An object of the present invention is to provide a battery state estimation device capable of accurately estimating a deterioration state of an entire battery system in consideration of an SOH distribution of battery cells. The battery state estimation device according to the present invention estimates an SOH of a battery cell by using a correspondence between a time derivative of an output voltage during a pause period of the battery cell and a battery temperature, and estimates a deterioration state of an entire battery system by using the SOHs of a plurality of battery cells (see FIG. 1).

A prediction method of battery health includes: obtaining an environment temperature, and a discharge capacity and an operating parameter of a battery; determining an estimated operating parameter in a preset temperature at the discharge capacity according to the environment temperature, the discharge capacity and the operating parameter; determining an estimated capacity of the battery according to the estimated operating parameter; and determining a health level of the battery according to the estimated capacity of the battery and a reference capacity, at the discharge capacity of the battery.

A prediction method of battery health includes: obtaining an environment temperature, and a discharge capacity and an operating parameter of a battery; determining an estimated operating parameter in a preset temperature at the discharge capacity according to the environment temperature, the discharge capacity and the operating parameter; determining an estimated capacity of the battery according to the estimated operating parameter; and determining a health level of the battery according to the estimated capacity of the battery and a reference capacity, at the discharge capacity of the battery.

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. Compared with an existing fault diagnosis method for a system without a state constraint, the present application solves the problem of fault diagnosis of a system with a state constraint by extending the state constraint of the system to the system output vector.

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. Compared with an existing fault diagnosis method for a system without a state constraint, the present application solves the problem of fault diagnosis of a system with a state constraint by extending the state constraint of the system to the system output vector.

A method for determining a low voltage defect of a lithium secondary battery is provided.

A method for determining a low voltage defect of a lithium secondary battery is provided.

The present invention relates to a method for determining a state of charge of a battery, including: (a) acquiring a state of charge of the battery at a current sampling time point tn; (b) acquiring a voltage Vn, a temperature Tn, and a charging rate Cn of the battery at the current sampling time point tn, and a voltage Vi of the battery at a sampling time point ti, and calculating a voltage difference Vn−Vi between the voltage Vn and the voltage Vi; (c) when the voltage difference Vn−Vi is greater than or equal to a preset voltage threshold, calculating a voltage change rate; and (d) when the voltage change rate is greater than or equal to a preset voltage change rate threshold for the first time, acquiring a corrected state of charge of the battery as an actual state of charge of the battery.

The present invention relates to a method for determining a state of charge of a battery, including: (a) acquiring a state of charge of the battery at a current sampling time point tn; (b) acquiring a voltage Vn, a temperature Tn, and a charging rate Cn of the battery at the current sampling time point tn, and a voltage Vi of the battery at a sampling time point ti, and calculating a voltage difference Vn−Vi between the voltage Vn and the voltage Vi; (c) when the voltage difference Vn−Vi is greater than or equal to a preset voltage threshold, calculating a voltage change rate; and (d) when the voltage change rate is greater than or equal to a preset voltage change rate threshold for the first time, acquiring a corrected state of charge of the battery as an actual state of charge of the battery.

An estimation device 4 is provided with: an acquisition unit 41 for acquiring time-series data about an SOC of a power storage element; a specification unit 41 for specifying a fluctuation range of the SOC in the time-series data and an SOC representative value representing an SOC region in the fluctuation range; and an estimation unit 41 for estimating a shape change of the power storage element on the basis of the specified fluctuation range and the specified representative value.