EV Battery SOH Control via Charging Station AI Feedback
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Solution Overview
Problem
Existing battery management systems fail to efficiently monitor and update charging/discharging control logic based on the degradation state of electric vehicle batteries, leading to uneven performance degradation and reduced service life.
Innovation Solution
A battery performance management system that collects and analyzes big data from electric vehicle charging stations to diagnose battery health (SOH) using artificial intelligence, and updates control factors for charging/discharging based on this data, optimizing battery operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized monitoring and AI-based SOH diagnosis is implemented, then battery service life is extended and management accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
A remote server acts as an intermediary between charging stations and battery management systems. The server collects performance evaluation information from multiple charging stations, processes data using AI models to diagnose SOH, and sends control factors back to individual vehicles. This distributes computational complexity to a centralized intermediary rather than requiring complex systems in each vehicle or charging station.
Solution Approach 2:
The system enables batteries to essentially self-diagnose and self-optimize. By collecting performance data during normal charging operations and using AI models to determine SOH and optimal control factors, the battery management system automatically adjusts charging/discharging parameters without requiring manual intervention or complex real-time monitoring infrastructure at each location.
2Measurement precision
If performance evaluation information is collected from multiple charging stations, then diagnosis accuracy is improved, but data collection and processing time increases
Solution Approach 1:
Performance evaluation information is collected during routine charging operations at charging stations, rather than requiring separate diagnostic sessions. The system accumulates data from multiple charging events over time, performing preliminary data gathering during normal battery usage. This eliminates the need for dedicated diagnostic time while building up a comprehensive dataset for accurate SOH diagnosis.
3Productivity
If control factors are updated based on SOH changes, then battery performance is optimized, but frequency of updates increases system complexity
Solution Approach 1:
The system implements a feedback mechanism where SOH diagnosis results directly trigger control factor updates. The remote server continuously monitors performance evaluation information, diagnoses SOH changes, and automatically adjusts control factors for charging/discharging operations. This closed-loop feedback ensures performance optimization while automating the update process to manage complexity.
Data Source
AI summary
A battery performance management system and method using an electric vehicle charging station. The battery performance management server collects battery performance evaluation information including identification information and operation characteristic accumulative information of a battery, identification information and driving characteristic accumulative information of the electric vehicle, and latest charging characteristic information of the battery from a plurality of charging stations through a network. The server determines a current state of health (SOH) corresponding to the collected battery performance evaluation information by using an artificial intelligence model that is trained in advance to receive the battery performance evaluation information and output a SOH of the battery. The server determines a latest control factor corresponding to the current SOH, and transmits the latest control factor to the charging station so that the charging station may transmit the latest control factor to a control system of the electric vehicle to update the control factor.