Power Tool Battery Prediction Control for Abrupt Shutdown Prevention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Power tools with energy storage devices, such as battery packs, face challenges in managing their lifespan due to complex influencing factors, leading to abrupt disconnections and uncomfortable user experiences, as existing battery management systems struggle to predict and manage energy consumption effectively.
Innovation Solution
A method involving the measurement of one-dimensional or multi-dimensional state vectors of energy storage devices, combined with consumption value measurement, uses a predictive model to estimate future operation states, allowing for proactive control and regulation of power output to extend battery life and prevent sudden tool interruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If battery management systems use abrupt disconnection when critical limits are exceeded, then battery safety is protected, but user convenience deteriorates due to tool interruptions
Solution Approach 1:
The system performs preliminary actions by reducing power output before critical battery limits are exceeded. The control device proactively adjusts power consumption based on predicted battery states, preventing critical situations from occurring rather than reacting with abrupt disconnection after thresholds are violated. This maintains both safety and operational continuity.
Solution Approach 2:
The system applies preliminary anti-action by counteracting potential harmful effects before they manifest. By predicting future battery states and reducing power consumption in advance, the system prevents the harmful effect of abrupt disconnections and tool interruptions, while still protecting against battery damage.
2Reliability
If battery management systems monitor multiple state parameters, then battery lifespan protection is improved, but system complexity increases
Solution Approach 1:
The system introduces a prediction model as an intermediary that processes multiple battery state parameters and consumption data. This mediator translates complex multi-parameter monitoring into simplified predictive outputs, enabling comprehensive battery protection without requiring complex control logic. The prediction model acts as a bridge between detailed state monitoring and straightforward power management decisions.
3Duration of action of stationary object
If power output is reduced proactively based on prediction, then battery life is extended, but power tool performance may deteriorate
Solution Approach 1:
The system applies dynamics by continuously adapting power output based on real-time predictions and actual battery states. Rather than static power reduction, the control device dynamically adjusts power consumption to extend battery life while minimizing impact on tool performance. The system flexibly modulates power delivery to balance longevity and performance requirements.
Solution Approach 2:
The system changes operational parameters by adjusting power consumption levels based on predicted battery states. By modifying power delivery parameters proactively, the system extends battery lifespan while attempting to maintain acceptable tool performance. The control device varies power parameters to optimize the trade-off between battery longevity and operational capability.
Data Source
Figure 1
Figure 2
AI summary
An energy storage device (1) for a power tool is to be operated more reliably. For this purpose, a one-dimensional or multi-dimensional state vector of the energy storage device (1) and a consumption value with respect to the power output of the energy storage device (1) are measured. A predictive value for the power tool or the energy storage device (1) is then determined from the state vector and the consumption value using a model. Finally, the power tool or the energy storage device (1) can be controlled or regulated depending on the predictive value.