Battery Failure Rate Prediction Using Aging and Climate Simulation
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Solution Overview
Problem
Existing statistical methodologies for predicting the failure rate of electric vehicle batteries are limited by their mechanical reliability analysis roots, which do not effectively account for the electrochemical properties of batteries.
Innovation Solution
An apparatus and method that utilize aging simulation to predict battery capacity fade, combining extended driving profiles and climate conditions to generate probability weights, and calculate a parts per million (PPM) value based on these simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing statistical methodology for mechanical reliability analysis is used, then the analysis framework is established, but it cannot effectively account for electrochemical properties of batteries
Solution Approach 1:
The patent transforms mechanical reliability parameters into electrochemical-specific parameters by introducing capacity fade data, cycle degradation, and calendar degradation as key metrics. This allows the statistical methodology to adapt to battery's electrochemical properties while maintaining the reliability analysis framework.
Solution Approach 2:
The patent segments the failure rate prediction into distinct components: cycle degradation (from charging/discharging cycles) and calendar degradation (from storage over time). This segmentation allows separate modeling of different degradation mechanisms specific to electrochemical systems.
2Measurement precision
If aging simulation with extended driving profiles and climate conditions is implemented, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary aging simulations to generate capacity fade data and degradation patterns before conducting the actual failure rate prediction. This pre-computed data is then used in the statistical analysis, reducing the need for complex real-time simulations while maintaining prediction accuracy.
Solution Approach 2:
The patent introduces probability weights as an intermediary element that bridges the gap between simulation results and failure rate prediction. These weights represent the likelihood of different degradation scenarios occurring, allowing complex simulation data to be transformed into actionable reliability metrics.
3Reliability
If multiple driving profiles and climate conditions are combined, then comprehensive battery degradation is captured, but data processing requirements increase
Solution Approach 1:
The patent merges multiple driving profiles and climate conditions into unified extended driving profiles that capture the combined effects of different operational scenarios. This consolidation reduces data volume while preserving the comprehensive degradation information needed for accurate reliability prediction.
Data Source
AI summary
Disclosed is an apparatus for predicting a failure rate, the apparatus comprising: an aging simulator configured to generate a plurality of capacity fades by simulating a life analysis on a plurality of extended driving profiles generated by combining at least two driving profiles extracted from original data, wherein a plurality of climate conditions are applied to the life analysis; and part per million (PPM) simulator configured to generate a plurality of probability weights based on a plurality of extended weights corresponding to the plurality of extended driving profiles and weights corresponding to the plurality of climate conditions, and calculate a PPM value based on the plurality of capacity fades and the plurality of probability weights.