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Insights and reviews on battery lifetime prediction from research to practice

Journal of Energy Chemistry 2024年第7期94卷 716-739页

作者： Xudong Qu Dapai Shi jingyuan zhao Manh-Kien Tran Zhenghong Wang Michael Fowler Yubo Lian Andrew FBurke Hubei Longzhong Laboratory Hubei University of Arts and ScienceXiangyang 441000HubeiChina Institute of Transportation Studies University of California DavisDavisCA 95616USA Department of Chemical Engineering University of WaterlooWaterlooOntario N2L 3G1Canada BYD Automotive Engineering Research Institute Shenzhen 518118GuangdongChina

The rising demand for energy storage solutions,especially in the electric vehicle and renewable energy sectors,highlights the importance of accurately predicting battery health to enhance their longevity and reliability.This article comprehensively examines various methods used to forecast battery health,including physics-based models,empirical models,and equivalent circuit models,among others.It delves into the promise of data-driven prognostics,utilizing both conventional machine learning and cuttingedge deep neural network techniques.The advantages and limitations of hybrid models are thoroughly analyzed,with a focus on the benefits of integrating diverse data sources to improve prognostic precision.Through practical case studies,the article showcases the effectiveness and flexibility of these approaches.It also critically addresses the challenges encountered in applying battery health prognostics in realworld scenarios,such as issues of scalability,complexity,and data anomalies.Despite these challenges,the article underscores the emerging opportunities brought about by recent technological,academic,and research advancements.These include the development of digital twin models for batteries,the use of data-centric AI and standardized benchmarking,the potential integration of blockchain technology for enhanced data security and transparency,and the synergy between edge and cloud computing to boost data analysis and processing.The primary goal of this article is to enrich the understanding of current battery health prognostic techniques and to inspire further research aimed at overcoming existing hurdles and tapping into new opportunities.It concludes with a visionary perspective on future research directions and potential developments in this evolving field,encouraging both researchers and practitioners to explore innovative solutions.

关键词： Batteries Lifetime Health Machine learning Deep learning Field Real world

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Battery prognostics and health management for electric vehicles under industry 4.0

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能源化学:英文版 2023年第9期84卷 30-33页

作者： jingyuan zhao Andrew F.Burke Institute of Transportation Studies University of California DavisDavisCA 95616USA

Transportation electrification is essential for decarbonizing transport. Currently, lithium-ion batteries are the primary power source for electric vehicles (EVs). However, there is still a significant journey ahead before EVs can establish themselves as the dominant force in the global automotive market. Concerns such as range anxiety, battery aging, and safety issues remain significant challenges.

关键词： Lithium-ion battery Prognostics and health management Machine learning Cloud Artificial intelligence Digital twins Lifelong learning

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Specialized deep neural networks for battery health prognostics:Opportunities and challenges

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Journal of Energy Chemistry 2023年第12期87卷 416-438,I0011页

作者： jingyuan zhao Xuebing Han Minggao Ouyang Andrew F.Burke Institute of Transportation Studies University of CaliforniaDavisCA 95616USA State Key Laboratory of Automotive Safety and Energy Tsinghua UniversityBeijing 100084China

Lithium-ion batteries are key drivers of the renewable energy revolution,bolstered by progress in battery design,modelling,and management.Yet,achieving high-performance battery health prognostics is a significant challenge.With the availability of open data and software,coupled with automated simulations,deep learning has become an integral component of battery health prognostics.We offer a comprehensive overview of potential deep learning techniques specifically designed for modeling and forecasting the dynamics of multiphysics and multiscale battery systems.Following this,we provide a concise summary of publicly available lithium-ion battery test and cycle datasets.By providing illustrative examples,we emphasize the efficacy of five techniques capable of enhancing deep learning for accurate battery state prediction and health-focused management.Each of these techniques offers unique benefits.(1)Transformer models address challenges using self-attention mechanisms and positional encoding methods.(2) Transfer learning improves learning tasks within a target domain by leveraging knowledge from a source domain.(3) Physics-informed learning uses prior knowledge to enhance learning algorithms.(4)Generative adversarial networks(GANs) earn praise for their ability to generate diverse and high-quality outputs,exhibiting outstanding performance with complex datasets.(5) Deep reinforcement learning enables an agent to make optimal decisions through continuous interactions with its environment,thus maximizing cumulative rewards.In this Review,we highlight examples that employ these techniques for battery health prognostics,summarizing both their challenges and opportunities.These methodologies offer promising prospects for researchers and industry professionals,enabling the creation of specialized network architectures that autonomously extract features,especially for long-range spatial-temporal connections across extended timescales.The outcomes could include improved accuracy,faster training,and enhanced generalization.

关键词： Lithium-ion batteries State of health Lifetime Deep learning Transformer Transfer learning Physics-informed learning Generative adversarial networks Reinforcement learning Open data

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Boosting extraction of Pb in contaminated soil via interfacial solar evaporation of multifunctional sponge

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Green Energy & Environment 2023年第5期8卷 1459-1468页

作者： Pan Wu Xuan Wu Yida Wang jingyuan zhao Haolan Xu Gary Owens Future Industries Institute UniSA STEMUniversity of South AustraliaMawson Lakes CampusSA 5095Australia

Interfacial solar water evaporation is a reliable way to accelerate water evaporation and contaminant remediation.Embracing the recent advance in photothermal technology,a functional sponge was prepared by coating a sodium alginate(SA)impregnated sponge with a surface layer of reduced graphene oxide(rGO)to act as a photothermal conversion medium and then subsequently evaluated for its ability to enhance Pb extraction from contaminated soil driven by interfacial solar evaporation.The SA loaded sponge had a Pb adsorption capacity of 107.4 mg g^(-1).Coating the top surface of the SA sponge with rGO increased water evaporation performance to 1.81 kg m^(-2)h^(-1)in soil media under one sun illumination and with a wind velocity of 2 m s^(-1).Over 12 continuous days of indoor evaporation testing,the Pb extraction efficiency was increased by 22.0%under 1 sun illumination relative to that observed without illumination.Subsequently,Pb extraction was further improved by 48.9%under outdoor evaporation conditions compared to indoor conditions.Overall,this initial work shows the significant potential of interfacial solar evaporation technologies for Pb contaminated soil remediation,which should also be applicable to a variety of other environmental contaminants.

关键词： Photothermal materials Interfacial solar evaporation Reduced graphene oxide Pb extraction Soil remediation

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DI-3-n-butylphthalide exerts neuroprotective effects by modulating hypoxia-inducible factor 1-alpha ubiquitination to attenuate oxidative stress-induced apoptosis

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Neural Regeneration Research 2023年第11期18卷 2424-2428页

作者： Shuai Li jingyuan zhao Yan Xi Jiaqi Ren Yanna Zhu Yan Lu Deshi Dong Department of Pharmacy The First Affiliated Hospital of Dalian Medical UniversityDalianLiaoning ProvinceChina Regenerative Medicine Center The First Affiliated Hospital of Dalian Medical UniversityDalianLiaoning ProvinceChina Department of Cardiology Institute of Cardiovascular DiseasesThe First Affiliated Hospital of Dalian Medical UniversityDalianLiaoning ProvinceChina

DI-3-n-butylphthalide is used to treat mild and moderate acute ischemic stroke.However,the precise underlying mechanism requires further investigation.In this study,we investigated the molecular mechanism of DI-3-n-butylphthalide action by various means.We used hydrogen peroxide to induce injury to PC12cells and RAW264.7 cells to mimic neuronal oxidative stress injury in stroke in vitro and examined the effects of DI-3-n-butylphthalide.We found that DI-3-nbutylphthalide pretreatment markedly inhibited the reduction in viability and reactive oxygen species production in PC12 cells caused by hydrogen peroxide and inhibited cell apoptosis.Furthermore,DI-3-n-butylphthalide pretreatment inhibited the expression of the pro-apoptotic genes Bax and Bnip3.DI-3-nbutylphthalide also promoted ubiquitination and degradation of hypoxia inducible factor 1α,the key transcription factor that regulates Bax and Bnip3 genes.These findings suggest that DI-3-n-butylphthalide exhibits a neuroprotective effect on stroke by promoting hypoxia inducible factor-1α ubiquitination and degradation and inhibiting cell apoptosis.

关键词： blood-brain barrier Dl-3-n-butylphthalide hypoxia inducible factor 1α mitochondria neuroprotection oxidative stress reactive oxygen species stroke transcription factor ubiquitination

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Review on supercapacitors:Technologies and performance evaluation

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Journal of Energy Chemistry 2021年第8期30卷 276-291,I0006页

作者： jingyuan zhao Andrew F.Burke Institute of Transportation Studies University of California-Davis95616 DavisUSA Computer Science and Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences518118 ShenzhenChina

The development of electrochemical capacitors(i.***.supercapacitors)have attracted a lot of attention in recent years because of the increasing demand for efficient,high-power energy storage.Electrochemical capacitors(ECs)are particularly attractive for transportation and renewable energy generation applications,taking advantage of their superior power capability and outstanding cycle life.Over the past decade,various advanced electrode materials and cell design are being studied to improve the energy density of ECs.Hybrid Li-ion capacitors and pseudo-capacitors that utilize fast surface redox reactions of metal oxide and doped polymers are the prime candidates being considered.This paper is concerned with the metrics being used to describe the performance of ECs and how the metrics are evaluated by testing devices and how the data from the testing are best interpreted.Emphasize is on relating testing of advanced ECs using materials more complex than activated carbons to testing electric double-layer capacitors(EDLCs)using carbon in both electrodes.A second focus of the paper is projecting the potential of the advanced materials and ionic liquid electrolytes for the development of complete EC cells having an energy density more than a factor of ten greater the energy density of the EDLC devices currently on the market.This potential was evaluated by calculating the performance(energy and power)of a series of ECs that utilize the advanced materials that have been studied by electrochemists over the past 10-15 years.The capacitance and resistance of the advanced ECs were calculated utilizing specific capacitance(F/g or F/cm^(3))and porosity data for the electrode materials and ionic conductivity of the electrolytes.It was concluded that hybrid ECs can be developed with energy densities of at least 50 Wh/kg,70 Wh/L with efficient power greater than 3 k W/kg.Continued research on micro-porous carbons with specific capacitance of 200 F/g and greater is needed.to achieve these EC performance goals.

关键词： Electrochemical capacitors Metrics Evaluation Cell design Applications

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Implantable nerve guidance conduits:Material combinations,multi-functional strategies and advanced engineering innovations

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Bioactive Materials 2022年第5期7卷 57-76页

作者： Yixin Yan Ruotong Yao jingyuan zhao Kaili Chen Lirong Duan Tian Wang Shujun Zhang Jinping Guan zhaozhu Zheng Xiaoqin Wang Zekun Liu Yi Li Gang Li National Engineering Laboratory for Modern Silk College of Textile and Clothing EngineeringSoochow UniversitySuzhou215123China Department of Materials University of ManchesterOxford RoadManchesterM139PLUK Department of Materials Imperial College LondonSW72AZUK Wilson College of Textiles North Carolina State UniversityRaleigh27695USA Research Centre of Printed Flexible Electronics School of Materials Science and EngineeringHarbin Institute of TechnologyShenzhen518055China

Nerve guidance conduits(NGCs)have attracted much attention due to their great necessity and applicability in clinical use for the peripheral nerve repair.Great efforts in recent years have been devoted to the development of high-performance NGCs using various materials and strategies.The present review provides a comprehensive overview of progress in the material innovation,structural design,advanced engineering technologies and multi functionalization of state-of-the-art nerve guidance conduits NGCs.Abundant advanced engineering technologies including extrusion-based system,laser-based system,and novel textile forming techniques in terms of weaving,knitting,braiding,and electrospinning techniques were also analyzed in detail.Findings arising from this review indicate that the structural mimetic NGCs combined with natural and synthetic materials using advanced manufacturing technologies can make full use of their complementary advantages,acquiring better biomechanical properties,chemical stability and biocompatibility.Finally,the existing challenges and future opportunities of NGCs were put forward aiming for further research and applications of NGCs.

关键词： Nerve guidance conduits Micro-nanofibers Multi-functional Surface modification Microenvironment

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Differential attacks on reduced SIMON versions with dynamic key-guessing techniques

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Science China(Information Sciences) 2018年第9期61卷 282-284页

作者： Ning WANG Xiaoyun WANG Keting JIA jingyuan zhao Key Laboratory of Cryptologic Technology and Information Security Ministry of EducationShandong University School of Mathematics Shandong University Institute for Advanced Study Tsinghua University Department of Computer Science and Technology Tsinghua University Beijing Research Institute of Telemetry

In 2013,NSA published the specifications of two lightweight block cipher families SIMON and SPECK[1].Since the SIMON family was announced,it has attracted a lot of attention of the cryptanalysts.In this article,we use... 详细信息

关键词： MON SI Differential attacks on reduced SIMON versions with dynamic key-guessing techniques

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美国国立综合癌症网络临床实践指南:胰腺癌(2023.V2)更新解读

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临床外科杂志 2024年第1期32卷 34-37页

作者：赵婧原吴河水华中科技大学同济医学院附属协和医院胰腺外科武汉430022

胰腺癌恶性程度高、早期诊断困难、预后极差,已成为继肺癌、前列腺癌(男性)、乳腺癌(女性)、结直肠癌后第4大癌症相关死亡原因^([1])。根治性切除是胰腺癌可能治愈的唯一方式。胰腺位置深在,胰腺癌症状不明显,待发现时大多已处于局部进... 详细信息

胰腺癌恶性程度高、早期诊断困难、预后极差,已成为继肺癌、前列腺癌(男性)、乳腺癌(女性)、结直肠癌后第4大癌症相关死亡原因^([1])。根治性切除是胰腺癌可能治愈的唯一方式。胰腺位置深在,胰腺癌症状不明显,待发现时大多已处于局部进展期或已经出现了远处转移而失去手术时机。随着循证医学证据的更新,众多学者认为胰腺癌是一种系统性疾病,需要在多学科诊疗模式指导下进行系统性、个体化治疗。

关键词：美国国立综合癌症网络胰腺癌更新解读

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西安市“源-汇”景观对碳排放量与可吸入颗粒物的协同效应

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水利水电技术（中英文） 2024年第1期55卷 88-102页

作者：任文静赵敬源马西娜韩力运城学院美术与工艺设计系山西运城044000 长安大学建筑学院陕西西安710061 西安科技大学建筑与土木工程学院陕西西安710054

【目的】大气污染与温室效应等环境问题,已成为影响城市可持续发展的重要因素,在减污降碳的实施路径方面亟需开展相应研究。【方法】以西安市主城区为研究对象,通过构建景观指数加权变异模型,分析城市“源-汇”景观格局的结构特征;基于... 详细信息

【目的】大气污染与温室效应等环境问题,已成为影响城市可持续发展的重要因素,在减污降碳的实施路径方面亟需开展相应研究。【方法】以西安市主城区为研究对象,通过构建景观指数加权变异模型,分析城市“源-汇”景观格局的结构特征;基于反距离加权插值与约束性排序分析法,探究碳排放量与可吸入颗粒物的空间分布特点,揭示“源-汇”景观对“碳污”的协同影响。【结果】结果显示,“源-汇”景观格局的特征尺度为距离研究区几何中心3 km、7 km、14 km的幅度边界处;碳排放量、PM_(2.5)、PM_(10)的反距离加权插值模型判定系数R^(2)分别为0.8541、0.8238与0.7885;“碳污”与景观指数之间的多元多重回归模型判定系数R^(2)为0.7540;16个“源-汇”景观关键指数对“碳污”的累计解释率达到66.2%,与“碳污”相关性的累计贡献率达到87.9%。【结论】结果表明,研究区环路界限可反映景观空间结构的扩展过程与分布特点;冬季采暖期碳排放量与可吸入颗粒物的空间异质性显著,呈现出不同的空间分布特点;通过对街区内绿地斑块密度、最大斑块指数、绿地率、香农多样性指数以及建筑密度与建筑高度变异系数进行差异化空间配置,可对“碳污”具有显著的协同消减影响。研究结果为城市景观规划、碳排放量与空气污染治理提供了科学依据,可促进城市生态环境的可持续发展。

关键词： “源-汇”景观碳排放量可吸入颗粒物协同效应空间配置西安市人类活动影响因素

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