Battery State Determination via Statistical Process Control
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Solution Overview
Problem
Existing battery state determination methods face challenges in accurately and stably identifying rapid changes in battery state, particularly in distinguishing actual changes from measurement or state variable errors and normal scatter, which can lead to incorrect decisions regarding battery health and capacity loss.
Innovation Solution
A method and device for battery state determination that employs statistical process checks, using quality control charts to assess characteristic variables such as state of charge and loss of capacity, by defining observation windows and identifying rapid changes or implausible values within defined limits, thereby filtering out spurious data and ensuring accurate battery state monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional battery state determination methods are used, then basic battery monitoring is achieved, but the ability to reliably distinguish actual rapid changes in battery state from measurement errors and normal scatter is insufficient
Solution Approach 1:
The patent implements feedback by continuously monitoring characteristic variables and comparing them against statistically determined control limits. The system uses feedback loops where measurement results are fed back into the statistical evaluation model, allowing the control limits to be dynamically adjusted based on historical data and actual battery behavior patterns, thereby improving the reliability of distinguishing real changes from measurement noise
Solution Approach 2:
The patent introduces statistical process control charts as an intermediary layer between raw measurements and battery state conclusions. These control charts act as mediators that filter and evaluate characteristic variables, using statistical methods to distinguish between normal measurement scatter and actual rapid changes in battery state, thus enhancing measurement precision without requiring direct modification of the battery or its sensors
2Stability of the object's composition
If statistical process checks are implemented to filter spurious data, then the stability of battery state results is improved, but the complexity of the determination system increases
Solution Approach 1:
The patent applies self-service by enabling the statistical process control system to automatically update its control limits and evaluation criteria based on historical measurement data. The system performs self-calibration and adaptive learning, where the statistical models continuously improve their accuracy by processing accumulated battery data, reducing the need for manual intervention and complex external calibration equipment
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting statistical parameters such as control limit thresholds and observation window sizes based on battery operating conditions. The system modifies these parameters adaptively to maintain optimal stability while managing computational complexity, allowing the determination system to remain relatively simple while achieving high result stability through intelligent parameter adaptation
Data Source
AI summary
A method for determining the state of a battery includes receiving measurements representative of at least one of a battery terminal voltage and a battery terminal current from a first unit and using a second unit to determine at least one characteristic variable for a battery state from at least one of the measured battery terminal voltage and the measured battery current. The method also includes using a microprocessor to statistically assess the at least one characteristic variable for the battery state by performing a statistical process check. The method further includes defining an observation window for the at least one characteristic variable within which the at least one characteristic variable is assumed to be steady-state and identifying an implausible value, which is not caused by the battery, for the at least one characteristic variable if the scatter of the at least one characteristic variable exceeds a defined scatter limit.