Battery Recharacterization Using Zero-Current Charge Pulses
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Solution Overview
Problem
Current battery management systems (BMS) face challenges in accurately characterizing battery parameters, particularly for lithium iron phosphate (LFP) batteries, due to voltage hysteresis and inaccurate state of charge estimation, leading to wide operational margins that can cause damaging effects and hinder wider adoption of these chemistries.
Innovation Solution
Implementing a method that uses zero current periods and known charge pulses to determine battery parameters through an inverted open circuit voltage model, enabling precise estimation of battery state of health and capacity using on-board electrochemical impedance spectroscopy (EIS) systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conservative boundaries are selected for operating conditions, then safety margins are improved, but accuracy of battery state estimation deteriorates
Solution Approach 1:
The system performs preliminary characterization of battery parameters at different states of charge and ages during manufacturing and initial use. This pre-characterization data is stored and used to inform future operating decisions, allowing the system to establish safe boundaries based on actual battery behavior rather than conservative defaults, thereby improving both safety margins and estimation accuracy simultaneously
2Device complexity
If look-up tables based on lab test data are used, then implementation complexity is reduced, but accuracy of battery characterization deteriorates due to aging
Solution Approach 1:
The system transitions from static look-up tables to dynamic parameter updates. Battery parameters are periodically re-estimated during normal operation using measured voltage, current, and temperature data. This dynamic adaptation allows the system to maintain high characterization accuracy throughout the battery's life while keeping implementation complexity manageable through efficient algorithms and processors
Solution Approach 2:
The system implements feedback mechanisms where actual battery performance data is continuously monitored and compared against model predictions. Discrepancies are used to update and refine the battery parameters and state-of-charge estimates. This closed-loop approach ensures accuracy is maintained despite aging effects, while the feedback algorithms are designed to be computationally efficient for practical implementation
3Reliability
If wide margins are used in operating conditions, then safety is improved, but productivity of battery usage deteriorates
Solution Approach 1:
The system performs preliminary characterization of battery parameters at different states of charge and ages during manufacturing and initial use. This pre-characterization data is stored and used to inform future operating decisions, allowing the system to establish safe boundaries based on actual battery behavior rather than conservative defaults, thereby improving both safety margins and estimation accuracy simultaneously
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 enhances the accuracy of battery parameter estimation, allowing narrower operational margins and improving the safety and efficiency of battery operations, particularly for LFP batteries, by minimizing errors and reducing the need for conservative charging practices.
Implementation Method 1
a battery pack also including a second battery circuit, the method comprising: with the first battery circuit at a first state of charge, enforcing a first zero current period in which the first battery circuit is neither charged nor discharged
Implementation Method 2
Electrochemical Impedance Spectroscopy (EIS) is a non-destructive technique for measuring electrical impedances of a material at multiple frequencies to obtain information about internal physical and chemical processes
Data Source
AI summary
Methods and systems for estimating a battery parameter for a rechargeable battery. A sequence of a first zero current period, a first battery measurement of the first battery circuit, delivery of a known quantity of charge to the first battery circuit, a second zero current period, and a second battery measurement is performed. The known quantity of charge may be obtained from various sources. The battery parameter is estimated using an inverted open circuit voltage model, the first and second battery voltage measurements, and the known quantity of charge. The battery parameter may be a battery capacity and/or battery state of health.