CN-122008938-A - Intelligent charging pile system and charging control method

CN122008938ACN 122008938 ACN122008938 ACN 122008938ACN-122008938-A

Abstract

The application discloses an intelligent charging pile system and a charging control method, and relates to the field of charging piles, wherein the intelligent charging pile system comprises a data acquisition module, a health evaluation module and a charging strategy optimization module; the system comprises a data acquisition module, a health evaluation module and a charging strategy optimization module, wherein the data acquisition module is used for acquiring basic state information of a battery of an electric automobile in real time in the charging process of the electric automobile and extracting features of the basic state information to obtain health features, the health evaluation module is used for processing the health features by adopting a deep learning model to obtain a battery health state value and a residual life prediction value of the battery in real time, and the charging strategy optimization module is used for adjusting charging parameters in real time based on the battery health state value of the battery in real time.

Inventors

LI YUAN
WANG JINGJING

Assignees

内蒙古工业大学

Dates

Publication Date: 20260512
Application Date: 20260317

Claims (10)

1. An intelligent charging stake system, characterized in that, the intelligent charging stake system includes: the system comprises a data acquisition module, a health evaluation module and a charging strategy optimization module; The data acquisition module is used for acquiring basic state information of the battery of the electric vehicle in real time in the charging process of the electric vehicle, and extracting features of the basic state information to obtain health features; The health evaluation module is used for processing the health characteristics by adopting a deep learning model to obtain a battery real-time battery health state value and a residual life prediction value; the charging strategy optimization module is used for adjusting the charging parameters in real time based on the battery state of health value in real time.
2. The intelligent charging stake system of claim 1, wherein the basic status information of the battery includes a voltage, a current and a temperature of the battery, and the data acquisition module includes: The device comprises a voltage acquisition unit, a current Hall sensor and a temperature sensor interface; the voltage acquisition unit is used for acquiring a voltage curve of the battery in real time; the current Hall sensor is used for collecting a current curve of the battery in real time; the temperature sensor interface is used for collecting the temperature of each sampling point of the battery in real time; The filtering module is used for filtering the basic state information of the battery; the feature extraction module is used for extracting features of the filtered basic state information of the battery.
3. The intelligent charging stake system of claim 1, wherein the deep learning model includes a shared encoder, a summing module, and a ELASTIC NET regularized linear regression model and a Micro-transducer module in parallel, wherein an output of the ELASTIC NET regularized linear regression model and a first output of the Micro-transducer module are connected through the summing module, and a second output of the Micro-transducer module is connected with the shared encoder.
4. The intelligent charging stake system of claim 1, further comprising a bi-directional communication and protocol adapter module, wherein the electric vehicle battery management system is coupled to the data collection module via the bi-directional communication and protocol adapter module.
5. The intelligent charging pile system according to claim 1, further comprising a cloud data management platform, wherein the data acquisition module, the health assessment module and the charging strategy optimization module are all connected with the cloud data management platform.
6. The intelligent charging pile system according to claim 2, wherein the feature extraction module comprises a time domain feature extraction sub-module and a temperature feature extraction sub-module; The time domain feature extraction submodule is used for processing the voltage curve and the current curve of the battery after filtering to obtain the peak value, the slope and the integral area of the voltage curve and the peak value, the slope and the integral area of the current curve; the temperature characteristic extraction submodule is used for processing the temperature of each sampling point of the battery after filtering to obtain the temperature rise rate and the thermal distribution gradient.
7. The intelligent charging stake system of claim 1, wherein the charging parameters are adjusted in real time based on a battery real-time battery state of health value, specifically: If the battery health state value is more than or equal to a first set threshold value, adjusting the charging power to be the maximum allowable current, and adjusting the charging power to be full power; If the second set threshold value is less than or equal to the battery health state value < the first set threshold value, the charging current is adjusted to be 0.8 times of the standard value, and the charging power is adjusted to be 90% of the full power; If the battery state of health value is less than the second set threshold, the charging current is adjusted to be 0.7 times of the standard value, and the charging power is adjusted to be 70% of the full power.
8. The intelligent charging pile system according to claim 4, wherein the two-way communication and protocol adaptation module comprises three parallel signal monitoring channels, a first signal monitoring channel collects CP-PWM signals of GB/T protocol, a second signal monitoring channel collects PLC-SLAC initialization signals of CCS protocol, and a third signal monitoring channel collects basic CAN frames in the CHAdeMO standard.
9. The intelligent charging stake system of claim 3, wherein the shared encoder is a single layer MLP.
10. A charging control method, characterized in that the charging control method is applied to the intelligent charging pile system according to any one of claims 1 to 9, the charging control method comprising: Acquiring basic state information of a battery of the electric automobile in real time in the charging process of the electric automobile, and extracting features of the basic state information to obtain health features; processing the health characteristics by adopting a deep learning model to obtain a battery health state value and a residual life prediction value of the battery in real time; and adjusting the charging parameters in real time based on the real-time battery state of health value of the battery.

Description

Intelligent charging pile system and charging control method Technical Field The application relates to the field of charging piles, in particular to an intelligent charging pile system and a charging control method. Background The current charging pile has a single function, only supplies power and does not diagnose, and the current main current charging pile only realizes energy transmission and cannot sense the aging state of the battery. In addition, the existing technology has three defects of detection and charge fracture, algorithm and hardware disconnection, no feedback in diagnosis and the like, and cannot meet the infrastructure requirements of plug and play, charge and diagnosis and charge coordination and the like. Disclosure of Invention The application aims to provide an intelligent charging pile system and a charging control method, which can meet the requirements of plug-and-play, charge-and-diagnosis-and-charge coordination. In order to achieve the purpose, the application provides a scheme that in the first aspect, the application provides an intelligent charging pile system, which comprises a data acquisition module, a health evaluation module and a charging strategy optimization module. The data acquisition module is used for acquiring basic state information of the battery of the electric vehicle in real time in the charging process of the electric vehicle, and extracting features of the basic state information to obtain health features. The health evaluation module is used for processing the health characteristics by adopting the deep learning model to obtain a battery health state value and a residual life prediction value of the battery in real time. The charging strategy optimization module is used for adjusting the charging parameters in real time based on the battery state of health value in real time. In a second aspect, the application provides a charging control method applied to the intelligent charging pile system, and the charging control method comprises the steps of acquiring basic state information of an electric vehicle battery in real time in the charging process of the electric vehicle, and extracting features of the basic state information to obtain health features. And processing the health characteristics by adopting a deep learning model to obtain a battery health state value and a residual life predicted value of the battery in real time. And adjusting the charging parameters in real time based on the real-time battery state of health value of the battery. According to the specific embodiment of the application, the intelligent charging pile system and the charging control method have the technical effects that the data acquisition module is used for acquiring basic state information of the battery of the electric automobile in real time in the charging process of the electric automobile and extracting features of the basic state information to obtain health features, the health assessment module is used for processing the health features by adopting a deep learning model to obtain a battery health state value and a residual life prediction value in real time, and the state of the lithium battery can be monitored accurately in real time while charging, so that the requirements of plug-and-play charging and instant diagnosis are met. The charging strategy optimization module is used for adjusting the charging parameters in real time based on the battery health state value in real time, and adjusting the charging parameters in real time based on the real-time feedback of SOH so as to realize the requirements of diagnosis, charging and coordination, and prolong the service life of the battery while ensuring the charging speed. Drawings In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Fig. 1 is a flowchart of a use and operation process of an intelligent charging pile system according to an embodiment of the present application. Fig. 2 is a flowchart of a fast protocol adaptive recognition method according to an embodiment of the present application. Fig. 3 is a flow chart of an internal process of the intelligent charging pile system according to the embodiment of the application. Detailed Description The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of