MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

In the recent years, lithium-ion batteries have become the battery technology of choice for portable devices, electric vehicles and grid storage. While increasing numbers of car manufacturers are introducing electrified models into their offering, range anxiety and the length of time required to recharge the batteries are still a common concern. The high currents needed to accelerate the charging process have been known to reduce energy efficiency and cause accelerated capacity and power fade. Fast charging is a multiscale problem, therefore insights from atomic to system level are required to understand and improve fast charging performance. The present paper reviews the literature on the physical phenomena that limit battery charging speeds, the degradation mechanisms that commonly result from charging at high currents, and the approaches that have been proposed to address these issues. Special attention is paid to low temperature charging. Alternative fast charging protocols are presented and critically assessed. Safety implications are explored, including the potential influence of fast charging on thermal runaway characteristics. Finally, knowledge gaps are identified and recommendations are made for the direction of future research. The need to develop reliable onboard methods to detect lithium plating and mechanical degradation is highlighted. Robust model-based charging optimisation strategies are identified as key to enabling fast charging in all conditions. Thermal management strategies to both cool batteries during charging and preheat them in cold weather are acknowledged as critical, with a particular focus on techniques capable of achieving high speeds and good temperature homogeneities.

Lithium-ion battery fast charging: A review

The lithium ion battery is widely used in electric vehicles (EV) The battery degradation is the key scientific problem in battery research The battery aging limits its energy storage and power output capability, as well as the performance of the EV including the cost and life span Therefore, a comprehensive review on the key issues of the battery degradation among the whole life cycle is provided in this paper Firstly, the battery internal aging mechanisms are reviewed considering different anode and cathode materials for better understanding the battery fade characteristic Then, to get better life performance, the influence factors affecting battery life are discussed in detail from the perspectives of design, production and application Finally, considering the difference between the cell and system, the battery system degradation mechanism is discussed

A review on the key issues of the lithium ion battery degradation among the whole life cycle

Contents: Variety and Quality. Variability loss and tolerance. Determining tolerances. Tolerance design and experimental design. Offline and online quality control. Parameter design and tolerance design: case study. Experimental design for smaller is better characteristics. Experimental design for larger is better characteristics. Bypassing the S/N ratio: spring experiment. Experimental design for dynamic characteristics.

Introduction to quality engineering. designing quality into products a

Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Internal heating of lithium-ion batteries using alternating current based on the heat generation model in frequency domain

https://iopscience.iop.org/article/10.1149/2.0461706jes/pdf

Investigating Lithium Plating in Lithium-Ion Batteries at Low Temperatures Using Electrochemical Model with NMR Assisted Parameterization

Alternating current (AC) is capable of heating up a lithium-ion battery efficiently before charging at low temperature. Generally, the lower the frequency of the AC current, the higher the heat generation rate. Yet at low frequency, there is a risk of lithium-ion deposition during the half cycle of charging. This study develops a temperature-adaptive, deposition-free AC preheating method. The electrochemical impedance spectroscopies (EIS) of both positive and negative electrodes are measured at different temperatures using a three-electrode lithium-ion cell. The equivalent electric circuit (EEC) models are fitted through the EIS data. The maximum permissible amplitudes of the heating current without incurring lithium deposition at different frequencies are determined at each temperature using the fitted EEC model and the lithium deposition potential. Combining the maximum permissible AC current and the heat generation rate model in the frequency domain, a multistep AC preheating method, in which the amplitude is adjusted according to the cell temperature, is developed. Using this method, the cell can be heated from −20°C to 5°C within 800 s at 100 Hz frequency with the multistep temperature-adaptive amplitude profile.

https://iopscience.iop.org/article/10.1149/2.0961602jes/pdf

Hao Ge

Papers

Internal heating of lithium-ion batteries using alternating current based on the heat generation model in frequency domain

Investigating Lithium Plating in Lithium-Ion Batteries at Low Temperatures Using Electrochemical Model with NMR Assisted Parameterization

Temperature-Adaptive Alternating Current Preheating of Lithium-Ion Batteries with Lithium Deposition Prevention

Path dependence of lithium ion cells aging under storage conditions

Dynamic Electrochemical Impedance Spectroscopy of a Three-Electrode Lithium-Ion Battery during Pulse Charge and Discharge