Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability

This project was funded by an industry-academia collaborative grant EPSRC EP/R511687/1 awarded by Engineering and Physical Sciences Research Council (EPSRC) & University of Edinburgh United Kingdom program Impact Acceleration Account (IAA). P. Dechent was supported by Bundesministerium für Bildung und Forschung Germany ( BMBF 03XP0302C ). Publisher Copyright: © 2022 The Author(s)The testing of battery cells is a long and expensive process, and hence understanding how large a test set needs to be is very useful. This work proposes an automated methodology to estimate the smallest sample size of cells required to capture the cell-to-cell variability seen in a larger population. We define cell-to-cell variation based on the slopes of a linear regression model applied to capacity fade curves. Our methodology determines a sample size which estimates this variability within user specified requirements on precision and confidence. The sample size is found using the distributional properties of the slopes under a normality assumption, and an implementation of the approach is available on GitHub. For the five datasets in the study, we find that a sample size of 8–10 cells (at a prespecified precision and confidence) captures the cell-to-cell variability of the larger datasets. We show that prior testing knowledge can be leveraged with machine learning models to operationally optimise the design of new cell-testing, leading up to a 75% reduction in experimental costs.publishersversionpublishe

"Knees" in lithium-ion battery aging trajectories

Lithium-ion batteries can last many years but sometimes exhibit rapid, nonlinear degradation that severely limits battery lifetime. In this work, we review prior work on "knees" in lithium-ion battery aging trajectories. We first review definitions for knees and three classes of "internal state trajectories" (termed snowball, hidden, and threshold trajectories) that can cause a knee. We then discuss six knee "pathways", including lithium plating, electrode saturation, resistance growth, electrolyte and additive depletion, percolation-limited connectivity, and mechanical deformation -- some of which have internal state trajectories with signals that are electrochemically undetectable. We also identify key design and usage sensitivities for knees. Finally, we discuss challenges and opportunities for knee modeling and prediction. Our findings illustrate the complexity and subtlety of lithium-ion battery degradation and can aid both academic and industrial efforts to improve battery lifetime.Comment: Submitted to the Journal of the Electrochemical Societ

Position statement of the EADV Artificial Intelligence (AI) Task Force on AI‐assisted smartphone apps and web‐based services for skin disease

Background: As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer. Objective: This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI‐assisted smartphone applications (apps) and web‐based services for skin diseases with emphasis on skin cancer detection.MethodsAn initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance. Results: Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non‐medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web‐based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users. Conclusions: The utilisation of AI‐assisted smartphone apps and web‐based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice

Delphi Consensus Among International Experts on the Diagnosis, Management, and Surveillance for Lentigo Maligna

Introduction: Melanoma of the lentigo maligna (LM) type is challenging. There is lack of consensus on the optimal diagnosis, treatment, and follow-up. Objectives: To obtain general consensus on the diagnosis, treatment, and follow-up for LM. Methods: A modified Delphi method was used. The invited participants were either members of the International Dermoscopy Society, academic experts, or authors of published articles relating to skin cancer and melanoma. Participants were required to respond across three rounds using a 4-point Likert scale). Consensus was defined as >75% of participants agreeing/strongly agreeing or disagreeing/strongly disagreeing. Results: Of the 31 experts invited to participate in this Delphi study, 29 participants completed Round 1 (89.9% response rate), 25/31 completed Round 2 (77.5% response rate), and 25/31 completed Round 3 (77.5% response rate). Experts agreed that LM diagnosis should be based on a clinical and dermatoscopic approach (92%) followed by a biopsy. The most appropriate primary treatment of LM was deemed to be margin-controlled surgery (83.3%), although non-surgical modalities, especially imiquimod, were commonly used either as alternative off-label primary treatment in selected patients or as adjuvant therapy following surgery; 62% participants responded life-long clinical follow-up was needed for LM. Conclusions: Clinical and histological diagnosis of LM is challenging and should be based on macroscopic, dermatoscopic, and RCM examination followed by a biopsy. Different treatment modalities and follow-up should be carefully discussed with the patient

Investigation of capacity recovery during rest period at different states-of-charge after cycle life test for prismatic Li(Ni1/3Mn1/3Co1/3)O2-graphite cells

In this publication two strategies are introduced to assess irreversible capacity loss during shallow cycling at different average SOCs. Due to superposed reversible capacity effects, a simple evaluation of capacity trend is not sufficient. Those reversible effects are related to contributions of the anode overhang (geometrical oversized anode) and to the homogeneity of lithium distribution (HLD). For both strategies the cycling test is additionally followed by a calendaric aging test to recover capacity. While the contribution of HLD can be assessed by storing the cells at the same average SOC as during cycling, the contribution of the anode overhang is evaluated for a defined low SOC. During the storage phase in all cases the extractable capacity rises supporting the reversible capacity theory. Moreover, the HLD, measured with differential capacity analysis and capacity difference analysis, rises as well; this is the case for all test conditions exhibiting the reversible nature of HLD and its influence on extractable capacity. The irreversible capacity losses are compared to an alternative method, called ‘slope method’, assuming that the aging is nearly linear and that the linear part at the end of test is mainly attributed to irreversible aging. While the results of both methods are in the same order of magnitude, the relaxation method can be applied, not only to static, but also to any dynamic aging profiles

A Review on Aging-Aware System Simulation for Plug-In Hybrids

The lithium-ion battery is a vital powertrain component in plug-in hybrid electric vehicles (PHEVs). The fuel reduction potential and cost-effectiveness of these vehicles depend on the sizing of the powertrain components and their utilization, which is defined by the energy management system (EMS). The battery is affected by power and capacity reduction over the lifetime of the vehicle, which needs to be considered during the design process to ensure the performance goals throughout the vehicle’s lifetime. Current literature regarding battery aging usually contains experimental results, which are not transformed into a useful aging model for system simulations. Consequently, battery aging is often neglected, which is why this article intends to help researchers understand the degradation process of batteries in PHEVs and consider this in their simulation and dimensioning process. First, PHEV powertrain topologies and components are presented. Afterward, battery degradation mechanisms and recent findings are explained, followed by appropriate modeling approaches for different simulation targets. Finally, current aging-aware EMS literature is systematically reviewed, and the integration of the aging models is analyzed, so researchers in system simulation areas can improve their powertrain models

High-Precision Monitoring of Volume Change of Commercial Lithium-Ion Batteries by Using Strain Gauges

This paper proposes a testing method that allows the monitoring of the development of volume expansion of lithium-ion batteries. The overall goal is to demonstrate the impact of the volume expansion on battery ageing. The following findings are achieved: First, the characteristic curve shape of the diameter change depended on the state-of-charge and the load direction of the battery. The characteristic curve shape consisted of three areas. Second, the characteristic curve shape of the diameter change changed over ageing. Whereas the state-of-charge dependent geometric alterations were of a reversible nature. An irreversible effect over the lifetime of the cell was observed. Third, an s-shaped course of the diameter change indicated two different ageing effects that led to the diameter change variation. Both reversible and irreversible expansion increased with ageing. Fourth, a direct correlation between the diameter change and the capacity loss of this particular lithium-ion battery was observed. Fifth, computer tomography (CT) measurements showed deformation of the jelly roll and post-mortem analysis showed the formation of a covering layer and the increase in the thickness of the anode. Sixth, reproducibility and temperature stability of the strain gauges were shown. Overall, this paper provides the basis for a stable and reproducible method for volume expansion analysis applied and established by the investigation of a state-of-the-art lithium-ion battery cell. This enables the study of volume expansion and its impact on capacity and cell deat