Secondary Battery Capacity Measurement Using Voltage Derivative Curves
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Solution Overview
Problem
Existing secondary battery capacity measurement systems face challenges in accurately estimating the state of charge (SOC) and maximum capacity of batteries without leaving the operating mode, especially in scenarios where current cannot be held at 0 A, and require extensive time and data acquisition to account for battery deterioration, leading to inefficiencies and errors.
Innovation Solution
A secondary battery capacity measurement system that uses derivative curves from separated waveform models of positive and negative electrodes to estimate SOC and maximum capacity, integrating measured data to generate a reference derivative curve that accounts for battery deterioration, allowing for real-time estimation within normal operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maximum capacity measurement is performed by fully charging and completely discharging the battery with a minute current, then measurement accuracy is improved, but measurement time increases significantly and operating efficiency drops
Solution Approach 1:
The invention changes the measurement parameters by using constant current charging/discharging instead of minute current, and uses voltage derivative characteristics (dV/dQ) to estimate capacity. This allows rapid capacity estimation without the time-consuming complete charge-discharge cycles, resolving the contradiction between measurement accuracy and measurement time.
Solution Approach 2:
The invention replaces the mechanical/physical process of complete charge-discharge measurement with a computational approach using voltage derivative analysis. By substituting the physical measurement process with mathematical modeling of voltage characteristics, the system achieves accurate capacity estimation without the time penalty of traditional methods.
2Measurement precision
If SOC calibration operation is performed to correct A/D conversion errors in current sensor, then measurement accuracy is improved, but operation complexity and time increase
Solution Approach 1:
The invention enables the system to self-correct A/D conversion errors by using the voltage derivative characteristics during normal charging/discharging operations. The voltage change rate information inherently contains the correction data needed, eliminating the need for separate calibration operations and reducing system complexity.
Solution Approach 2:
The system uses feedback from voltage derivative measurements to continuously correct SOC calculations. By monitoring the relationship between voltage change and capacity change during operation, the system automatically compensates for sensor errors without requiring external calibration interventions.
3Duration of action of stationary object
If the battery operates within a prescribed SOC range (20%-80%) to suppress deterioration, then battery life is extended, but the ability to perform accurate capacity estimation deteriorates due to limited voltage variation
Solution Approach 1:
The invention changes the measurement parameter from absolute voltage to voltage derivative (dV/dQ). This transformation allows accurate capacity estimation even within the limited 20%-80% SOC range, because the derivative characteristics remain detectable and meaningful within this range, eliminating the trade-off between battery life extension and measurement accuracy.
Data Source
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AI summary
A secondary battery capacity measurement system includes a data convertor, an SOC computer, and a maximum capacity computer. The data convertor determines a partial derivative characteristic curve of a capacity-to-voltage derivative over voltage, the partial derivative characteristic curve indicating a characteristic of a capacity-to-voltage derivative, from a set of historical data of time-sequentially-measured values of voltage and current. The SOC computer computes a difference between the partial derivative characteristic curve and a reference derivative curve indicating a reference characteristic of the capacity-to-voltage derivative, and fits the partial derivative characteristic curve to the reference derivative curve by reducing the difference to estimate an SOC. The maximum capacity computer estimates a maximum value of capacity, from the partial derivative characteristic curve and the reference derivative curve. The reference derivative curve is given by a complex of first and second characteristic derivative curves respectively derived from positive and negative electrode materials.