Battery SOH Estimation via Iterative Charging Time Feedback
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Solution Overview
Problem
Existing methods for estimating the state of health (SOH) of batteries in electric vehicles face challenges due to measurement errors in internal resistance and current, leading to inaccuracies in calculating the SOH, especially when direct measurement is difficult and errors accumulate over time.
Innovation Solution
A method that iteratively adjusts the estimated charging required time using a battery meta-model and weighted least squares to minimize errors between measured and estimated values, allowing for accurate SOH estimation even without prior SOH data, by selecting cells with lowest, average, and maximum voltages and calculating residuals vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If internal resistance is indirectly calculated using Ohm's law from measured voltage and current, then the calculation is simple, but measurement errors in voltage and current lead to large errors in SOH estimation
Solution Approach 1:
The patent implements an iterative feedback mechanism where the estimated SOH is continuously refined by comparing predicted charging times with actual measured charging times. The system adjusts the SOH estimate in each iteration based on the error between predicted and observed values, gradually converging to an accurate SOH value that compensates for measurement errors in voltage and current.
Solution Approach 2:
The patent replaces the direct electrical measurement approach (Ohm's law calculation) with a time-based measurement approach. Instead of relying on accurate voltage and current measurements to calculate internal resistance, the system measures charging time directly, which is less sensitive to measurement errors, and uses this time data to infer SOH through iterative optimization.
2Ease of operation
If SOC is estimated by integrating charging/discharging current, then SOH can be estimated using the estimated SOC, but measurement errors in current accumulate over time and decrease accuracy of SOC and SOH estimation
Solution Approach 1:
The patent uses feedback from actual charging time measurements to correct and refine the SOH estimate iteratively. Instead of relying on accumulated current integration errors, the system continuously compares predicted charging behavior with observed charging time and adjusts the SOH estimate accordingly, preventing error accumulation.
Solution Approach 2:
The patent introduces charging time as an intermediary measurement that bridges the gap between current integration and SOH estimation. Rather than directly using integrated current (which accumulates errors), the system uses charging time as a more stable intermediate parameter that is less sensitive to measurement errors, from which SOH is then inferred.
3Measurement precision
If charging required time is measured for SOH estimation, then direct measurement is obtained, but iterative optimization requires multiple calculations increasing computational complexity
Solution Approach 1:
The patent implements a self-correcting iterative process where each calculation cycle uses the results of the previous cycle to improve accuracy. The system automatically refines the SOH estimate by using the error from one iteration to adjust the estimate in the next iteration, progressively converging to an accurate value without requiring external intervention or complex computational resources.
Solution Approach 2:
The patent changes the parameter being optimized from direct internal resistance calculation to charging time-based SOH estimation. By using charging time as the primary measurement parameter and iteratively adjusting SOH to minimize the difference between predicted and actual charging times, the system achieves accurate results with computationally efficient operations.
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 reduces estimation errors, improves reliability, and efficiently estimates battery capacity using limited calculation resources, effectively addressing the mismatch between actual and stored SOH, and enabling accurate aging state assessment.
Implementation Method 1
calculating an estimated value of the charging required time for each of the voltage intervals by a preset battery meta-model
Implementation Method 2
estimating the SOH having lowest error between the measured value of the charging required time and the estimating value of the charging required time
Data Source
AI summary
A method for estimating a state of health of a battery includes calculating a measured value of a charging required time for each of preset voltage intervals in a preset voltage range in which the battery is charged; calculating an estimated value of the charging required time for each of the voltage intervals by a preset battery meta-model; and estimating the SOH having lowest error between the measured value of the charging required time and the estimating value of the charging required time by allowing the estimated value of the charging required time to iteratively approach the measured value of the charging required time.