Pattern Recognition Battery State-of-Health Diagnosis
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Solution Overview
Problem
Existing battery state-of-health monitoring systems rely on complex physics-based mathematical models, which are difficult to obtain accurately, making it challenging to determine when a vehicle battery may fail.
Innovation Solution
A pattern recognition system and method using historical and statistical data from testing samples to classify the state-of-health of a vehicle battery during the engine cranking phase, employing a pre-processing unit to normalize data and a classifier with a trained state-of-health decision boundary to determine the battery's condition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based mathematical models are used for battery state-of-health determination, then measurement precision may be improved, but device complexity increases significantly
Solution Approach 1:
The patent uses measured voltage and current data from battery operation to create empirical representations of battery behavior, replacing complex physics-based models with data-driven patterns that capture essential characteristics without requiring detailed physical understanding
Solution Approach 2:
The patent replaces the mechanical/mathematical physics-based modeling approach with an electrical/data-based approach using voltage-current measurements and pattern recognition algorithms, substituting complex theoretical models with empirical data analysis
2Measurement precision
If physics-based mathematical models are used for battery state-of-health determination, then measurement precision may be improved, but ease of operation deteriorates
Solution Approach 1:
The patent creates simplified empirical models based on measured data patterns, replacing difficult-to-implement physics-based models with straightforward voltage-current relationship analysis that is easier to deploy in practical applications
Solution Approach 2:
The patent transforms the problem from requiring multiple complex physical parameters to using simple voltage and current measurements, changing the parameter set to more accessible and easier-to-measure quantities while maintaining determination accuracy
Data Source
AI summary
A method is provided for determining a state-of-health of a battery in a vehicle-during an engine cranking phase. An engine cranking phase is initiated. Characteristic data is recorded that includes battery voltage data and engine cranking speed data during the engine cranking phase. The characteristic data is provided to a pre-processing unit. The pre-processing unit normalizes the characteristic data for processing within a classifier. The normalized data is input o the classifier for determining the vehicle battery state-of-health. The classifier has a trained state-of-health decision boundary resulting from a plurality of trials in which predetermined characterization data is collected with known classes. The battery state-of-health is classified based on the trained state-of-health decision boundary.