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Co-estimation for capacity and state of charge for lithium-ion batteries using improved adaptive extended Kalman filter EI SCIE
期刊论文 | 2021 , 40 | JOURNAL OF ENERGY STORAGE
WoS CC Cited Count: 2
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Abstract :

Accurate state of charge (SOC) plays a dominant role in safety control and energy management of battery system. In this paper, an improved adaptive extended Kalman filter (IAEKF) is proposed to realize co-estimation for battery capacity and SOC. Firstly, the online OCV identified by forgetting factor recursive least squares (FFRLS) is innovatively regarded as the observation state, so battery capacity and SOC can be integrated into a second-order filter to realize co-estimation. Secondly, the polynomial relationship between OCV, SOC and T is established to enhance the temperature adaptability of IAEKF. Notably, compared with adaptive extended Kalman filter (AEKF), IAEKF can improve estimation process by producing a fast convergence speed under big OCV errors and assure a slower slope when the OCV errors turn small. Besides, the forgetting factor can simplify the moving window of the adaptive update for the process and measurement noise. With sophisticated Federal Urban Driving Schedule test (FUDS), the estimation accuracy of the proposed algorithm is verified with SOC error band controlled between +/- 1.2% within the first 50s and relative capacity error limited within 2% after convergence. Furthermore, the verification results also prove that IAEKF based co-estimation algorithm has strong anti-interference ability when facing disturbance, including erroneous initial SOC settings, unknown battery capacity and various ambient temperatures.

Keyword :

Temperature Capacity Lithium-ion battery Improved adaptive extended Kalman filter State of charge

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GB/T 7714 Nian, Peng , Shuzhi, Zhang , Xiongwen, Zhang . Co-estimation for capacity and state of charge for lithium-ion batteries using improved adaptive extended Kalman filter [J]. | JOURNAL OF ENERGY STORAGE , 2021 , 40 .
MLA Nian, Peng 等. "Co-estimation for capacity and state of charge for lithium-ion batteries using improved adaptive extended Kalman filter" . | JOURNAL OF ENERGY STORAGE 40 (2021) .
APA Nian, Peng , Shuzhi, Zhang , Xiongwen, Zhang . Co-estimation for capacity and state of charge for lithium-ion batteries using improved adaptive extended Kalman filter . | JOURNAL OF ENERGY STORAGE , 2021 , 40 .
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An application-oriented multistate estimation framework of lithium-ion battery used in electric vehicles EI SCIE
期刊论文 | 2021 | INTERNATIONAL JOURNAL OF ENERGY RESEARCH
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Abstract :

Considering prediction accuracy and adaptability to unpredictable operating conditions simultaneously, this paper presents an application-oriented multistate estimation framework of lithium-ion battery used in electric vehicles. Under static and dynamic operating conditions, three commonly used online model parameters identification algorithms, including extended Kalman filter (EKF), particle swarm optimization, and recursive least square, are compared first, whose comparison results show that EKF's comprehensive performance is optimal. Taking identified open-circuit voltage as observation information, two first-order EKFs are established to online estimate state-of-charge (SOC) and state-of-energy (SOE). To maintain high accuracy and reliability under unpredicted operating conditions, fixed accumulation charge and fixed accumulation energy are innovatively seen as triggers, successfully realizing periodically capacity (state-of-health) and maximum available energy prediction with estimated SOC and SOE. Finally, with identified model parameters and estimated battery states, peak discharge/charge power can be further calculated in real time. Notably, parameters tuning for multistate estimation is also discussed in this work. Furthermore, the feasibility and prediction accuracy of the proposed multistate estimation framework is verified with sophisticated driving simulation under different temperatures. The validation results indicate that the presented framework can provide precise and reliable multistate estimation with relatively low computation cost. Highlights An application-oriented multistate estimation framework is proposed Different online model parameters identification methods are compared Parameters tuning for multistate estimation is discussed Fixed accumulation charge is innovatively taken as capacity updating trigger The feasibility of the proposed framework is verified under various temperatures

Keyword :

state-of-charge maximum available energy state-of-health state-of-energy state-of-power

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GB/T 7714 Zhang, Shuzhi , Peng, Nian , Zhang, Xiongwen . An application-oriented multistate estimation framework of lithium-ion battery used in electric vehicles [J]. | INTERNATIONAL JOURNAL OF ENERGY RESEARCH , 2021 .
MLA Zhang, Shuzhi 等. "An application-oriented multistate estimation framework of lithium-ion battery used in electric vehicles" . | INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2021) .
APA Zhang, Shuzhi , Peng, Nian , Zhang, Xiongwen . An application-oriented multistate estimation framework of lithium-ion battery used in electric vehicles . | INTERNATIONAL JOURNAL OF ENERGY RESEARCH , 2021 .
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A variable multi-time-scale based dual estimation framework for state-of-energy and maximum available energy of lithium-ion battery SCIE
期刊论文 | 2021 | INTERNATIONAL JOURNAL OF ENERGY RESEARCH
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Abstract :

To lower the computation burden and enhance co-estimation reliability under unpredicted operating conditions, this paper presents a novel variable multi-time-scale based dual estimation framework for state-of-energy (SOE) and maximum available energy. Through forgetting factor recursive least squares (FFRLS) based model parameters identification method, the first-order RC model is online built firstly to simulate battery dynamics. Subsequently, identified model parameters are inputted into an adaptive extended Kalman filter to predict SOE. Meanwhile, with battery data and two estimated SOE, inaccurate maximum available energy can be further updated by FFRLS when energy accumulation reaches pre-defined threshold. Especially, to determine the optimal macro time-scale considering co-estimation performance comprehensively, a multi-objective decision analysis method by fusion of analytic hierarchy process and the entropy weight is innovatively proposed. The dual estimation accuracy and robustness ability of the proposed framework are verified with experimental data of Federal Urban Driving Schedule tests conducted under various temperatures, whose results show that the presented method has satisfactory co-estimation accuracy and robustness ability. Furthermore, the comparison with other algorithms not only indicates the necessity of maximum available energy updating on SOE prediction but also the superiority of the presented framework on dual estimation accuracy and computational cost.

Keyword :

variable multi-time-scale dual estimation framework multi-objective decision method comparison with other algorithms state-of-energy maximum available energy

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GB/T 7714 Zhang, Shuzhi , Peng, Nian , Zhang, Xiongwen . A variable multi-time-scale based dual estimation framework for state-of-energy and maximum available energy of lithium-ion battery [J]. | INTERNATIONAL JOURNAL OF ENERGY RESEARCH , 2021 .
MLA Zhang, Shuzhi 等. "A variable multi-time-scale based dual estimation framework for state-of-energy and maximum available energy of lithium-ion battery" . | INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2021) .
APA Zhang, Shuzhi , Peng, Nian , Zhang, Xiongwen . A variable multi-time-scale based dual estimation framework for state-of-energy and maximum available energy of lithium-ion battery . | INTERNATIONAL JOURNAL OF ENERGY RESEARCH , 2021 .
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A comparative study of different online model parameters identification methods for lithium-ion battery SCIE
期刊论文 | 2021 , 64 (10) , 2312-2327 | SCIENCE CHINA-TECHNOLOGICAL SCIENCES
WoS CC Cited Count: 1
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Abstract :

Precise states estimation for the lithium-ion battery is one of the fundamental tasks in the battery management system (BMS), where building an accurate battery model is the first step in model-based estimation algorithms. To date, although the comparative studies on different battery models have been performed intensively, little attention is paid to the comparison among different online parameters identification methods regarding model accuracy, robustness ability, adaptability to the different battery operating conditions and computation cost. In this paper, based on the Thevenin model, the three most widely used online parameters identification methods, including extended Kalman filter (EKF), particle swarm optimization (PSO), and recursive least square (RLS), are evaluated comprehensively under static and dynamic tests. It is worth noting that, although the built model's terminal voltage may well follow a measured curve, these identified model parameters may significantly out of reasonable range, which means that the error between measured and predicted terminal voltage cannot be seen as a gist to determine which model is the most accurate. To evaluate model accuracy more rigorously, battery state-of-charge (SOC) is further estimated based on identified model parameters under static and dynamic tests. The SOC prediction results show that EKF and RLS algorithms are more suitable to be used for online model parameters identification under static and dynamic tests, respectively. Moreover, the random offset is added into originally measured data to verify the robustness ability of different methods, whose results indicate EKF and RLS have more satisfactory ability against imprecisely sampled data under static and dynamic tests, respectively. Considering model accuracy, robustness ability, adaptability to the different battery operating conditions and computation cost simultaneously, EKF is recommended to be adopted to establish battery model in real application among these three most widely used methods.

Keyword :

online model parameters identification methods Thevenin model state-of-charge comprehensive performance lithium-ion battery

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GB/T 7714 Zhang, ShuZhi , Zhang, XiongWen . A comparative study of different online model parameters identification methods for lithium-ion battery [J]. | SCIENCE CHINA-TECHNOLOGICAL SCIENCES , 2021 , 64 (10) : 2312-2327 .
MLA Zhang, ShuZhi 等. "A comparative study of different online model parameters identification methods for lithium-ion battery" . | SCIENCE CHINA-TECHNOLOGICAL SCIENCES 64 . 10 (2021) : 2312-2327 .
APA Zhang, ShuZhi , Zhang, XiongWen . A comparative study of different online model parameters identification methods for lithium-ion battery . | SCIENCE CHINA-TECHNOLOGICAL SCIENCES , 2021 , 64 (10) , 2312-2327 .
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Joint estimation method for maximum available energy and state-of-energy of lithium-ion battery under various temperatures EI SCIE
期刊论文 | 2021 , 506 | Journal of Power Sources
WoS CC Cited Count: 2
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Abstract :

Accurate estimation for state-of-energy (SOE), defined as the ratio of residual available energy to maximum available energy, is an important task in battery management system. Nevertheless, the reduction in maximum available energy caused by battery degradation may lower SOE estimation accuracy, which is almost ignored in existing SOE estimation algorithms. To ensure precise SOE prediction over battery's whole lifetime, a joint estimation method for maximum available energy and SOE is proposed in this paper. Firstly, the parameters of the first-order RC battery model are online identified by forgetting factor recursive least square, where rough SOE is inferred directly from identified open-circuit-voltage (OCV). Considering OCV change at adjacent sampling time, a third-order extended Kalman filter is established to correct OCV and estimate maximum available energy with identified parameters and rough SOE. Finally, the predicted maximum available energy is further transmitted into adaptive extended Kalman filter to estimate SOE. The feasibility, prediction accuracy and robustness ability are verified with Federal Urban Driving Schedule tests under the temperature range of 0 °C–50 °C. Validation results indicate that the proposed joint estimation method can still provide accurate maximum available energy and SOE prediction results even though there exist various forms of interferences. © 2021 Elsevier B.V.

Keyword :

Battery management systems Forecasting Extended Kalman filters Lithium-ion batteries Open circuit voltage

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GB/T 7714 Zhang, Shuzhi , Zhang, Xiongwen . Joint estimation method for maximum available energy and state-of-energy of lithium-ion battery under various temperatures [J]. | Journal of Power Sources , 2021 , 506 .
MLA Zhang, Shuzhi 等. "Joint estimation method for maximum available energy and state-of-energy of lithium-ion battery under various temperatures" . | Journal of Power Sources 506 (2021) .
APA Zhang, Shuzhi , Zhang, Xiongwen . Joint estimation method for maximum available energy and state-of-energy of lithium-ion battery under various temperatures . | Journal of Power Sources , 2021 , 506 .
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A multi time-scale framework for state-of-charge and capacity estimation of lithium-ion battery under optimal operating temperature range EI SCIE
期刊论文 | 2021 , 35 | Journal of Energy Storage
WoS CC Cited Count: 6
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Abstract :

To reduce computation cost and improve state-of-charge (SOC) estimation accuracy over battery's whole lifetime, a multi time-scale framework is proposed to co-estimate SOC and capacity in this paper. With forgetting factor recursive least square, model parameters are online identified firstly, which are later transmitted into adaptive extended Kalman filter to predict SOC in real-time. Subsequently, the difference between two estimated SOC before and after macro time-scale is calculated and innovatively seen as measurement information of extended Kalman filter to further update capacity periodically. Considering battery optimal operating temperatures, Federal Urban Driving Schedule tests under 20°C, 30°C and 40°C are performed to verify the feasibility, co-estimation accuracy and adaptability to different macro time-scales of the presented method. The validation results show that the mean absolute error (MAE) and root mean square error (RMSE) of SOC estimation results with three different macro time-scales under optimal operating temperature range can be roughly limited within 1%, while most MAE and RMSE of capacity prediction results is below 1% and 2%, respectively. Moreover, the comparison with other three typical co-estimation methods is also conducted, whose results indicate that the proposed algorithm has more superior comprehensive performance on co-estimation accuracy and convergence speed. © 2021

Keyword :

Battery management systems Charging (batteries) Mean square error Lithium-ion batteries Temperature Time measurement Extended Kalman filters

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GB/T 7714 ZHANG, Shuzhi , ZHANG, Xiongwen . A multi time-scale framework for state-of-charge and capacity estimation of lithium-ion battery under optimal operating temperature range [J]. | Journal of Energy Storage , 2021 , 35 .
MLA ZHANG, Shuzhi 等. "A multi time-scale framework for state-of-charge and capacity estimation of lithium-ion battery under optimal operating temperature range" . | Journal of Energy Storage 35 (2021) .
APA ZHANG, Shuzhi , ZHANG, Xiongwen . A multi time-scale framework for state-of-charge and capacity estimation of lithium-ion battery under optimal operating temperature range . | Journal of Energy Storage , 2021 , 35 .
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A novel one-way transmitted co-estimation framework for capacity and state-of-charge of lithium-ion battery based on double adaptive extended Kalman filters EI SCIE
期刊论文 | 2021 , 33 | Journal of Energy Storage
WoS CC Cited Count: 4
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Abstract :

Precise capacity and state-of-charge (SOC) estimation is crucial to assure safe and reliable operation of lithium-ion battery. To lower the influence of cross interference between these two estimated states and possible divergence existing in two-way transmitted co-estimation framework, a novel double adaptive extended Kalman filters (AEKFs) based one-way transmitted co-estimation framework for capacity and SOC is proposed in this paper. Firstly, the model parameters of the first-order RC model and open-circuit-voltage (OCV) are online obtained by forgetting factor recursive least squares. With the first derivative of OCV versus SOC, the SOC inferred through OCV-SOC table and identified parameters are inputted into AEKF1 to online estimate capacity. Subsequently, estimated capacity is further transmitted into AEKF2 to predict SOC. By simulation driving cycles, the proposed co-estimation framework is compared with AEKF based SOC algorithm without capacity calibration, whose results indicate that the presented algorithm can lower the impact of inaccurate initial capacity value on SOC estimation to more effectively track SOC. Moreover, through robustness analysis results, it is clearly found that initial erroneous SOC values will not influence capacity estimation results due to the one-way transmitted characteristic of the proposed co-estimation framework and SOC can still be estimated accurately and robustly. © 2020

Keyword :

Extended Kalman filters Lithium-ion batteries Charging (batteries) Battery management systems Open circuit voltage Adaptive filtering

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GB/T 7714 Shuzhi, Zhang , Xu, Guo , Xiongwen, Zhang . A novel one-way transmitted co-estimation framework for capacity and state-of-charge of lithium-ion battery based on double adaptive extended Kalman filters [J]. | Journal of Energy Storage , 2021 , 33 .
MLA Shuzhi, Zhang 等. "A novel one-way transmitted co-estimation framework for capacity and state-of-charge of lithium-ion battery based on double adaptive extended Kalman filters" . | Journal of Energy Storage 33 (2021) .
APA Shuzhi, Zhang , Xu, Guo , Xiongwen, Zhang . A novel one-way transmitted co-estimation framework for capacity and state-of-charge of lithium-ion battery based on double adaptive extended Kalman filters . | Journal of Energy Storage , 2021 , 33 .
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Covalent triazine framework anchored with atomically dispersed iron as an efficient catalyst for advanced oxygen reduction EI SCIE
期刊论文 | 2021 , 628 | COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS
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Abstract :

Developing non-precious metal electrocatalysts with high-performance is an urgent need for market entry of proton exchange membrane fuel cells (PEMFCs). Transition metal-nitrogen-carbon catalysts are suggested as efficient oxygen reduction reaction (ORR) electrocatalysts in PEMFCs. However, uncontrollable agglomeration or inhomogeneous microstructure are often generated during the thermolysis of metal/nitrogen/carbon-containing precursors, which results in incomplete active site exposure and inferior mass transport. In this study, a facile step-wise polymerization, subsequent pyrolysis method and then with NH3 activation is explored to construct highly efficient Fe modified all-triazine C3N3 framework for cathodic reaction of fuel cells. Due to its high specific surface area (641 m(2) g(-1)), uniform distribution of active species, micro/mesoporous structure, conductive network and high pyridinic N and graphitic N content, the as-made Fe-C3N3-750-NH3 catalyst delivers atomic sized Fe species, dominant four-electron pathway, attractive ORR performance and good stability relative to commercial Pt/C electrocatalyst. Inexpensive raw materials and facile preparation combined with superior electrocatalytic performance make Fe-C3N3-750-NH3 a promising ORR catalyst, opening new avenues for application of nanostructured polymers in fuel cells.

Keyword :

Covalent triazine framework Fe-N-C catalyst Non-precious metal Oxygen reduction

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GB/T 7714 Chai, Dan , Min, Xiaoteng , Harada, Takashi et al. Covalent triazine framework anchored with atomically dispersed iron as an efficient catalyst for advanced oxygen reduction [J]. | COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS , 2021 , 628 .
MLA Chai, Dan et al. "Covalent triazine framework anchored with atomically dispersed iron as an efficient catalyst for advanced oxygen reduction" . | COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS 628 (2021) .
APA Chai, Dan , Min, Xiaoteng , Harada, Takashi , Nakanishi, Shuji , Zhang, Xiongwen . Covalent triazine framework anchored with atomically dispersed iron as an efficient catalyst for advanced oxygen reduction . | COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS , 2021 , 628 .
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Low thermal expansion material Bi0.5Ba0.5FeO3-delta in application for proton-conducting ceramic fuel cells cathode (vol 44, pg 21127, 2019) SCIE Scopus
期刊论文 | 2020 , 45 (15) , 9278-9278 | INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
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GB/T 7714 Cui, Jiajia , Wang, Junkai , Zhang, Xiongwen et al. Low thermal expansion material Bi0.5Ba0.5FeO3-delta in application for proton-conducting ceramic fuel cells cathode (vol 44, pg 21127, 2019) [J]. | INTERNATIONAL JOURNAL OF HYDROGEN ENERGY , 2020 , 45 (15) : 9278-9278 .
MLA Cui, Jiajia et al. "Low thermal expansion material Bi0.5Ba0.5FeO3-delta in application for proton-conducting ceramic fuel cells cathode (vol 44, pg 21127, 2019)" . | INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 45 . 15 (2020) : 9278-9278 .
APA Cui, Jiajia , Wang, Junkai , Zhang, Xiongwen , Li, Guojun , Wu, Kai , Cheng, Yonghong et al. Low thermal expansion material Bi0.5Ba0.5FeO3-delta in application for proton-conducting ceramic fuel cells cathode (vol 44, pg 21127, 2019) . | INTERNATIONAL JOURNAL OF HYDROGEN ENERGY , 2020 , 45 (15) , 9278-9278 .
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An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery EI SCIE
期刊论文 | 2020 , 32 | Journal of Energy Storage | IF: 6.583
WoS CC Cited Count: 23
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Abstract :

Precise state of charge (SOC) estimation is crucial to assure safe and reliable operation of lithium-ion battery in electric vehicles. Adaptive unscented Kalman filter (AUKF) has been intensively applied to estimate SOC due to its features of self-correction and high accuracy. Nevertheless, the estimation by traditional AUKF cannot proceed when error covariance matrix is non-positive definite, greatly influencing the stability of SOC estimation. To address this issue, an improved AUKF is proposed in this paper. Firstly, the forgetting factor recursive least square is employed to online identify parameters of electrical equivalent circuit model. With these identified parameters, an improved AUKF, whose Cholesky decomposition for error covariance matrix of tradition AUKF is replaced by singular value decomposition, is applied here to provide online accurate SOC estimation. The feasibility of the proposed method is verified by experimental data under Federal Urban Driving Schedule test. The validation results of robustness present that the algorithm has satisfactory robustness against inaccurate initial SOC. Moreover, through the comparison with traditional AUKF, it can be easily concluded that the proposed method can achieve precise and stable SOC estimation even though error covariance matrix is non-positive definite. © 2020

Keyword :

Charging (batteries) Errors Adaptive filtering Kalman filters Equivalent circuits Covariance matrix Lithium-ion batteries Singular value decomposition Electric network parameters Battery management systems

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GB/T 7714 Zhang, Shuzhi , Guo, Xu , Zhang, Xiongwen . An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery [J]. | Journal of Energy Storage , 2020 , 32 .
MLA Zhang, Shuzhi et al. "An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery" . | Journal of Energy Storage 32 (2020) .
APA Zhang, Shuzhi , Guo, Xu , Zhang, Xiongwen . An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery . | Journal of Energy Storage , 2020 , 32 .
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