Active State Excitation for Battery Control Under Uncertain Internal States
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Solution Overview
Problem
Existing automated control systems for batteries and other physical systems face challenges in determining responses to varying conditions and managing uncertainty, particularly when internal states are not directly observable, leading to inefficiencies in operation and optimization.
Innovation Solution
The implementation of a model realization system that performs non-destructive active excitation of battery and other physical systems to generate incremental parametric non-linear state models, allowing for real-time control and optimization of operations by analyzing responses to injected pulses and using intelligent sensors to collect data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated control systems use traditional modeling approaches for batteries and physical systems, then device complexity is reduced, but measurement precision and reliability deteriorate due to uncertainty in internal states and unobservable conditions
Solution Approach 1:
The patent introduces an intermediary modeling layer that acts as a mediator between the physical system and control system. This layer uses incremental parametric non-linear state models to represent internal battery states (temperature, chemistry) that are not directly observable, allowing the control system to make decisions based on modeled state estimates rather than direct measurements, thereby improving measurement precision without requiring direct access to all internal parameters
Solution Approach 2:
The patent transforms the control approach by changing from static models to incremental parametric non-linear state models. These models dynamically adjust parameters based on operating conditions (charge/discharge rates, temperature), allowing the system to adapt to varying internal states and improve measurement precision across different operational regimes without increasing fundamental system complexity
2Reliability
If automated control systems implement comprehensive monitoring of internal states, then reliability improves, but loss of energy increases due to continuous measurement and control activities
Solution Approach 1:
The patent implements partial monitoring by focusing measurement and modeling efforts on the most critical internal states that significantly impact reliability (such as temperature and charge state). Rather than continuously monitoring all possible parameters, the system selectively models key states using the incremental parametric approach, achieving improved reliability while minimizing the energy overhead associated with comprehensive monitoring
Solution Approach 2:
The system performs preliminary modeling and state estimation before control actions are executed. By using the incremental parametric non-linear state models to predict internal states and potential issues in advance, the control system can make proactive decisions that improve reliability without requiring continuous high-energy monitoring during critical operations
3Adaptability or versatility
If static models and constraints are used to control physical systems, then device complexity is minimized, but adaptability deteriorates when operating conditions change
Solution Approach 1:
The patent replaces static models with dynamic incremental parametric non-linear state models that automatically adapt to changing operating conditions. These models capture the time-varying nature of battery and physical system behavior, adjusting their parameters based on current state and operating conditions, thereby providing the necessary adaptability while maintaining a structured approach that limits complexity growth
Data Source
AI summary
Techniques are described for implementing automated control systems to control operations of target physical systems and/or their components (e.g., a fuel cell, wind turbine, HVAC unit, etc.), such as based at least in part on models of their dynamic non-linear behaviors that are generated by gathering and analyzing information about their operations under varying conditions. The techniques may include, for each of multiple levels of inputs to the system/component and/or other factors, injecting a corresponding signal input into the system/component, and using active sensors to collect time changes of the responses to these pulses. Information about the inputs and the responses is used to generate an incremental parametric model representing the internal state and behavioral dynamics of the system/component, which is further used to control additional ongoing operations of the system/component (e.g., to control whether and how much output is produced in a current or future time period).