Adaptive Charging Control for Aerosol Devices
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Solution Overview
Problem
Existing aerosol generating devices for smoking alternatives lack efficient battery charging strategies that adapt to usage patterns and charge levels, leading to suboptimal battery life and performance.
Innovation Solution
An adaptive charging module within the aerosol generating device or charging case that learns charging and usage patterns to adjust charging rates, setting thresholds based on anticipated usage, user habits, and device properties to optimize battery charging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a constant high charging rate is used, then charging speed is improved, but battery lifespan and safety deteriorate
Solution Approach 1:
The charging rate is made dynamic rather than constant. The system adjusts the charging rate in real-time based on battery state (charge level, temperature, age) and usage patterns. When the battery reaches certain charge thresholds or temperature limits, the charging rate is automatically reduced to prevent damage, thus resolving the contradiction between fast charging and battery longevity.
Solution Approach 2:
The system changes charging parameters (rate, current, voltage) based on battery state and learned usage patterns. By monitoring battery health metrics and adjusting charging parameters dynamically, the system optimizes both charging speed and battery preservation, preventing the harmful effects of constant high-rate charging.
2Reliability
If charging thresholds are set low, then battery lifespan is extended, but device readiness for use deteriorates
Solution Approach 1:
The system incorporates feedback loops that monitor battery charge levels, usage patterns, and user behavior. Based on this feedback, the system dynamically adjusts charging thresholds and rates. For example, if the system detects that the user typically charges overnight, it can use lower thresholds during that period, but switch to higher thresholds during daytime when quick readiness is needed, thus balancing lifespan extension with device availability.
Solution Approach 2:
The system performs preliminary learning of user charging patterns and device usage habits. By analyzing historical data about when and how the device is used, the system can proactively set optimal charging thresholds before charging begins, ensuring both battery protection and timely readiness based on predicted user needs.
3Device complexity
If fixed charging rates are used, then device complexity is reduced, but adaptability to usage patterns deteriorates
Solution Approach 1:
The charging system performs self-learning and self-adjustment without requiring complex user input or manual configuration. The adaptive charging module automatically monitors usage patterns, analyzes charging behavior, and adjusts charging parameters autonomously. This maintains relative simplicity while achieving high adaptability to individual user patterns and device characteristics.
Solution Approach 2:
The system performs preliminary analysis of usage patterns during initial device use and continues to learn over time. By pre-processing and analyzing usage data, the system builds a model of user behavior that enables automatic adaptation of charging parameters, achieving versatility without proportionally increasing operational complexity.
Data Source
AI summary
An apparatus and a method is described comprising: charging a battery of an aerosol generating device in a first mode of operation when a charge level of the battery is below a first threshold; and charging the battery of the aerosol generating device in a second mode of operation when the charge level of the battery is above the first threshold.