Lithium battery remaining charging time prediction method for guaranteeing electrical safety

Abstract

The invention discloses a lithium battery remaining charging time prediction method for guaranteeing electrical safety. The lithium battery remaining charging time prediction method is characterized in that: detection data are obtained through a charging data detection step; a battery charge state model prediction step and a remaining charging time model prediction step are used for acquiring battery charge states and outputting the remaining charging time through model prediction; in a battery charge increment comparison step, the battery charge states in the battery charge state model prediction step are compared to obtain a result; and in a model retraining step, models in the battery charge state model prediction step and the remaining charging time model prediction step are iteratively updated according to the result. The lithium battery remaining charging time prediction method has the advantages that the prediction model adaptively matches the aging state of a lithium battery, calculation resources are reasonably distributed at the local end and the cloud end, the hardware dependence is low and the like, the electrical safety of lithium battery charging is ensured through accurate prediction of the remaining charging time, and the application is wide.

Description

technical field [0001] The invention relates to a method for predicting the remaining charging time of a lithium battery, in particular to a method for predicting the remaining charging time of a lithium battery to ensure electrical safety. Background technique [0002] Lithium batteries have been widely used in many industries due to their small size, light weight, and low energy density. For example, in the electric vehicle industry (including electric cars and electric bicycles), with the increasing congestion of urban roads and the country's active promotion of new energy vehicles, the penetration rate of electric vehicles is getting higher and higher. Against the above background, the demand for charging lithium batteries of electric vehicles is also increasing. [0003] During the charging process of lithium batteries, the prediction of the remaining charging time has strong practical significance, including the following aspects: [0004] For electrical safety, accu...

AI Technical Summary

Problems solved by technology

Among the above methods, the open circuit voltage method needs to interrupt the charging process, so it is actually difficult to accurately measure the open circuit voltage, so it is difficult to estimate the remaining charging time

The ampere-hour integration method is suitable for calculating the increase of battery SOC from one time point to another time point, but because the initial SOC is unknown, the ampere-hour integration method is also difficult to determine the accurate current battery SOC value

The model-based method and data-driven method need to be calculated based on a large amount of data, so they are not suitable for local operation of the charging device, and must rely on the cloud with stronger computing resources.

[0011] Second, predictive models cannot track battery aging status in real time

The above method, even if the battery SOC and remaining charging time are accurately predicted at a certain point in time, because the battery will age during use, there will be differences between the obtained prediction model and the battery aging state, resulting in the prediction model error amplification

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