Electric vehicle residual charging time prediction method and system based on cloud sparse charging data
A technology of charging time and sparse data, applied in the field of electric vehicles, can solve the problems such as the inability to guarantee the full coverage of the battery life cycle by data samples, the inability to accurately predict the remaining charging time of the vehicle, and the jumping of the remaining charging time, so as to avoid uncertainty. and low life cycle coverage problems, improve user experience and vehicle brand competitiveness, and determine the effect of travel plans
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[0070] The remaining charging time prediction method of the electric vehicle full life cycle based on the cloud sparse charging data provided in accordance with the present invention includes the following steps:
[0071] Step 1: Get the battery charging test data, preprocessing the sparse input data set after cleaning; the predictive charging time adaptive network model, division training set and test set based on pre-treatment data, and cross-validation of the model and the model Statistical assessment, eventually, the battery predictive charging time adaptive network model is arranged in the cloud, specifically see figure 1 .
[0072] Step 1.1: Use a large number of cyclic test data (or based on this model battery, based on this model battery), each cycle test discharge portion guarantees the same magnification from full charge to zero charge, each cycle test charging portion is guaranteed With the same charging strategy, the charging strategy of each batte...
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