Battery State Estimator Dual Sampling Rates
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Solution Overview
Problem
Existing battery state estimation algorithms for electric vehicles face challenges in accurately capturing both fast dynamics for state of charge (SOC) estimation and slow dynamics for power prediction due to their reliance on a single sampling rate and simple linear models, which results in compromised accuracy and robustness across various applications.
Innovation Solution
A method employing two different sampling rates to estimate battery parameters, where terminal voltage and current are sampled at a high rate to determine open circuit voltage and high frequency resistance, and then re-sampled at a low rate to capture slow dynamics, allowing for improved SOC estimation and power prediction using a simple battery model with limited frequency modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single sampling rate is used for battery parameter estimation, then computational complexity is reduced, but accuracy in capturing both fast dynamics (SOC) and slow dynamics (power) is compromised
Solution Approach 1:
The patent segments the battery parameter estimation process into two distinct phases with different sampling rates: a fast sampling rate phase for capturing SOC dynamics and a slow sampling rate phase for capturing power dynamics. This segmentation allows each phase to be optimized for its specific temporal characteristics, resolving the contradiction between computational simplicity and comprehensive accuracy.
Solution Approach 2:
The patent dynamically adapts the sampling rate based on the specific estimation task at hand. The system switches between fast and slow sampling rates depending on whether SOC or power needs to be estimated, making the sampling strategy dynamic rather than static. This dynamic approach captures the essence of different battery dynamics without requiring continuous high-rate sampling.
2Quantity of substance
If a simple linear model with limited frequency modes is used, then memory and computational cost are minimized, but estimation accuracy for multiple applications is reduced due to inability to cover all feature frequencies
Solution Approach 1:
The patent applies local quality by using different sampling rates for different aspects of battery behavior. Instead of using a single uniform sampling rate throughout, the system uses fast sampling when SOC estimation is needed and slow sampling when power estimation is needed. This localized optimization of sampling rate to specific estimation tasks maintains memory efficiency while improving overall estimation accuracy.
Solution Approach 2:
The patent changes the sampling rate parameter dynamically based on the estimation task. By adjusting the sampling rate from fast to slow depending on whether SOC or power is being estimated, the system optimizes the balance between computational resources and estimation accuracy for each specific application scenario.
3Measurement precision
If high sampling rate is used to capture fast dynamics for SOC estimation, then SOC estimation accuracy is improved, but computational burden and memory requirements increase
Solution Approach 1:
The patent segments the estimation process into SOC estimation phase (using fast sampling) and power estimation phase (using slow sampling). By segmenting the tasks, the system only uses high computational resources when necessary for SOC estimation, rather than continuously maintaining high sampling rates for all estimations, thus reducing overall memory and computational burden.
Solution Approach 2:
The patent applies partial action by using high sampling rate only for the specific purpose of SOC estimation rather than for all battery parameter estimation. This selective application of high-rate sampling only where needed (for SOC) avoids excessive computational and memory resources being wasted on parameters that can be accurately estimated at lower sampling rates.
4Productivity
If low sampling rate is used to reduce computational cost, then power prediction is simplified, but accuracy in capturing fast dynamics and SOC changes is compromised
Solution Approach 1:
The patent implements dynamic sampling rate adjustment where the system automatically selects between fast and slow sampling rates based on the current estimation task. When SOC estimation is required, the system dynamically switches to fast sampling to capture rapid changes; when power prediction is the sole task, it uses slow sampling for computational efficiency. This dynamic behavior resolves the contradiction between efficiency and accuracy.
Solution Approach 2:
The patent changes the sampling rate parameter adaptively based on the estimation objectives. By adjusting this critical parameter from high to low depending on whether fast dynamics capture or computational efficiency is the priority, the system optimizes the trade-off between productivity and measurement precision for different operational contexts.
Data Source
AI summary
A method for estimating vehicle battery parameters that uses two different sampling rates. The method samples a battery terminal voltage and current at a high sampling rate to estimate the battery open circuit voltage and high frequency resistance. The battery state of charge (SOC) is derived from the open circuit voltage. Next, the battery terminal voltage and current are re-sampled at a low sampling rate. Other battery parameters can be extracted from the low-rate sampled signals. Next, all of the battery parameters that were obtained from the two sampling rates are used together to predict battery power.