Electrochemical impedance spectroscopy (EIS) is widely used in fuel cell impedance analysis. However, for ohmic resistance (R Ω), EIS has some disadvantages such as long test period and complex data analysis with equ...Electrochemical impedance spectroscopy (EIS) is widely used in fuel cell impedance analysis. However, for ohmic resistance (R Ω), EIS has some disadvantages such as long test period and complex data analysis with equivalent circuits. Therefore, the current interruption method is explored to measure the value of RΩ in direct methanol fuel cells (DMFC) at different temperatures and current densities. It is found that RΩ decreases as temperature increase, and decreases initially and then increases as current density increases. These results are consistent with those measured by the EIS technique. In most cases, the ohmic resistances with current interruption (R iR ) are larger than those with EIS (R EIS ), but the difference is small, in the range from –0.848% to 5.337%. The errors of R iR at high current densities are less than those of R EIS . Our results show that the R iR data are reliable and easy to obtain in the measurement of ohmic resistance in DMFC.展开更多
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
基金Supported by the National High Technology Research and Development Program of China （2007AA05Z150） the National Natural Science Foundation of China （50911140287 50973055）
文摘Electrochemical impedance spectroscopy (EIS) is widely used in fuel cell impedance analysis. However, for ohmic resistance (R Ω), EIS has some disadvantages such as long test period and complex data analysis with equivalent circuits. Therefore, the current interruption method is explored to measure the value of RΩ in direct methanol fuel cells (DMFC) at different temperatures and current densities. It is found that RΩ decreases as temperature increase, and decreases initially and then increases as current density increases. These results are consistent with those measured by the EIS technique. In most cases, the ohmic resistances with current interruption (R iR ) are larger than those with EIS (R EIS ), but the difference is small, in the range from –0.848% to 5.337%. The errors of R iR at high current densities are less than those of R EIS . Our results show that the R iR data are reliable and easy to obtain in the measurement of ohmic resistance in DMFC.
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.
