Estimation of energy storage capacity in aerosol generation systems
A non-invasive method using charge profile data segments and machine learning estimates energy storage capacity in aerosol generators, addressing the inefficiencies of traditional capacity determination methods, enhancing user experience and maintenance efficiency.
JP2026520302APending Publication Date: 2026-06-23PHILIP MORRIS PRODUCTS SA
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- PHILIP MORRIS PRODUCTS SA
- Filing Date
- 2024-04-25
- Publication Date
- 2026-06-23
AI Technical Summary
Technical Problem
Existing methods for determining the capacity of energy storage units in aerosol generators are intrusive, time-consuming, and limit user awareness of the device's charge status, impacting user experience and maintenance efficiency.
Method used
A computer-implemented method estimates the capacity of an energy storage unit by analyzing charge profile data segments, collected non-invasively during normal use, using machine learning models to correlate usage sessions and charge profiles, reducing the need for invasive testing.
Benefits of technology
Provides accurate, timely, and non-intrusive estimation of energy storage capacity, enhancing user experience by enabling informed maintenance and improving device longevity through predictive capacity management.
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Smart Images
Figure 2026520302000001_ABST
Abstract
A computer-implemented method for estimating the capacity of an energy storage unit of an aerosol generator or an auxiliary device configured to charge an aerosol generator with electrical energy, the method comprising: collecting charge profile data of the energy storage unit, wherein the charge profile data includes a charge profile; selecting a data segment of the charge profile within the charge profile data; and determining the estimated capacity of the energy storage unit from the data segment of the charge profile.
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