METHOD AND SYSTEM FOR ESTIMATING STATE OF CHARGE IN BATTERY CLUSTERS, ELECTRONIC DEVICE, AND STORAGE MEDIA
Dossier Overview
Applicant
Shanghai Makesens Energy Storage Technology Co., Ltd.
Inventor
Peng DING; Qiong WEI; Enhai ZHAO; Danfei GU; Pingchao HAO; Pei SONG; Xiao YAN; Jie ZHANG
IPC Classification
CPC Classification
A method and a system for estimating a state of charge of a battery cluster, an electronic device, and a storage media are provided. The method comprises acquiring target data related to the state of charge; estimating, by an ampere-hour integration method, the state of charge based on the target data, to obtain a first estimated value; inputting the target data into a state-of-charge prediction model to estimate the state of charge and obtain a second estimated value, wherein the prediction model is obtained by training based on sample data; and determining a final estimated value of the state of charge based on the first estimated value, the second estimated value, and a first distance between the target data and the sample data. The method combines the ampere-hour integration method and the prediction model to estimate the state of charge, effectively improving the accuracy of the state of charge estimation.
Original (Chinese)
A method and a system for estimating a state of charge of a battery cluster, an electronic device, and a storage media are provided. The method comprises acquiring target data related to the state of charge; estimating, by an ampere-hour integration method, the state of charge based on the target data, to obtain a first estimated value; inputting the target data into a state-of-charge prediction model to estimate the state of charge and obtain a second estimated value, wherein the prediction model is obtained by training based on sample data; and determining a final estimated value of the state of charge based on the first estimated value, the second estimated value, and a first distance between the target data and the sample data. The method combines the ampere-hour integration method and the prediction model to estimate the state of charge, effectively improving the accuracy of the state of charge estimation.
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