| 1. |
- Guo, Wenchao, et al.
(author)
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Early diagnosis of battery faults through an unsupervised health scoring method for real-world applications
- 2023
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In: IEEE Transactions on Transportation Electrification. - 2332-7782. ; In Press
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Journal article (peer-reviewed)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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| 2. |
- Qian, Kun, et al.
(author)
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The Impact of Considering State of Charge Dependent Maximum Charging Powers on the Optimal Electric Vehicle Charging Scheduling
- 2023
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In: IEEE Transactions on Transportation Electrification. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7782 .- 2372-2088. ; 9:3, s. 4517-4530
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Journal article (peer-reviewed)abstract
- Intelligent charging solutions facilitate mobility electrification. Mathematically, electric vehicle (EV) charging scheduling formulations are constrained optimization problems. Therefore, accurate constraint modeling is theoretically and practically relevant for scheduling. However, the current scheduling literature lacks an accurate problem formulation, including the joint modeling of the nonlinear battery charging profile and minimum charging power constraints. The minimum charging power constraint prevents allocating inexecutable charging profiles. Furthermore, if the problem formulation does not consider the battery charging profile, the scheduling execution may deviate from the allocated charging profile. An insignificant deviation indicates that simplified modeling is acceptable. After providing the problem formulation targeting the maximum possible vehicle battery state of charge (SoC) on departure, the numerical assessment shows how the constraint consideration impacts the scheduling performance in typical charging scenarios (weekday workplace and weekend public charging where the grid supplies up to forty vehicles). The simulation results show that the nonlinear battery charging constraint is practically negligible: For many connected EVs, the grid limit frequently overrules that constraint. The resulting difference between the final mean SoCs using and not using accurate modeling does not exceed 0.2%. Consequently, the results justify simplified modeling (excluding the nonlinear charging profile) for similar scenarios in future contributions.
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| 3. |
- Sharma, Nimananda, 1988, et al.
(author)
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A Mechanical-Hardware-in-the-Loop Test Bench for Verification of Multi-Motor Drivetrain Systems
- 2023
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In: IEEE Transactions on Transportation Electrification. - 2332-7782. ; 9:1, s. 1698-1707
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Journal article (peer-reviewed)abstract
- Multi-motor drivetrain systems utilizing more than one electric machine for propulsion can provide possibilities to improve energy efficiency and vehicle dynamic performance of battery electric vehicles (BEV). However, laboratory testing of such drivetrain systems using a dyno test bench can be costly. A solution can be to use a mechanical-hardware-in-the-loop (MHIL) test bench, which combines real-time simulations of the intended working environment with the dyno test bench. To utilize the MHIL approach for multi-motor drivetrain systems, one drivetrain is implemented in the dyno test bench, while the remaining are simulated using a real-time simulator. Therefore, providing a less expensive solution for laboratory testing of drivetrain components and control methods in their intended environment. In this work, an MHIL test bench for a multi-motor drivetrain system is designed and experimentally verified. A BEV with two independently driven front wheels is considered for modeling. To interface the dyno test bench with real-time simulation, two different methods, namely open-loop and closed-loop, are proposed and verified in experiments by prototyping an MHIL test bench. In addition, an anti-slip control is implemented and evaluated experimentally to demonstrate the suitability of the proposed MHIL test bench in the verification of control methods.
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| 4. |
- Wei, Zhongbao, et al.
(author)
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Multi-Level Data-Driven Battery Management: From Internal Sensing to Big Data Utilization
- 2023
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In: IEEE Transactions on Transportation Electrification. - 2332-7782. ; 9:4, s. 4805-4823
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Journal article (peer-reviewed)abstract
- Battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of data-driven battery management from a multi-level perspective. The widely-explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multi-dimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for enhanced battery management is further overviewed. This belongs to the upper and the macroscopic level of the data-driven BMS framework. With this endeavor, we aim to motivate new insights into the future development of next-generation data-driven battery management.
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| 5. |
- Yan, Yuming, et al.
(author)
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Hybrid-Magnet Variable Flux Memory Machine With Improved Field Regulation Capability for Electric Vehicle Applications
- 2023
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In: IEEE Transactions on Transportation Electrification. - 2332-7782. ; 9:1, s. 586-597
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Journal article (peer-reviewed)abstract
- This article aims to investigate a novel hybrid-magnet variable flux memory machine (VFMM) with improved field regulation capability for electric vehicle applications. Two sets of permanent magnets (PMs), i.e., high-energy-density neodymium-iron-boron PM and low coercive force (LCF) PM, are arranged in a delta array to improve field regulation capability and torque density. The machine topology, key features, and operating principle of the presented machine are demonstrated. Based on the simplified magnetic circuit model, the underlying design tradeoffs of the presented machine are revealed qualitatively. The nonlinear magnetic circuit model of the presented machine taking saturation and hybrid-magnet leakage flux into consideration is built for machine analysis. Electromagnetic performance comparisons are carried out by finite element analysis between the presented VFMM and the existing VFMM. The results show that the presented machine can achieve significantly improved flux regulation capability while maintaining the high output torque due to the improved arrangement of hybrid magnetic branches and enhanced flux concentration effect. Finally, the investigated VFMM is prototyped. The operating principle and predicted results are verified by experimental results.
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| 6. |
- Yu, Xiaoran, et al.
(author)
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Remaining-useful-lifetime prediction of proton exchange membrane fuel cell considering model uncertainty quantification on the full-time scale
- 2023
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In: IEEE Transactions on Transportation Electrification. - 2332-7782. ; In Press
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Journal article (peer-reviewed)abstract
- A prognostics and health management (PHM) system with prediction at its core optimizes the durability of the proton exchange membrane fuel cell (PEMFC). However, the aging behavior model has some uncertainty due to limited knowledge, affecting the predictive performance in remaining useful life (RUL) prediction. To address this issue, an RUL prediction method based on the Bayesian framework considering uncertainty quantification on the full-time scale is proposed. Firstly, the state of health (SOH) of the PEMFC is estimated, and the behavior of uncertainty is quantified. Afterwards, a long short-term memory (LSTM) neural network is employed to make a prediction for its behavior. Finally, the RUL of PEMFC is predicted based on historical SOH and the predicted behavior of uncertainty. Validation indicates that the proposed method can make a long-term prediction and provide RUL prediction with high accuracy. Under the dynamic operating condition, in terms of long-term prediction, compared to unscented Kalman filter, adaptive unscented Kalman filter, double-input-echo-state-network and bidirectional LSTM, the proposed method decreases the error by 88.12%, 41.99%, 13.82% and 3.21%, respectively. And under the dynamic operating condition, the proposed method shows good stability. Moreover, the robustness of this method has also been verified.
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| 7. |
- Zhang, Huang, 1993, et al.
(author)
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Interpretable Battery Cycle Life Range Prediction Using Early Cell Degradation Data
- 2023
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In: IEEE Transactions on Transportation Electrification. - 2332-7782. ; 9:2, s. 2669-2682
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Journal article (peer-reviewed)abstract
- Battery cycle life prediction using early degradation data has many potential applications throughout the battery product life cycle. For that reason, various data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. However, managing the rapidly increasing amounts of batteries at end-of-life with lower economic and technical risk requires prediction of cycle life with quantified uncertainty, which is still lacking. The interpretability (i.e., the reason for high prediction accuracy) of these advanced data-driven methods is also worthy of investigation. Here, a Quantile Regression Forest (QRF) model, having the advantage of not assuming any specific distribution of cycle life, is introduced to make cycle life range prediction with uncertainty quantified as the width of the prediction interval, in addition to point predictions with high accuracy. The hyperparameters of the QRF model are optimized with a proposed alpha-logistic-weighted criterion so that the coverage probabilities associated with the prediction intervals are calibrated. The interpretability of the final QRF model is explored with two global model-agnostic methods, namely permutation importance and partial dependence plot.
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