Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long lifespan.The monitoring of battery states and failure identification are indis...Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long lifespan.The monitoring of battery states and failure identification are indispensable for guaranteeing the secure and optimal functionality of the batteries.The impedance spectrum has garnered growing interest due to its ability to provide a valuable understanding of material characteristics and electrochemical processes.To inspire further progress in the investigation and application of the battery impedance spectrum,this paper provides a comprehensive review of the determination and utilization of the impedance spectrum.The sources of impedance inaccuracies are systematically analyzed in terms of frequency response characteristics.The applicability of utilizing diverse impedance features for the diagnosis and prognosis of batteries is further elaborated.Finally,challenges and prospects for future research are discussed.展开更多
The current impedance spectroscopy measurement techniques face difficulties in diagnosing solar cell faults due to issues such as cost,complexity,and accuracy.Therefore,a novel system was developed for precise broadba...The current impedance spectroscopy measurement techniques face difficulties in diagnosing solar cell faults due to issues such as cost,complexity,and accuracy.Therefore,a novel system was developed for precise broadband impedance spectrum measurement of solar cells,which was composed of an oscilloscope,a signal generator,and a sampling resistor.The results demonstrate concurrent accurate measurement of the impedance spectrum(50 Hz-0.1 MHz)and direct current voltametric characteristics.Comparative analysis with Keithley 2450 data yields a global relative error of approximately 6.70%,affirming the accuracy.Among excitation signals(sine,square,triangle,pulse waves),sine wave input yields the most accurate data,with a root mean square error of approximately 13.3016 and a global relative error of approximately 4.25%compared to theoretical data.Elevating reference resistance expands the half circle in the impedance spectrum.Proximity of reference resistance to that of the solar cell enhances the accuracy by mitigating line resistance influence.Measurement error is lower in high-frequency regions due to a higher signal-to-noise ratio.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
Physical-layer security issues in wireless systems have attracted great attention.In this paper,we investigate the spectrum anti-jamming(AJ)problem for data transmissions between devices.Considering fast-changing phys...Physical-layer security issues in wireless systems have attracted great attention.In this paper,we investigate the spectrum anti-jamming(AJ)problem for data transmissions between devices.Considering fast-changing physical-layer jamming attacks in the time/frequency domain,frequency resources have to be configured for devices in advance with unknown jamming patterns(i.e.the time-frequency distribution of the jamming signals)to avoid jamming signals emitted by malicious devices.This process can be formulated as a Markov decision process and solved by reinforcement learning(RL).Unfortunately,stateof-the-art RL methods may put pressure on the system which has limited computing resources.As a result,we propose a novel RL,by integrating the asynchronous advantage actor-critic(A3C)approach with the kernel method to learn a flexible frequency pre-configuration policy.Moreover,in the presence of time-varying jamming patterns,the traditional AJ strategy can not adapt to the dynamic interference strategy.To handle this issue,we design a kernelbased feature transfer learning method to adjust the structure of the policy function online.Simulation results reveal that our proposed approach can significantly outperform various baselines,in terms of the average normalized throughput and the convergence speed of policy learning.展开更多
文摘Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long lifespan.The monitoring of battery states and failure identification are indispensable for guaranteeing the secure and optimal functionality of the batteries.The impedance spectrum has garnered growing interest due to its ability to provide a valuable understanding of material characteristics and electrochemical processes.To inspire further progress in the investigation and application of the battery impedance spectrum,this paper provides a comprehensive review of the determination and utilization of the impedance spectrum.The sources of impedance inaccuracies are systematically analyzed in terms of frequency response characteristics.The applicability of utilizing diverse impedance features for the diagnosis and prognosis of batteries is further elaborated.Finally,challenges and prospects for future research are discussed.
基金supported by National Natural Science Foundation of China(Nos.12064027,62065014,12464010)2022 Jiangxi Province Highlevel and High-skilled Leading Talent Training Project Selected(No.63)+1 种基金Jiujiang“Xuncheng Talents”(No.JJXC2023032)Nanchang Hangkong University Education Reform Project(No.JY21069).
文摘The current impedance spectroscopy measurement techniques face difficulties in diagnosing solar cell faults due to issues such as cost,complexity,and accuracy.Therefore,a novel system was developed for precise broadband impedance spectrum measurement of solar cells,which was composed of an oscilloscope,a signal generator,and a sampling resistor.The results demonstrate concurrent accurate measurement of the impedance spectrum(50 Hz-0.1 MHz)and direct current voltametric characteristics.Comparative analysis with Keithley 2450 data yields a global relative error of approximately 6.70%,affirming the accuracy.Among excitation signals(sine,square,triangle,pulse waves),sine wave input yields the most accurate data,with a root mean square error of approximately 13.3016 and a global relative error of approximately 4.25%compared to theoretical data.Elevating reference resistance expands the half circle in the impedance spectrum.Proximity of reference resistance to that of the solar cell enhances the accuracy by mitigating line resistance influence.Measurement error is lower in high-frequency regions due to a higher signal-to-noise ratio.
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partially supported by the National Natural Science Foundation of China under Grant U2001210,61901216,61827801the Natural Science Foundation of Jiangsu Province under Grant BK20190400。
文摘Physical-layer security issues in wireless systems have attracted great attention.In this paper,we investigate the spectrum anti-jamming(AJ)problem for data transmissions between devices.Considering fast-changing physical-layer jamming attacks in the time/frequency domain,frequency resources have to be configured for devices in advance with unknown jamming patterns(i.e.the time-frequency distribution of the jamming signals)to avoid jamming signals emitted by malicious devices.This process can be formulated as a Markov decision process and solved by reinforcement learning(RL).Unfortunately,stateof-the-art RL methods may put pressure on the system which has limited computing resources.As a result,we propose a novel RL,by integrating the asynchronous advantage actor-critic(A3C)approach with the kernel method to learn a flexible frequency pre-configuration policy.Moreover,in the presence of time-varying jamming patterns,the traditional AJ strategy can not adapt to the dynamic interference strategy.To handle this issue,we design a kernelbased feature transfer learning method to adjust the structure of the policy function online.Simulation results reveal that our proposed approach can significantly outperform various baselines,in terms of the average normalized throughput and the convergence speed of policy learning.
