Power battery SOC estimation method based on dimensionality reduction observer

Abstract

The invention discloses a power battery SOC (State of Charge) estimation method based on a dimensionality reduction observer. The method includes the following steps: firstly, establishment of a battery model structure; secondly, recognition of battery unknown parameters; and thirdly, estimation of battery state of charge (SOC). For a lithium ion battery, the dimensionality reduction observer is designed while internal resistance and a relaxation effect of the battery are taken into consideration, and the SOC of the battery is estimated in real time through an open-circuit voltage method and a coulombic metering method. The computation complexity is low, the estimation precision is high, and errors caused by SOC initial value selection and system uncertainty are effectively overcome.

Description

technical field [0001] The invention relates to the technical field of power batteries of new energy electric vehicles, in particular to a power battery SOC estimation method based on a dimensionality reduction observer. Background technique [0002] As one of the core technologies of energy-saving and new energy vehicles, battery technology has always been the key to hindering the development of the new energy vehicle industry. The current research directions for power batteries mainly include battery model establishment, battery fault diagnosis, battery life prediction, battery State of Health (SOH) estimation and battery State of Charge (SOC) estimation. Among them, the battery model mainly reflects the relationship between the internal state variables of the battery and the external characteristics of the battery, which is the prerequisite for battery fault diagnosis and state estimation; the SOC of the battery describes the current remaining power inside the battery, wh...

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Problems solved by technology

Among them, the method based on the internal resistance model is affected by the temperature of the AC impedance and the influence of the DC impedance by the calculation time. If the time is short, only the ohmic resistance can be measured, and if the time is long, the internal resistance becomes complicated, and it becomes difficult to accurately measure the internal resistance of the monomer. , so it is seldom used in real vehicles; the open circuit voltage method requires the battery to stand for a long time and return to a stable state from the working state, which makes the measurement difficult, so this method is only applicable to the parking state when used alone; the Coulomb measurement method can Provide accurate current measurement results to achieve high-precision estimation, but cannot estimate the initial SOC of the battery; the neural network method requires a large amount of reference data for training, and the estimation results are greatly affected by the training data and training methods; the Kalman filter method involves The algorithm is complex, the amount of calculation is large, and the hardware requirements are harsh

Therefore, any estimation method used alone cannot meet the actual requirements of accuracy and easy realization.

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