Battery State Estimation Using High-Frequency Empirical Model
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Solution Overview
Problem
Existing battery state estimation techniques are less than optimal due to challenges in accurately estimating the state of charge (SOC) and state of power (SOP) in real-time, particularly when local SOC disparities and transient voltage effects are present, leading to inaccuracies in battery management.
Innovation Solution
A method using a controller that applies a mathematical model to sensor-based measurements, incorporating an empirical model with filters and basis functions to estimate the full state vector of the battery, including open-circuit voltage and higher-frequency voltage transients, to derive accurate real-time SOC and SOP estimates, while compensating for transient effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time battery state estimation is performed during charging or discharging operations, then the battery management system can enable optimal control and power flow decision making, but the estimation accuracy deteriorates due to local SOC disparities and transient voltage effects
Solution Approach 1:
The patent segments the battery state estimation into multiple components: bulk SOC estimation using OCV, and local SOC disparities captured by the porous electrode model. This segmentation allows simultaneous tracking of both average state and spatial variations, resolving the contradiction between real-time capability and accuracy during dynamic operations
Solution Approach 2:
The patent introduces a porous electrode transient model as an intermediary between the bulk OCV measurement and the actual local SOC distribution. This intermediary model bridges the gap by predicting how concentration gradients and transient effects translate from bulk measurements to local conditions, enabling accurate real-time estimation despite transient voltage effects
2Measurement precision
If the battery is allowed to rest at open circuit to achieve equilibrium voltage, then the SOC measurement accuracy improves through unique OCV-SOC relationship, but the response time deteriorates due to insufficient time for local SOC disparities to resolve
Solution Approach 1:
The patent performs preliminary action by using the porous electrode transient model to predict and compensate for local SOC disparities during dynamic operations. Instead of waiting for equilibrium, the model proactively estimates the equilibrium state by accounting for concentration gradients and transient effects, providing accurate SOC estimation without requiring actual rest periods
Solution Approach 2:
The patent changes the estimation approach from direct OCV measurement (which requires equilibrium) to a model-based estimation that uses OCV as one input among several. The porous electrode model transforms the problem by incorporating transient parameters and concentration gradient information, allowing accurate SOC estimation during dynamic operations without requiring the system to be at equilibrium
Data Source
AI summary
An electrical system includes a battery, sensors, and a controller. The sensors output measured signals indicative of an actual state of the battery, including respective actual voltage, current, and temperature signals for each battery cell. The controller, in conducting a method, generates an estimated state of the battery, including a predicted voltage of the battery, doing so responsive to the signals using an open-circuit voltage and an output of an empirical model. An operating state of the electrical system is controlled using the estimated state. The empirical model includes low-pass/band-pass filters and a high-pass filter each with a different time-constant, the time-constants being spread over a time-constant range. Each low-pass/band-pass filter branches through a basis function(s) whose output(s) are multiplied by a respective resistance value to generate higher-frequency voltage transients. The controller sums the open-circuit voltage and voltage transients to derive the predicted voltage.