Battery Monitoring Score for User Habit-Based Lifespan Prediction
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Solution Overview
Problem
Existing battery management systems lack consumer-friendly indicators for battery information, quantitative judgment, and comprehensive user management guides, leading to inefficient battery operation and reduced lifespan.
Innovation Solution
A battery monitoring system that calculates a battery management score based on charging, driving, and parking habits using AI, predicts battery lifespan and detects abnormal behavior, providing user-friendly indicators and guides for improved battery management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly technical terms and jargons are used in battery management information, then professional accuracy is maintained, but user understanding and accessibility deteriorate
Solution Approach 1:
The patent introduces an intermediary translation layer that converts technical battery parameters (voltage, current, temperature, SOC, SOH) into consumer-friendly indicators (battery lifespan prediction, abnormal behavior detection, management scores). This mediator preserves the accuracy of technical measurements while making the information accessible to non-expert users through intuitive visual displays and plain language explanations.
2Measurement precision
If comprehensive battery monitoring parameters are collected, then battery management precision is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the comprehensive battery monitoring system into distinct functional modules: data collection module (gathering voltage, current, temperature), analysis module (calculating SOC, SOH, lifespan prediction), and output module (displaying management scores and indicators). Each module handles specific tasks independently, reducing overall system complexity while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
The patent introduces intermediary processing layers that aggregate and simplify raw battery data into meaningful metrics. Instead of directly processing all raw sensor data, the system uses intermediate calculations (SOC, SOH) and composite indicators (management scores) to bridge the gap between complex measurements and user-friendly outputs, reducing computational burden and system complexity.
3Measurement precision
If AI-based prediction algorithms are implemented, then battery lifespan prediction accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The patent implements partial AI processing by applying machine learning algorithms selectively to the most critical prediction tasks (lifespan estimation, abnormal behavior detection) while using simpler rule-based methods for routine monitoring. This partial application of complex algorithms reduces computational energy consumption while maintaining sufficient prediction accuracy for key battery management decisions.
Data Source
AI summary
A battery monitoring system includes a data receiver configured to receive battery information data and vehicle information data from a data collecting device connected to a vehicle, a battery management score calculator configured to calculate, based on the battery information data and the vehicle information data, factors affecting battery degradation among a charging habit, a driving habit, and a parking habit of a user, calculate, based on the factors, a battery management score, and store the battery management score in a database, and an information transmitter configured to transmit the battery management score to a terminal.