With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal c...With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal changes of lithium batteries.Based on Fabry-Perot interferometer(FPI)structure which is made of a glass plate and an optical fiber pigtail,the ultrasound imaging system possesses a high sensitivity of 558 mV/kPa at 500 kHz with the noise equivalent pressure(NEP)of only 63.5 mPa.For the frequency response,the ultrasound sensitivity is higher than 13.1 mV/kPa within the frequency range from 50 kHz to 1 MHz.Meanwhile,the battery imaging system based on the proposed sensor has a superior resolution as high as 0.5 mm.The performance of battery safety monitoring is verified,in which three commercial lithium-ion ferrous phosphate/graphite(LFP||Gr)batteries are imaged and the state of health(SOH)for different batteries is obtained.Besides,the wetting process of an anode-free lithium metal batteries(AFLMB)is clearly observed via the proposed system,in which the formation process of the pouch cell is analyzed and the gas-related"unwetting"condition is discovered,representing a significant advancement in battery health monitoring field.In the future,the commercial usage can be realized when sensor array and artificial intelligence technology are adopted.展开更多
A high EMS current-mode SPI interface for battery monitor IC（BMIC） is presented to form a daisychain bus configuration for the cascaded BMICs and the communication between the MCU and master BMIC.Based on analog and...A high EMS current-mode SPI interface for battery monitor IC（BMIC） is presented to form a daisychain bus configuration for the cascaded BMICs and the communication between the MCU and master BMIC.Based on analog and digital mixed filtering technique,the proposed daisy-chain can avoid the isolated communication issue in electromagnetic interference environment,and reduce the extensively required I/O ports of MCU,at the same time reduce the system cost.The proposed daisy-chain interface was introduced in a 6-ch battery monitor IC which was fabricated with 0.35μ m 30 V BCD process.The measurement result shows that the presented daisy-chain SPI interface achieves better EMS performance with different EMI injection while just consuming a total operation current up to 1 m A.展开更多
With the increasing demand for batteries,the real-time in situ monitoring of the physical/chemical state within the“black box”is critical to improving battery performance.Consequently,the development of a cost-effec...With the increasing demand for batteries,the real-time in situ monitoring of the physical/chemical state within the“black box”is critical to improving battery performance.Consequently,the development of a cost-effective and in situ battery monitoring system that does not interfere with the normal operation of the battery is imminent.Traditional monitoring techniques are constrained by size,reliability,and scalability.Optical fiber sensors offer a distinctive advantage in enabling highly sensitive,multiparameter in situ measurements in the harsh electrochemical environment of batteries.By decoding these characteristic parameters,it helps to establish the evolution mechanism of the battery’s safety state.Additionally,the integration of advanced lab-on-fiber technology with battery monitoring systems has attracted considerable attention.This review summarizes the recent advances in optical fiber sensing technology in the fields of battery temperature and mechanical stress/strain and provides an outlook on the future challenges and development of smart batteries.展开更多
The potential of acoustic signatures to be used for State-of-Charge(SoC)estimation is demonstrated using artificial neural network regression models.This approach represents a streamlined method of processing the enti...The potential of acoustic signatures to be used for State-of-Charge(SoC)estimation is demonstrated using artificial neural network regression models.This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual,and often arbitrary,waveform peak selection.For applications where computational economy is prioritised,simple metrics of statistical significance are used to formally identify the most informative waveform features.These alone can be exploited for SoC inference.It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value,challenging the more common peak selection methods which focus on the latter.Although later echoes represent greater through-thickness coverage,and are intuitively more information-rich,their presence is not guaranteed.Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states.It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly,while also improving the estimation accuracy.Most importantly,it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates,without any complementary voltage information.This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed.Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell.Mean estimation errors as low as 0.75%are achieved on a SoC scale of 0-100%.展开更多
基金supports from China National Funds for Distinguished Young Scientists(62425505)National Natural Science Foundation of China(U22A20206)+1 种基金the China Postdoctoral Science Foundation(2023M731188)the Fundamental Research Funds for the Central Universities(2024BRA012).
文摘With the rapid development of lithium batteries,it’s of great significance to ensure the safe use of it.An ultrasound imaging system based on fiber optic ultrasound sensor has been developed to monitor the internal changes of lithium batteries.Based on Fabry-Perot interferometer(FPI)structure which is made of a glass plate and an optical fiber pigtail,the ultrasound imaging system possesses a high sensitivity of 558 mV/kPa at 500 kHz with the noise equivalent pressure(NEP)of only 63.5 mPa.For the frequency response,the ultrasound sensitivity is higher than 13.1 mV/kPa within the frequency range from 50 kHz to 1 MHz.Meanwhile,the battery imaging system based on the proposed sensor has a superior resolution as high as 0.5 mm.The performance of battery safety monitoring is verified,in which three commercial lithium-ion ferrous phosphate/graphite(LFP||Gr)batteries are imaged and the state of health(SOH)for different batteries is obtained.Besides,the wetting process of an anode-free lithium metal batteries(AFLMB)is clearly observed via the proposed system,in which the formation process of the pouch cell is analyzed and the gas-related"unwetting"condition is discovered,representing a significant advancement in battery health monitoring field.In the future,the commercial usage can be realized when sensor array and artificial intelligence technology are adopted.
基金Project supported by the National Natural Science Foundation of China(No.61334003)
文摘A high EMS current-mode SPI interface for battery monitor IC（BMIC） is presented to form a daisychain bus configuration for the cascaded BMICs and the communication between the MCU and master BMIC.Based on analog and digital mixed filtering technique,the proposed daisy-chain can avoid the isolated communication issue in electromagnetic interference environment,and reduce the extensively required I/O ports of MCU,at the same time reduce the system cost.The proposed daisy-chain interface was introduced in a 6-ch battery monitor IC which was fabricated with 0.35μ m 30 V BCD process.The measurement result shows that the presented daisy-chain SPI interface achieves better EMS performance with different EMI injection while just consuming a total operation current up to 1 m A.
基金support by the National Key Research and Development Program(2023YFB2503700)the Tsinghua University-China Petrochemical Corporation Joint Institute for Green Chemical Engineering(224247)+1 种基金the Beijing Municipal Science&Technology Commission(Z2311-00006123003)the National Science Foundation of China(22071133).
文摘With the increasing demand for batteries,the real-time in situ monitoring of the physical/chemical state within the“black box”is critical to improving battery performance.Consequently,the development of a cost-effective and in situ battery monitoring system that does not interfere with the normal operation of the battery is imminent.Traditional monitoring techniques are constrained by size,reliability,and scalability.Optical fiber sensors offer a distinctive advantage in enabling highly sensitive,multiparameter in situ measurements in the harsh electrochemical environment of batteries.By decoding these characteristic parameters,it helps to establish the evolution mechanism of the battery’s safety state.Additionally,the integration of advanced lab-on-fiber technology with battery monitoring systems has attracted considerable attention.This review summarizes the recent advances in optical fiber sensing technology in the fields of battery temperature and mechanical stress/strain and provides an outlook on the future challenges and development of smart batteries.
基金funding and support from the Faraday Institution(EP/S003053/1)as part of the Multi-Scale Modelling(FIRG025)and LiSTAR(FIRG014)projectsThe Royal Academy of Engineering is acknowledged for the financial support of Shearing(CiET1718\59)Brett under the Research Chairs and Senior Research Fellowships scheme(RCSRF2021/13/53).
文摘The potential of acoustic signatures to be used for State-of-Charge(SoC)estimation is demonstrated using artificial neural network regression models.This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual,and often arbitrary,waveform peak selection.For applications where computational economy is prioritised,simple metrics of statistical significance are used to formally identify the most informative waveform features.These alone can be exploited for SoC inference.It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value,challenging the more common peak selection methods which focus on the latter.Although later echoes represent greater through-thickness coverage,and are intuitively more information-rich,their presence is not guaranteed.Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states.It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly,while also improving the estimation accuracy.Most importantly,it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates,without any complementary voltage information.This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed.Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell.Mean estimation errors as low as 0.75%are achieved on a SoC scale of 0-100%.
