Battery Lifespan Prediction Using Dual-Model SoC and Temperature Correction
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Solution Overview
Problem
Current battery life prediction methods lack accuracy in estimating the remaining life of secondary batteries, particularly lithium-ion batteries, due to variations in usage patterns and environmental conditions.
Innovation Solution
A battery life prediction apparatus and method that utilizes operational data to generate two results using a current integration method and a life prediction function, with a controller managing and correcting parameters such as State of Charge (SoC), Capacity Rate (CP-rate), and temperature to ensure the accuracy of battery life prediction by minimizing differences between results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery usage data is collected and analyzed to predict lifespan, then prediction accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments battery lifespan prediction into multiple independent factors (cycle count, charge/discharge patterns, temperature history, voltage curves) that can be collected and analyzed separately. Each factor is processed through dedicated modules that contribute to the overall prediction without requiring complete system redesign, thus improving accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary components such as battery management system (BMS) modules that act as mediators between raw battery data and prediction algorithms. These intermediaries pre-process and structure data before analysis, reducing the computational burden on the main system while maintaining high prediction accuracy through layered processing architecture.
2Reliability
If real-time battery monitoring is implemented, then prediction reliability is improved, but energy consumption and processing load increase
Solution Approach 1:
The patent implements periodic monitoring at strategically selected intervals rather than continuous real-time monitoring. Data collection occurs at key events such as charge completion, discharge thresholds, or temperature milestones, maintaining prediction reliability by capturing critical battery states while significantly reducing energy consumption compared to continuous monitoring.
Solution Approach 2:
The patent applies different monitoring intensities to different battery operating conditions. High-frequency monitoring is activated only during critical states (extreme temperatures, high charge rates, abnormal voltage deviations), while normal operating conditions use lower-frequency monitoring, thus maintaining reliability during critical moments while minimizing overall energy consumption.
Data Source
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AI summary
A battery life prediction apparatus according to an embodiment disclosed herein includes a data obtaining unit for obtaining operational data of a battery and a controller for predicting a life of the battery based on a current integration method by using the operational data to generate a first result, predicting the life of the battery based on a life prediction function by using the operational data to generate a second result, and managing the life prediction function based on the first result and the second result.