| 1. |
- Thaduri, Adithya, et al.
(författare)
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Electronics Parts Change Control and Supply Chain Responsibilities
- 2015
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Konferensbidrag (populärvet., debatt m.m.)abstract
- The rapid growth of the information and automation industries has spurred dramatic changes in the parts that make up electronic products and systems. In particular, changes are continuously being made to increase performance, reduce feature size and power, and of course reduce costs while meeting environmental and other legal regulations. All changes introduce uncertainty and this uncertainty can affect the operation of the supply chain, as well as the final results. The more frequent changes are made, the more complex the operations of the supply chain become and the more attention is required to assess the changes. This presentation will discuss some of the key issues with changes and how advanced supply chain methods are being used to address the changes. Issues concerning obsolescence, the use of application specific parts, counterfeit parts, qualification, reliability and operational availability will also be presented.
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| 2. |
- Zhang, Yizhou, 1991, et al.
(författare)
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A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data
- 2022
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Ingår i: Journal of Power Sources. - : Elsevier BV. - 0378-7753. ; 526
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Tidskriftsartikel (refereegranskat)abstract
- Accurately predicting batteries’ ageing trajectory and remaining useful life is not only required to ensure safe and reliable operation of electric vehicles (EVs) but is also the fundamental step towards health-conscious use and residual value assessment of the battery. The non-linearity, wide range of operating conditions, and cell to cell variations make battery health prediction challenging. This paper proposes a prediction framework that is based on a combination of global models offline developed by different machine learning methods and cell individualised models that are online adapted. For any format of raw data collected under diverse operating conditions, statistic properties of histograms can be still extracted and used as features to learn battery ageing. Our framework is trained and tested on three large datasets, one being retrieved from 7296 plug-in hybrid EVs. While the best global models achieve 0.93% mean absolute percentage error (MAPE) on laboratory data and 1.41% MAPE on the real-world fleet data, the adaptation algorithm further reduced the errors by up to 13.7%, all requiring low computational power and memory. Overall, this work proves the feasibility and benefits of using histogram data and also highlights how online adaptation can be used to improve predictions.
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| 3. |
- Zhang, Yongzhi, 1991, et al.
(författare)
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Aging characteristics-based health diagnosis and remaining useful life prognostics for lithium-ion batteries
- 2019
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Ingår i: eTransportation. - : Elsevier BV. - 2590-1168. ; 1
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Tidskriftsartikel (refereegranskat)abstract
- This paper developed methods for improving the practicability of battery health diagnosis and remaining useful life prognostics. Battery state of health was estimated using a feature extraction-based method based on the charging voltage curve. Battery remaining useful life was predicted by identifying recognizable aging stages. Acceleration aging test data for 9 cells at constant current rates including 0.5C, 1C, 1.5C, and 2C, and dynamic current rates were used to validate the developed methods. The capacity estimates were accurate with estimation errors less than 1% at most cycles. The remaining useful life was predicted within 0.3 s at dynamic current rates, with the prediction errors at most cycles less than 10 after 300 cycles and the 95% confidence intervals covering about 20 cycles for each prediction.
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| 4. |
- Zou, Changfu, 1987, et al.
(författare)
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A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors
- 2018
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Ingår i: Journal of Power Sources. - : Elsevier BV. - 0378-7753. ; 390, s. 286-296
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Forskningsöversikt (refereegranskat)abstract
- Electrochemical energy storage systems play an important role in diverse applications, such as electrified transportation and integration of renewable energy with the electrical grid. To facilitate model-based management for extracting full system potentials, proper mathematical models are imperative. Due to extra degrees of freedom brought by differentiation derivatives, fractional-order models may be able to better describe the dynamic behaviors of electrochemical systems. This paper provides a critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors. Starting with the basic concepts and technical tools from fractional-order calculus, the modeling principles for these energy systems are presented by identifying disperse dynamic processes and using electrochemical impedance spectroscopy. Available battery/supercapacitor models are comprehensively reviewed, and the advantages of fractional types are discussed. Two case studies demonstrate the accuracy and computational efficiency of fractional-order models. These models offer 15–30% higher accuracy than their integer-order analogues, but have reasonable complexity. Consequently, fractional-order models can be good candidates for the development of advanced b attery/supercapacitor management systems. Finally, the main technical challenges facing electrochemical energy storage system modeling, state estimation, and control in the fractional-order domain, as well as future research directions, are highlighted.
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