Adaptive Fitting Capacity Prediction Method for Lithium‑Ion Batteries

Lithium-ion batteries have become the mainstream power source for electric vehicles because of their excellent performance.However,lithium-ion batteries still experience aging and capacity attenuation during usage.It is therefore critical to accu-rately predict battery remaining capacity for increasing battery safety and prolonging battery life.This paper first adopts the metabolism grey algorithm and a simplified electrochemical model to predict battery capacity under different operating conditions.To improve the prediction performance where the capacity changes nonlinearly,a decoupling analysis of battery capacity loss is then conducted based on the simplified electrochemical model.Finally,an adaptive fitting method is devel-oped for capacity prediction,aiming at improving the prediction accuracy at the inflection point of battery capacity diving.The prediction results indicate that the developed adaptive fitting method can achieve high prediction accuracy under battery capacity attenuation at different discharge stages with errors lower than 2.2%.And the battery capacity decay shows linear variation,and the proposed method effectively forecast the inflection point of battery capacity diving.

A Prediction Method of Trend-Type Capacity Index Based on Recurrent Neural Network

Due to the increase in the types of business and equipment in telecommunications companies,the performance index data collected in the operation and maintenance process varies greatly.The diversity of index data makes it very difficult to perform high-precision capacity prediction.In order to improve the forecasting efficiency of related indexes,this paper designs a classification method of capacity index data,which divides the capacity index data into trend type,periodic type and irregular type.Then for the prediction of trend data,it proposes a capacity index prediction model based on Recurrent Neural Network(RNN),denoted as RNN-LSTM-LSTM.This model includes a basic RNN,two Long Short-Term Memory(LSTM)networks and two Fully Connected layers.The experimental results show that,compared with the traditional Holt-Winters,Autoregressive Integrated Moving Average(ARIMA)and Back Propagation(BP)neural network prediction model,the mean square error(MSE)of the proposed RNN-LSTM-LSTM model are reduced by 11.82%and 20.34%on the order storage and data migration,which has greatly improved the efficiency of trend-type capacity index prediction.

State-of-the-art review of soft computing applications in underground excavations

Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity,compared to the traditional methods.This paper presents an overview of some soft computing techniques as well as their applications in underground excavations.A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting(XGBoost),Multivariate Adaptive Regression Splines(MARS),Artificial Neural Networks(ANN),and Support Vector Machine(SVM) in estimating the maximum lateral wall deflection induced by braced excavation.This study also discusses the merits and the limitations of some soft computing techniques,compared with the conventional approaches available.

Semen Analysis and Fecundity Association Among Women with Polycystic Ovary Syndrome Experiencing Ovulatory Dysfunction Treated by Ovulation Induction

In this study,normal values of semen analysis were set for a general infertile population of couples among which most women had normal ovulation.The predictive capacity values of sperm quality,including concentration,motile count,progressive motile count,and morphology,are unclear for women with polycystic ovary syndrome(PCOS).A secondary analysis was conducted based on a randomized controlled trial investigating infertility among women with PCOS experiencing ovulatory disorder between 2011 and 2016 in China.A total of 1000 women received ovulation induction(acupuncture and clomiphene).We randomized the women with PCOS in 27 hospitals in China who received one of four interventions(acupuncture plus clomiphene,sham acupuncture plus clomiphene,acupuncture plus placebo,or sham acupuncture plus placebo).Semen analysis was performed for every male partner according to the World Health Organization(WHO)criteria.The outcomes included conception,clinical pregnancy,and live birth.Logistic regression was used to evaluate the predictive value of semen analysis among ovulatory women for conception,clinical pregnancy,and live birth.Among the 1000 couples,the number of couples who attained ovulation,conception,clinical pregnancy,and live birth were 780,320,235,and 205,respectively.Semen volume and motility were applied and used as prediction parameters for conception(area under the curve(AUC)of 0.62(95%confidence interval(CI),0.55–0.69)),clinical pregnancy(AUC of 0.67(95%CI:0.61–0.73)),and live birth(AUC of 0.57(95%CI:0.50–0.64)).No poor calibration was shown for these models in Hosmer–Lemeshow tests.The predictive capacity of semen analysis for treatment outcome in PCOS women with PCOS experiencing with ovulatory dysfunction is limited.