Battery SOH Estimation During Charging Without Offline Testing
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Solution Overview
Problem
Existing methods for determining Battery State of Health (SOH) are energy-intensive, require batteries to be taken offline, or necessitate expensive and complex equipment, and often rely on assumptions that do not accurately reflect individual battery degradation in diverse operating environments.
Innovation Solution
A method and system that monitor and determine battery SOH during normal charging cycles by recognizing the battery via a unique identifier, collecting data on voltage, current, and temperature, and comparing this data across multiple charging instances to assess the battery's health without the need for offline testing or expensive equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional offline SOH measurement methods are used, then measurement accuracy is improved, but battery downtime increases and energy is wasted
Solution Approach 1:
The system performs preliminary data collection during normal charging operations, accumulating voltage, current, and temperature data before SOH analysis is needed. This preliminary action during routine charging enables accurate SOH determination without requiring separate offline testing periods, thus reducing battery downtime while maintaining measurement accuracy.
Solution Approach 2:
The system continuously monitors battery parameters during normal charging operations rather than interrupting service for dedicated measurement periods. By utilizing the charging process itself for data collection, the system maintains continuous useful action (charging) while simultaneously performing SOH assessment, eliminating the need to take the battery offline.
2Measurement precision
If electrochemical techniques are used for SOH measurement, then measurement accuracy is improved, but system complexity increases
Solution Approach 1:
The system uses charging process parameters (voltage, current, temperature) as intermediary measurements that can be obtained through standard charging equipment rather than complex electrochemical analysis tools. These intermediary measurements during normal charging provide sufficient information for SOH assessment without requiring partial disassembly or specialized electrochemical measurement equipment.
Solution Approach 2:
The system replaces complex electrochemical measurement systems with electrical and thermal parameter monitoring during charging. Instead of using sophisticated electrochemical analysis equipment that requires battery disassembly, the system substitutes simpler voltage, current, and temperature sensing that integrates naturally into the charging infrastructure.
3Device complexity
If mathematical models are used for SOH estimation, then device complexity is reduced, but measurement precision deteriorates due to assumptions
Solution Approach 1:
The system implements feedback mechanisms that use actual charging data (voltage, current, temperature) to continuously refine SOH estimates for individual batteries. This feedback approach allows the system to move beyond generic mathematical models by incorporating real-time operational data, improving precision while maintaining relatively simple device complexity through software-based analysis.
Solution Approach 2:
The system transitions from general mathematical models that assume uniform battery behavior to localized analysis of individual battery charging characteristics. By examining specific voltage, current, and temperature patterns unique to each battery during charging, the system achieves higher measurement precision tailored to local battery conditions without requiring complex equipment.
Data Source
AI summary
A method for determining the state of health of a battery by monitoring charging of the battery over multiple charging instances is provided. The method is implemented within charging stations by: recognizing the battery via a unique identifier; charging the battery; monitoring conditions of the battery during charging; storing data related to the current charging instance based at least partially on the monitored conditions; retrieving data related to at least one earlier charging instance of the uniquely identified battery; and making a determination of the uniquely identified battery's state of health based at least partially on a comparison of the data related to the current charging instance and the retrieved data related to at least one earlier charging instance.