EV Battery SOH Control Updates via Charging Station AI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery management systems for electric vehicles lack an efficient method to centrally monitor and update charging/discharging control logic based on battery performance degradation, leading to uneven battery life and potential safety issues.
Innovation Solution
A battery performance management system that collects and analyzes data from electric vehicle charging stations using artificial intelligence models to determine the State Of Health (SOH) of batteries, updating control factors for charging/discharging operations to optimize battery life and safety.
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 device complexity is reduced, but battery service life and reliability deteriorate
Solution Approach 1:
The system pre-establishes multiple sets of control logic corresponding to different SOH ranges (e.g., 80-100%, 60-80%, 40-60%, 20-40%). When battery degradation is detected, the system switches to the appropriate pre-prepared control logic set, avoiding the need for complex real-time optimization calculations while extending battery service life through degradation-aware control.
Solution Approach 2:
The control logic is made dynamic by automatically switching between different control strategies based on the battery's current SOH level. The system adapts charging/discharging parameters (current, voltage, power limits) according to the degradation state, allowing the control approach to evolve with battery aging without requiring complex manual reconfiguration.
2Productivity
If centralized monitoring of multiple batteries is implemented, then productivity and reliability are improved, but device complexity and loss of information increase
Solution Approach 1:
The server implements a universal data management system that handles multiple battery types and models through standardized protocols. The system collects, stores, and processes performance data from various battery sources using a unified approach, enabling centralized monitoring of multiple batteries without proportionally increasing system complexity.
Solution Approach 2:
The system creates and manages copies of control logic configurations and performance data across multiple batteries. By replicating standardized data structures and control parameters, the system efficiently handles information for multiple batteries simultaneously, reducing the management overhead compared to handling each battery individually.
3Reliability
If frequent updates of control logic are performed, then battery reliability is improved, but loss of time and device complexity increase
Solution Approach 1:
The system performs control logic updates at periodic intervals or when specific thresholds are reached, rather than continuously. Performance data is collected over defined periods, and control logic is revised based on accumulated degradation trends, reducing the time overhead while maintaining reliable battery management through regular updates.
Solution Approach 2:
The system implements feedback mechanisms where control logic updates are triggered by performance thresholds or degradation milestones. Rather than frequent arbitrary updates, the system monitors battery health and initiates control logic revisions only when necessary based on actual degradation patterns, optimizing the balance between reliability and time efficiency.
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.