Internal temperature estimation method for lithium battery based on discrete sliding-mode observer

Abstract

The invention discloses an internal temperature estimation method for a lithium battery based on a discrete sliding-mode observer. The method comprises the following steps that a lithium battery discrete state space model for internal temperature estimation is established; a working voltage, a working current, a surface temperature and an external environment temperature of the lithium battery under a working condition are collected in real time; parameters of the lithium battery discrete state space model are identified online; and the discrete sliding-mode observer is constructed to estimate the internal temperature of the lithium battery in real time. The method has a good temperature estimation effect, can ensure the convergence of the observer strictly, and is highly robust for modeling errors, model parameter perturbation and external disturbance of the lithium battery thermal model.

Description

technical field [0001] The invention relates to the technical field of lithium batteries for vehicles, in particular to a method for estimating the internal temperature of a lithium battery based on a discrete sliding mode observer. Background technique [0002] Lithium batteries have the advantages of high energy density, no memory effect, low environmental pollution, and long cycle life, and are increasingly used in electric vehicles. As a key component of electric vehicles, lithium batteries have an important impact on the performance, life and safety of lithium batteries if their temperature is too high or too low, which in turn affects the power, economy and safety of the vehicle. [0003] Battery temperature is an important parameter for battery management system to perform thermal management, and its value is of great significance for estimating the remaining power of the battery and formulating thermal management strategies. [0004] At present, there are two main m...

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Problems solved by technology

[0007] 1) The Kalman filter algorithm requires the process noise and observation noise to be white noise and the statistical properties such as the mean and variance of the noise are known, which is difficult to meet in practical applications, not only because the statistical properties of the noise are difficult to obtain, but also because the white noise also exists only under ideal conditions;

[0008] 2) The Kalman filter algorithm has high requirements on the accuracy of the lithium battery temperature prediction model. When the parameters in the model change due to factors such as temperature, aging, and life, it will cause a large temperature prediction error and even diverge.

[0010] 1) Only the relationship between the internal resistance of the battery and the internal temperature of the battery is considered, and the standard relationship between the internal temperature of the battery and the internal resistance of the battery is obtained offline. However, during the working process of the lithium battery, in addition to the temperature, the internal resistance of the battery is also affected by this parameter. Changes due to the influence of other factors, such as the number of cycles, charge and discharge rate, state of charge, aging degree, etc., so there will be a large estimation error

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