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Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data

Energy and AI 2024年第2期16卷 256-268页

作者： haijun ruan Niall Kirkaldy Gregory J.Offer Billy Wu Dyson School of Design Engineering Imperial College LondonSW72AZLondonUK Centre for E-Mobility and Clean Growth Research Coventry UniversityCoventryCV15FBUK Department of Mechanical Engineering Imperial College LondonSW72AZLondonUK The Faraday Institution Harwell Science and Innovation CampusOX110RADidcotUK

Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work;highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.

关键词： Lithium-ion battery Composite electrode Silicon Degradation diagnostic Explainable deep learning Partial charging

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A data-fusion-model method for state of health estimation of Li-ion battery packs based on partial charging curve

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Green Energy and Intelligent Transportation 2024年第5期3卷 1-12页

作者： Xingzi Qiang Wenting Liu Zhiqiang Lyu haijun ruan Xiaoyu Li School of Internet Anhui UniversityHefei230039China Institute for Clean Growth&Future Mobility Coventry UniversityUK School of Mechanical Engineering Hebei University of TechnologyTianjin300130China

The estimation of State of Health(SOH)for battery packs used in Electric Vehicles(EVs)is a complex task with significant importance,accompanied by several *** study introduces a data-fusion model approach to estimate the SOH of battery *** approach utilizes dual Gaussian Process Regressions(GPRs)to construct a data-driven and non-parametric aging model based on charging-based Aging Features(AFs).To enhance the accuracy of the aging model,a noise model is established to replace the random ***,the statespace representation of the aging model is ***,the Particle Filter(PF)is introduced to track the unknown state in the aging model,thereby developing the data-fusion-model for SOH *** performance of the proposed method is validated through aging experiments conducted on battery *** simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation,with maximum errors less than 1.5%.Compared to conventional techniques such as GPR and Support Vector Regression(SVR),the proposed method exhibits higher estimation accuracy and robustness.

关键词： Li-ion battery pack State of health Data-fusion-model method Particle filter Gaussian process regression Support vector regression

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A machine learning driven 3D+1D model for efficient characterization of proton exchange membrane fuel cells

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Energy and AI 2024年第3期17卷 281-294页

作者： Yuwei Pan haijun ruan Billy Wu Yagya N.Regmi Huizhi Wang Nigel P.Brandon Department of Earth Science and Engineering Imperial College LondonLondon SW72AZUK Dyson School of Design Engineering Imperial College LondonLondon SW72AZUK Centre for E-Mobility and Clean Growth Coventry UniversityCoventry CV15FBUK Manchester Fuel Cell Innovation Centre Department of Natural SciencesManchester Metropolitan UniversityManchester M15GDUK Department of Mechanical Engineering Imperial College LondonLondon SW72AZUK

The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.

关键词： Proton exchange membrane fuel cell Hierarchical model Neural network Hybrid model

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基于自产热和外传热的锂离子电池热学模型参数辨识方法

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电工技术学报 2024年第1期39卷 278-288页

作者：孙丙香宋东林阮海军张维戈郑凯元北京交通大学国家能源主动配电网技术研发中心北京100044 伦敦帝国理工学院戴森设计工程学院伦敦SW72AZ

锂离子电池热学模型参数(热容和热阻)的准确辨识对电池热电耦合建模及状态参数估计至关重要。然而传统测量方法成本高且测试周期长,如何利用充放电工况结合产热和传热机理研究快速热参数辨识方法具有重要意义。以8A×h软包锂离子电池为... 详细信息

锂离子电池热学模型参数(热容和热阻)的准确辨识对电池热电耦合建模及状态参数估计至关重要。然而传统测量方法成本高且测试周期长,如何利用充放电工况结合产热和传热机理研究快速热参数辨识方法具有重要意义。以8A×h软包锂离子电池为研究对象,建立分布式热路模型;设计双向脉冲工况实验,采用自适应粒子群算法(APSO)进行辨识;同时采用其他工况进行验证,实验和仿真温度误差小于0.1℃。另外,将热容和热阻转换为比热容和导热系数,并与其他文献中同类电池的参数进行比对,量级接近。研究结果表明,该方法可以有效解决层叠式软包锂离子电池热学模型参数辨识难的问题,且简便易行、成本低。

关键词：层叠式软包锂离子电池分布式热路模型热容和热阻自产热和外传热

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Generalised diagnostic framework for rapid battery degradation quantification with deep learning

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能源与人工智能(英文) 2022年第3期9卷 24-36页

作者： haijun ruan Jingyi Chen Weilong Ai Billy Wu Dyson School of Design Engineering Imperial College LondonLondonSW72AZUnited Kingdom The Faraday Institution Quad OneHarwell Science and Innovation CampusDidcotUnited Kingdom

Diagnosing lithium-ion battery degradation is challenging due to the complex, nonlinear, and path-dependent nature of the problem. Here, we develop a generalised and rapid degradation diagnostic method with a deep learning-convolutional neural network that quantifies degradation modes of batteries aged under various conditions in 0.012 s without feature engineering. Rather than performing extensive aging experiments, synthetic aging datasets for network training are generated. This dramatically lowers training cost/time, with these datasets covering almost all the aging paths, enabling a generalised degradation diagnostic framework. We show that the five thermodynamic degradation modes are correlated, and systematically elucidate their correlations. We thus propose a non-invasive comprehensive evaluation method and find the degradation diagnostic errors to be less than 1.22% for three leading commercial battery chemistries. The comparison with the traditional diagnostic methods confirms the high accuracy and fast nature of the proposed approach. Quantification of degradation modes with the partial discharge/charge data using the proposed diagnostic framework validates the real-world feasibility of this approach. This work, therefore, enables the promise of online identification of battery degradation and efficient analysis of large-data sets, unlocking potential for long lifetime energy storage systems.

关键词： Lithium-ion battery Degradation diagnostics Deep learning Battery degradation theory Synthetic training dataset Aging paths

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基于静态非合作博弈的电动汽车充电电价影响因素量化分析

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电工技术学报 2016年第21期31卷 75-85页

作者：孙丙香阮海军许文中姜久春龚敏明国家能源主动配电网技术研发中心(北京交通大学) 北京100044 北京交通大学北京电动车辆协同创新中心北京100044

电动汽车的大规模推广应用需要合理地制定充电电价,运用博弈论分析了电动汽车发展相关的主要三方——政府、充电设施运营商和用户的利益关系,构建了关于充电电价和电动汽车发展规模的三方博弈模型,并针对静态非合作博弈方式进行了博弈... 详细信息

电动汽车的大规模推广应用需要合理地制定充电电价,运用博弈论分析了电动汽车发展相关的主要三方——政府、充电设施运营商和用户的利益关系,构建了关于充电电价和电动汽车发展规模的三方博弈模型,并针对静态非合作博弈方式进行了博弈求解。以珠三角9个城市电动汽车数据为例,得到了分别以充电设施运营商和政府为博弈主导方的博弈均衡解,在此基础上定量分析了5个影响因素变动幅度改变的条件下,充电电价和电动汽车保有量的变化趋势,以及各影响因素对充电电价和电动汽车保有量的敏感程度。最后在政府给予充电设施运营商适当补贴的情况下得到双方可接受的博弈均衡解,并对用户的广义成本进行分析,合理的充电定价能够满足三方的利益诉求,有利于电动汽车的大规模推广。

关键词：电动汽车保有量充电电价静态非合作博弈

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锂离子电池变频变幅交流低温自加热策略

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电工技术学报 2019年第9期34卷 1798-1805页

作者：何锡添孙丙香阮海军王占国苏晓佳北京交通大学国家能源主动配电网技术研发中心北京100044 北京电动车辆协同创新中心北京100044

低温下锂离子电池的可用容量和功率大幅下降,而且充电困难。对锂离子电池进行低温加热是改善其性能的有效途径。该文建立了电池的热-电耦合模型,设计了一种变频变幅交流自加热策略,在保证极化电压幅值不变的条件下,以加热功率最大为目标... 详细信息

低温下锂离子电池的可用容量和功率大幅下降,而且充电困难。对锂离子电池进行低温加热是改善其性能的有效途径。该文建立了电池的热-电耦合模型,设计了一种变频变幅交流自加热策略,在保证极化电压幅值不变的条件下,以加热功率最大为目标,根据电池在各个温度下得到的最佳加热频率实时调整交流激励的频率和幅值。对比不同策略发现,采用变频变幅交流自加热策略,电池在700s内上升了47.67℃,相比恒频变幅加热策略,其温升速率最大可提高21.85%。所设计的变频变幅交流自加热策略具有良好的加热效果,有利于促进电动汽车在寒冷环境下的推广应用。

关键词：锂离子电池低温自加热热-电耦合模型变频变幅

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考虑地区等级升级的天然气管道壁厚优选方法

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油气储运 2022年第3期41卷 295-301页

作者：关旭辉马强阮海军汪晨武中国石油天然气管道工程有限公司上海分公司上海市200120

针对天然气管道运营后期地区等级升级造成管道风险不可接受的问题,尝试从壁厚设计角度解决该问题,提出一种考虑地区等级升级的管道壁厚优选方法。该方法在基于可靠性设计和评估(Reliability-Based Design and Assessment,RBDA)方法的基... 详细信息

针对天然气管道运营后期地区等级升级造成管道风险不可接受的问题,尝试从壁厚设计角度解决该问题,提出一种考虑地区等级升级的管道壁厚优选方法。该方法在基于可靠性设计和评估(Reliability-Based Design and Assessment,RBDA)方法的基础上,通过定量计算可靠度,保证管道所有地区等级风险满足社会风险的要求,同时进行管道全生命周期费用比选从而实现管道经济最优,最终选出合理管道壁厚。利用该方法对管径508 mm管道进行计算,并分别讨论地区等级升级时间、地区等级、管径对最优壁厚的影响。结果表明:管道最优壁厚随地区等级升级时间、地区等级、管径增大而增大。与RBDA方法相比,该方法考虑更加全面,可达到经济成本最小化及管道安全最大化,但该方法不能实现对未来地区等级升级的预测,需进一步研究。

关键词：地区等级升级管道全生命周期管道壁厚优选可靠性设计和评估定量分析

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锂离子电池分数阶等效电路模型低频参数演变规律研究

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重庆理工大学学报（自然科学） 2020年第2期34卷 6-14页

作者：龚敏明卞景季孙丙香王占国阮海军何锡添北京交通大学国家能源主动配电网技术研发中心北京100044 北京交通大学北京电动车辆协同创新中心北京100044

基于EIS建立了分数阶等效电路模型,对阻抗谱进行拟合时,发现分数阶模型能以更少的元件得到和多阶RC模型一样的精度;在低频区,分析了不同荷电状态(SOC)下模型的参数变化规律,对比了1阶RC电路的精度,结果显示分数阶模型有更小的拟合误差... 详细信息

基于EIS建立了分数阶等效电路模型,对阻抗谱进行拟合时,发现分数阶模型能以更少的元件得到和多阶RC模型一样的精度;在低频区,分析了不同荷电状态(SOC)下模型的参数变化规律,对比了1阶RC电路的精度,结果显示分数阶模型有更小的拟合误差。研究了模型参数随频率的演变规律,结果显示:模型的内阻元件几乎不随激励频率的变化而变化,常相位元件参数与激励的周期呈一次函数关系。

关键词：锂离子电池电化学阻抗谱分数阶模型参数演变

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Mechanism of phthalic acid collector in flotation separation of fluorite and rare earth

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Journal of Rare Earths 2022年第1期40卷 118-126页

作者： Sheng Chang Mei Li Kai Gao Dongliang Zhang haijun Duan Linlin Ma Zheng ruan College of Mining Inner Mongolia University of Science&TechnologyBaotou 014010China Key Laboratory of Green Extraction&Efficient Utilization of Light Rare-Earth Resources Ministry of Education Baotou 014010China Inner Mongolia Key Laboratory of Rare Earth Hydrometallurgy and Light Rare Earth Application Baotou 014010China Inner Mongolia Mining Exploitation Co. Ltd.Hohhot 010020China

The mechanism of phthalic acid,a dicarboxylic acid collector,in flotation separation of fluorite and rare earth(RE)was studied in this *** experimental data of flotation show that phthalic acid,as the collector,can realize highly efficient separation of fluorite and rare earth under weakly acidic *** adsorption mechanism of phthalic acid on the surface of fluorite and bastnaesite was analyzed in this paper by means of the zeta potential measurement,the Fourier transform infrared(FT-IR),the X-ray photoelectron spectroscopy(XPS)and the stability constant measurement of active metal ion and phthalic acid coo rdination *** to the zeta potential testing results,the surfaces of fluorite adsorb the collector phthalate ion with negative charge under weakly acidic conditions which,in turn,increases its electronegativity and results in the motion of its *** the reaction between phthalic acid and fluorite ores under weakly acidic conditions,the peak of the fluorite ores is found to have significant changes in the FT-IR results,indicating strong chemical adsorption on the surfaces of phthalic acid and fluorite *** to the XPS analysis,the peak of benzene ring of phthalic acid is as high as 2%on the surface of fluorite,while no obvious characteristic peak of benzene ring is found on the surface of *** to the pH potentiometric titration results,the stability constant Ktotal of calcium phthalate complex within the acid range is higher than the stability constant K’total of cerium phthalate complex,indicating that the complex generated between phthalic acid and Ca^(2+)is more stable than the complex generated between phthalic acid and Ce^(3+).The possible reason is that Ca^(2+),with the highest reticular density,plays a prevailing role in the octahedron structure of fluorite amidst the acidic *** the active point of flotation,Ca^(2+)works with the carboxyl groups of the collector phthalic acid(-C=O-)to form

关键词： Fluorite Bastnaesite Carboxylic acid collector Flotation Equilibrium constants of complexes Rare earths

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