Adaptive Battery State Estimation Using Electrode Ion Observers
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Solution Overview
Problem
Traditional battery management systems fail to accurately estimate the State of Charge (SOC) and State of Health (SOH) of batteries in real time due to outdated models that do not account for aging mechanisms, leading to inaccurate predictions and potential safety issues, especially in large electric vehicle batteries.
Innovation Solution
A battery monitoring system utilizing adaptive cathode and anode observers with an Enhanced Single Particle Model (ESPM) and sliding mode interconnected observers to dynamically estimate ion concentrations and update battery health parameters in real time, incorporating aging-sensitive parameters and open-loop communication between observers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional model-based systems are used to estimate SOC and SOH, then the system complexity is reduced, but the measurement precision and reliability of battery state estimation deteriorate due to outdated models that do not account for aging mechanisms
Solution Approach 1:
The system divides the battery estimation problem into separate adaptive observers for the cathode and anode, each independently estimating ion concentration in their respective electrodes. This segmentation allows each observer to focus on specific aging mechanisms relevant to its electrode type, improving estimation accuracy without requiring a single overly complex model that would need to account for all aging mechanisms simultaneously.
Solution Approach 2:
The adaptive observers dynamically adjust estimation parameters based on real-time battery data and aging mechanisms. The system changes parameters such as ion concentration estimates, transport parameters, and aging-sensitive parameters adaptively rather than using fixed model parameters, allowing the system to maintain high accuracy as the battery ages and its characteristics change over time.
2Reliability
If traditional battery management systems are used, then ease of operation is maintained, but reliability and safety deteriorate due to inaccurate models leading to incorrect SOC and SOH estimates
Solution Approach 1:
The adaptive observers continuously receive feedback from real-time battery measurements (voltage, current, temperature) and adjust their estimates of ion concentration, SOC, and SOH accordingly. This feedback mechanism ensures that the system maintains reliable and safe operation by constantly updating its understanding of battery state based on actual performance data, while the automated nature of the feedback loop maintains ease of operation.
Solution Approach 2:
The system performs self-updating of battery state estimates through the adaptive observers, which automatically adjust their parameters and calculations based on incoming data without requiring manual intervention. The observers self-correct their estimates as new data becomes available, maintaining reliability while requiring minimal operational input from users or external systems.
3Measurement precision
If real-time adaptive estimation is implemented, then measurement precision of battery state improves, but device complexity increases due to adaptive observers and predictive modeling
Solution Approach 1:
By segmenting the estimation task into separate cathode and anode observers, the system avoids the complexity of a single monolithic model that would need to handle all battery physics and aging mechanisms. Each segmented observer is simpler in structure but collectively they provide comprehensive real-time estimation accuracy.
Solution Approach 2:
The system uses dynamic adaptive estimation where the observers continuously adjust their parameters based on real-time data rather than relying on static models. This dynamic approach improves real-time accuracy while the modular adaptive structure keeps individual observer complexity manageable compared to a single comprehensive dynamic model.
Data Source
AI summary
A battery health monitoring system that utilizes adaptive cathode and adaptive anode observers to estimate the ion concentrations at the respective cathode and anode of a battery. Subsequently, the estimated ion concentrations can be used in a battery model to estimate the state of heath and state of charge of the battery. Additionally, the model and ion concentrations can be updated real time as aging components of the battery are evaluated in the output data from the battery.