Power Tool Battery Charger with Adaptive Maintenance Scheduling
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Solution Overview
Problem
Conventional power tool battery chargers rely on hard-coded thresholds that fail to adapt to changing conditions, limiting their ability to detect and respond to complex usage patterns, maintenance needs, and environmental factors, leading to suboptimal charging and maintenance procedures.
Innovation Solution
The integration of a machine learning controller or artificial intelligence system that collects and analyzes data on usage, maintenance, environmental, and operational conditions to determine optimal charging strategies, maintenance procedures, and non-use modes for power tool battery packs, using techniques such as supervised learning and neural networks to adjust parameters and thresholds dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hard-coded thresholds are used for charging control, then device complexity is reduced and ease of manufacture is improved, but adaptability to changing conditions and usage patterns deteriorates
Solution Approach 1:
The patent implements dynamic charging thresholds that automatically adjust based on real-time monitoring of battery conditions, usage patterns, and environmental factors. The system transitions from static hard-coded values to dynamic adaptive thresholds that learn and optimize charging parameters continuously, resolving the contradiction between manufacturing simplicity and operational adaptability.
Solution Approach 2:
The charging system performs self-optimization by automatically analyzing its own operational data and adjusting charging parameters without external intervention. The system monitors its performance, identifies patterns, and autonomously modifies charging thresholds to improve battery health and charging efficiency, eliminating the need for complex manual configuration while maintaining high adaptability.
2Reliability
If machine learning controllers are integrated to analyze usage data and determine maintenance procedures, then charging optimization and battery health are improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary intelligence layer that sits between the simple charging circuitry and the battery, performing complex analysis and decision-making. This intermediary component handles the sophisticated machine learning algorithms and data analysis, allowing the core charging system to remain relatively simple while achieving advanced battery health management through the mediating intelligent layer.
3Measurement precision
If real-time data collection and analysis are performed to determine maintenance timing and procedures, then maintenance precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The system implements selective monitoring where not all battery parameters are continuously analyzed at full resolution. Instead, the machine learning controller focuses computational resources on critical thresholds and high-risk conditions, performing detailed analysis only when necessary. This partial action approach maintains high maintenance precision for critical issues while reducing overall energy consumption compared to continuous full-spectrum monitoring.
Data Source
AI summary
A power tool battery charger includes a battery pack interface configured to receive a power tool battery pack and provide charging current to the battery pack and an electronic controller including a processor. The electronic controller can be configured to receive a set of data associated with the power tool battery pack and use of the power tool battery pack, determine a time for performing a maintenance procedure based on the set of data, determine one or more maintenance procedures to be performed on the power tool battery pack based on the set of data, and perform the one or more maintenance procedures on the power tool battery pack at the determined time. The maintenance procedures may include determining a maximum capacity of the battery pack, cell balancing of the battery pack, cooling the battery pack or heating the battery pack.