Vehicle Battery Life Prediction via Degradation Rate Convergence
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Solution Overview
Problem
Current methods for predicting the end of life of vehicle batteries, such as SLI batteries, fail to accurately account for various degradation mechanisms like corrosion and sulfation, leading to premature or delayed servicing, which affects driver satisfaction and vehicle performance, especially in autonomous vehicles where battery health is critical for safety and functionality.
Innovation Solution
A method that predicts the state of degradation of vehicle batteries by monitoring multiple metrics like internal resistance and capacity, normalizing them to temperature and state of charge, and defining thresholds based on past data and statistical methods, allowing for timely notification of remaining battery life and adjustment of driving behavior to extend battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery health prediction methods are used, then the prediction process is simple, but the prediction accuracy is low leading to premature or delayed servicing
Solution Approach 1:
The patent segments battery degradation into multiple independent metrics (internal resistance, capacity, voltage, current, temperature) rather than using a single composite health indicator. Each metric is monitored and analyzed separately to capture different degradation mechanisms, improving prediction accuracy while maintaining manageable complexity through modular measurement approaches.
Solution Approach 2:
The patent transforms battery health assessment from static threshold-based methods to dynamic parameter tracking by monitoring multiple electrical and thermal parameters over time. The system calculates degradation rates by comparing parameter changes against historical data and operating conditions, enabling more accurate predictions that adapt to varying battery states and environmental conditions.
2Reliability
If multiple battery metrics are monitored to account for different degradation mechanisms, then the prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The patent makes existing battery management system components multi-functional by using the same sensors and processors to simultaneously monitor multiple degradation metrics. The voltage, current, and temperature sensors serve both traditional state-of-charge estimation and the new multi-metric degradation analysis, eliminating the need for additional dedicated hardware and reducing overall system complexity.
Solution Approach 2:
The system uses the battery's own operational data (voltage, current, temperature during normal operation) to assess its health, eliminating the need for external testing equipment or separate diagnostic systems. The battery management system processes its own measurement data to generate degradation metrics and predictions, making the monitoring capability inherent to the battery system itself.
3Ease of operation
If battery servicing is performed based on traditional prediction methods, then maintenance scheduling is simple, but servicing occurs at wrong times causing driver dissatisfaction
Solution Approach 1:
The patent implements a feedback loop where the battery management system continuously monitors degradation metrics, compares actual degradation rates against predicted rates, and adjusts service recommendations accordingly. The system provides ongoing feedback to the driver about battery health status and estimated service timing, allowing for dynamic adjustment of maintenance schedules based on actual battery condition rather than fixed intervals.
Solution Approach 2:
The system performs preliminary degradation analysis and service timing predictions well before actual battery failure occurs by tracking multiple degradation metrics and projecting future battery state. This advance prediction allows service to be scheduled at optimal times before degradation reaches critical levels, preventing premature servicing while avoiding the inconvenience of unexpected battery failures.
Data Source
AI summary
Methods and systems are provided for reliably providing a prognosis of the life-expectancy of a vehicle battery. A state of degradation of the battery is predicted based on a rate of convergence of a metric, that is derived from a sensed vehicle operating parameter, towards a defined threshold, determined based on past history of the metric. The predicted state of degradation is then converted into an estimate of time or distance remaining before the component needs to serviced, and displayed to the vehicle operator. Vehicle control and communication strategies may be defined with respect to the predicted state of degradation.