EV Battery Lifetime Prediction Using SoH and Vehicle Data
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Solution Overview
Problem
Current battery diagnostic tests only measure performance degradation and do not predict the remaining useful lifetime of electric vehicle batteries, which is crucial for determining their residual value for reuse and providing accurate financial services.
Innovation Solution
A method and apparatus that collect battery and vehicle data to generate first and second battery value data, using current capacity and internal resistance-based determination units to assign weights for state of health (SoH) and consider vehicle data clusters to determine the battery's residual value, incorporating feedback for accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery diagnostic test is performed to measure performance degradation, then the degree of performance degradation is determined, but the remaining useful lifetime cannot be predicted
Solution Approach 1:
The patent applies preliminary action by collecting and analyzing usage history data before the battery reaches its end of life. The system continuously gathers data on charging cycles, temperature conditions, and usage patterns throughout the battery's lifecycle, enabling prediction of remaining useful lifetime before actual degradation occurs. This proactive data collection allows the system to forecast battery performance trends and estimate remaining useful life without waiting for performance degradation to manifest.
Solution Approach 2:
The patent implements feedback by using actual performance degradation measurements to refine and update the prediction model. The system compares predicted remaining useful lifetime with actual battery performance over time, using this feedback to adjust and improve the accuracy of future predictions. The model learns from real-world battery behavior patterns, continuously optimizing its ability to predict remaining useful lifetime based on accumulated usage history and actual degradation data.
2Measurement precision
If battery value is determined based on battery diagnostic test only, then the degree of performance degradation is known, but the residual value for reuse cannot be accurately determined
Solution Approach 1:
The patent merges battery diagnostic test data with usage history information to comprehensively determine battery residual value. The system combines multiple data sources including performance degradation measurements, charging cycle counts, temperature exposure history, and usage patterns into a unified assessment model. This integration of diverse information sources enables accurate determination of battery residual value for reuse decisions, considering both current performance state and historical usage conditions.
3Adaptability or versatility
If different usage environments are considered, then the degradation tendency varies, but a single diagnostic test cannot capture these variations
Solution Approach 1:
The patent applies self-service by using the battery management system within the electric vehicle to automatically collect and report usage history data. The vehicle's existing sensors and control systems continuously monitor charging cycles, temperature conditions, and usage patterns, storing this information without requiring external diagnostic equipment. This self-collected usage data provides comprehensive environmental context for predicting degradation tendencies across different usage conditions.
Solution Approach 2:
The patent handles different usage environments by adjusting prediction model parameters based on environmental conditions. The system modifies degradation rate parameters, temperature coefficients, and cycle life expectations according to the specific usage history and environmental conditions recorded. This parameter adaptation allows the same diagnostic system to accurately predict remaining useful lifetime across diverse usage scenarios without requiring physically different diagnostic devices.
Data Source
AI summary
A method of predicting the lifetime of a battery and an apparatus for performing the method can include collecting, by a battery information collection unit, information on the battery, generating, by a first battery value determination unit, first battery value data, generating, by a second battery value determination unit, second battery value data and determining, by a battery value determination unit, a battery value based on the first battery value data and the second battery value data. The first battery value data is a value of the battery, which is determined based on a battery diagnostic test, and the second battery value data is a value of the battery, which is determined based on vehicle data.