EV Battery SOH Control via AI Charging Station Feedback
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Solution Overview
Problem
Existing battery management systems fail to effectively monitor and adapt 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 battery performance evaluation information from multiple charging stations using an artificial intelligence model to determine State Of Health (SOH) and update control factors for charging/discharging, utilizing a centralized database to optimize battery operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If battery charging/discharging control logic is not updated based on degradation state, then system complexity is reduced, but battery service life is shortened
Solution Approach 1:
The battery management system automatically monitors battery performance parameters, determines SOH degradation state, and updates control logic without manual intervention. The system serves itself by collecting data from sensors, processing it through the determination unit, and autonomously adjusting charging/discharging parameters based on degradation level.
Solution Approach 2:
The system continuously monitors battery performance parameters (voltage, current, temperature, capacity) and uses this feedback to determine the SOH degradation state. Based on the determined degradation level, the control logic is updated and applied to subsequent charging/discharging operations, creating a closed-loop feedback system that adapts to battery aging.
2Reliability
If centralized monitoring of multiple batteries is implemented, then performance degradation is reduced, but device complexity increases
Solution Approach 1:
The battery management system is designed to monitor and manage multiple batteries of the same model simultaneously using a unified control architecture. The system collects performance parameters from all batteries, determines their respective SOH states, and applies appropriate control logic for each, enabling centralized management of multiple battery units through a single multi-functional platform.
Solution Approach 2:
The system divides the monitoring task into independent modules: data collection from individual batteries, centralized processing to determine SOH degradation state for each battery, and separate control logic application for each battery based on its specific degradation level. This segmentation allows efficient management of multiple batteries without overwhelming system complexity.
3Productivity
If control factors are updated based on SOH degradation, then battery performance is optimized, but measurement precision requirements increase
Solution Approach 1:
The system replaces complex physical measurement methods with an intelligent determination approach. Instead of requiring highly precise direct measurement of SOH degradation, the system uses sensors to collect battery performance parameters (voltage, current, temperature, capacity) and employs a determination unit to calculate and assess the SOH degradation state based on these measurements and pre-established degradation characteristics.
Solution Approach 2:
The system monitors changes in key battery parameters (capacity, internal resistance, voltage, temperature) over time and uses these parameter changes to determine the SOH degradation state. By tracking parameter evolution rather than requiring absolute precise measurement, the system can accurately assess degradation and update control factors accordingly.
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.