Non-linear Battery State of Charge Estimation via Curve Fitting
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Solution Overview
Problem
Conventional methods for determining battery system efficiency and state of charge in power grids are inaccurate due to reliance on unavailable information and simplistic approximations, limiting the deployment of battery systems.
Innovation Solution
A method that receives battery data over time, identifies sub-periods of charging or discharging, fits non-linear curves to this data to determine expected performance, and generates operating instructions based on performance models that account for output power and temperature, allowing for efficient battery operation without requiring specific battery type information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches are used to determine battery efficiency metrics, then the calculation process is simple, but the accuracy of the metrics is poor due to reliance on unavailable information and simplistic approximations
Solution Approach 1:
The system uses the battery system's own operational data (power in/out, state of charge, temperature) to generate performance models and efficiency metrics without requiring external information about battery type, chemistry, or capacity. The battery system essentially models itself through its observed behavior, eliminating the need for unavailable manufacturer specifications.
Solution Approach 2:
The patent introduces an intermediary performance modeling system that processes raw battery operational data and transforms it into accurate efficiency metrics. This intermediary layer (the curve-fitting and modeling process) bridges the gap between simple data collection and accurate efficiency determination, avoiding both simplistic approximations and overly complex direct measurement.
2Measurement precision
If detailed battery type information is required for accurate performance modeling, then the accuracy of performance metrics improves, but the ease of deployment decreases due to unavailable information
Solution Approach 1:
The performance modeling system determines battery characteristics through self-service by analyzing the battery's own operational data. Instead of requiring external information about battery type, chemistry, or capacity, the system extracts these characteristics implicitly from observed power, state of charge, and temperature relationships during charging and discharging cycles.
Solution Approach 2:
The patent inverts the conventional approach by not starting with battery type information to determine performance, but rather starting with operational data and deriving battery characteristics and performance models from there. This inversion eliminates the dependency on unavailable manufacturer specifications while maintaining modeling accuracy.
3Loss of energy
If conventional efficiency metrics are used, then the system operation is straightforward, but the round-trip efficiency losses are higher due to inaccurate performance understanding
Solution Approach 1:
The system implements feedback by continuously monitoring battery operational data (power in/out, state of charge, temperature) and using this feedback to update and refine performance models. This feedback loop enables the system to accurately track efficiency metrics and optimize operation to minimize round-trip losses, with the complexity of data processing justified by the energy savings achieved.
Solution Approach 2:
The patent replaces mechanical/simple efficiency calculation methods with a data-driven computational approach. Instead of using simplistic approximations or fixed efficiency values, the system uses curve-fitting and performance modeling based on actual operational data to dynamically determine efficiency metrics, reducing energy losses through more accurate performance understanding.
Data Source
AI summary
Systems, methods, and computer media for battery system management and non-linear estimation of battery state of charge are provided herein. Battery data is received for a time period over which a battery system has operated. The battery data represents the actual performance of the battery system over the time period. Sub-periods of charging or discharging can be identified in the time period. For the sub-periods of time, a curve can be fit to the battery data. Using the curves for the battery data for the sub-periods of time, an expected performance of the battery system, over a range of states-of-charge, can be determined. Operating instructions for the battery system can be provided based on the expected performance.