本文提出基于NB-IoT(Narrow Band Internet of Things,窄带物联网)技术的核辐射剂量仪研制方案,该仪器采用STM32F103系列单片机作为主控芯片,辐射剂量探测器选用G-M计数管,集成温湿度采集模块,通过BC20物联网模块将采集数据发送到OneNe...本文提出基于NB-IoT(Narrow Band Internet of Things,窄带物联网)技术的核辐射剂量仪研制方案,该仪器采用STM32F103系列单片机作为主控芯片,辐射剂量探测器选用G-M计数管,集成温湿度采集模块,通过BC20物联网模块将采集数据发送到OneNet云平台,云平台能够实时监控仪器所在位置的辐射剂量值以及温湿度值,同时显示仪器的北斗定位信息。该辐射剂量仪能够实现远程实时监测、定位,具备轻量化,低功耗,低成本,覆盖范围广等特点。展开更多
To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation alg...To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation algorithm is proposed by combining the online parameter identification method and the modified covariance extended Kalman filter(MVEKF)algorithm.Based on the parameters identified on line with the multiple forgetting factors recursive least squares methods,the newly-established algorithm recalculates the covariance in the iterative process with the modified estimation and updates the process gain which is used for the next state estimation to decrease errors of the filter.Experiments including constant pulse discharging and the dynamic stress test(DST)demonstrate that compared with the EKF algorithm,the MVEKF algorithm produces fewer estimation errors and can reduce the errors to 5%at most under the complex charging and discharging conditions of batteries.In the charging process under the DST condition,the EKF produces a larger deviation and lacks stability,while the MVEKF algorithm can estimate SOC stably and has a strong robustness.Therefore,the established MVEKF algorithm is suitable for complex and changeable working conditions of batteries for electric vehicles.展开更多
文摘本文提出基于NB-IoT(Narrow Band Internet of Things,窄带物联网)技术的核辐射剂量仪研制方案,该仪器采用STM32F103系列单片机作为主控芯片,辐射剂量探测器选用G-M计数管,集成温湿度采集模块,通过BC20物联网模块将采集数据发送到OneNet云平台,云平台能够实时监控仪器所在位置的辐射剂量值以及温湿度值,同时显示仪器的北斗定位信息。该辐射剂量仪能够实现远程实时监测、定位,具备轻量化,低功耗,低成本,覆盖范围广等特点。
基金The National Natural Science Foundation of China(No.51375086)。
文摘To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation algorithm is proposed by combining the online parameter identification method and the modified covariance extended Kalman filter(MVEKF)algorithm.Based on the parameters identified on line with the multiple forgetting factors recursive least squares methods,the newly-established algorithm recalculates the covariance in the iterative process with the modified estimation and updates the process gain which is used for the next state estimation to decrease errors of the filter.Experiments including constant pulse discharging and the dynamic stress test(DST)demonstrate that compared with the EKF algorithm,the MVEKF algorithm produces fewer estimation errors and can reduce the errors to 5%at most under the complex charging and discharging conditions of batteries.In the charging process under the DST condition,the EKF produces a larger deviation and lacks stability,while the MVEKF algorithm can estimate SOC stably and has a strong robustness.Therefore,the established MVEKF algorithm is suitable for complex and changeable working conditions of batteries for electric vehicles.
