Fine modeling method for lithium ion battery
A lithium-ion battery and battery model technology, applied in neural learning methods, measuring electricity, measuring electrical variables, etc., can solve the problem of low accuracy of lithium-ion battery models, achieve dynamic tracking, reduce control prediction requirements, and improve accuracy degree of effect
Pending Publication Date: 2021-11-23
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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[0004] The purpose of the present invention is to provide a lithium-ion battery model modeling method to solve the problem that the existing modeling method ignores that the battery model is actually a global nonlinear system, resulting in low accuracy of the established lithium-ion battery model
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[0017] The present invention proposes a refined modeling method for lithium-ion batteries. The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0018] The invention proposes a method for establishing a lithium-ion battery model, which is characterized in that the method first establishes a mathematical model corresponding to the equivalent circuit model of the battery, and establishes an ARX model of the object; then the lithium-ion battery is operated under different input current states The range is divided into a large number of different sub-time regions; the single cell voltage and current data are used as the input of the battery model, and the local ARX model coefficients are identified in each sub-time domain by the least square method; according to the model coefficients identified in each sub-time domain The modeling data set is composed, and the BP neural network is used for training...
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The invention provides a modeling method for a lithium ion battery, and belongs to the field of lithium ion battery modeling. The method comprises the following steps: firstly, establishing a mathematical model corresponding to an equivalent circuit model of a battery, and establishing an ARX (auto-regressive exogenous input) model of an object; dividing the working range of the lithium ion battery into a large number of different sub-time regions through different input current states; taking the voltage and current data of the single cell as the input of a battery model, and identifying a local ARX model coefficient in each sub-time domain through a least square method; forming a modeling data set according to the model coefficients identified in each sub-time domain, and training by using a BP neural network to obtain a function type coefficient approaching a battery global ARX model; and finally, substituting the function type coefficient into the global ARX model to obtain a global nonlinear LPV parameter real variable model. According to the method, the dynamic nonlinear characteristics of the lithium ion battery can be better described, the accuracy of the established battery model can be improved, and the industrial requirements on a battery model control method are reduced.
Description
technical field [0001] The invention belongs to the field of lithium-ion battery modeling, and particularly proposes a refined modeling method for lithium-ion batteries based on an LPV model. Background technique [0002] The lithium-ion battery itself has strong nonlinear and time-varying characteristics, because of its internal chemical reaction mechanism, its internal characteristic parameters such as polarization capacitance, polarization resistance, etc. change with battery aging, and the battery is charging and discharging. External factors such as current and temperature have a great influence on the battery, which greatly increases the difficulty of battery modeling. Therefore, the modeling of lithium-ion batteries and the corresponding parameter identification methods have always been hot spots and difficulties in theoretical research and engineering applications. . [0003] At present, the commonly used lithium-ion battery models mainly include electrochemical mod...
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Login to View More IPC IPC(8): G01R31/367G01R31/378G06N3/04G06N3/08
Inventor 夏向阳贺懿冰曾小勇
Owner CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY