Mobile Energy Storage Device Time-to-Charge Prediction
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Solution Overview
Problem
Existing mobile energy storage devices lack effective methods to estimate the time remaining until the state of charge reaches a particular level, especially in off-grid situations where multiple devices are powered or multiple power sources are used, making it difficult for users to manage energy efficiently.
Innovation Solution
An advanced mobile energy storage device (AMESD) with a processor that determines an estimate of time until the state of charge reaches a particular level by monitoring energy storage rate and state of charge, and optionally adjusts solar panel orientation for maximum energy collection, using wireless network communication and energy storage rate prediction algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If the battery stores more energy to extend operation time, then the device capacity increases, but the device weight and volume increase
Solution Approach 1:
The patent implements dynamic power management that continuously monitors battery state of charge, power consumption rates, and usage patterns to dynamically adjust power output and provide accurate time-to-empty predictions. This allows the device to optimize performance based on real-time conditions without requiring excessive battery capacity margins
Solution Approach 2:
The system incorporates feedback mechanisms that track actual power consumption versus predicted consumption, continuously refining estimates of remaining operation time. This feedback loop enables the device to provide accurate runtime information without needing oversized batteries, as users can trust the predicted duration based on actual usage patterns
2Loss of information
If the device provides accurate time-to-empty estimates, then user energy management improves, but the computational complexity increases
Solution Approach 1:
The patent applies simplified algorithms that focus on the most significant factors affecting battery discharge (current draw, battery voltage, temperature) rather than attempting to model all possible variables. This partial action approach provides sufficiently accurate predictions without requiring complex computational models
Solution Approach 2:
The system dynamically adjusts calculation parameters based on operating conditions, using different estimation methods for different states (e.g., heavy load vs. light load, different temperature ranges). This allows accurate predictions across varying conditions while keeping the computational burden manageable through context-aware parameter selection
3Adaptability or versatility
If multiple power sources are used simultaneously, then energy supply flexibility increases, but the difficulty of managing energy flow increases
Solution Approach 1:
The patent implements a universal power management architecture that handles multiple power sources (battery, solar panels, AC mains, DC inputs) through a single integrated control system. This multi-functional approach consolidates what could be multiple separate management systems into one unified controller that automatically selects and manages the optimal power source combination
Solution Approach 2:
The system automatically monitors available power sources, their output capabilities, and current consumption patterns to self-manage power distribution without user intervention. The controller autonomously determines when to charge the battery, when to draw from external sources, and how to allocate power to different outputs, reducing the perceived complexity for users
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
The AMESD provides users with accurate estimates of remaining charge time and optimizes solar energy collection, enhancing energy management and reliability in off-grid situations.
Implementation Method 1
A processor determines, for indication to a user, an estimate of time until the state of charge at least reaches one or more particular levels, the estimate determined at least from the state of charge in conjunction with the energy storage rate
Implementation Method 2
optionally adjusts solar panel orientation for maximum energy collection
Data Source
AI summary
An advanced mobile energy storage device includes an energy storage component for the storage of electrical energy and characterized by a state of charge representative of an amount of energy stored within the energy component and by an energy storage rate into and out of the energy storage component. At least one power input transfers electrical energy into the device for storage in the energy storage component. At least one power output transfers electrical energy out of the device from the energy storage component. A processor determines, for indication to a user, an estimate of time until the state of charge at least reaches one or more particular levels, the estimate determined at least from the state of charge in conjunction with the energy storage rate. The device can network with an external computing device and can generate solar adjustment information.