Battery temperature estimation method based on thermal-neural network coupling model

Abstract

The invention relates to a battery temperature estimation method based on a thermal-neural network coupling model, and belongs to the technical field of battery management. The method comprises the following steps: S1, selecting a to-be-tested battery, collecting and sorting the specification and key geometric parameters of the battery, and obtaining an experimental data set required by battery model establishment and temperature estimation; s2, a low-order thermal model of the battery is established based on a Chebyshev Galerkin approximation method by considering the thermal effect of the tab, parameter identification is carried out to obtain unknown parameters of the thermal model, and the key temperature of the battery is estimated in real time in combination with an extended Kalman filter (EKF) algorithm; s3, establishing and training a battery data driving model based on a long-short-term memory neural network, and determining a mapping relation between battery heat production, a state of charge (SOC) and an environment temperature and a battery key temperature; and S4, coupling the physical thermal model and the neural network model through an integrated learning algorithm adaboost, and optimizing the fusion weight of the physical thermal model and the neural network model, thereby realizing accurate battery temperature estimation.

Description

technical field [0001] The invention belongs to the technical field of battery management and relates to a battery temperature estimation method based on a thermal-neural network coupling model. Background technique [0002] As the core component of pure electric vehicles EVs, hybrid electric vehicles HEVs and plug-in hybrid electric vehicles PHEVs, the performance of power batteries directly determines the development of electric vehicles. Due to the outstanding advantages of flexible shape design, light weight, high specific energy, and compact layout, large-size power batteries are increasingly used in electric vehicles. Due to the poor consistency of large-scale laminated batteries during manufacturing, local hot spots are prone to occur during normal operation, which leads to uneven temperature and obvious local hot spots during use of this type of battery, and even triggers thermal runaway and ignition. Moreover, in order to dispel customers' concerns about the short ...

AI Technical Summary

Problems solved by technology

Estimating the temperature of the battery based on a thermal model can be roughly divided into: using a simple thermal model to estimate the average temperature of the battery, which is simple to calculate, but the estimation accuracy cannot reflect the actual situation of the battery temperature; using a numerical solution method to estimate the temperature distribution of the battery, This type of method can achieve accurate estimation, but the amount of calculation is large, and it is difficult to apply in practice; using the two-state thermal model, the temperature distribution inside the battery can be estimated in combination with the battery surface temperature measurement. This type of method model and algorithm are simple, and the accuracy is high However, a large number of temperature sensors need to be installed, and it is difficult to achieve popularization and application

The temperature estimation based on EIS measurement has a simple model, does not need to install a temperature sensor, and is not limited by the geometric shape, but this type of method can only obtain the average temperature

Therefore, some scholars have studied the temperature estimation based on the combination of thermal model and EIS measurement, and used the thermal-impedance model based on impedance measurement to estimate and predict the temperature distribution inside the battery cell. This method does not need to install a temperature sensor, It can also obtain rich temperature information, but this type of method requires high accuracy of measuring equipment, and the modeling stage takes a long time

In recent years, with the sweeping of the era of big data, the state estimation method based on machine learning and artificial intelligence has been widely used in the estimation and prediction of the SOC, SOH and remaining life of the power battery, but it is rarely used for the temperature estimation of the power battery. estimate

This type of method does not require a physical model, nor does it require an in-depth understanding of the heat generation and heat transfer mechanism of the battery, and is not limited by the geometric shape. However, it requires high data quantity and quality, takes a long time to calculate, and has poor generalization ability.

[0004] At present, there have been many studies on estimating the temperature of power batteries, but few scholars have studied the method of estimating the temperature of large-scale stacked batteries by combining thermal models and data-driven methods.

On the one hand, although the thermal model of large-scale stacked batteries can achieve relatively accurate temperature estimation, there are problems such as the need for in-depth exploration of the physical model and the complexity of the modeling process; on the other hand, the neural network model does not require a physical model to estimate the battery temperature. , but there are problems such as high requirements on the quantity and quality of data, and weak generalization ability

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