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CN-122022290-A - Energy supplementing management method and system

CN122022290ACN 122022290 ACN122022290 ACN 122022290ACN-122022290-A

Abstract

The embodiment of the invention relates to the technical field of new energy automobiles and discloses an energy supplementing management method and system, wherein the method comprises the steps of obtaining multi-mode data, wherein the multi-mode data comprises battery state data collected by a vehicle, vehicle environment data, charging environment data collected by charging equipment, power grid data collected by a power grid platform and user demand data uploaded by a user terminal; the method comprises the steps of preprocessing data of multi-mode data to obtain target multi-mode data, analyzing energy supplementing strategies based on a preset strategy decision model and the target multi-mode data to obtain optimal energy supplementing strategies, and sending the optimal energy supplementing strategies to a vehicle and charging equipment to enable the vehicle and the charging equipment to execute self-adaptive energy supplementing management, so that the problem that energy supplementing efficiency is poor due to the fact that a traditional energy supplementing mode cannot adapt to a changeable energy supply and demand relationship can be avoided, and accurate and efficient energy supplementing management is achieved by means of fusion of the multi-mode data and deep analysis of the energy supplementing strategies by combining the preset strategy decision model.

Inventors

  • CAI DONGHONG

Assignees

  • 阿维塔科技(重庆)股份有限公司

Dates

Publication Date
20260512
Application Date
20260115

Claims (10)

  1. 1. The energy supplementing management system comprises a cloud server, a vehicle interacting with the cloud server, charging equipment, a power grid platform and a user terminal, the energy supplementing management method is applied to a cloud server and is characterized by comprising the following steps: Acquiring multi-mode data, wherein the multi-mode data comprises battery state data acquired by the vehicle, vehicle environment data, charging environment data acquired by the charging equipment, power grid data acquired by the power grid platform and user demand data uploaded by the user terminal; Performing data preprocessing on the multi-modal data to obtain target multi-modal data; Analyzing the energy supplementing strategy based on a preset strategy decision model and the target multi-mode data to obtain an optimal energy supplementing strategy; And sending the optimal energy supplementing strategy to the vehicle and the charging equipment so as to enable the vehicle and the charging equipment to execute adaptive energy supplementing management.
  2. 2. The method of claim 1, wherein the sending the optimal energy replenishment strategy to the vehicle and charging device to cause the vehicle and charging device to perform adaptive energy replenishment management comprises: when the optimal energy supplementing strategy is quick charging, determining the maximum safe charging power according to the maximum output voltage and the maximum output current corresponding to the charging equipment and the charging parameters corresponding to the vehicle; determining output voltage and current corresponding to the charging equipment based on the maximum safe charging power; And generating a control instruction according to the output voltage and the current corresponding to the charging equipment, and sending the control instruction to the vehicle and the charging equipment so as to enable the vehicle and the charging equipment to execute self-adaptive energy supplementing management.
  3. 3. The method of claim 2, wherein the transmitting the optimal energy replenishment strategy to the vehicle and charging device to cause the vehicle and charging device to perform adaptive energy replenishment management further comprises: If the battery temperature in the battery state parameter is greater than a preset safety threshold, reducing the output power of the charging equipment in the optimal energy supplementing strategy to obtain an adjusted energy supplementing strategy; and sending the adjusted energy supplementing strategy to the charging equipment so that the charging equipment reduces output power according to the adjusted energy supplementing strategy.
  4. 4. The method of claim 1, wherein the analyzing the energy replenishment policy based on the preset policy decision model and the target multi-modal data to obtain an optimal energy replenishment policy comprises: extracting power grid load and real-time electricity price from the power grid data; Predicting charging parameters corresponding to the charging equipment and target battery electric quantity according to the battery state data, the vehicle environment data, the power grid load, the real-time electricity price and the departure time set by a user in the user demand data to obtain a prediction result; And analyzing the energy supplementing strategy based on a preset strategy decision model and the prediction result to obtain an optimal energy supplementing strategy.
  5. 5. The method of claim 1, wherein the analyzing the energy replenishment policy based on the preset policy decision model and the target multi-modal data to obtain an optimal energy replenishment policy comprises: Planning a driving route based on user demand data in the target multi-mode data to obtain route planning information; Predicting the energy demand based on the route planning information to obtain an energy demand prediction result; determining target multi-mode data according to the energy demand prediction result; And analyzing the energy supplementing strategy based on a preset strategy decision model and the target multi-mode data to obtain an optimal energy supplementing strategy.
  6. 6. The method of claim 5, wherein planning the driving route based on the user demand data in the target multi-modal data to obtain route planning information comprises: determining departure time and destination information according to user demand data in the target multi-mode data; And planning a driving route based on the departure time and the destination information to obtain route planning information.
  7. 7. An energy replenishment management method applied to a vehicle interacting with a cloud server, the method comprising: Uploading the collected battery state data and vehicle environment data to the cloud server; receiving an optimal energy supplementing strategy generated by the cloud server based on the battery state data and the vehicle environment data; and executing self-adaptive energy supplementing management according to the optimal energy supplementing strategy.
  8. 8. The energy supplementing management system is characterized by comprising a cloud server, a vehicle, charging equipment, a power grid platform and a user terminal, wherein the vehicle interacts with the cloud server; the cloud server is used for acquiring multi-mode data, wherein the multi-mode data comprises battery state data acquired by the vehicle, vehicle environment data, charging environment data acquired by the charging equipment, power grid data acquired by the power grid platform and user demand data uploaded by the user terminal; The cloud server is further used for carrying out data preprocessing on the multi-modal data to obtain target multi-modal data; the cloud server is further used for analyzing the energy supplementing strategy based on a preset strategy decision model and the target multi-mode data to obtain an optimal energy supplementing strategy; The cloud server is further configured to send the optimal energy compensating policy to the vehicle and the charging device, so that the vehicle and the charging device execute adaptive energy compensating management.
  9. 9. The energy management system of claim 8, wherein the system further comprises a photovoltaic power generation device and an energy storage device; The photovoltaic power generation equipment is used for charging the energy storage equipment preferentially when the photovoltaic power generation output power is larger than the load demand power and the power grid load acquired by the power grid platform is lower than a preset threshold value; The energy storage equipment is used for preferentially supplying power to the load when the photovoltaic power generation output power is smaller than the load demand power or the power grid load acquired by the power grid platform is higher than the preset threshold value.
  10. 10. The energy replenishment management system as recited in claim 8, wherein the controller in the vehicle is configured to obtain a current vehicle battery temperature upon receiving an optimal energy replenishment policy issued by the cloud server; and the controller in the vehicle is also used for preheating and controlling the vehicle battery when the current vehicle battery temperature is lower than a preset optimal charging temperature.

Description

Energy supplementing management method and system Technical Field The embodiment of the invention relates to the technical field of new energy automobiles, in particular to an energy supplementing management method and system. Background Conventional energy replenishment management techniques, when faced with complex real-world scenarios, expose a number of drawbacks. In terms of data utilization, it is difficult to fully mine and utilize a large amount of data acquired in real time. In terms of decision accuracy, a fixed algorithm is difficult to adapt to complex and changeable energy supply and demand relations, so that energy supplementing efficiency is poor. Disclosure of Invention In view of the above problems, embodiments of the present invention provide an energy supplementing management method and system, which are used to solve the problem that the traditional energy supplementing mode cannot adapt to the changeable energy supply and demand relationship, resulting in poor energy supplementing efficiency. According to one aspect of the embodiment of the invention, the energy supplementing management system comprises a cloud server, a vehicle interacting with the cloud server, a charging device, a power grid platform and a user terminal, wherein the energy supplementing management method is applied to the cloud server and comprises the following steps: Acquiring multi-mode data, wherein the multi-mode data comprises battery state data acquired by the vehicle, vehicle environment data, charging environment data acquired by the charging equipment, power grid data acquired by the power grid platform and user demand data uploaded by the user terminal; Performing data preprocessing on the multi-modal data to obtain target multi-modal data; Analyzing the energy supplementing strategy based on a preset strategy decision model and the target multi-mode data to obtain an optimal energy supplementing strategy; And sending the optimal energy supplementing strategy to the vehicle and the charging equipment so as to enable the vehicle and the charging equipment to execute adaptive energy supplementing management. According to another aspect of the embodiment of the present invention, there is provided an energy replenishment management method applied to a vehicle interacting with a cloud server, the method including: Uploading the collected battery state data and vehicle environment data to the cloud server; receiving an optimal energy supplementing strategy generated by the cloud server based on the battery state data and the vehicle environment data; and executing self-adaptive energy supplementing management according to the optimal energy supplementing strategy. According to another aspect of the embodiment of the invention, an energy supplementing management system is provided, and the system comprises a cloud server, a vehicle interacting with the cloud server, charging equipment, a power grid platform and a user terminal; the cloud server is used for acquiring multi-mode data, wherein the multi-mode data comprises battery state data acquired by the vehicle, vehicle environment data, charging environment data acquired by the charging equipment, power grid data acquired by the power grid platform and user demand data uploaded by the user terminal; The cloud server is further used for carrying out data preprocessing on the multi-modal data to obtain target multi-modal data; the cloud server is further used for analyzing the energy supplementing strategy based on a preset strategy decision model and the target multi-mode data to obtain an optimal energy supplementing strategy; The cloud server is further configured to send the optimal energy compensating policy to the vehicle and the charging device, so that the vehicle and the charging device execute adaptive energy compensating management. The embodiment of the invention acquires multi-modal data, wherein the multi-modal data comprises battery state data acquired by a vehicle, vehicle environment data, charging environment data acquired by charging equipment, power grid data acquired by a power grid platform and user demand data uploaded by a user terminal, performs data preprocessing on the multi-modal data to obtain target multi-modal data, analyzes an energy supplementing strategy based on a preset strategy decision model and the target multi-modal data to obtain an optimal energy supplementing strategy, and sends the optimal energy supplementing strategy to the vehicle and the charging equipment so as to enable the vehicle and the charging equipment to execute self-adaptive energy supplementing management, thereby avoiding the problem that the energy supplementing efficiency is poor due to the fact that the traditional energy supplementing mode cannot adapt to a changeable energy supply and demand relation. The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and may be implemented accordin