Power Tool Battery Charger With Adaptive ML Charging Control
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Solution Overview
Problem
Conventional power tool battery chargers rely on hard-coded thresholds that fail to adapt to changing conditions, such as inconsistent power sources or user-specific usage patterns, leading to suboptimal charging operations.
Innovation Solution
The integration of a machine learning controller that processes usage data and environmental factors to dynamically adjust charging parameters, such as rate and timing, based on historical data and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hard-coded thresholds are used to control charging operations, then the charging process is simple to implement, but the system cannot adapt to changing conditions such as inconsistent power sources or user-specific usage patterns
Solution Approach 1:
The charging control system transitions from static hard-coded thresholds to dynamic machine learning-based control that adapts to changing conditions. The electronic controller continuously monitors power source characteristics, battery state, and usage patterns, adjusting charging parameters in real-time based on learned patterns rather than fixed rules.
Solution Approach 2:
The system implements feedback loops where the electronic controller monitors charging progress, battery response, and power source stability, then uses this information to adjust charging operations. The machine learning controller learns from historical charging data and user feedback to continuously improve charging strategies.
2Productivity
If machine learning controller is integrated to dynamically adjust charging parameters, then charging optimization is improved, but device complexity increases
Solution Approach 1:
The machine learning controller enables the charging system to self-optimize by automatically learning from usage patterns and power source characteristics. The system performs self-diagnosis and self-adjustment of charging parameters without requiring manual intervention or complex external control, making the complexity manageable through autonomous operation.
Solution Approach 2:
The system dynamically changes charging parameters such as charging rate, voltage, and current based on real-time conditions and learned patterns. The electronic controller adjusts these parameters continuously to optimize charging efficiency while preventing battery damage, rather than using fixed parameter sets.
3Reliability
If charging operations are optimized based on usage patterns and environmental conditions, then battery health and efficiency are improved, but data processing requirements increase
Solution Approach 1:
The machine learning controller pre-processes and analyzes usage patterns, power source characteristics, and environmental conditions before charging begins. By preparing charging strategies in advance based on historical data and predicted conditions, the system reduces real-time data processing requirements while maintaining optimized charging performance.
Data Source
AI summary
A power tool battery charger includes a housing, at least one charging circuit coupled to the housing, and an electronic controller coupled to the housing. The electronic controller is configured to receive power tool device data from a power tool device, which may be the same or another power tool battery charger, a battery pack, and/or a power tool. The power tool device data indicate various data associated with the power tool device. Charger operation data are generated by the electronic controller based on the power tool device data, and can include a charging rate, charging target, and/or time indication for when to adjust the charging rate and/or charging target of the at least one charging circuit. A machine learning or artificial intelligence controller can also be used when generating the charger operation data. The at least one charging circuit is then operated based on the charger operation data.