Method and device for predicting health of single battery of energy storage power station system
A system battery and health prediction technology, which is applied to battery circuit devices, measurement devices, circuit devices, etc., can solve the problems of difficulty in capturing abnormal changes in detection variables, slow detection, and insufficient accuracy
Pending Publication Date: 2021-05-07
EAST CHINA UNIV OF SCI & TECH
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[0004] However, due to the different application scenarios and application requirements of the energy storage power station on the grid side and the rapid iteration of technology, in the existing technology, the overall monitoring of the energy storage power station is not accurate enough. When the internal battery of a single energy storage cabinet When thermal runaway occurs, it may not be detected or detected slowly; while BMS can only detect faults in real time, it is difficult to capture abnormal changes in detected variables in the future, and cannot predict the occurrence of faults in advance
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[0053] In order to make the object, technical solution and advantages of the present invention clearer, preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined arbitrarily with each other.
[0054] The steps shown in the flowcharts of the figures may be implemented in a computer system, such as a set of computer-executable instructions. Also, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown or described herein.
[0055] A method for predicting the health of a battery cell in an energy storage power station system according to the present invention, the energy storage power station includes a plurality of lithium ion battery cells, such as figure 1 As shown, th...
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The invention provides a method for predicting the health of a single battery of an energy storage power station system. The method comprises the steps of collecting alarm information and original monitoring data of a battery, preprocessing the original monitoring data according to the alarm information, and constructing a health data set; for the health data set, taking data of the latest period of time, establishing a health degree monitoring model by adopting a kernel density estimation probability density function method, and determining a control limit of a monitoring variable of each single battery; normalizing the original detection data, and constructing a prediction model of a monitoring variable based on a long and short term memory neural network model to obtain a prediction value of the monitoring variable; and comparing the predicted value with a control limit, positioning the faulted battery cell, and early warning the overrun moment of the monitoring variable. The invention further provides a corresponding health prediction device. According to the method, online prediction of the health state of the single battery of the energy storage power station system can be realized, and timely replacement and maintenance can be carried out before a single battery fault occurs, so that normal operation of the energy storage power station system is ensured.
Description
technical field [0001] The invention relates to the field of lithium-ion battery health management, and more particularly to a method and device for predicting the health of a lithium-ion battery cell. Background technique [0002] As a new energy battery, lithium-ion batteries are not only more environmentally friendly but also have superior performance, and are widely used in energy storage power stations. In the energy storage power station system, an extremely complex system composed of hundreds or thousands of lithium-ion battery cells has highly nonlinear, temperature-sensitive characteristics, obvious aging characteristics, and inconsistencies, which also lead to battery packs may Thermal runaway will occur due to the heat release and accumulation of its own chemical reaction or the influence of external heat sources, which will seriously affect the safety performance of the energy storage power station. [0003] Energy storage technology is an important aspect of gr...
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IPC IPC(8): G01R31/392G01R31/382G01R31/367H02J7/00G06N3/04
CPCG01R31/392G01R31/382G01R31/367H02J7/005G06N3/04
Inventor 杨文赵芝芸朱奕楠郭世雄
Owner EAST CHINA UNIV OF SCI & TECH