Dynamic Time Constant Estimation for Battery State-of-Charge
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Solution Overview
Problem
Existing methods for estimating battery state-of-charge (SOC) using open-circuit voltage (OCV) are inaccurate and require excessive idle periods, as they rely on pre-characterized time constants and may not account for dynamic operational conditions.
Innovation Solution
Dynamically updating the relaxation time constant during system operation using regression analysis on electrical measurements collected during vehicle use, allowing for more accurate SOC estimation and reduced measurement collection intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-characterized time constant values are used with statistical mathematical techniques, then SOC estimation can be obtained without waiting for full relaxation, but the OCV values may be inaccurate and excessive idle periods are still required
Solution Approach 1:
The patent applies dynamics by making the time constant τ dynamic rather than fixed. The system dynamically identifies τ for each battery based on its actual relaxation behavior using measured voltage data during idle periods. This allows the estimation algorithm to adapt to the specific battery's characteristics, achieving accurate OCV prediction with shorter idle periods without requiring pre-characterized universal time constant values.
Solution Approach 2:
The system uses feedback by continuously monitoring the battery voltage during the idle period and using this measured data to refine the identification of the time constant τ. The measured voltage values are fed back into the estimation algorithm to adjust and optimize the time constant value, thereby improving the accuracy of OCV prediction and reducing the required idle period duration.
2Measurement precision
If the battery is allowed to reach a fully relaxed state before measurement, then OCV measurement accuracy is maximized, but considerable time is required for the voltage to stabilize
Solution Approach 1:
The patent applies partial action by performing regression analysis on a subset of voltage measurements taken during the idle period rather than requiring measurements from the entire relaxation period. By selectively using measurements from the most informative time window and applying regression techniques, the system achieves accurate OCV estimation without waiting for complete voltage stabilization, thus reducing the required relaxation time while maintaining measurement accuracy.
Solution Approach 2:
The system performs preliminary identification of the time constant τ during the idle period before the final OCV calculation is made. This preliminary action allows the algorithm to prepare the necessary parameters in advance, enabling accurate OCV prediction to be made as soon as sufficient voltage data is collected, rather than waiting for full relaxation to occur.
3Device complexity
If pre-characterized time constant values are used, then the system complexity is reduced, but the method cannot account for dynamic operational conditions and battery variations
Solution Approach 1:
The patent applies self-service by enabling the battery management system to automatically identify its own time constant τ using its own measured voltage data during normal operation. The system performs self-characterization without requiring external pre-characterization or complex lookup tables, thereby maintaining relatively simple system architecture while achieving high adaptability to different batteries and operational conditions through autonomous parameter identification.
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
This approach provides more accurate SOC estimation and reduced measurement times, enhancing battery life, reliability, and vehicle range prediction in electric and hybrid vehicles.
Implementation Method 1
V(t)=OCV−αe−t/τ where V(t) is a voltage measurement taken at time t, α is the overpotential and τ is the time constant
Data Source
AI summary
A method for estimating the state-of-charge of a battery. The method includes collecting a plurality of voltage measurements during operation of the system containing the battery and determining a time-constant of relaxation and an open-circuit voltage corresponding to the battery based, at least in part, on the voltage measurements. The method further includes estimating the state-of-charge of the battery based, at least in part, on the open-circuit voltage.