Battery Usage Scenario Modeling for Accurate End-of-Life Prediction
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Solution Overview
Problem
Rechargeable secondary batteries in battery systems have varying lifespans due to usage patterns and environmental factors, necessitating an accurate method to predict their end-of-life for effective management and maintenance.
Innovation Solution
A battery management system that estimates the state of health (SOH) of batteries by integrating electric current changes and usage patterns, generates usage scenarios based on environmental and usage data, and predicts the end-of-life time using machine learning models, allowing for accurate lifespan prediction and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery lifespan prediction is based on simple usage data, then the prediction process is simple and fast, but the prediction accuracy is insufficient
Solution Approach 1:
The patent segments the battery system into multiple components: battery module information acquisition unit, vehicle usage information acquisition unit, prediction model input unit, and prediction result output unit. The prediction model itself is segmented into multiple input factors including SOH, temperature, charging habits, and usage patterns. This segmentation allows complex data to be processed systematically while maintaining prediction accuracy.
Solution Approach 2:
The patent introduces a prediction model as an intermediary between raw battery data and lifespan prediction results. This intermediary processes multiple input parameters (SOH, temperature, charging habits, usage patterns) and transforms them into accurate lifespan predictions. The intermediary handles the complexity internally while presenting simplified results to users.
2Measurement precision
If comprehensive battery and usage data are collected for accurate prediction, then prediction accuracy improves, but data collection complexity and processing requirements increase
Solution Approach 1:
The patent creates a universal data collection framework that gathers multiple types of information through integrated units. The battery module information acquisition unit and vehicle usage information acquisition unit work together to collect diverse data (electrical parameters, temperature, usage patterns, charging habits) using a unified approach, reducing the overall difficulty of data collection while ensuring comprehensive coverage for accurate prediction.
Solution Approach 2:
The patent performs preliminary data collection and organization before prediction. The acquisition units continuously gather and store battery module information and vehicle usage information in advance, preparing the data structure and format needed for prediction. This preliminary action reduces the complexity of real-time data processing during prediction operations.
Data Source
AI summary
A method of predicting a battery lifespan includes estimating a state of health (SOH) of a battery by integrating an amount of electric current while a state of charge (SOC) of the battery mounted in each of a plurality of systems changes, dividing the plurality of systems into a plurality of groups according to a usage pattern collected by each of the plurality of systems at every predetermined period, generating a usage scenario of the battery in each of the plurality of systems, using a usage environment of each of the plurality of groups and the usage pattern, and predicting, for each of the plurality of systems, an end-of-life time of the battery using the usage scenario and the SOH of the battery.