| 1. |
- Bian, Xiaolei, et al.
(författare)
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A Novel Model-based Voltage Construction Method for Robust State-of-health Estimation of Lithium-ion Batteries
- 2021
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Ingår i: IEEE Transactions on Industrial Electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0046 .- 1557-9948. ; 68:12, s. 12173-12184
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Tidskriftsartikel (refereegranskat)abstract
- Accurate estimation of the state-of-health (SOH) is vital to the life management of lithium-ion batteries (LIBs). This paper proposes a fusion-type SOH estimation method by combining the model-based feature extraction and data-based state estimate. Particularly, a novel model-based voltage construction method is proposed to eliminate the unfavorable numerical condition and reshape the disturbance-free incremental capacity (IC) curves. Leveraging the modified IC curves, a set of informative features-of-interest are extracted and evaluated, while eventually several cautiously-selected ones are used to estimate the SOH of LIB accurately. Furthermore, the impact of model order on the estimation performance is scrutinized, to give insights into the parameterization in practical applications. Long-term cycling tests on different types of LIB cells are used for evaluation. The proposed method is validated with a good robustness to the cell inconsistency, temperature uncertainty, noise corruption, and a satisfied generality to different battery chemistries.
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| 2. |
- Bian, Xiaolei, et al.
(författare)
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State-of-Health Estimation of Lithium-Ion Batteries by Fusing an Open Circuit Voltage Model and Incremental Capacity Analysis
- 2022
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Ingår i: IEEE transactions on power electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8993 .- 1941-0107. ; 37:2, s. 2226-2236
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Tidskriftsartikel (refereegranskat)abstract
- The state of health (SOH) is a vital parameter enabling the reliability and life diagnostic of lithium-ion batteries. A novel fusion-based SOH estimator is proposed in this study, which combines an open circuit voltage (OCV) model and the incremental capacity analysis. Specifically, a novel OCV model is developed to extract the OCV curve and the associated features-of-interest (FOIs) from the measured terminal voltage during constant-current charge. With the determined OCV model, the disturbance-free incremental capacity (IC) curves can be derived, which enables the extraction of a set of IC morphological FOIs. The extracted model FOI and IC morphological FOIs are further fused for SOH estimation through an artificial neural network. Long-term degradation data obtained from different battery chemistries are used for validation. Results suggest that the proposed fusion-based method manifests itself with high estimation accuracy and high robustness.
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| 3. |
- Dong, Guangzhong, 1991, et al.
(författare)
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A Hierarchical Approach for Finite-time H- State Observer and Probabilistic Lifetime Prediction of Lithium-Ion Batteries
- 2021
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Ingår i: IEEE Transactions on Energy Conversion. - 1558-0059 .- 0885-8969. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- Accurate state-of-charge (SOC) estimation and lifetime prognosis of lithium-ion batteries are of great significance for reliable operations of energy storage systems. This paper proposes a novel two-layer hierarchical approach for online SOC estimation and remaining-useful-life (RUL) prediction based on a robust observer and Gaussian-process-regression (GPR). At the bottom layer, an equivalent-circuit model is first developed to describe battery dynamics. Second, a combination method of a recursive least square method and a finite time H-1 observer is designed to estimate battery open-circuit-voltage (OCV) and SOC through stability and robustness analysis. Next, the estimated OCV and SOC are fed into the top layer to generate the incremental-capacity-analysis-based aging feature, through which a robust signature associated with battery aging is identified. The feature is further employed for RUL prediction based on GPR. The salient advantages of the proposed approach are that it can provide robust parameter estimation in a given finite-time interval, and the GPR-based RUL prediction can tackle longterm uncertainties in a principled Bayesian manner. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed SOC observer and lifetime prediction methods.
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| 4. |
- Han, Yini, et al.
(författare)
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Optimization of land use pattern reduces surface runoff and sediment loss in a Hilly-Gully watershed at the Loess Plateau, China
- 2016
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Ingår i: Forest Systems. - : Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA). - 2171-9845. ; 25:1, s. 45-54
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Tidskriftsartikel (refereegranskat)abstract
- Aim of study: The aim is to find a way increasing gain yield and lessen area of farmland, and then increasing vegetation cover, improving environment and alleviating soil erosion.Area of study: The Hilly-Gully region at the loess plateau of China.Material and methods: In this study, an adjusted and optimized land use pattern was developed in Luoyugou watershed in the Yellow River valley based on the gradient distribution of land use types, and its effect on water and sediment transport was simulated using the SWAT model and GIS, with remote sensing images, land use maps and hydrologic data.Main results: The results indicate: average simulated runoff and sediment for the period 1986-2000 under conditions of the three land use pattern (2011, 2008 and optimized land use) reduced by 0.002-0.013 m3/s (2.7-17.6%) and 0.66 million tons, respectively. The runoff and sediment data obtained were compared with observed data from 2008, which showed that runoff and sediment production would be reduced by 467625 m3 and 22754 tons, respectively.Research highlights: The adjustment of the land use pattern in comprehensive consideration of vegetation and geography have a positive effect on water and sediment transport which will be important for decision making and water resources management, and provides a reference for future environmental management and ecological construction in the loess plateau Hilly-Gully region.
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| 5. |
- He, Jiangtao, et al.
(författare)
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State-of-Health Estimation of Lithium-Ion Batteries Using Incremental Capacity Analysis Based on Voltage-Capacity Model
- 2020
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Ingår i: IEEE Transactions on Transportation Electrification. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7782. ; 6:2, s. 417-426
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Hu, Jian, et al.
(författare)
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Disturbance-Immune and Aging-Robust Internal Short Circuit Diagnostic for Lithium-Ion Battery
- 2022
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Ingår i: IEEE Transactions on Industrial Electronics. - 0278-0046 .- 1557-9948. ; 69:2, s. 1988-1999
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Tidskriftsartikel (refereegranskat)abstract
- The accurate diagnostic of internal short circuit (ISC) is critical to the safety of lithium-ion battery (LIB), considering its consequence to disastrous thermal runaway. Motivated by this, this paper proposes a novel ISC diagnostic method with a high robustness to measurement disturbances and the capacity fading. Particularly, a multi-state-fusion ISC diagnostic method leveraging polarization dynamics instead of the conventional charge depletion is proposed within a model-switching framework. This is well proven to eliminate the vulnerability of diagnostic to battery aging. Within this framework, the recursive total least squares method with variant forgetting (RTLS-VF) is exploited, for the first time, to mitigate the adverse effect of measurement disturbances, which contributes to an unbiased estimation of the ISC resistance. The proposed method is validated both theoretically and experimentally for high diagnostic accuracy as well as the strong robustness to battery degradation and disturbance.
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| 7. |
- Hu, Jian, et al.
(författare)
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Residual Statistics-Based Current Sensor Fault Diagnosis for Smart Battery Management
- 2022
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Ingår i: IEEE Journal of Emerging and Selected Topics in Power Electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-6777 .- 2168-6785. ; 10:2, s. 2435-2444
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Tidskriftsartikel (refereegranskat)abstract
- Current sensor fault diagnostic is critical to the safety of lithium-ion batteries (LIBs) to prevent over-charging and over-discharging. Motivated by this, this article proposes a novel residual statistics-based diagnostic method to detect two typical types of sensor faults, leveraging only the 50 current-voltage samples at the startup phase of the LIB system. In particular, the load current is estimated by using particle swarm optimization (PSO)-based model matching with measurable initial system states. The estimation residuals are analyzed statistically with Monte-Carlo simulation, from which an empirical residual threshold is generated and used for accurate current sensor fault diagnostic. The residual evaluation process is well proved with high robustness to the measurement noises and modeling uncertainties. The proposed method is validated experimentally to be effective in current sensor fault diagnosis with low miss alarm rate (MAR) and false alarm rate (FAR).
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| 8. |
- Li, Yang, 1984, et al.
(författare)
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Adaptive Ensemble-Based Electrochemical-Thermal Degradation State Estimation of Lithium-Ion Batteries
- 2022
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Ingår i: IEEE Transactions on Industrial Electronics. - 0278-0046 .- 1557-9948. ; 69:7, s. 6984-6996
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Tidskriftsartikel (refereegranskat)abstract
- A computationally efficient state estimation method for lithium-ion (Li-ion) batteries is proposed based on a degradation-conscious high-fidelity electrochemical-thermal model for advanced battery management systems. The computational burden caused by the high-dimensional nonlinear nature of the battery model is effectively eased by adopting an ensemble-based state estimator using the singular evolutive interpolated Kalman filter (SEIKF). Unlike the existing schemes, it shows that the proposed algorithm intrinsically ensures mass conservation without imposing additional constraints, leading to a battery state estimator simple to tune and fast to converge. The model uncertainty caused by battery degradation and the measurement errors are properly addressed by the proposed scheme as it adaptively adjusts the error covariance matrices of the SEIKF. The performance of the proposed adaptive ensemble-based Li-ion battery state estimator is examined by comparing it with some well-established nonlinear estimation techniques that have been used previously for battery electrochemical state estimation, and the results show that excellent performance can be provided in terms of accuracy, computational speed, as well as robustness.
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| 9. |
- Li, Yang, 1984, et al.
(författare)
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Constrained Ensemble Kalman Filter for Distributed Electrochemical State Estimation of Lithium-Ion Batteries
- 2021
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Ingår i: IEEE Transactions on Industrial Informatics. - 1941-0050 .- 1551-3203. ; 17:1, s. 240-250
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Tidskriftsartikel (refereegranskat)abstract
- This article proposes a novel model-based estimator for distributed electrochemical states of lithium-ion (Li-ion) batteries. Through systematic simplifications of a high-order electrochemical–thermal coupled model consisting of partial differential-algebraic equations, a reduced-order battery model is obtained, which features an equivalent circuit form and captures local state dynamics of interest inside the battery. Based on the physics-based equivalent circuit model, a constrained ensemble Kalman filter (EnKF) is pertinently designed to detect internal variables, such as the local concentrations, overpotential, and molar flux. To address slow convergence issues due to weak observability of the battery model, the Li-ion's mass conservation is judiciously considered as a constraint in the estimation algorithm. The estimation performance is comprehensively examined under a wide operating range. It demonstrates that the proposed EnKF-based nonlinear estimator is able to accurately reproduce the physically meaningful state variables at a low computational cost and is significantly superior to its prevalent benchmarks for online applications.
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| 10. |
- Li, Yang, 1984, et al.
(författare)
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Electrochemical Model-Based Fast Charging: Physical Constraint-Triggered PI Control
- 2021
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Ingår i: IEEE Transactions on Energy Conversion. - 1558-0059 .- 0885-8969. ; 36:4, s. 3208-3220
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a new fast charging strategy for lithium-ion (Li-ion) batteries. The approach relies on an experimentally validated high-fidelity model describing battery electrochemical and thermal dynamics that determine the fast charging capability. Such a high-dimensional nonlinear dynamic model can be intractable to compute in real-time if it is fused with the extended Kalman filter or the unscented Kalman filter that is commonly used in the community of battery management. To significantly save computational efforts and achieve rapid convergence, the ensemble transform Kalman filter (ETKF) is selected and tailored to estimate the nonuniform Li-ion battery states. Then, a health- and safety-aware charging protocol is proposed based on successively applied proportional-integral (PI) control actions. The controller regulates charging rates using online battery state information and the imposed constraints, in which each PI control action automatically comes into play when its corresponding constraint is triggered. The proposed physical constraint-triggered PI charging control strategy with the ETKF is evaluated and compared with several prevalent alternatives. It shows that the derived controller can achieve close to the optimal solution in terms of charging time and trajectory, as determined by a nonlinear model predictive controller, but at a drastically reduced computational cost.
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