A battery analysis method and system based on electrochemical parameters

Abstract

The invention relates to a battery analysis method and system based on electrochemical parameters. According to the method, collected battery macroscopic parameters are mutually coupled with electrochemical-based microcosmic parameters obtained by further calculation through a curve fitting algorithm, so that the feature parameters input into a trained machine learning model contain more effectiveinformation; two-stage data cleaning is carried out through edge calculation and cloud calculation, so that the prediction precision of the machine learning model is fundamentally improved; through analysis of battery big data, fault early warning and out-of-control alarm can be realized according to outlier detection of real-time data; and a relatively accurate prediction model can be obtained through training of massive historical data, and accurate estimation of a battery state is realized.

Description

technical field [0001] The invention belongs to the technical field of battery analysis, and in particular relates to a battery analysis method and system based on electrochemical parameters. Background technique [0002] Energy crisis and environmental pollution are two major problems facing the world today. Governments and automobile manufacturers are actively taking measures to deal with them. The development of renewable energy technology is an important way to solve this series of problems. As an important branch of new energy technology, Green, clean and efficient new energy vehicle technology can reduce dependence on fossil energy and reduce emissions of pollutants and greenhouse gases, and ultimately replace traditional fuel vehicles. Compared with traditional vehicles, electric vehicles have the advantages of being clean, energy-saving, pollution-free and low-noise. The core of the development of electric vehicles is to solve the problems of cost, battery life and s...

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Problems solved by technology

[0004] Aiming at the technical problems existing in the above-mentioned prior art that real-time and accurate fault warning and state prediction cannot be guaranteed at present, the present invention provides a battery analysis method based on electrochemical parameters. The chemical microscopic parameters are coupled with each other, so that the characteristic parameters contain more effective information, fundamentally improve the prediction accuracy of the machine learning model, and can use the trained machine learning model to improve the quality of the battery performance detection and prediction results

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