A system for controlling charging based on new energy, comprising a power grid charging module, a power generating module, an interaction module, a charging interface, a control module, and a heat dissipation device, wherein:
- the power grid charging module includes a first circuit connected with a power grid, and the power grid charging module is configured to deliver first electrical energy provided by the power grid to the charging interface;
- the power generating module is configured to provide second electrical energy and deliver the second electrical energy to the charging interface;
- the interaction module is in communication connection with the control module, and the interaction module is configured to obtain a charging request;
- the charging interface is in communication connection with the control module;
- the charging interface is configured to transfer at least one of the first electrical energy or the second electrical energy delivered by at least one of the power grid charging module and the power generating module to charge the electric vehicle in response to receiving a charging instruction;
- the heat dissipation device is connected with the power grid, and the heat dissipation device is in communication connection with the control module;
- the heat dissipation device is configured to dissipate heat from at least one of the power generating module and the charging interface;
- and
- the control module is configured to:
- in response to obtaining the charging request, determine the charging instruction and send the charging instruction to the charging interface based on the charging request;
- wherein the charging instruction includes adopting at least one of the power grid charging module and the power generating module for charging;
- determine heat dissipation efficacy data based on first operation information of a converter included in the charging interface, second operation information of a battery, power generating module information, a candidate heat dissipation parameter, and current electricity price information via an efficacy determination model, wherein the efficacy determination model is a machine learning model;
- the efficacy determination model is obtained through training on a training set, a validation set, and a test set, wherein the training set, the validation set, and the test set include a data set consisting of first operation information of sample converters, second operation information of sample batteries, sample power generating module information, sample candidate heat dissipation parameters, and sample current electricity price information for a predetermined time period drawn from historical data, wherein:
- data volumes of the training set, the validation set, and the test set constitute a first predetermined ratio with no data crossover;
- and
- a sample statistical difference in the training set is greater than a predetermined difference threshold, and the predetermined difference threshold relates to a statistical value of actual heat dissipation efficacy data;
- determine the heat dissipation parameter of the heat dissipation device based on the heat dissipation efficacy data;
- and
- control the heat dissipation device to operate to dissipate heat from at least one of the power generating module and the charging interface based on the heat dissipation parameter, wherein the heat dissipation parameter includes heat dissipation power, heat dissipation duration, and target temperature.