An Adaptive Electric Vehicle SOC Estimation Method Based on Big Data

Abstract

The invention discloses an adaptive electric vehicle SOC estimation method based on big data. The time, longitude, latitude, SOC value, vehicle speed, total mileage value of the odometer, and total output current of the battery pack are collected in advance from the vehicle. and the total output voltage of the battery pack as the training data set, calculate the acceleration, distance, and energy consumption values ​​according to the time, speed, latitude and longitude, current, and voltage values ​​in the training data set, and then calculate the characteristic speed, acceleration, distance, and dependent variable energy consumption values It is used to construct an extremely random decision tree model, and then the SOC prediction model based on mileage and energy consumption is obtained from the total mileage value, energy consumption value and SOC value of the odometer, so that the SOC prediction model based on mileage and energy consumption is obtained according to the genetic algorithm. The SOC prediction model, every T time, the model will update the data in the training data set, so as to achieve the effect of adaptive prediction.

Description

technical field [0001] The invention relates to the field of electric vehicle SOC estimation, in particular to an adaptive electric vehicle SOC estimation method based on big data. Background technique [0002] In recent years, with the rapid development of lithium-ion battery technology, the status of electric vehicles is increasing day by day. However, due to the obvious nonlinear and time-varying characteristics of the state of charge (SOC) of lithium-ion batteries, the prediction of SOC has always been a key and difficult point in the field of electric vehicles. Therefore, the driver's mileage anxiety phenomenon often occurs. And the long-term SOC prediction is also of great significance to intelligent transportation, unmanned driving and other aspects. [0003] At present, there are mainly three methods commonly used to estimate SOC: the first is the estimation method based on the battery model represented by the ampere integral method, the open circuit voltage method...

AI Technical Summary

Problems solved by technology

However, due to the obvious nonlinear and time-varying characteristics of the state of charge (SOC) of lithium-ion batteries, the prediction of SOC has always been a key and difficult point in the field of electric vehicles. Therefore, the driver's mileage anxiety often occurs

When using the first estimation method based entirely on the battery model, the error of this type of method will gradually accumulate as the forecast time span grows, so the forecast result may have a large error; the recursive algorithm relies on the battery model, And with the increase of the single prediction time span, the prediction accuracy rate drops significantly, and the uncertainty of the recursive algorithm may continue to accumulate during the calculation process, which may seriously affect the results and even cause the algorithm to diverge; the third type of machine learning method is insufficient The advantage is that a large amount of data support is required, the amount of calculation is large, and the algorithm model is not easy to be trained

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