Prediction model fusion-based battery life prediction method and storage medium

Abstract

The invention provides a battery life prediction method based on prediction model fusion and a storage medium. According to the battery life prediction method, a long and short memory network model isnested in a particle filtering model; fusion model with simple structure, training a long and short memory network model by using existing historical data to obtain a degradation trend equation, anddetermining a state transition equation of a particle filtering model; the problem that a particle filtering model excessively depends on an empirical model is solved; the particle filtering model canobtain the uncertain expression of the residual life by using the weighted sum of the particles to approach the predicted value of the capacity, and in addition, a new sample obtained on line is added to the original training sample set to retrain the model, so that the model parameter is updated in time, the adaptability is better, and the prediction of the residual cycle life of the cadmium-nickel storage battery can be realized.

Description

technical field [0001] The invention belongs to the technical field of battery life prediction, and in particular relates to a battery life prediction method based on prediction model fusion and a storage medium. Background technique [0002] No matter the electric locomotive or the diesel locomotive, the battery and the charger are connected in parallel to form the energy source of the locomotive control circuit. Once the battery fails, it will be impossible to maintain the normal use of the lighting, wireless communication devices and emergency devices in the car, which will endanger the lives and property of passengers. will pose a great threat. Through investigation, it is found that the batteries used in high-speed railway vehicles are mostly alkaline nickel-cadmium batteries, which are generally replaced according to the number of running kilometers or service life in actual use. At this time, the battery life often has a large margin, and replacement in advance will ...

AI Technical Summary

Problems solved by technology

[0006] In view of this, the present invention provides a battery life prediction method and storage medium based on predictive model fusion to solve the problem of establishing an accurate battery life due to excessive reliance on failure mechanisms when predicting the battery life based on a single particle filter model. The problem of the relatively difficult degradation model, and the problem of solving the uncertainty of the output of the prediction model based on a single neural network, and at the same time solve the technical problem that the existing technology cannot update the model online

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