Machine Learning Battery Fault Detection System
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Solution Overview
Problem
Lead-acid batteries in vehicles often fail to achieve their targeted life expectancy due to abnormal operation or manufacturing flaws, leading to premature aging and reduced customer satisfaction.
Innovation Solution
A control system utilizing machine learning to analyze key battery parameters, comparing them to a large dataset of known batteries to identify premature aging, and triggering alerts or mitigation actions when a high confidence level is reached, allowing for timely replacement or operational adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery monitoring methods are used, then the system is simple and easy to implement, but it cannot accurately predict premature battery failure
Solution Approach 1:
The patent replaces traditional rule-based battery monitoring systems with a machine learning-based predictive system. The control system uses trained machine learning models that analyze multiple battery parameters (voltage, current, temperature, state of charge) to predict premature failures with high accuracy, substituting mechanical/threshold-based monitoring with intelligent algorithms that adapt to different battery types and operating conditions.
Solution Approach 2:
The system monitors multiple battery parameters simultaneously (voltage, current, temperature, state of charge) and uses machine learning to analyze changes in these parameters over time. By tracking parameter trends and deviations from expected behavior patterns, the system can detect premature aging and predict failures before they occur, going beyond simple threshold monitoring.
2Reliability
If machine learning analysis is applied to predict battery failure, then prediction accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent implements machine learning models during an offline training phase where historical battery data is collected and used to train predictive algorithms. Once trained, the models are deployed to the control system and can make rapid predictions during online operation. This preliminary training action separates the computationally intensive model development from the real-time prediction process, enabling fast and reliable failure predictions during actual battery operation.
3Measurement precision
If continuous monitoring of multiple battery parameters is implemented, then detection accuracy improves, but energy consumption increases
Solution Approach 1:
The control system performs multiple functions using the same collected data: it monitors battery parameters for normal operation, trains machine learning models for predictive analytics, and provides real-time failure predictions. By making the data collection and processing system multi-functional, the patent avoids the need for separate dedicated hardware for each function, thereby reducing overall energy consumption while maintaining high detection accuracy.
Data Source
AI summary
A control system, responsive to receiving values of one or more parameters of a battery of a vehicle for a particular time in service, generates via a machine learning determination a level of confidence at which the values of the one or more parameters of the battery match values of one or more parameters of a set of batteries for a same time in service, and responsive to the level exceeding a predefined threshold, causes a mitigation action to be implemented.