The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ...The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health(SOH)of batteries based on ICA that is applicable under differing charging conditions.This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile.This approach's efficacy is contingent upon precisely acquiring the equivalent impedance.To obtain the equivalent impedance throughout the batteries'lifespan while minimizing testing costs,this study employs a current interrupt technique in conjunction with a long short-term memory(LSTM)network to develop a predictive model for equivalent impedance.Following the derivation of ICA curves using voltage profiles under quasi-static conditions,the research explores two scenarios for SOH estimation:one utilizing only incremental capacity(IC)features and the other incorporating both IC features and IC sampling.A genetic algorithm-optimized backpropagation neural network(GABPNN)is employed for the SOH estimation.The proposed generalized framework is validated using independent training and test datasets.Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions.These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04%for RMSE and 0.90%for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0%and 70%,which constitutes a major advancement compared to established ICA methods.It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.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
Capacity estimation plays a crucial role in battery management systems,and is essential for ensuring the safety and reliability of lithium-sulfur(Li-S)batteries.This paper proposes a method that uses a long short-term...Capacity estimation plays a crucial role in battery management systems,and is essential for ensuring the safety and reliability of lithium-sulfur(Li-S)batteries.This paper proposes a method that uses a long short-term memory(LSTM)neural network to estimate the state of health(SOH)of Li-S batteries.The method uses health features extracted from the charging curve and incre-mental capacity analysis(ICA)as input for the LSTM network.To enhance the robustness and accuracy of the network,the Adam algorithm is employed to optimize specific hyperparameters.Experimental data from three different groups of batteries with varying nominal capac-ities are used to validate the proposed method.The results demonstrate the effectiveness of the method in accurately estimating the capacity degradation of all three batteries.Also,the study examines the impact of different lengths of network training sets on capacity estimation.The results reveal that the ICA-LSTM model achieves a prediction accuracy of mean absolute error 4.6%and mean squared error 0.21%with three different training set lengths of 20%,40%,and 60%.The analysis demonstrates that the lightweight model maintains high SOH estimation accu-racy even with a small training set,and exhibits strong adaptive and generalization capabilities when applied to different Li-S batteries.Overall,the proposed method,supported by experimental validation and analysis,demonstrates its efficacy in ensuring accurate and reliable SOH estimation,thereby enhancing the safety and per-formance of Li-S batteries.Index Terms—Adam algorithm,incremental capacity analysis,Li-S battery,long short-term memory,state of health.展开更多
Herein,incremental capacity-differential voltage (IC-DV) at a high C-rate (HC) is used as a non-invasive diagnostic tool in lithium-ion batteries,which inevitably exhibit capacity fading caused by multiple mechanisms ...Herein,incremental capacity-differential voltage (IC-DV) at a high C-rate (HC) is used as a non-invasive diagnostic tool in lithium-ion batteries,which inevitably exhibit capacity fading caused by multiple mechanisms during charge/discharge cycling.Because battery degradation modes are complex,the simple output of capacity fading does not yield any useful data in that respect.Although IC and DV curves obtained under restricted conditions (<0.1C,25℃) were applied in non-invasive analysis for accurate observation of degradation symptoms,a facile,rapid diagnostic approach without intricate,complex calculations is critical in on-board applications.Herein,Li Ni_(0.5)Mn_(0.3)Co_(0.2)O_(2)(NMC532)/graphite pouch cells were cycled at 4 and 6C and the degradation characteristics,i.e.,loss of active materials (LAM) and loss of lithium inventory (LLI),were parameterized using the IC-DV curves.During the incremental current cycling,the initial steep LAM and LLI slopes underwent gradual transitions to gentle states and revealed the gap between low-and high-current measurements.A quantitative comparison of LAM at high and low C-rate showed that a IC;revealed the relative amount of available reaction region limited by cell polarization.However,this did not provide a direct relationship for estimating the LAM at a low C-rate.Conversely,the limiting LLI,which is calculated at a C-rate approaching 0,was obtained by extrapolating the LLI through more than two points measured at high C-rate,and therefore,the LLI at 0.1C was accurately determined using rapid cycling.展开更多
基金funded by the Bavarian State Ministry of ScienceResearch and Art(Grant number:H.2-F1116.WE/52/2)。
文摘The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health(SOH)of batteries based on ICA that is applicable under differing charging conditions.This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile.This approach's efficacy is contingent upon precisely acquiring the equivalent impedance.To obtain the equivalent impedance throughout the batteries'lifespan while minimizing testing costs,this study employs a current interrupt technique in conjunction with a long short-term memory(LSTM)network to develop a predictive model for equivalent impedance.Following the derivation of ICA curves using voltage profiles under quasi-static conditions,the research explores two scenarios for SOH estimation:one utilizing only incremental capacity(IC)features and the other incorporating both IC features and IC sampling.A genetic algorithm-optimized backpropagation neural network(GABPNN)is employed for the SOH estimation.The proposed generalized framework is validated using independent training and test datasets.Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions.These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04%for RMSE and 0.90%for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0%and 70%,which constitutes a major advancement compared to established ICA methods.It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.
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.
基金This work is supported by the Zhejiang Province Natural Science Foundation(No.LY22E070007)National Natural Science Foundation of China(No.52007170).
文摘Capacity estimation plays a crucial role in battery management systems,and is essential for ensuring the safety and reliability of lithium-sulfur(Li-S)batteries.This paper proposes a method that uses a long short-term memory(LSTM)neural network to estimate the state of health(SOH)of Li-S batteries.The method uses health features extracted from the charging curve and incre-mental capacity analysis(ICA)as input for the LSTM network.To enhance the robustness and accuracy of the network,the Adam algorithm is employed to optimize specific hyperparameters.Experimental data from three different groups of batteries with varying nominal capac-ities are used to validate the proposed method.The results demonstrate the effectiveness of the method in accurately estimating the capacity degradation of all three batteries.Also,the study examines the impact of different lengths of network training sets on capacity estimation.The results reveal that the ICA-LSTM model achieves a prediction accuracy of mean absolute error 4.6%and mean squared error 0.21%with three different training set lengths of 20%,40%,and 60%.The analysis demonstrates that the lightweight model maintains high SOH estimation accu-racy even with a small training set,and exhibits strong adaptive and generalization capabilities when applied to different Li-S batteries.Overall,the proposed method,supported by experimental validation and analysis,demonstrates its efficacy in ensuring accurate and reliable SOH estimation,thereby enhancing the safety and per-formance of Li-S batteries.Index Terms—Adam algorithm,incremental capacity analysis,Li-S battery,long short-term memory,state of health.
基金supported by the projects of the Korea Electric Power Corporation(R19TA05)。
文摘Herein,incremental capacity-differential voltage (IC-DV) at a high C-rate (HC) is used as a non-invasive diagnostic tool in lithium-ion batteries,which inevitably exhibit capacity fading caused by multiple mechanisms during charge/discharge cycling.Because battery degradation modes are complex,the simple output of capacity fading does not yield any useful data in that respect.Although IC and DV curves obtained under restricted conditions (<0.1C,25℃) were applied in non-invasive analysis for accurate observation of degradation symptoms,a facile,rapid diagnostic approach without intricate,complex calculations is critical in on-board applications.Herein,Li Ni_(0.5)Mn_(0.3)Co_(0.2)O_(2)(NMC532)/graphite pouch cells were cycled at 4 and 6C and the degradation characteristics,i.e.,loss of active materials (LAM) and loss of lithium inventory (LLI),were parameterized using the IC-DV curves.During the incremental current cycling,the initial steep LAM and LLI slopes underwent gradual transitions to gentle states and revealed the gap between low-and high-current measurements.A quantitative comparison of LAM at high and low C-rate showed that a IC;revealed the relative amount of available reaction region limited by cell polarization.However,this did not provide a direct relationship for estimating the LAM at a low C-rate.Conversely,the limiting LLI,which is calculated at a C-rate approaching 0,was obtained by extrapolating the LLI through more than two points measured at high C-rate,and therefore,the LLI at 0.1C was accurately determined using rapid cycling.
