SELF-ADAPTIVE LITHIUM-ION BATTERY METHOD USING KNOWLEDGE-REINFORCED MACHINE LEARNING AND KALMAN FILTERING, ELECTRONIC DEVICE, AND STORAGE MEDIUM

    公开(公告)号:US20250044753A1

    公开(公告)日:2025-02-06

    申请号:US18365621

    申请日:2023-08-04

    Abstract: The present disclosure provides a self-adaptive lithium-ion battery method using knowledge-reinforced machine learning and Kalman filtering, an electronic device, and a storage medium. The method includes training and synchronizing an artificial neural network with dual extended Kalman filters to capture battery capacity data of each of lithium-ion batteries; integrating prior knowledge with Gaussian process regression to form an integrated knowledge-reinforced Gaussian process regression; training a stochastic capacity degradation model by employing integrated knowledge-reinforced Gaussian process regression with captured battery capacity data to obtain a trained stochastic capacity degradation model; performing capacity prediction using trained stochastic capacity degradation model to obtain remaining useful life of one or more testing lithium-ion batteries; generating an air mass flow rate and a charging/discharging rate by a controller; and inputting the air mass flow rate and the charging/discharging rate into a battery thermal management system to improve battery RULs by adjusting lithium-ion battery temperature.

    HANDHELD SPECTRORADIOMETER SYSTEM, COMPUTER-READABLE MEDIA, AND CALIBRATION METHODS

    公开(公告)号:US20200264095A1

    公开(公告)日:2020-08-20

    申请号:US16793350

    申请日:2020-02-18

    Abstract: Non-transitory computer-readable media, spectroradiometer systems, and methods for calibrating a spectroradiometer. In one embodiment, a non-transitory computer-readable medium includes instructions that, when executed by an electronic processor, cause the electronic processor to perform a set of operations. The set of operations includes receiving spectral data regarding an object-of-interest that is captured by a handheld spectroradiometer, detecting a characteristic of the object-of-interest by performing a spectral analysis on the spectral data that is received, and controlling a display to display the characteristic of the object-of-interest.

    Nonlinear power flow control for networked AC/DC microgrids

    公开(公告)号:US10666054B2

    公开(公告)日:2020-05-26

    申请号:US15982850

    申请日:2018-05-17

    Abstract: A method for designing feedforward and feedback controllers for integration of stochastic sources and loads into a nonlinear networked AC/DC microgrid system is provided. A reduced order model for general networked AC/DC microgrid systems is suitable for HSSPFC control design. A simple feedforward steady state solution is utilized for the feedforward controls block. Feedback control laws are provided for the energy storage systems. A HSSPFC controller design is implemented that incorporates energy storage systems that provides static and dynamic stability conditions for both the DC random stochastic input side and the AC random stochastic load side. Transient performance was investigated for the feedforward/feedback control case. Numerical simulations were performed and provided power and energy storage profile requirements for the networked AC/DC microgrid system overall performance. The HSSPFC design can be implemented in the Matlab/Simulink environment that is compatible with real time simulation/controllers.

    Nonlinear power flow control for networked AC/DC microgrids

    公开(公告)号:US20180366952A1

    公开(公告)日:2018-12-20

    申请号:US15982850

    申请日:2018-05-17

    Abstract: A method for designing feedforward and feedback controllers for integration of stochastic sources and loads into a nonlinear networked AC/DC microgrid system is provided. A reduced order model for general networked AC/DC microgrid systems is suitable for HSSPFC control design. A simple feedforward steady state solution is utilized for the feedforward controls block. Feedback control laws are provided for the energy storage systems. A HSSPFC controller design is implemented that incorporates energy storage systems that provides static and dynamic stability conditions for both the DC random stochastic input side and the AC random stochastic load side. Transient performance was investigated for the feedforward/feedback control case. Numerical simulations were performed and provided power and energy storage profile requirements for the networked AC/DC microgrid system overall performance. The HSSPFC design can be implemented in the Matlab/Simulink environment that is compatible with real time simulation/controllers.

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