A capacity increase is often observed in the early stage of Li-ion battery cycling.This study explores the phenomena involved in the capacity increase from the full cell,electrodes,and materials perspective through a ...A capacity increase is often observed in the early stage of Li-ion battery cycling.This study explores the phenomena involved in the capacity increase from the full cell,electrodes,and materials perspective through a combination of non-destructive diagnostic methods in a full cell and post-mortem analysis in a coin cell.The results show an increase of 1%initial capacity for the battery aged at 100%depth of discharge(DOD)and 45℃.Furthermore,large DODs or high temperatures accelerate the capacity increase.From the incremental capacity and differential voltage(IC-DV)analysis,we concluded that the increased capacity in a full cell originates from the graphite anode.Furthermore,graphite/Li coin cells show an increased capacity for larger DODs and a decreased capacity for lower DODs,thus in agreement with the full cell results.Post-mortem analysis results show that a larger DOD enlarges the graphite dspace and separates the graphite layer structure,facilitating the Li+diffusion,hence increasing the battery capacity.展开更多
Electrochemical impedance spectroscopy(EIS) is an effective technique for Lithium-ion battery state of health diagnosis, and the impedance spectrum prediction by battery charging curve is expected to enable battery im...Electrochemical impedance spectroscopy(EIS) is an effective technique for Lithium-ion battery state of health diagnosis, and the impedance spectrum prediction by battery charging curve is expected to enable battery impedance testing during vehicle operation. However, the mechanistic relationship between charging curves and impedance spectrum remains unclear, which hinders the development as well as optimization of EIS-based prediction techniques. In this paper, we predicted the impedance spectrum by the battery charging voltage curve and optimized the input based on electrochemical mechanistic analysis and machine learning. The internal electrochemical relationships between the charging curve,incremental capacity curve, and the impedance spectrum are explored, which improves the physical interpretability for this prediction and helps define the proper partial voltage range for the input for machine learning models. Different machine learning algorithms have been adopted for the verification of the proposed framework based on the sequence-to-sequence predictions. In addition, the predictions with different partial voltage ranges, at different state of charge, and with different training data ratio are evaluated to prove the proposed method have high generalization and robustness. The experimental results show that the proper partial voltage range has high accuracy and converges to the findings of the electrochemical analysis. The predicted errors for impedance spectrum are less than 1.9 mΩ with the proper partial voltage range selected by the corelative analysis of the electrochemical reactions inside the batteries. Even with the voltage range reduced to 3.65–3.75 V, the predictions are still reliable with most RMSEs less than 4 mO.展开更多
Fast charging is considered a promising protocol for raising the charging efficiency of electric vehicles.However,high currents applied to Lithium-ion(Li-ion)batteries inevitably accelerate the degradation and shorten...Fast charging is considered a promising protocol for raising the charging efficiency of electric vehicles.However,high currents applied to Lithium-ion(Li-ion)batteries inevitably accelerate the degradation and shorten their lifetime.This work designs a multi-step fast-charging method to extend the lifetime of LiNi0.5Co0.2Mn0.3O2(NMC)/graphite Li-ion batteries based on the studies of half cells and investigates the aging mechanisms for different charging methods.The degradation has been studied from both full cell behaviour and materials perspectives through a combination of non-destructive diagnostic methods and post-mortem analysis.In the proposed multi-step charging protocol,the state-of-charge(SOC)profile is subdivided into five ranges,and the charging current is set differently for different SOC ranges.One of the designed multi-step fast charging protocols is shown to allow for a 200 full equivalent cycles longer lifetime as compared to the standard charging method,while the charging time is reduced by 20%.From the incremental capacity analysis and electrical impedance spectroscopy,the loss of active materials and lithium inventory on the electrodes,as well as an increase in internal resistance for the designed multistep constant-current-constant-voltage(MCCCV)protocol have been found to be significantly lower than for the standard charging method.Post-mortem analysis shows that cells aged by the designed MCCCV fast charging protocol exhibit less graphite exfoliation and crystallization damage,as well as a reduced solid electrolyte interphase(SEI)layer growth on the anode,leading to a lower Rseiresistance and extended lifetime.展开更多
基金supported by a grant from the China Scholarship Council(202006370035 and 202006220024)supported by the National Natural Science Foundation of China(52107229)。
文摘A capacity increase is often observed in the early stage of Li-ion battery cycling.This study explores the phenomena involved in the capacity increase from the full cell,electrodes,and materials perspective through a combination of non-destructive diagnostic methods in a full cell and post-mortem analysis in a coin cell.The results show an increase of 1%initial capacity for the battery aged at 100%depth of discharge(DOD)and 45℃.Furthermore,large DODs or high temperatures accelerate the capacity increase.From the incremental capacity and differential voltage(IC-DV)analysis,we concluded that the increased capacity in a full cell originates from the graphite anode.Furthermore,graphite/Li coin cells show an increased capacity for larger DODs and a decreased capacity for lower DODs,thus in agreement with the full cell results.Post-mortem analysis results show that a larger DOD enlarges the graphite dspace and separates the graphite layer structure,facilitating the Li+diffusion,hence increasing the battery capacity.
基金supported by a grant from the China Scholarship Council (202006370035)a fund from Otto Monsteds Fund (4057941073)。
文摘Electrochemical impedance spectroscopy(EIS) is an effective technique for Lithium-ion battery state of health diagnosis, and the impedance spectrum prediction by battery charging curve is expected to enable battery impedance testing during vehicle operation. However, the mechanistic relationship between charging curves and impedance spectrum remains unclear, which hinders the development as well as optimization of EIS-based prediction techniques. In this paper, we predicted the impedance spectrum by the battery charging voltage curve and optimized the input based on electrochemical mechanistic analysis and machine learning. The internal electrochemical relationships between the charging curve,incremental capacity curve, and the impedance spectrum are explored, which improves the physical interpretability for this prediction and helps define the proper partial voltage range for the input for machine learning models. Different machine learning algorithms have been adopted for the verification of the proposed framework based on the sequence-to-sequence predictions. In addition, the predictions with different partial voltage ranges, at different state of charge, and with different training data ratio are evaluated to prove the proposed method have high generalization and robustness. The experimental results show that the proper partial voltage range has high accuracy and converges to the findings of the electrochemical analysis. The predicted errors for impedance spectrum are less than 1.9 mΩ with the proper partial voltage range selected by the corelative analysis of the electrochemical reactions inside the batteries. Even with the voltage range reduced to 3.65–3.75 V, the predictions are still reliable with most RMSEs less than 4 mO.
基金the support from the China Scholarship Council(202006370035 and 202006220024)the Otto M?nsted Fond(22-70-1620)。
文摘Fast charging is considered a promising protocol for raising the charging efficiency of electric vehicles.However,high currents applied to Lithium-ion(Li-ion)batteries inevitably accelerate the degradation and shorten their lifetime.This work designs a multi-step fast-charging method to extend the lifetime of LiNi0.5Co0.2Mn0.3O2(NMC)/graphite Li-ion batteries based on the studies of half cells and investigates the aging mechanisms for different charging methods.The degradation has been studied from both full cell behaviour and materials perspectives through a combination of non-destructive diagnostic methods and post-mortem analysis.In the proposed multi-step charging protocol,the state-of-charge(SOC)profile is subdivided into five ranges,and the charging current is set differently for different SOC ranges.One of the designed multi-step fast charging protocols is shown to allow for a 200 full equivalent cycles longer lifetime as compared to the standard charging method,while the charging time is reduced by 20%.From the incremental capacity analysis and electrical impedance spectroscopy,the loss of active materials and lithium inventory on the electrodes,as well as an increase in internal resistance for the designed multistep constant-current-constant-voltage(MCCCV)protocol have been found to be significantly lower than for the standard charging method.Post-mortem analysis shows that cells aged by the designed MCCCV fast charging protocol exhibit less graphite exfoliation and crystallization damage,as well as a reduced solid electrolyte interphase(SEI)layer growth on the anode,leading to a lower Rseiresistance and extended lifetime.
