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 ...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.展开更多
Cathode material of spent lithium-ion batteries was refined to obtain high value-added cobalt and lithium products based on the chemical behaviors of metal in different oxidation states. The active substances separate...Cathode material of spent lithium-ion batteries was refined to obtain high value-added cobalt and lithium products based on the chemical behaviors of metal in different oxidation states. The active substances separated from the cathode of spent lithium-ion batteries were dissolved in H2SO4 and H2O2 solution, and precipitated as CoC2O4·2H2O microparticles by addition of (NH4)2C2O4. After collection of the CoC2O4·2H2O product by filtration, the Li2CO3 precipitates were obtained by addition of Na2CO3 in the left filtrate. The experimental study shows that 96.3% of Co (mass fraction) and 87.5% of Li can be dissolved in the solution of 2 mol/L H2SO4 and 2.0% H2O2 (volume fraction), and 94.7% of Co and 71.0% of Li can be recovered respectively in the form of CoC2O4·2H2O and Li2CO3.展开更多
基金supported by Fundamental Research Program of Shanxi Province(No.202203021211088)Shanxi Provincial Natural Science Foundation(No.202204021301049).
文摘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.
基金Project (51078286) supported by the National Natural Science Foundation of ChinaProject (2008BAC46B02) supported by the National Key Technologies R&D Program of China+1 种基金Project (2011SQRL110) supported by the Excellent Youth Foundation of Anhui Education Department, ChinaProject (KJ2011z053) supported by the Natural Science Foundation of Anhui Education Department, China
文摘Cathode material of spent lithium-ion batteries was refined to obtain high value-added cobalt and lithium products based on the chemical behaviors of metal in different oxidation states. The active substances separated from the cathode of spent lithium-ion batteries were dissolved in H2SO4 and H2O2 solution, and precipitated as CoC2O4·2H2O microparticles by addition of (NH4)2C2O4. After collection of the CoC2O4·2H2O product by filtration, the Li2CO3 precipitates were obtained by addition of Na2CO3 in the left filtrate. The experimental study shows that 96.3% of Co (mass fraction) and 87.5% of Li can be dissolved in the solution of 2 mol/L H2SO4 and 2.0% H2O2 (volume fraction), and 94.7% of Co and 71.0% of Li can be recovered respectively in the form of CoC2O4·2H2O and Li2CO3.