Secondary Battery Internal Resistance Estimation via Segmentation
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Solution Overview
Problem
Existing methods for estimating the internal resistance of secondary batteries, such as those described in JP 2014-149280 A and JP 2012-185122 A, lack accuracy and do not effectively consider the impact of capacity deviations between the positive and negative electrodes, leading to incomplete degradation assessment.
Innovation Solution
An internal resistance estimation method that acquires initial and temperature course information, calculates increased and reduced resistance values based on temperature and state of charge changes, and incorporates resistance increase coefficients to accurately estimate the internal resistance of secondary batteries, considering capacity deviations and temperature variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing estimation methods (JP 2014-149280 A, JP 2012-185122 A) are used to estimate internal resistance, then the estimation can be performed with simplified procedures, but the measurement precision and accuracy of degradation assessment deteriorate due to not considering capacity deviations between electrodes
Solution Approach 1:
The internal resistance is segmented into two distinct components: increased resistance value (due to aging/degradation) and reduced resistance value (due to capacity deviation between electrodes). This segmentation allows each component to be calculated using different approaches - the increased value uses temperature course information and resistance increase coefficients, while the reduced value uses capacity ratio information, thereby improving overall estimation accuracy without requiring complete procedural redesign
Solution Approach 2:
The method performs preliminary calculations of the increased resistance value based on temperature course information before final estimation. By pre-calculating the temperature-dependent resistance increase and storing resistance increase coefficients, the system prepares degradation data in advance, which then can be combined with capacity deviation information to produce the final accurate internal resistance estimate
2Reliability
If simplified estimation procedures are used, then the ease of operation is improved, but the reliability of degradation assessment deteriorates due to incomplete consideration of battery usage conditions
Solution Approach 1:
The estimation method segments the internal resistance into increased value (from aging) and reduced value (from capacity deviation), allowing each segment to be calculated with appropriate complexity level. The increased value uses temperature and time data, while the reduced value uses capacity ratios, enabling reliable degradation assessment without requiring uniformly complex procedures throughout
Solution Approach 2:
The method introduces resistance increase coefficients as intermediary parameters that mediate between temperature course information and the final internal resistance estimation. These coefficients serve as pre-calculated lookup values that simplify the estimation process while maintaining reliability by capturing the complex temperature-dependent degradation behavior
3Productivity
If real-time monitoring is implemented with accurate estimation, then the productivity and battery performance optimization are improved, but the device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary calculations of temperature course information and stores resistance increase coefficients in advance. By pre-processing temperature data and preparing coefficient lookup tables, the system reduces real-time computational requirements, enabling productivity optimization through accurate real-time monitoring without excessive computational complexity
Solution Approach 2:
The estimation method uses readily available battery operating data (temperature, capacity ratios) that the battery system already generates during normal operation. By utilizing self-generated data without requiring external measurement equipment or complex additional sensors, the system achieves productivity optimization while minimizing device complexity
Data Source
AI summary
An internal resistance estimation method for a secondary battery includes: acquiring an initial internal resistance value and temperature course information including a battery temperature and time information about time at which the battery temperature has been recorded; estimating an increased amount of the internal resistance value after a lapse of a predetermined time from the predetermined reference time on the basis of the temperature course information; estimating a reduced amount of the internal resistance value of the secondary battery resulting from a relative change between a range of use of a state of charge of the positive electrode and a range of use of a state of charge of the negative electrode on the basis of the temperature course information; and calculating an internal resistance variation in the lapse of the predetermined time on the basis of the increased amount and the reduced amount.