Training method of battery state prediction model, and battery state prediction method and device

Abstract

The invention discloses a training method of a battery state prediction model, a battery state prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an electrochemical model, wherein the electrochemical model is constructed by measurement operation data and attribute data of a battery; performing charge and discharge simulation based on the electrochemical model to obtain simulation operation data of the battery under different simulation conditions; and taking the simulation operation data and the measurement operation data as training samples, inputting the training samples into a neural network, adjusting network parameters of the neural network and model parameters of the electrochemical model according to an output result of the neural network, and determining the neural network meeting an iteration stop condition as a battery state prediction model. According to the invention, the training samples of the neural network are expanded and enriched by means of the electrochemical model, and the electrochemical model is optimized based on the output result of the neural network, so that the electrochemical model provides more accurate training samples for the neural network, and the accuracy of model training is improved.

Description

technical field [0001] The invention relates to the technical field of batteries, in particular to a training method for a battery state prediction model, a battery state prediction method and device, electronic equipment, and a storage medium. Background technique [0002] With the development of new energy sources, the usage of rechargeable batteries such as lithium batteries is increasing rapidly. In the actual use of lithium batteries, due to the influence of factors such as the environment in which the batteries are located, series and parallel connections, the batteries often cannot complete the full life cycle in an ideal way. Abnormal life attenuation, even smoke, spontaneous combustion and other accidents occur from time to time. Therefore, it is very important to detect the state of the battery. [0003] The traditional battery management system (BMS) generally only roughly estimates the state of the battery from the measured values ​​of the battery voltage, curr...

AI Technical Summary

Problems solved by technology

There are a few solutions to predict the state of the battery based on the electrochemical model, but due to limited computing power, it is difficult to establish a real-time and effective electrochemical model, resulting in inaccurate early warning of battery failure

There is also a prediction of the state of the battery based on machine learning, but limited by the acquisition and labeling of training samples, it is impossible to obtain an accurate model, and it is impossible to accurately predict the state of the battery.

Images