuracy of 2% of the nominal capacity is possible for current rates up to approximately C/4 if partial charging curves between 10% and 80% SOC are used, while a maximum current rate of C/15 should be used for accurate estimation of the degradation modes.
... Model-based methods for determining the SOC can be considered. When these techniques are applied, a battery should first be modeled using the equivalent circuit model (ECM) [34], the state-space model [35], and the numerical model [36]. Therefore, model-based or observerbased digital filtering techniques such as the Kalman filter (KF) and advanced extended Kalman filter (EKF) [37], which are based on robust recursive tools and consider the nonlinearity and uncertainty of the battery model, are relevant to real-time applications. ...
State of charge (SOC) is a crucial index for a battery’s energy assessment. Its estimation is becoming an increasing challenge in order to assure the battery's safety and efficiency. To this end, many methods can be found in the scientific literature with various accuracy and complexity. However, accurate SOC is highly dependent on the adopted methodology. This paper investigates five methods for estimating battery SOC for lithium-ion (Li-ion) manufacturers. For this purpose, five methods were selected and then used in practice, including the modified Coulomb counting method, the extended Kalman filter, the neural network (NN), and two other techniques based on machine learning known as the support vector machines and the K-nearest neighbor algorithm (KNN), respectively. A detailed analysis based on statistical assessment is performed on an experimental test that covers multiple cycles of charge and discharge modes. The KNN method proved to be more accurate than the EKF approach, which is extensively used for estimating the SOC of Li-ion batteries. The algorithm demonstrated great predictive accuracy, with most predictions having a relative error near zero and a maximum error value of roughly 0.26%. All of the findings validate the methodology's reliability and efficiency.
