AI optimized charging pile charging curve dynamic adjustment energy efficiency improvement system

Abstract

The invention discloses an AI-optimized charging pile charging curve dynamic adjustment energy efficiency improvement system, and relates to the fields of artificial intelligence, intelligent charging, energy management and new energy automobiles. Aiming at the problems that a fixed charging curve of a traditional charging pile is difficult to adapt to dynamic factors, the energy efficiency is low and the service life of a battery is influenced, the system collects multi-source data such as a vehicle battery state, environmental parameters and power grid loads in real time, dynamically optimizes the charging curve through an AI algorithm, accurately adjusts output parameters of the charging pile, and is matched with energy efficiency analysis feedback and user interaction functions. Experiments show that the average charging energy efficiency is improved by 12%, the unit energy consumption is reduced by 11%, the charging time is shortened by 8%, and the health degree of the battery is improved. The system has been popularized and applied in charging operation enterprises, smart parks and the like, the charging efficiency is improved, the battery life is prolonged, and the user experience is optimized.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence, intelligent charging, energy management and new energy vehicle technology, specifically to a system that uses AI algorithms to dynamically optimize and adjust the charging curve of a charging pile to improve energy efficiency, which falls under the category of intelligent charging and energy efficiency management technology. Background Technology

[0002] With the popularization of new energy vehicles, charging piles have been widely deployed as their infrastructure. Traditional charging piles mostly use fixed or preset charging curves, which are difficult to adjust in real time according to dynamic factors such as different vehicle battery status, ambient temperature, and grid load. This results in low charging efficiency, high energy consumption, and a significant impact on battery life. Although some existing systems support simple charging power adjustment, they lack intelligent optimization capabilities based on big data and AI, and cannot achieve refined management of the charging process and maximize energy efficiency. Summary of the Invention

[0003] This invention proposes an AI-optimized charging pile charging curve dynamic adjustment energy efficiency improvement system. By collecting multi-source data such as vehicle battery status, environmental parameters, and grid load in real time, it utilizes artificial intelligence algorithms to dynamically optimize the charging curve, achieving adaptive adjustment and energy efficiency improvement during the charging process. The system includes a data acquisition module, an AI charging curve optimization module, a charging control module, an energy efficiency analysis and feedback module, and a user interaction platform. This system can intelligently adjust the charging strategy according to actual conditions, improving charging efficiency, extending battery life, and reducing energy consumption.

Claims

1. An AI-optimized charging pile charging curve dynamic adjustment energy efficiency improvement system, characterized in that, include: The multi-source data acquisition module is used to collect vehicle battery status parameters (including SOC, temperature, and health), environmental parameters (including ambient temperature and humidity), grid load parameters, and historical charging data in real time. The AI ​​charging curve optimization module is used to dynamically generate the optimal charging curve based on the multi-source data using artificial intelligence algorithms (including but not limited to machine learning, deep learning, etc.) to achieve intelligent adjustment of charging current and voltage. The charging control module is used to adjust the output parameters of the charging pile in real time and precisely control the charging process based on the optimal charging curve output by the AI ​​charging curve optimization module. The energy efficiency analysis and feedback module is used to perform energy efficiency analysis on each charging process, evaluate energy consumption, charging efficiency and impact on battery life, and feed the analysis results back to the AI ​​charging curve optimization module to continuously optimize the charging strategy. The user interaction platform is used to display information such as charging status, energy efficiency reports, and optimization suggestions to users and maintenance personnel, and supports user interaction operations.

2. The system according to claim 1, characterized in that, The multi-source data acquisition module can interface with various external systems such as vehicle BMS, power grid management system, and environmental monitoring equipment to achieve automatic data acquisition and synchronization.

3. The system according to claim 1, characterized in that, The AI ​​charging curve optimization module can adaptively adjust the charging curve according to different vehicle models, battery types, and environmental conditions to achieve personalized charging optimization.

4. The system according to claim 1, characterized in that, The energy efficiency analysis and feedback module can perform multi-dimensional statistics and analysis on indicators such as charging energy efficiency, unit energy consumption, and changes in battery health, and automatically generate suggestions for improving energy efficiency.

5. The system according to claim 1, characterized in that, The user interaction platform supports real-time monitoring of the charging process, querying of historical charging data, downloading of energy efficiency reports, and personalized optimization suggestions.