Notebook computer residual electric quantity estimation method based on improved Elman neural network

Abstract

The invention discloses a notebook computer residual electric quantity estimation method based on an improved Elman neural network, and the method is suitable for estimating the residual electric quantity of a battery when a notebook computer enters a dormant state, and comprises the steps: constructing an original data set; preprocessing the data set; dividing the data set; constructing a neural network model structure; training a neural network model; optimizing the neural network model; evaluating the neural network model and embedding the neural network model into the battery management system; estimating the residual electric quantity of the notebook computer. Compared with the prior art, the method has the advantages that the number of the input data is increased, so that the adverse effect of single data on the residual electric quantity estimation precision is avoided, the attention mechanism layer is established to perform reasonable weight distribution on the input characteristic data, and the problem of distraction of the model is effectively solved, so that the accuracy of an estimation result is improved; and the influence of the change of the discharge current of the battery on the residual electric quantity estimation precision is avoided.

Description

technical field [0001] The present invention relates to the field of battery technology, and more particularly, relates to a method for estimating the remaining power of a notebook computer based on an improved Elman neural network. Background technique [0002] Notebook computers are important portable office devices with limited battery power. Therefore, accurate estimation of the remaining battery power is the key to the battery management system of the notebook computer and the user's reasonable arrangement of computer running tasks and time, which helps to overcome "battery anxiety". [0003] At present, the methods for estimating the remaining battery capacity mainly include the ampere-hour integration method, the open circuit voltage method, the Kalman filter method and the data-driven method. Due to the strong computing power of the notebook computer itself, the estimation of the remaining battery power based on data has a good application prospect. The existing da...

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Problems solved by technology

The existing data-driven method for estimating the remaining battery capacity generally uses the voltage, current, and temperature of the battery at a certain moment as the input of the neural network to estimate the remaining battery capacity, but this method has the following problems: ① The amount of input data If it is too small, the measurement error of a single quantity will have a great impact on the estimation accuracy of the remaining power; ②The weight distribution of the input characteristic data is not considered, which is not conducive to the improvement of the estimation accuracy; ③The timing dependence is serious, and it is difficult to change the discharge current Accurately estimate the remaining capacity of the battery

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