| 1. |
- Deng, Zhongwei, et al.
(författare)
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Battery health evaluation using a short random segment of constant current charging
- 2022
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Ingår i: iScience. - : Elsevier BV. - 2589-0042. ; 25:5
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Tidskriftsartikel (refereegranskat)abstract
- Accurately evaluating the health status of lithium-ion batteries (LIBs) is significant to enhance the safety, efficiency, and economy of LIBs deployment. However, the complex degradation processes inside the battery make it a thorny challenge. Data-driven methods are widely used to resolve the problem without exploring the complex aging mechanisms; however, random and incomplete charging-discharging processes in actual applications make the existing methods fail to work. Here, we develop three data-driven methods to estimate battery state of health (SOH) using a short random charging segment (RCS). Four types of commercial LIBs (75 cells), cycled under different temperatures and discharging rates, are employed to validate the methods. Trained on a nominal cycling condition, our models can achieve high-precision SOH estimation under other different conditions. We prove that an RCS with a 10mV voltage window can obtain an average error of less than 5%, and the error plunges as the voltage window increases.
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| 2. |
- Deng, Zhongwei, et al.
(författare)
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Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data
- 2022
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Ingår i: IEEE transactions on power electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-8993 .- 1941-0107. ; 37:5, s. 5021-5031
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Tidskriftsartikel (refereegranskat)abstract
- The rapid development of battery technology has promoted the deployment of electric vehicles (EVs). To ensure the healthy and sustainable development of EVs, it is urgent to solve the problems of battery safety monitoring, residual value assessment, and predictive maintenance, which heavily depends on the accurate state-of-health (SOH) estimation of batteries. However, many published methods are unsuitable for actual vehicle conditions. To this end, a data-driven method based on the random partial charging process and sparse Gaussian process regression (GPR) is proposed in this article. First, the random capacity increment sequences (oQ) at different voltage segments are extracted from the partial charging process. The average value and standard deviation of oQ are used as features to indicate battery health. Second, correlation analysis is conducted for three types of batteries, and high correlations between the features and battery SOH are verified at different temperatures and discharging current rates. Third, by using the proposed features as inputs, sparse GPR models are constructed to estimate the SOH. Compared with other data-driven methods, the sparse GPR has the highest estimation accuracy, and its average maximum absolute errors are only 2.88%, 2.52%, and 1.51% for three different types of batteries, respectively.
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| 3. |
- Guo, Wenchao, et al.
(författare)
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Early diagnosis of battery faults through an unsupervised health scoring method for real-world applications
- 2023
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Ingår i: IEEE Transactions on Transportation Electrification. - 2332-7782. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- Battery fault diagnosis is critical to ensure the safe and reliable operation of electric vehicles or energy storage systems. Early diagnosis of battery faults can enable timely maintenance and reduce potential accidents. However, the lead time for detection is still relatively insufficient, and the identification of target vehicle with unidentified fault type has generally been neglected. To fill the gap, an unsupervised health scoring method for early diagnosis of battery faults is proposed in this paper. First, considering the properties of field data, new features and four types of feature sets related to battery health and fault status are derived for each cell. Then, a novel strategy is proposed to transform a typical classification problem into a quantitative scoring problem by performing multiple clustering. To produce ample clustering results, three cluster algorithms based on different principles are used and the features are randomly divided into feature subsets. By coupling temperature information, early determination of thermal runaway faults can be achieved. Finally, the real-world cloud data of three typical accidents are employed for verification, the results indicate that the proposed approach can innovatively achieve the detection of the abnormal cells at the level of days in advance, demonstrating excellent performance.
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| 4. |
- Guo, Wenchao, et al.
(författare)
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Rapid online health estimation for lithium-ion batteries based on partial constant-voltage charging segment
- 2023
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Ingår i: Energy. - 0360-5442. ; 281
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Tidskriftsartikel (refereegranskat)abstract
- Battery health evaluation is vital for ensuring the security and reliability of lithium-ion batteries. However, the currently proposed methods generally require high-quality input data for feature extraction in online applications. To overcome this obstacle, this paper proposes a rapid online health estimation method only based on partial constant-voltage (CV) charging segment. Firstly, through primary analysis of battery test data, the evolution of CV charging current is confirmed to be correlated with battery capacity. Subsequently, the current evolution constant of CV charging phase is mathematically formulated and quantitatively characterized using a novel health indicator (HI). Besides, charging time and charging capacity are also extracted as HIs to comprehensively capture the CV charging behavior and enhance the robustness of data-driven models. Considering the user's charging habits, an optimized CV segment is determined, enabling a significant reduction in data size and coverage. Finally, three data-driven methods are employed to construct health estimation models by using the extracted HIs, and the best performance is achieved by Gaussian process regression with MAE and RMSE lower than 0.8% and 1%, respectively. Remarkably, the proposed method demonstrates superiority in dealing with sparse sampling, and satisfactory results with 2.9% error under the sparsity of 10 s are obtained.
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| 5. |
- Wu, Xiaohua, et al.
(författare)
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Online Adaptive Model Identification and State of Charge Estimation for Vehicle-Level Battery Packs
- 2024
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Ingår i: IEEE Transactions on Transportation Electrification. - 2332-7782. ; 10:1, s. 596-607
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Tidskriftsartikel (refereegranskat)abstract
- Accurate state of charge (SOC) estimation of traction batteries plays a crucial role in energy and safety management for electric vehicles. Existing studies focus primarily on cell battery SOC estimation. However, numerical instability and divergence problems might occur for a large-size lithium-ion battery pack consisting of many cells. This paper proposes a high-performance online model identification and SOC estimation method based on an adaptive square root unscented Kalman filter (ASRUKF) and an improved forgetting factor recursive least squares (IFFRLS) for vehicle-level traction battery packs. The model parameters are identified online through the IFFRLS, where the conventional method might encounter numerical stability problems. By updating the square root of the covariance matrix, the divergence problem in the traditional unscented Kalman filter is solved in the ASRUKF algorithm, where the positive semi-definiteness of the covariance matrix is guaranteed. Combined with the adaptive noise covariance matched filtering algorithm and real-time compensation of system error, the proposed method solves the problem of ever-degrading estimation accuracy in the presence of time-varying noise with unknown statistical characteristics. Using a 66.2-kWh vehicle battery pack, we experimentally verified that the proposed algorithm could achieve high estimation accuracy with guaranteed numerical stability. The maximum error of SOC estimation can be bounded by 1%, and the root-mean-square error is as low as 0.47% under real-world vehicle operating conditions.
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