Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in th...Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in the future. However, one of the obstacles hindering the future development of battery technology is how to accurately evaluate and monitor battery health, which affects the entire lifespan of battery use. It is not enough to assess battery health comprehensively through the state of health(SoH) alone, especially when nonlinear aging occurs in onboard applications. Here, for the first time, we propose a brand-new health evaluation indicator—state of nonlinear aging(SoNA) to explain the nonlinear aging phenomenon that occurs during the battery use, and also design a knee-point identification method and two SoNA quantitative methods. We apply our health evaluation indicator to build a complete LIB full-lifespan grading evaluation system and a ground-to-cloud service framework, which integrates multi-scenario data collection, multi-dimensional data-based grading evaluation, and cloud management functions. Our works fill the gap in the LIBs’ health evaluation of nonlinear aging, which is of great significance for the health and safety evaluation of LIBs in the field of echelon utilization such as vehicles and energy storage. In addition, this comprehensive evaluation system and service framework are expected to be extended to other battery material systems other than LIBs, yet guiding the design of new energy ecosystem.展开更多
High-performance batteries greatly benefit from accurate,early predictions of future capacity loss,to advance the management of the battery and sustain desirable application-specific performance characteristics for as...High-performance batteries greatly benefit from accurate,early predictions of future capacity loss,to advance the management of the battery and sustain desirable application-specific performance characteristics for as long as possible.Li-ion cells exhibit a slow capacity degradation up to a knee-point,after which the degradation ac-celerates rapidly until the cell’s End-of-Life.Using capacity degradation data,we propose a robust method to identify the knee-point within capacity fade curves.In a new approach to knee research,we propose the concept‘knee-onset’,marking the beginning of the nonlinear degradation,and provide a simple and robust identifica-tion mechanism for it.We link cycle life,knee-point and knee-onset,where predicting/identifying one promptly reveals the others.On data featuring continuous high C-rate cycling(1C–8C),we show that,on average,the knee-point occurs at 95%capacity under these conditions and the knee-onset at 97.1%capacity,with knee and its onset on average 108 cycles apart.After the critical identification step,we employ machine learning(ML)techniques for early prediction of the knee-point and knee-onset.Our models predict knee-point and knee-onset quantitatively with 9.4% error using only information from the first 50 cycles of the cells’life.Our models use the knee-point predictions to classify the cells’expected cycle lives as short,medium or long with 88–90% accuracy using only information from the first 3–5 cycles.Our accuracy levels are on par with existing literature for End-of-Life prediction(requiring information from 100-cycles),nonetheless,we address the more complex problem of knee prediction.All estimations are enriched with confidence/credibility metrics.The uncertainty regarding the ML model’s estimations is quantified through prediction intervals.These yield risk-criteria insurers and manufacturers of energy storage applications can use for battery warranties.Our classification model provides a tool for cell man-ufacturers to speed up the validation of 展开更多
With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction e...With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction exhibited dramatic drawbacks,i.e.,volatile capacity nonlinear fading trajectories create obstacles to the accurate multistep ahead prediction due to the complex working conditions of batteries.Herein,a novel deep learning model is proposed to achieve a universal and accurate Li-ion battery aging prognosis.Two battery datasets with various electrode types and cycling conditions are developed to validate the proposed approaches.Knee-point probability(KPP),extracted from the capacity loss curve,is first proposed to detect knee points and improve state-of-health(SOH) predictive accuracy,especially during periods of rapid capacity decline.Using one-cycle data of partial raw voltage as the model input,the SOH and KPP can be simultaneously predicted at multistep ahead,whereas the conventional method showed worse accuracy.Furthermore,to explore the underlying characteristics among various degradation tendencies,an online model update strategy is developed by leveraging the adversarial adaptationinduced transfer learning technique.This work gains new sights into the comprehensive Li-ion battery management and prognosis framework through decomposing capacity degradation trajectories and adversarial learning on the unlabeled samples.展开更多
Objective:To observe the therapeutic effect differences among five-knee-point acupuncture combined with Chinese medication package warm compress therapy of Shēntòng Zhúyū Decoction(身痛逐瘀汤 generalized p...Objective:To observe the therapeutic effect differences among five-knee-point acupuncture combined with Chinese medication package warm compress therapy of Shēntòng Zhúyū Decoction(身痛逐瘀汤 generalized pain stasis-expelling decoction),simple five-knee-point acupuncture and simple Chinese medication package warm compress therapy of Shēntòng Zhúyū Decoction in treating knee osteoarthritis(KOA).Methods:A total of 126 KOA patients were randomized into a five-knee-point acupuncture combined with Chinese medication package warm compress therapy group(combined treatment group),where there were 42 cases,including 28 cases of unilateral KOA and 14 cases of bilateral KOA,totally 56 affected knees involved,a Chinese medication package warm compress therapy group(medication package group,42 cases,including 22 cases of unilateral KOA,20 cases of bilateral KOA,totally 62 affected knees involved)and a five-knee-point acupuncture group(five-knee-point group,42 cases,including 27 cases of unilateral KOA,15 cases of bilateral KOA,totally 57 affected knees involved).The basic health education was provided in all of the groups.Additionally,in the combined treatment group,acupuncture was applied to the five knee points on the affected side for 30 min.The warm compress with herbal package of Shēntòng Zhúyū Decoction was given for 10 to 15 min.In the medication package group,the warm compress with Shēntòng Zhúyū Decoction was exerted on the affected area for 10 to 15 min.In the fiveknee-point group,acupuncture was applied to SP 10,ST 34,EX-LE 2,EX-LE 4 and ST 35 and the needles were retained for 30 min.The treatment in each group was given once a day,consecutively for 2 weeks.Before and after treatment,the visual analogue scale(VAS)and Lysholm knee scale were adopted to evaluate the pain degree and knee joint motor function in KOA patients.The clinical therapeutic effects were evaluated too.Results:A total of 121 cases accomplished the final observation and 5 cases were dropped out in the three groups,in which,2 展开更多
基金financially supported by the National Natural Science Foundation of China(NSFC,U20A20310,52107230,52176199,52102470)the support of the research project Model2Life(03XP0334),funded by the German Federal Ministry of Education and Research(BMBF)。
文摘Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in the future. However, one of the obstacles hindering the future development of battery technology is how to accurately evaluate and monitor battery health, which affects the entire lifespan of battery use. It is not enough to assess battery health comprehensively through the state of health(SoH) alone, especially when nonlinear aging occurs in onboard applications. Here, for the first time, we propose a brand-new health evaluation indicator—state of nonlinear aging(SoNA) to explain the nonlinear aging phenomenon that occurs during the battery use, and also design a knee-point identification method and two SoNA quantitative methods. We apply our health evaluation indicator to build a complete LIB full-lifespan grading evaluation system and a ground-to-cloud service framework, which integrates multi-scenario data collection, multi-dimensional data-based grading evaluation, and cloud management functions. Our works fill the gap in the LIBs’ health evaluation of nonlinear aging, which is of great significance for the health and safety evaluation of LIBs in the field of echelon utilization such as vehicles and energy storage. In addition, this comprehensive evaluation system and service framework are expected to be extended to other battery material systems other than LIBs, yet guiding the design of new energy ecosystem.
文摘High-performance batteries greatly benefit from accurate,early predictions of future capacity loss,to advance the management of the battery and sustain desirable application-specific performance characteristics for as long as possible.Li-ion cells exhibit a slow capacity degradation up to a knee-point,after which the degradation ac-celerates rapidly until the cell’s End-of-Life.Using capacity degradation data,we propose a robust method to identify the knee-point within capacity fade curves.In a new approach to knee research,we propose the concept‘knee-onset’,marking the beginning of the nonlinear degradation,and provide a simple and robust identifica-tion mechanism for it.We link cycle life,knee-point and knee-onset,where predicting/identifying one promptly reveals the others.On data featuring continuous high C-rate cycling(1C–8C),we show that,on average,the knee-point occurs at 95%capacity under these conditions and the knee-onset at 97.1%capacity,with knee and its onset on average 108 cycles apart.After the critical identification step,we employ machine learning(ML)techniques for early prediction of the knee-point and knee-onset.Our models predict knee-point and knee-onset quantitatively with 9.4% error using only information from the first 50 cycles of the cells’life.Our models use the knee-point predictions to classify the cells’expected cycle lives as short,medium or long with 88–90% accuracy using only information from the first 3–5 cycles.Our accuracy levels are on par with existing literature for End-of-Life prediction(requiring information from 100-cycles),nonetheless,we address the more complex problem of knee prediction.All estimations are enriched with confidence/credibility metrics.The uncertainty regarding the ML model’s estimations is quantified through prediction intervals.These yield risk-criteria insurers and manufacturers of energy storage applications can use for battery warranties.Our classification model provides a tool for cell man-ufacturers to speed up the validation of
基金supported by the financial support from the National Key Research and Development Program of China(2022YFB3807200)the Fundamental Research Funds for the Central Universities(2242022K330047)+3 种基金the dual creative talents from Jiangsu Province(JSSCBS20210152,JSSCBS20210100)the National Natural Science Foundation of Jiangsu Province(BK20221456,BK20200375)the Natural Science Foundation of China with(22109021)the Research Fund Program of Guangdong Provincial Key Lab of Green Chemical Product Technology(6802008024)。
文摘With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction exhibited dramatic drawbacks,i.e.,volatile capacity nonlinear fading trajectories create obstacles to the accurate multistep ahead prediction due to the complex working conditions of batteries.Herein,a novel deep learning model is proposed to achieve a universal and accurate Li-ion battery aging prognosis.Two battery datasets with various electrode types and cycling conditions are developed to validate the proposed approaches.Knee-point probability(KPP),extracted from the capacity loss curve,is first proposed to detect knee points and improve state-of-health(SOH) predictive accuracy,especially during periods of rapid capacity decline.Using one-cycle data of partial raw voltage as the model input,the SOH and KPP can be simultaneously predicted at multistep ahead,whereas the conventional method showed worse accuracy.Furthermore,to explore the underlying characteristics among various degradation tendencies,an online model update strategy is developed by leveraging the adversarial adaptationinduced transfer learning technique.This work gains new sights into the comprehensive Li-ion battery management and prognosis framework through decomposing capacity degradation trajectories and adversarial learning on the unlabeled samples.
文摘Objective:To observe the therapeutic effect differences among five-knee-point acupuncture combined with Chinese medication package warm compress therapy of Shēntòng Zhúyū Decoction(身痛逐瘀汤 generalized pain stasis-expelling decoction),simple five-knee-point acupuncture and simple Chinese medication package warm compress therapy of Shēntòng Zhúyū Decoction in treating knee osteoarthritis(KOA).Methods:A total of 126 KOA patients were randomized into a five-knee-point acupuncture combined with Chinese medication package warm compress therapy group(combined treatment group),where there were 42 cases,including 28 cases of unilateral KOA and 14 cases of bilateral KOA,totally 56 affected knees involved,a Chinese medication package warm compress therapy group(medication package group,42 cases,including 22 cases of unilateral KOA,20 cases of bilateral KOA,totally 62 affected knees involved)and a five-knee-point acupuncture group(five-knee-point group,42 cases,including 27 cases of unilateral KOA,15 cases of bilateral KOA,totally 57 affected knees involved).The basic health education was provided in all of the groups.Additionally,in the combined treatment group,acupuncture was applied to the five knee points on the affected side for 30 min.The warm compress with herbal package of Shēntòng Zhúyū Decoction was given for 10 to 15 min.In the medication package group,the warm compress with Shēntòng Zhúyū Decoction was exerted on the affected area for 10 to 15 min.In the fiveknee-point group,acupuncture was applied to SP 10,ST 34,EX-LE 2,EX-LE 4 and ST 35 and the needles were retained for 30 min.The treatment in each group was given once a day,consecutively for 2 weeks.Before and after treatment,the visual analogue scale(VAS)and Lysholm knee scale were adopted to evaluate the pain degree and knee joint motor function in KOA patients.The clinical therapeutic effects were evaluated too.Results:A total of 121 cases accomplished the final observation and 5 cases were dropped out in the three groups,in which,2
