A review of data-driven whole-life state of health prediction for lithium-ion batteries:Data preprocessing,aging characteristics,algorithms,and future challenges

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.

Boosting battery state of health estimation based on self-supervised learning

State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

State of Health Estimation of LiFePO_(4) Batteries for Battery Management Systems

When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.

Design of a Prognostics and Health Management System for Electromechanical Equipment Through Time Stress Analysis

Time stress includes all kinds of environment and operating stress such as shock, vibration, temperature and electric current that the electromechanical system suffers in the manufacture, transport and operating process. In this paper, the conception of time stress and prognostics and health management （ PHM） system are introduced. Then, in order to improve the false alarm recognition and fault prediction capabilities of the electromechanical equipment, a novel PHM architecture for electromechanical equipment is put forward based on a built-in test （BIT） system design technology and time stress analysis method. Finally, the structure, the design and implementing method and the functions of each module of this PHM system are described in detail.

Life prediction of ZPW-2000A track circuit equipment based on SVDD and gray prediction

Evaluation of the health state and prediction of the remaining life of the track circuit are important for the safe operation of the equipment of railway signal system.Based on support vector data description(SVDD)and gray prediction,this paper illustrates a method of life prediction for ZPW-2000A track circuit,which combines entropy weight method,SVDD,Mahalanobis distance and negative conversion function to set up a health state assessment model.The model transforms multiple factors affecting the health state into a health index named H to reflect the health state of the equipment.According to H,the life prediction model of ZPW-2000A track circuit equipment is established by means of gray prediction so as to predict the trend of health state of the equipment.The certification of the example shows that the method can visually reflect the health state and effectively predict the remaining life of the equipment.It also provides a theoretical basis to further improve the maintenance and management for ZPW-2000A track circuit.

Review on Lithium-ion Battery PHM from the Perspective of Key PHM Steps

Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews are still continuously updated over time.In this paper,we browsed extensive literature related to battery PHM from 2018to 2023 and summarized advances in battery PHM field,including battery testing and public datasets,fault diagnosis and prediction methods,health status estimation and health management methods.The last topic includes state of health estimation methods,remaining useful life prediction methods and predictive maintenance methods.Each of these categories is introduced and discussed in details.Based on this survey,we accordingly discuss challenges left to battery PHM,and provide future research opportunities.This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.

Estimation of Battery State of Health Using Back Propagation Neural Network

100 pieces of 26650-type Lithium iron phosphate（LiFePO4） batteries cycled with a fixed charge and discharge rate are tested, and the influence of the battery internal resistance and the instantaneous voltage drop at the start of discharge on the state of health（SOH） is discussed. A back propagation（BP） neural network model using additional momentum is built up to estimate the state of health of Li-ion batteries. The additional 10 pieces are used to verify the feasibility of the proposed method. The results show that the neural network prediction model have a higher accuracy and can be embedded into battery management system（BMS） to estimate SOH of LiFePO4 Li-ion batteries.

Design Technology Research of Aircraft Engine Health Management (EHM) Technologies

Aircraft engine is an important guarantee for aircraft safety, and its failure mode and health management have become the top priority. However, there are very few researches on aircraft engine health management. This article mainly summarizes the current research status of aircraft engine health management (EHM) from the aspect of aircraft electronic system, focuses on the overall structure, functional areas and key technologies of EHM system design, points out the design requirements of EHM system, and finally proposes EHM system. The design must improve the monitoring accuracy of the sensor to meet the monitoring requirements of more than 0.1%. High-precision monitoring data is more conducive to engine fault detection and processing, and EHM will therefore develop in the direction of real-time, intelligent, integrated and networked.

Intelligent Evidence-Based Management for Data Collection and Decision-Making Using Algorithmic Randomness and Active Learning

We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.

基于Android的个人健康管理系统的客户端设计开发

移动医疗App开发了健康管理系统,但在用户健康数据挖掘和利用方面略显不足,难以发挥良好的健康管理作用。设计了一款基于Android的个人健康管理系统客户端,对用户健康数据进行系统收集和妥善管理,根据检测数据对用户的健康风险进行计算,为个人健康管理提供数据支持,有助于提升身体素质,具有广阔的发展空间和现实意义。

基于Adaboost-PSO-SVM的铝电解槽健康状态诊断方法研究

针对铝电解槽在铝电解生产过程中故障频发的问题,提出了一种基于支持向量机(support vector machine,SVM)的铝电解槽健康状态诊断模型,考虑传统的支持向量机只能适用于二分类问题,采用自适应推进算法(adaptive boosting,Adaboost)将支持向量机的二分类问题转化为多分类问题用于求解铝电解槽健康状态诊断问题,充分考虑了子模型的权重,强化了模型的适用性。并利用粒子群优化算法(particle swarm optimization,PSO)对其超参数寻优,提高模型的预测精度。实验结果表明,提出的铝电解槽健康状态诊断模型的准确率和Macro-F1分数分别达到94.70%和0.9453,相较于其他传统模型均有显著提升。

水中兵器锂原电池健康状态预测技术探究

锂原电池作为高效、清洁能源,在水中兵器装备中广泛应用,但是在长期贮存和使用过程中,暴露出了一些故障及安全隐患,因此对电池进行健康状态预测及对故障进行机理研究极为重要。针对锂原电池的故障及应用需求,梳理分析现有研究基础以及困难等,借鉴锂离子电池的成熟技术,探究性地对锂原电池故障模式、故障机理进行了分析,提出了状态预测技术的研究思路和方法,为水中兵器锂原电池健康状态预测技术体系的构建和研究提供了参考。

基于数实结合的测控装备健康监测系统设计与实现

针对测控业务领域装备种类多、型号杂、布站分散、技术保障人力不足、远程技术协助手段落后等实际问题,结合测控装备自身特点和运维保障规律,提出了采用数字技术构建功能级数字测控装备,并利用数字装备和实体装备间物联网络使数实装备状态数据一致,实现基于数实结合的测控装备健康监测,实现装备状态数实同步,并可利用数实同步有效的评估装备状态、进行故障诊断和健康趋势预估,实现了运维保障数字化。

JUST回转支承故障试验数据分析

回转支承作为军用雷达的关键部件,对保证设备安全使用、提高效益具有重大作用。通过数据驱动方法对回转支承运行状态进行实时监测与诊断,已成为该技术领域的研究热点。然而回转支承面临服役工况复杂、故障试验样本稀少等问题,使其故障诊断技术研究一直饱受数据不足的困扰,也严重制约了军用机械装备寿命预测与健康管理技术的发展与应用。为此开展了回转支承故障试验,通过对回转支承运转过程中的多向振动、声发射信号进行采集,形成了故障试验数据集。该数据集包含回转支承在9种工况下的多向振动信号和声发射信号,且明确标注了回转支承的信号采集时间、故障标签、转速、负载、采集次数等多种相关信息,为基于数据驱动的回转支承故障诊断与雷达伺服系统健康管理提供数据支撑和技术保障。

论健康促进实现的法治保障——以“健康融入万策”框架为基础

在现代社会,健康是所有政策的综合结果,我国语境下的“健康促进”应解释为健康权的完整实现,其实现进路应运用“健康融入万策”框架,从“政策影响健康”走向“健康影响政策”。但目前健康融入万策在规范体系内的适用范围、具体机制等内容都存在未竟之处。健康融入万策是一种政策制定方式,规范体系应该为健康融入万策确定范式,其适用范围应分三个阶段逐步扩大。健康融入万策的规范体系应当至少包含政策论证制定机制、跨部门合作机制、健康审查机制和健康报告机制四大部分。政策论证制定机制应厘清行政组织的规范定位、明确政策制定的具体下位系统、构建信息共享的开放平台。跨部门合作机制应当包含公权力部门之间的合作系统及非政府主体的合作系统。健康审查机制应明确其适用范围并引入健康影响评估。健康报告机制应通过规范予以制度化、明确化。

压接式IGBT健康管理方法综述

压接式IGBT功率器件是新型电力系统应用装备中的核心部件,对其健康管理可提升IGBT使用寿命与运行可靠性,保障新型电力系统的安全稳定。首先介绍压接式IGBT器件的封装结构和主要失效模式;其次以特征参数类型的不同,对现有健康状态监测方法进行分类阐述和分析;然后归纳现有压接式IGBT寿命预测方法的原理和特点;最后对现有的健康管理技术进行综合对比分析,并指出压接式IGBT健康管理方法需要进一步研究的问题和未来发展趋势。

基于SCSSA-CNN-BiLSTM的行驶工况下锂电池寿命预测

随着锂离子电池广泛应用,电池寿命预测的重要性日益突显。锂离子电池剩余寿命(RUL)的准确预测是其健康管理的关键组成部分。基于此提出了一种名为SCSSA-CNN-BiLSTM的算法,旨在实现应用于整车的锂离子电池RUL预测。采用卷积神经网络(CNN)和双向长短时记忆神经网络(BiLSTM),并结合了正余弦和柯西变异的麻雀优化算法(sine-cosine and Cauchy mutation sparrow search algorithm, SCSSA),形成了一种新型的混合神经网络,以提高锂离子电池RUL预测的准确性和稳定性。CNN用于电池健康状态(SOH)深度特征的全面提取,而BiLSTM以双向方式研究这些深度特征,并通过密集层生成锂离子电池的RUL预测。为验证所提出方法的有效性,首先使用NASA的电池数据,将多个常用模型与所提出的混合神经网络模型进行比较。研究结果显示,混合模型的决定系数(R2)提高了4%~23%,RUL绝对误差降至1,这表明模型具备更高的预测准确性。随后,在整车层面进行了CLTC动态工况下的循环试验,并对寿命衰减数据进行了预测。最终的结果显示,SCSSA-CNN-BiLSTM模型对应的均方根误差(RMSE)、R2分别为1.64、0.98 Ah,展现出了良好的预测和泛化性能。

电动汽车动力锂离子电池可靠性关键技术综述

随着动力锂离子电池在电动汽车行业发挥关键性作用,保证其工作可靠性已成为当下研究热点。文中综述锂离子电池材料和制造工艺;详细归纳总结电池状态估算方法以及剩余寿命预测方法,并讨论各种方法的优缺点;从电池管理系统层面,先后梳理均衡管理系统和热管理系统相关知识;从电动汽车混合储能系统层面阐述实际工况下性能退化机理及相关技术。最后总结电动汽车动力锂离子电池与可靠性相关的四个方面关键技术的现状,并展望未来发展可能。

糖尿病前期社区健康管理存在的问题及策略建议

背景2型糖尿病是危害我国人群健康的重大公共卫生问题,糖尿病前期人群是庞大的糖尿病后备军,对其进行适当干预可以预防或延缓糖尿病,但现阶段社区健康管理效果不佳。目的了解糖尿病前期社区健康管理实践中存在的问题及其影响因素,提出系统且可操作的糖尿病前期健康管理措施及相关策略建议。方法于2023年3—4月系统检索中国知网、万方数据知识服务平台、维普网、PubMed、Web of Science数据库中与糖尿病前期社区健康管理相关的文献,并基于多方视角于2023年4—5月在上海市社区、医院、疾病预防控制中心对20名社区卫生服务中心工作人员、卫生行政人员、临床内分泌科医生、疾病预防控制中心健康管理工作人员、糖尿病患者或前期人群及其家属、存在糖尿病危险因素者进行糖尿病前期社区健康管理现状、有关态度及看法等观点、服务接受程度等问题的访谈,基于文献和访谈形成的问题集进行鱼骨图分析,梳理糖尿病前期社区健康管理相关问题之间的层次并绘制鱼骨图。结果最终纳入14篇相关文献,总结基于文献和访谈归纳出的22条当前糖尿病前期社区健康管理所存在的问题,得出患者方面、干预范围、服务能力和信息系统4个方面的问题,并提出疾病风险认知水平、自我管理技能水平、经费预算、工作经验、工作量、服务可及性、电子健康档案建设水平和信息共享范围8个影响因素。结论糖尿病前期人群是社区健康管理的重要对象,政策变迁过程反映出对糖尿病前期人群的重视加强,但多方证据证明其目前仍是薄弱环节。针对当前实践中存在的问题,需提高社区卫生服务人员相关专业知识技能等管理能力,基于医联体建设优化信息系统平台,进一步形成集筛查、管理、干预于一体的更具可行性的连续性糖尿病前期健康管理模式。