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.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

State of health prediction for lithium-ion batteries based on ensemble Gaussian process regression

The performance of lithium-ion batteries(LIBs)gradually declines over time,making it critical to predict the battery’s state of health(SOH)in real-time.This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR)to predict the SOH of LIBs.Firstly,the degradation process of an LIB is analyzed through indirect health indicators(HIs)derived from voltage and temperature during discharge.Next,the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA),and the EGPR is employed to estimate the SOH of LIBs.Finally,the proposed model is tested under various experimental scenarios and compared with other machine learning models.The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration(NASA)LIB.The root mean square error(RMSE)is maintained within 0.20%,and the mean absolute error(MAE)is below 0.16%,illustrating the proposed approach’s excellent predictive accuracy and wide applicability.

A Health State Prediction Model Based on Belief Rule Base and LSTM for Complex Systems

In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.

Acceptability of Immunization against COVID-19 by the Populations of the Kasenga State Health Area in the Uvira Health Zone, DR Congo

Introduction: COVID-19 was an emerging disease putting all public health systems in countries around the world in a state of emergency. To be able to prevent its spread and morbidity and mortality, several appropriate strategies were necessary, such as vaccination. The latter has been the subject of controversy. The objective of the present study is therefore to evaluate the factors associated with the acceptance of this vaccine within the population of the Kasenga State Health Area. A result which will shed light on future strategies to be put in place for possible new vaccines. Methodology: Is a prospective and analytical cross-sectional study conducted over a period of approximately 1 month from January 5 to February 5, 2024. A survey questionnaire in Kobotoolbox was useful for collecting data. STATA software was very important for us in analyzing the data collected. Results: Prevalence of vaccination against COVID-19 among the population of the Kasenga State Health Area is 37.5% (28.4 - 45.6). The study revealed that reluctance is observed among most of the population for different reasons, including, first and foremost, the deliberate aspect of not wanting to take the vaccine (46.6%) and rumors that this antigen is dangerous and harmful (32.9%). 72.5% of respondents believe that the COVID-19 vaccine is a fabrication, unhealthy and that the disease itself never existed. The study proved that there was a statistical relationship between age (p = 0.001) and adherence to vaccination. And the refusal of respondents to recommend the vaccine to loved ones was a factor associated with non-adherence to vaccination (OR = 7.901, 95% IC [3.028 - 20.615], p = 0.000). Conclusion: Vaccination against COVID-19 was not well accepted by the population of the study site. Raising public awareness and involving community leaders and political-administrative authorities, which has not been done well, would play an important role in the good perception of the disease, of the vaccine and therefore in its adherence.

Statistical Models for Condition Monitoring and State of Health Estimation of Lithium-Ion Batteries for Ships

Battery systems are increasingly being used for powering ocean going ships,and the number of fully electric or hybrid ships relying on battery power for propulsion is growing.To ensure the safety of such ships,it is important to monitor the available energy that can be stored in the batteries,and classification societies typically require the state of health(SOH)to be verified by independent tests.This paper addresses statistical modeling of SOH for maritime lithium-ion batteries based on operational sensor data.Various methods for sensor-based,data-driven degradation monitoring will be presented,and advantages and challenges with the different approaches will be discussed.The different approaches include cumulative degradation models and snapshot models,models that need to be trained and models that need no prior training,and pure data-driven models and physics-informed models.Some of the methods only rely on measured data,such as current,voltage,and temperature,whereas others rely on derived quantities such as state of charge.Models include simple statistical models and more complicated machine learning techniques.Insight from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

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.

Estimation of state of health based on charging characteristics and back-propagation neural networks with improved atom search optimization algorithm

With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation(Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity(IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.

Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.

Challenges and opportunities for battery health estimation:Bridging laboratory research and real-world applications

Addressing climate change demands a significant shift away from fossil fuels,with sectors like electricity and transportation relying heavily on renewable energy.Integral to this transition are energy storage systems,notably lithium-ion batteries.Over time,these batteries degrade,affecting their efficiency and posing safety risks.Monitoring and predicting battery aging is essential,especially estimating its state of health(SOH).Various SOH estimation methods exist,from traditional model-based approaches to machine learning approaches.

The Place of Human Resource Management in Lagos State Healthcare Delivery: A Statistical Overview

Background: Behind every great system is an organized team;this is especially true in the healthcare industry, where a dedicated human resources team can effectively recruit employees, train staff, and implement safety measures in the workplace. The importance of human resources in the healthcare industry cannot be overstated, with benefits ranging from providing an orderly and effectively run facility to equipping staff with the most accurate and up-to-date training. Proper human resources management is critical in providing high-quality health care. A refocus on human resources management in healthcare requires more research to develop new policies. Effective human resources management strategies are greatly needed to achieve better outcomes and access to health care worldwide. Methods: This study leveraged NOI Polls census data on Health Facility Assessment for Lagos State. One thousand two hundred fifty-six health care facilities were assessed in Lagos State;numbers of Health workers were documented alongside their area of specialization. Also, demographic characterizations of the facilities, such as LGA, Ownership type, Facility Level Care, and Category of the facility, were also documented. Descriptive statistics alongside cross tabulation was done to present the various area of specialization of the health workers. Multiple response analysis was done to understand the distribution of human resources across the health facilities. At the same time, Chi-square and correlation tests were conducted to test the independence of various categories recorded while understanding the relationships among selected specialties. Results: The study revealed that Nurses were the most common health specialist in the Lagos State health facilities. At the same time, Gynecologists and General surgeons are the two medical specialists mostly common in health facilities. Midwives are the second most common health specialist working full time, while Generalist medical doctors make up the top three health specialists working full time. Nurses and Midwives had the highest number in Lagos State, while Pulmonologists were currently the lowest human resource available in Lagos State health care system. It was also noted that health facility distribution across Lagos’s urban and rural areas was even. In contrast, distribution based on other factors such as ownership type, Facility level of care, and facility category was slightly skewed. Conclusion: The distribution of health workers in health facility across LGA in Lagos State depend on Ownership type, Facility level of care, and category of the facility.

Depression among Health Care Workers in Khartoum State, Sudan, 2022

Introduction: Depression is a serious issue affecting healthcare workers and is a leading cause of disability for both genders. Furthermore, it is one of the leading causes of mortality and morbidity, responsible for 4.4 percent of global disability. An estimated 350 million people are currently living with depression worldwide. Objectives: to estimate the prevalence of depression among healthcare workers in Khartoum State in 2022 and determine the associated factors. Methods: A cross-sectional survey was conducted among healthcare workers in Khartoum State, Sudan, in 2022 using a self-administered electronic questionnaire. Depression was screened using the self-reporting questionnaire (PHQ-9). Descriptive statistics in the form of frequencies and percentages were used to display the data. Odds ratios (ORs) with a 95% confidence interval were estimated using bivariate and multivariate logistic regression analysis to determine associations between depression and related factors. Results: A total of 341 valid responses were received, with a mean age of 33.91. The overall prevalence of depression (PHQ-9 > 8) was 258 (75.6%). The prevalence was significantly associated with marital status (single and divorced), occupation (psychologist), and working department (Emergency Department), showing a p-value of Conclusion: Depression is a serious mental health disorder that affects all people, including healthcare workers, and is a growing problem in Sudan. To address this, healthcare organizations must implement policies and strategies to reduce inequality and protect healthcare workers. A multidisciplinary approach that includes mental health professionals, the Ministry of Health, and universities is needed to prioritize mental health issues and ensure quality care and the overall well-being of both healthcare workers and patients.

Maternal Mortality Rates among Im/Migrant Populations in the United States

Introduction: Maternal mortality rates have more than doubled in the U.S over the last two decades, making it one of the few places in the world where maternal mortality is increasing. Differences in maternal mortality among certain races and ethnicities are known but few studies examine maternal mortality among immigrants. Since immigrants represent 13.7% of the U.S. population, it is essential to examine immigrant subsets to understand maternal mortality among this vulnerable population. Methods: A literature search identified 318 articles on maternal mortality and immigrants, with 12 articles from the U.S. The keywords included maternal mortality, United States, migrants, asylum seekers, immigrants, and disparities. Maternal mortality statistics were obtained from the World Health Organization and Center for Disease Control. Results: Studies analyzed in this review found an overall lower maternal mortality rate among immigrant women compared to U.S.-born women, except for Hispanic immigrant women. Black women had the highest maternal mortality rate, regardless of immigration status. Conclusion: Although the literature points to lower maternal mortality among immigrants, the data is still somewhat mixed, making it challenging to draw comprehensive conclusions. Additional research examining maternal mortality among Im/migrants in the U.S. is needed to guide future training among healthcare professionals and policymakers.

A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries

In the field of energy storage,it is very important to predict the state of charge and the state of health of lithium-ion batteries.In this paper,we review the current widely used equivalent circuit and electrochemical models for battery state predictions.The review demonstrates that machine learning and deep learning approaches can be used to construct fast and accurate data-driven models for the prediction of battery performance.The details,advantages,and limitations of these approaches are presented,compared,and summarized.Finally,future key challenges and opportunities are discussed.

State of charge and health estimation of batteries for electric vehicles applications:key issues and challenges

Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil fuel depletion,increases in air pollution,accelerating energy demands,global warming,and climate change,have paved the way for the electrification of the transport sector.EVs can address all of the aforementioned issues.Portable power supplies have become the lifeline of the EV world,especially lithium-ion(Li-ion)batteries.Li-ion batteries have attracted considerable attention in the EV industry,owing to their high energy density,power density,lifespan,nominal voltage,and cost.One major issue with such batteries concerns providing a quick and accurate estimation of a battery’s state and health;therefore,accurate determinations of the battery’S performance and health,as well as an accurate prediction of its life,are necessary to ensure reliability and efficiency.This study conducts a review of the technological briefs of EVs and their types,as well as the corresponding battery characteristics.Various aspects of recent research and developments in Li-ion battery prognostics and health monitoring are summarized,along with the techniques,algorithms,and models used for current/voltage estimations,state-of-charge(SoC)estimations,capacity estimations,and remaining-useful-life predictions.

Perception of Adolescents on the Attitudes of Providers on Their Access and Use of Reproductive Health Services in Delta State, Nigeria

This paper examines the perception of adolescents on the attitudes of providers on their access and use of reproductive health services (ARHS) in Delta State, Nigeria, with a view of assessing the impact of providers’ attitude on the use of adolescents’ reproductive health services in Delta State. The study adopted a survey design to collect primary data using questionnaires and focus group discussions (FGDs) from adolescents in a sample of schools. A sample size of 1500 respondents was taken from 12 schools in six Local Government Areas in three Senatorial Districts in Delta State, Nigeria. The locations of the schools were such that six each were in rural and urban communities respectively. The result from the study was that unfriendly attitudes of providers which keep adolescents waiting, inadequate duration of consultations, judgmental attitudes of some providers, lack of satisfactory services provision and lack of confidentiality will put off adolescents from accessing and using adolescents’ reproductive health services irrespective of their sex, age, class, religion, residence, ethnic group, parents’ education or income levels. The paper concludes that medical personnel take all these issues very seriously when dealing with adolescents to enhance access and use adolescents’ reproductive health services in Delta State and indeed Nigeria.

Concepts of body constitution, health and sub-health from traditional Chinese medicine perspective

This paper described and discussed the important literature and ideas about the concepts, types and measurement of body constitution, in terms of healthy,sub-healthy and disease status. In view of traditional Chinese medicine, ‘‘healthy" state is a status of relative balance of Yin and Yang to keep our bodily homeostasis. If there are significant physical and/or psychological stressors, such as loss of a beloved one and failure in study or work, the body can no longer keep its own bodily condition balanced and subsequently enter a state of ‘‘sub-health"(sub-optimal health).‘‘unhealthy" body constitution such as ‘‘Dampnessheat", ‘‘Cold-dampness" and ‘‘Heat- or Cold- dryness"with a subnormal body temperature and humidity and clinical manifestations such as insomnia, malaise and overweight will be presented. Immediate, appropriate strategies such as modification of life-style and seeking medical treatment can prevent evolution of an illness.Otherwise, the body will enter a disease status with a‘‘pathological" body constitution of ‘‘Yin or Yang deficiency'', ‘‘Blood-stasis" and/or ‘‘Phlegm-dampness". To be complimentary with health promotion and disease prevention in Western medicine, understanding about an individual's body constitution, together with itsdeterminants(e.g., healthy eating and lifestyle behaviors), can contribute to a more proactive, holistic and individualized healthcare.

Lithium battery state of charge and state of health prediction based on fuzzy Kalman filtering

This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The model parameters are estimated by searching least square error optimization algorithm.Precisely defined by this method,the model parameters allow to accurately determine the capacity of the battery,which in turn allows to specify the SOC prediction value used as a basis for the SOH value.Application of the extended Kalman filter(EKF)removes the need of prior known initial SOC,and applying the fuzzy logic helps to eliminate the measurement and process noise.Simulation results obtained during the urban dynamometer driving schedule(UDDS)test show that the maximum error in estimation of the battery SOC is 0.66%.Battery capacity is estimate by offline updated Kalman filter,and then SOH will be predicted.The maximum error in estimation of the battery capacity is 1.55%.