Battery State of Charge Estimation Using Multi-Parameter Fusion
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Solution Overview
Problem
Existing battery charge estimation methods, such as voltage-based measurements and coulomb counting, face inaccuracies due to factors like ambient temperature, discharge rate, battery age, and sensor errors, especially when batteries are not fully charged or discharged, and require calibration and account for hysteresis effects.
Innovation Solution
A state of charge estimation model that combines voltage, current, and temperature measurements with prior current values, using machine learning or lookup tables to provide accurate estimates, and can be integrated with other estimation methods like coulomb counting using filters to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voltage-based measurements are used to estimate battery charge, then the measurement process is simple, but accuracy deteriorates when the battery is not close to fully charged or fully discharged
Solution Approach 1:
The patent combines multiple measurement techniques (voltage-based measurements, coulomb counting, and other estimation methods) into a unified state of charge estimation system. By merging these different approaches, the system overcomes the limitations of individual methods and achieves accurate state of charge estimates across the entire battery charge range, not just at extreme levels.
2Duration of action of moving object
If coulomb counting techniques are used to measure battery charge, then continuous monitoring is possible, but sensor measurement errors accumulate over time causing serious inaccuracies
Solution Approach 1:
The patent implements a feedback mechanism where the state of charge estimation model continuously receives current measurements and adjusts estimates based on observed deviations. The system uses voltage measurements and other data to correct accumulated errors from coulomb counting, creating a closed-loop system that maintains accuracy over extended monitoring periods.
3Ease of manufacture
If voltage measurement techniques are used, then no calibration is needed, but hysteresis effects reduce measurement accuracy
Solution Approach 1:
The patent accounts for hysteresis effects by incorporating multiple parameters into the state of charge estimation model, including current, voltage, temperature, and prior current values. By changing from a single-parameter voltage-based approach to a multi-parameter model, the system compensates for hysteresis and achieves accurate measurements without requiring calibration procedures.
4Device complexity
If simple voltage-based indicators are used, then device complexity is low, but accuracy is insufficient for critical applications like electric vehicles and medical equipment
Solution Approach 1:
The patent creates a universal state of charge estimation model that can be applied across multiple applications ranging from consumer electronics to critical systems like electric vehicles and medical equipment. The multi-functional model adapts to different accuracy requirements and can be integrated with existing battery management systems, providing enhanced accuracy without requiring completely separate systems for different applications.
Data Source
AI summary
The present disclosure provides techniques and solutions for obtaining state of charge estimates for one or more battery cells. A set of values is obtained for a set of one or more battery cells. The set of values includes a least one voltage measurement, at least one present current measurement, and at least one temperature measurement. The set of input values is submitted to a state of charge estimation model, as well as at least one prior current value for the set of one or more battery cells. A state of charge estimate is received for the set of one or more battery cells. In various implementations, the state of charge estimation model can be implemented as a machine learning model or as a lookup table. An estimate from the charge estimation model may be combined with one or more other state of charge estimates.