Abstract: Embodiments of the present disclosure provide a virtual-machine memory integrity protection method, apparatus, electronic device, and storage medium.

Abstract: An uncertain noisy filtering-based fault diagnosis method for a power battery management system is described. The method includes establishing an electro-thermal coupling model of the power battery system; extending an output vector of the system according to a state constraint of a power battery, and expanding a state vector of the system according to a fault of the power battery system to obtain an augmented system of the power battery system; obtaining an estimation interval of a power battery sensor fault by using a zonotope Kalman filtering method; judging whether the power battery management system has a fault according to upper and lower bounds of fault estimation; if a fault occurs, determining a fault type and a fault time according to a result.

Abstract: The present disclosure discloses an uncertain noisy filtering-based fault diagnosis method for a power battery management system and belongs to the field of power battery fault diagnosis. The method comprises: establishing an electro-thermal coupling model of a power battery system; extending an output vector of the system according to a state constraint of a power battery, and expanding a state vector of the system according to a fault of the power battery system to obtain an augmented system of the power battery system; obtaining an estimation interval of a power battery sensor fault by using a zonotope Kalman filtering method; judging whether the power battery management system has a fault according to upper and lower bounds of fault estimation, and if a fault occurs, determining a fault type and a fault time according to a result.

Abstract: The disclosure relates to a fault detection method for a Buck converter based on an inverse Kalman filter, which belongs to the field of fault detection for power converters. The method includes: establishing a hybrid discrete model of a non-ideal Buck converter in Continuous Conduction Mode (CCM); acquiring a state equation and an observation equation of the Buck converter; acquiring the input voltage, the switching state, the inductive current and the output voltage in a working state, and determining the state matrix of the Buck converter; estimating a parameter matrix at time k according to the state matrix, the state equation and the observation equation; determining an estimated value of each component according to the estimated parameter matrix of the Buck converter at time k; and determining whether the Buck converter is faulty or not according to the estimated value of each component.

Abstract: The disclosure relates to a fault detection method for a Buck converter based on an inverse Kalman filter, which belongs to the field of fault detection for power converters. The method includes: establishing a hybrid discrete model of a non-ideal Buck converter in Continuous Conduction Mode (CCM); acquiring a state equation and an observation equation of the Buck converter; acquiring the input voltage, the switching state, the inductive current and the output voltage in a working state, and determining the state matrix of the Buck converter; estimating a parameter matrix at time k according to the state matrix, the state equation and the observation equation; determining an estimated value of each component according to the estimated parameter matrix of the Buck converter at time k; and determining whether the Buck converter is faulty or not according to the estimated value of each component.