Battery ECU Frequency Mapping for Replaced Battery Degradation
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Solution Overview
Problem
Existing battery degradation estimation methods fail to accurately assess the degree of degradation in replaced batteries, as frequency data stored in control devices before replacement is insufficient for estimating the new battery's condition.
Innovation Solution
A battery system that includes a control device capable of estimating battery degradation using frequency data of battery temperature and a degradation coefficient, which creates new frequency data based on the battery's full charge capacity and temperature-dependent degradation rate to accurately determine the degree of degradation after replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If frequency data stored in control device memory before battery replacement is used, then the control device structure is simple, but the degree of degradation of the replaced battery cannot be estimated accurately
Solution Approach 1:
The control device performs preliminary actions by acquiring the estimated full charge capacity value of the replaced battery before creating new frequency data. This preliminary data collection enables the subsequent calculation of degradation degree and creation of accurate frequency data specific to the replaced battery, resolving the contradiction between simple structure and accurate estimation.
Solution Approach 2:
The invention changes the parameters used for degradation estimation by creating new frequency data based on the replaced battery's specific characteristics (full charge capacity value) rather than using generic pre-replacement data. This parameter transformation enables accurate degradation estimation for the specific replaced battery while maintaining a relatively simple control device structure.
2Measurement precision
If new frequency data is created based on full charge capacity for replaced batteries, then degradation estimation accuracy improves, but the processing complexity increases
Solution Approach 1:
The control device performs self-service by automatically acquiring the full charge capacity value, calculating the degradation degree, and creating new frequency data without requiring external intervention. This automation reduces processing complexity while maintaining high degradation estimation accuracy, as the system handles all operations internally through integrated processing steps.
Solution Approach 2:
The invention replaces manual or complex mechanical data collection methods with electronic data processing. By using electronic acquisition of full charge capacity values and automated calculation algorithms, the system achieves accurate degradation estimation with reduced processing complexity compared to manual methods.
3Loss of information
If frequency data covers the entire temperature range, then the data comprehensiveness is high, but memory usage increases and may overflow
Solution Approach 1:
The invention applies local quality by creating frequency data specifically tailored to the high-temperature regions where degradation is accelerated, rather than uniformly distributing data across the entire temperature range. This localized approach maintains comprehensiveness for degradation-critical regions while reducing overall memory usage and preventing overflow.
Solution Approach 2:
The invention changes the parameter distribution in frequency data by concentrating data points in high-temperature regions where degradation occurs more rapidly. This parameter transformation ensures comprehensive coverage of degradation-critical conditions while optimizing memory usage by reducing data points in low-temperature regions where degradation is minimal.
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 precise estimation of battery degradation in replaced batteries, reducing the likelihood of memory overflow and improving estimation accuracy by creating frequency data tailored to high-temperature regions where degradation is accelerated, thus ensuring reliable battery management.
Implementation Method 1
a temperature sensor configured to detect a battery temperature that is a temperature of the battery
Implementation Method 2
the degree of degradation (amount of damage and amount of degradation) of a battery is estimated according to Arrhenius law using the frequency distribution (history) of the battery temperature
Data Source
AI summary
The battery ECU updates the frequency data of the area having the temperature and SOC of the battery as parameters, and estimates the degree of degradation (amount of degradation) from the frequency data and the degradation coefficient that increases as the temperature increases. When the battery is replaced, the battery ECU obtains an estimated full charge capacity value of the replaced battery, and calculates a degree of degradation of the replaced battery from a difference from the full charge capacity at the time of the new battery. Then, new frequency data is created based on the degree of degradation. The new frequency data is created as frequency data of an area having a high temperature and a large SOC, and the other area is null.