Multi-feature lithium battery health state online estimation method and device
A state-of-health, lithium battery technology, applied in measurement devices, electricity measurement, electric vehicles, etc., can solve the problems of high data dependence, general estimation effect of data-driven methods, etc., to improve dynamic performance, easy extraction, improve efficiency and The effect of accuracy
Pending Publication Date: 2022-08-05
QUANZHOU INST OF EQUIP MFG
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However, these methods are still offline training, and when the number of samples is small, the estimation effect of the data-driven method is general, and it often shows a high dependence on the data during use.
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[0032] like figure 1 As shown, the multi-feature lithium battery state-of-health online estimation method includes the following steps:
[0033] Step S1. Perform an aging cycle test on the No. 5, No. 6 and No. 7 lithium batteries in the NASA data set at room temperature and constant temperature. The lithium battery includes two processes of constant current charging and constant voltage charging during the charging process. In charging mode, the charging current is 1.5A, and when the voltage reaches 4.2V, it continues to charge in constant voltage charging mode.
[0034] Figure 2-1 It is shown that with the increase of the number of cycles, the charging time of the constant current charging mode decreases, and the polarization degree of the lithium battery gradually decreases. It can be seen that the charging time of the constant current charging mode is related to the health state of the lithium battery, so obtain The charging time of the constant current charging mode as th...
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The invention provides a multi-feature lithium battery health state online estimation method and device. The multi-feature lithium battery health state online estimation method comprises the following steps: S1, obtaining a first feature F1, a second feature F2, a third feature F3, a fourth feature F4, a fifth feature F5 and a sixth feature F6; s2, establishing a hypothesis function formula Y = theta0 + theta1F1 + theta2F2 +... + theta6F6 of a multiple linear regression model by taking the first to sixth features in the step S1 as an input vector X = [F1, F2, F3, F4, F5 and F6], and preliminarily determining a parameter vector phi = [theta0, theta1,..., theta6] by using a gradient descent algorithm; and S3, randomly generating N initialization particles according to the parameter vector phi, updating each initialization particle by using Gaussian white noise, performing importance sampling and resampling, and updating the parameter vector and a predicted value of the multiple linear regression model. According to the method, multiple features are extracted from different angles to construct a more accurate multiple linear regression model, model parameters are updated online, and the efficiency and accuracy of online estimation are improved.
Description
technical field [0001] The present invention relates to a method and device for online estimation of the health state of a lithium battery with multiple features. Background technique [0002] Effective management and control of lithium batteries is one of the core technologies that differentiate new energy vehicles from ordinary gasoline vehicles. With the increase in the number of charge-discharge cycles in use, the aging phenomenon of lithium-ion batteries has a greater impact on the service life of lithium-ion batteries and the safe driving range of electric vehicles. State of Health (SOH) of lithium-ion batteries is an important indicator to identify the aging degree of lithium-ion batteries, and it is also a key parameter of lithium-ion battery management systems. Therefore, predicting the health status of lithium-ion batteries can timely avoid possible safety accidents and prolong battery life. [0003] The estimation methods of battery SOH can be divided into two c...
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IPC IPC(8): G01R31/392G01R31/367
CPCG01R31/392G01R31/367Y02T10/70
Inventor 林名强严晨昊
Owner QUANZHOU INST OF EQUIP MFG