Power Tool Battery Pack Control Using Neural State Estimation
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Solution Overview
Problem
Existing battery packs and management systems for power tools rely on pre-formulated charging and discharging strategies based on laboratory data, which fail to optimize efficiency in different environments and require on-site failure analysis, lacking adaptability and real-time performance optimization.
Innovation Solution
Integration of a battery pack with a sensor, electronic controller, and machine learning programs, including physics-informed neural networks, to process sensor data for real-time adjustments of charging and discharging, protection thresholds, and mode selection, enabling adaptive battery management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-formulated charging and discharging strategies based on laboratory data are used, then the battery management system is simple to implement, but the charging and discharging efficiency cannot be optimized in different environments
Solution Approach 1:
The patent implements dynamic charging and discharging strategies by using machine learning models that continuously adapt to real-time sensor data and environmental conditions. The system transitions from static pre-formulated strategies to dynamic adaptive control, where parameters such as charging current and discharging thresholds are adjusted based on actual battery state and environmental factors.
Solution Approach 2:
The battery management system performs self-optimization through embedded machine learning models that automatically learn from operational data and environmental conditions. The system self-adjusts charging and discharging parameters without requiring external intervention or complex configuration, enabling adaptive behavior while maintaining relatively simple system architecture.
2Productivity
If pre-formulated battery charging and discharging strategies are used, then the system is easy to manufacture and deploy, but real-time performance optimization cannot be achieved
Solution Approach 1:
The patent pre-trains machine learning models with laboratory data before deployment, enabling the system to start with optimized performance characteristics. This preliminary action allows the models to achieve real-time performance optimization from the beginning of deployment without requiring extensive on-site training or complex configuration procedures.
Solution Approach 2:
The patent replaces traditional rule-based control mechanisms with machine learning-based intelligent control. This substitution enables real-time performance optimization by allowing the system to learn complex patterns and make adaptive decisions, while the embedded nature of the models keeps the deployment process relatively simple.
3Reliability
If laboratory-based charging strategies are used, then the system structure is simple, but failure analysis requires on-site data collection and complex procedures
Solution Approach 1:
The patent implements continuous feedback loops where sensor data from battery operation is fed into machine learning models that monitor battery health and predict potential failures. This feedback mechanism enables real-time reliability assessment and early warning of issues, replacing complex on-site failure analysis with continuous automated monitoring and prediction.
Solution Approach 2:
The machine learning models perform preliminary failure prediction and diagnosis by analyzing operational patterns and sensor data in real-time. This preliminary action identifies potential issues before they manifest as actual failures, enabling preventive maintenance and reducing the need for complex on-site failure analysis procedures.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances battery performance by optimizing charging and discharging efficiency, predicting failures, and providing real-time adaptability to environmental conditions, thereby improving user experience and extending battery life.
Implementation Method 1
the electronic controller includes an electronic processor and a memory, the memory includes a machine learning program for execution by the electronic processor, and the electronic controller is configured to: receive the sensor data; process the sensor data using the machine learning program, where the machine learning program includes a trained neural network model
Implementation Method 2
the physics-informed neural network (PINN) model uses a heat transfer partial differential equation as a constraint of a neural network
Data Source
AI summary
A battery pack includes: a sensor that generates sensor data indicating an operating parameter of the battery pack; and an electronic controller including a machine learning program. The electronic controller is configured to: receive the sensor data; process the sensor data using the machine learning program, where the machine learning program includes a trained neural network model; and use the machine learning program to generate an output based on the sensor data, where the output indicates at least one of a state of charge (SOC), a state of temperature (SOT), a state of health (SOH), and a state of power (SOP) of a cell. The effective utilization of a battery is improved.