It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics,energy storage,and electric vehicles.Herein,by integr...It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics,energy storage,and electric vehicles.Herein,by integrating regular real-time current short pulse tests with data-driven Gaussian process regression algorithm,an efficient battery estimation has been successfully developed and validated for batteries with capacity ranging from 100%of the state of health(SOH)to below 50%,reaching an average accuracy as high as 95%.Interestingly,the proposed pulse test strategy for battery capacity measurement could reduce test time by more than 80%compared with regular long charge/discharge tests.The short-term features of the current pulse test were selected for an optimal training process.Data at different voltage stages and state of charge(SOC)are collected and explored to find the most suitable estimation model.In particular,we explore the validity of five different machine-learning methods for estimating capacity driven by pulse features,whereas Gaussian process regression with Matern kernel performs the best,providing guidance for future exploration.The new strategy of combining short pulse tests with machine-learning algorithms could further open window for efficiently forecasting lithium-ion battery remaining capacity.展开更多
Abuse of Lithium-ion batteries,both physical and electrochemical,can lead to significantly reduced operational capabilities.In some instances,abuse can cause catastrophic failure,including thermal runaway,combustion,a...Abuse of Lithium-ion batteries,both physical and electrochemical,can lead to significantly reduced operational capabilities.In some instances,abuse can cause catastrophic failure,including thermal runaway,combustion,and explosion.Many different test standards that include abuse conditions have been developed,but these generally consider only one condition at a time and only provide go/no-go criteria.In this work,different types of cell abuse are implemented concurrently to determine the extent to which simultaneous abuse conditions aggravate cell degradation and failure.Vibrational loading is chosen to be the consistent type of physical abuse,and the first group of cells is cycled at different vibrational frequencies.The next group of cells is cycled at the same frequencies,with multiple charge pulses occurring during each discharge.The final group of cells is cycled at the same frequencies,with a partial nail puncture occurring near the beginning of cycling.The results show that abusing cells with vibrational loading or vibrational loading with current pulses does not cause a significant decrease in operational capabilities while abusing cells with vibrational loading and a nail puncture drastically reduces operational capabilities.The cells with vibration only experience an increase in internal resistance by a factor of 1.09–1.26,the cells with vibration and current pulses experience an increase in internal resistance by a factor of 1.16–1.23,and all cells from each group reach their rated lifetime of 500 cycles without reaching their end-of-life capacity.However,the cells with vibration and nail puncture experience an increase in internal resistance by a factor of 6.83–22.1,and each cell reaches its end-of-life capacity within 50 cycles.Overall,the results show that testing multiple abuse conditions simultaneously provides a better representation of the extreme limitations of cell operation and should be considered for inclusion in reference test standards.展开更多
基金support from Shenzhen Municipal Development and Reform Commission(Grant Number:SDRC[2016]172)Shenzhen Science and Technology Program(Grant No.KQTD20170810150821146)Interdisciplinary Research and Innovation Fund of Tsinghua Shenzhen International Graduate School,and Shanghai Shun Feng Machinery Co.,Ltd.
文摘It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics,energy storage,and electric vehicles.Herein,by integrating regular real-time current short pulse tests with data-driven Gaussian process regression algorithm,an efficient battery estimation has been successfully developed and validated for batteries with capacity ranging from 100%of the state of health(SOH)to below 50%,reaching an average accuracy as high as 95%.Interestingly,the proposed pulse test strategy for battery capacity measurement could reduce test time by more than 80%compared with regular long charge/discharge tests.The short-term features of the current pulse test were selected for an optimal training process.Data at different voltage stages and state of charge(SOC)are collected and explored to find the most suitable estimation model.In particular,we explore the validity of five different machine-learning methods for estimating capacity driven by pulse features,whereas Gaussian process regression with Matern kernel performs the best,providing guidance for future exploration.The new strategy of combining short pulse tests with machine-learning algorithms could further open window for efficiently forecasting lithium-ion battery remaining capacity.
基金Funding for this research has been provided by the Office of Naval Research(ONR)under the Grant N00014-20-1-2227(Program Manager:Dr.Maria Medeiros and Dr.Corey Love).
文摘Abuse of Lithium-ion batteries,both physical and electrochemical,can lead to significantly reduced operational capabilities.In some instances,abuse can cause catastrophic failure,including thermal runaway,combustion,and explosion.Many different test standards that include abuse conditions have been developed,but these generally consider only one condition at a time and only provide go/no-go criteria.In this work,different types of cell abuse are implemented concurrently to determine the extent to which simultaneous abuse conditions aggravate cell degradation and failure.Vibrational loading is chosen to be the consistent type of physical abuse,and the first group of cells is cycled at different vibrational frequencies.The next group of cells is cycled at the same frequencies,with multiple charge pulses occurring during each discharge.The final group of cells is cycled at the same frequencies,with a partial nail puncture occurring near the beginning of cycling.The results show that abusing cells with vibrational loading or vibrational loading with current pulses does not cause a significant decrease in operational capabilities while abusing cells with vibrational loading and a nail puncture drastically reduces operational capabilities.The cells with vibration only experience an increase in internal resistance by a factor of 1.09–1.26,the cells with vibration and current pulses experience an increase in internal resistance by a factor of 1.16–1.23,and all cells from each group reach their rated lifetime of 500 cycles without reaching their end-of-life capacity.However,the cells with vibration and nail puncture experience an increase in internal resistance by a factor of 6.83–22.1,and each cell reaches its end-of-life capacity within 50 cycles.Overall,the results show that testing multiple abuse conditions simultaneously provides a better representation of the extreme limitations of cell operation and should be considered for inclusion in reference test standards.
