Electric vehicle lithium battery residual life prediction method based on XGBoost-LSTM optimization model

Abstract

The invention relates to the technical field of electric vehicle lithium batteries, and discloses an electric vehicle lithium battery residual life prediction method based on an XGBoost-LSTM optimization model, and the method comprises an electric vehicle lithium battery information online collection technology. Experiments are carried out through actual electric vehicle charging data, classification can be carried out based on the appropriate data size, different and appropriate training models are selected, the RUL prediction problem in the electric vehicle lithium battery coverage total attenuation process is solved, and the method has remarkable significance in improving the long-term prediction performance and prediction accuracy of the battery RUL and has good application prospects. The accumulated feature influence and the meta-reinforcement learning algorithm are introduced into battery RUL prediction, battery health state information hidden in the accumulated features and the change rule of the battery health state information are fully excavated, meanwhile, the high small sample learning capacity of the meta-reinforcement learning algorithm is exerted, and high-precision prediction can be achieved in the whole life process of the battery.

Description

technical field [0001] The invention relates to the technical field of lithium batteries for electric vehicles, in particular to a method for predicting the remaining life of lithium batteries for electric vehicles based on an XGBoost-LSTM optimization model. Background technique [0002] Electric vehicles usually use power batteries as the device and source of energy storage. The existing power batteries are usually chemical batteries. After a period of charging and discharging, the maximum capacity of the battery will decrease. Usually, the battery health (State Of Health, SOH) refers to the ratio of the capacity of the battery after being fully charged to the rated capacity. SOH can reflect the current state of health of the battery. Obtaining the SOH of the lithium battery of the electric vehicle can effectively help calculate the remaining life of the electric vehicle and the remaining power of the power battery. Remaining Useful Life (RUL) refers to the number of cycle...

AI Technical Summary

Problems solved by technology

[0004] 1. Algorithms for early life prediction based on a small amount of data are very important to prevent battery failure. However, the amount of training data for most prediction algorithms accounts for 40%-70% of the total data. Therefore, it is necessary to develop a small sample training algorithm to extract more effective batteries. The health characteristic factor is the key and challenge to realize early prediction. The prediction accuracy of the battery life model obtained by fitting the capacity decline data not only depends on the accuracy of the life model itself, but also is limited by the amount of data. Too much data will increase the The time cost and complexity of large RUL prediction, insufficient data will lead to inaccurate prediction models, in addition, most models can only have high accuracy and strong feasibility in short-term prediction when predicting lithium battery RUL , the long-term prediction performance needs to be improved by adjusting parameters, data size, or combining multiple models. Noise data will be generated during the data collection process. The prediction accuracy can be improved by improving the detection standard and processing the noise data. The prediction model The accuracy of is also attributed to various influencing factors that are complex and mutually coupled;

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