Lithium battery health state estimation method based on convolutional neural network and transfer learning

Abstract

The invention discloses a lithium battery health state estimation method based on a convolutional neural network and transfer learning. According to the method, based on transfer learning, a basic model is pre-trained offline by using complete cycle data of an accelerated aging experiment and about 7.5% of cycle data of the last small part in the life cycle of a waste battery, and then fine adjustment is performed on parameters of the basic model by using normal speed aging data of only 15% cycle in front of the new battery so as to perform online estimation on a health state of the battery atany moment. The method is advantaged in that the accelerated aging experiment greatly shortens the service life of the battery, the last small part of cycle data of the waste battery is easy to obtain, and the former 15% of cycle data of the new battery is further easy to obtain, a large amount of time for collecting training data is saved, the size of model input data is reduced, and the calculation process is faster.

Description

technical field [0001] The invention belongs to the technical field of automation, and relates to a method for estimating the state of health of a lithium battery based on a convolutional neural network and transfer learning. Background technique [0002] The existing lithium battery state of health estimation methods mainly include two categories: model-based and data-driven. The model-based method has high requirements on the complex physical mechanism inside the battery. The data-driven method is mainly to manually extract important features from the original battery voltage, current, capacity and other data, as the input of some traditional machine learning models. Among them, the capacity increment analysis method is widely used. This method converts the voltage plateau reflecting the first-order phase transition of the battery on the original charge-discharge voltage capacity curve into a clearly identifiable ΔQ / ΔV peak on the capacity increment curve, and then extrac...

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Problems solved by technology

S.Shen et al first introduced deep learning into the study of battery health state estimation in the literature A deep learning method for online capacity estimation of lithium-ion batteries (Journal of Energy Storage, vol.25, p.100817, 2019), however This method is based on 10 years of cyclical experimental data, and the data collection is quite time-consuming, but the present invention aims to improve the accuracy of the health state estimation model and overcome the shortcomings of relying too much on data in the past

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