Traction Battery EIS Control for Fast Internal State Detection
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Solution Overview
Problem
Existing methods for detecting the internal state of a traction battery in electrified vehicles, such as state-of-charge and power capability, are hindered by the long measurement times required for electrochemical impedance spectroscopy (EIS), which are impractical for on-board or charging station applications due to battery state changes during driving or charging.
Innovation Solution
Implementing adaptive sampled electrochemical impedance spectroscopy (EIS) with a battery energy control module (BECM) that uses adaptive sampling to reduce EIS measurement time by 50-75% while maintaining accuracy, allowing for real-time detection of battery state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electrochemical impedance spectroscopy (EIS) measurement methods are used to detect battery internal state, then measurement accuracy is maintained, but measurement time becomes excessively long making it impractical for on-board applications
Solution Approach 1:
The patent applies partial action by performing EIS measurements only at selected frequency points rather than continuously across the entire frequency spectrum. The controller identifies specific frequency points where impedance measurements provide the most valuable information for determining battery internal state, thereby reducing total measurement time while maintaining detection accuracy.
Solution Approach 2:
The patent implements dynamic adaptation by adjusting the EIS measurement process based on real-time battery operating conditions. The controller modifies measurement parameters such as frequency selection and sampling intervals according to the current state of charge, temperature, and load conditions, optimizing the balance between measurement speed and accuracy for each specific operating scenario.
2Productivity
If adaptive sampled EIS measurements are implemented to reduce measurement time, then productivity is improved, but measurement complexity increases
Solution Approach 1:
The patent employs feedback mechanisms where the controller continuously monitors battery operating parameters and uses this information to adjust the EIS measurement strategy. The controller analyzes previous measurement results and current operating conditions to dynamically select optimal frequency points and measurement intervals, creating a closed-loop system that adapts to changing battery states while managing complexity through intelligent control algorithms.
Solution Approach 2:
The patent changes measurement parameters dynamically based on operating conditions. The controller adjusts frequency selection, sampling rates, and measurement duration according to state of charge levels, temperature conditions, and current load requirements. This parameter adaptation allows the system to maintain high productivity across diverse operating scenarios while managing computational complexity through condition-based parameter selection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid and accurate detection of battery state, including power capability and state-of-charge, facilitating efficient battery management and vehicle control, even in dynamic vehicle conditions.
Implementation Method 1
The controller is configured to control the battery based on adaptive sampled electrochemical impedance spectroscopy (EIS) measurements of the battery
Data Source
AI summary
A system, such as an electrified vehicle, includes a battery, such as a traction battery. The controller controls the battery based on adaptive sampled electrochemical impedance spectroscopy (EIS) measurements of the battery. The controller may perform the adaptive sampled EIS measurements. The controller may use the adaptive sampled EIS measurements to identify values of parameters of a model, such as an equivalent circuit model (ECM), of the battery. The controller may use the model, with the identified values of the parameters, to detect an internal state of the battery whereby the controller detects the internal state of the battery based on the adaptive sampled EIS measurements.