Battery Charge Management Using Forecast-Based Preparation Modes
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Solution Overview
Problem
Current battery management systems fail to optimally manage the state of charge of energy storage devices, leading to reduced longevity and inefficient energy utilization, as they do not effectively balance the need to maintain battery health with the requirement to provide energy when needed.
Innovation Solution
A battery manager system that controls energy source and load switches based on parameters like user input, sensor data, and forecast data to maintain an optimal state of charge, switching between modes such as maintain, preparation, and in-use modes to maximize battery longevity and energy availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the battery is maintained at high state of charge to ensure energy availability when needed, then energy availability is improved, but battery longevity deteriorates
Solution Approach 1:
The system performs preliminary charging actions based on forecast data before energy is needed. The controller determines a charge start time based on predicted energy requirements and forecasts, charging the battery in advance to ensure energy availability without maintaining high state of charge continuously, thus preserving battery longevity while ensuring reliability when needed.
Solution Approach 2:
The system dynamically adjusts the state of charge based on real-time conditions and forecasts. Rather than maintaining a static high charge level, the controller continuously monitors energy requirements, weather forecasts, and battery status to optimize charge levels, switching between maintaining charge and preserving battery life based on current needs.
2Reliability
If the battery is charged continuously to maximize energy availability, then energy availability is improved, but energy efficiency deteriorates due to unnecessary charging
Solution Approach 1:
The system charges the battery in advance based on forecast data and predicted energy requirements, rather than continuously charging. The controller calculates the optimal charge start time to reach desired charge levels by the time energy is needed, avoiding unnecessary charging cycles and reducing energy waste while ensuring availability.
Solution Approach 2:
The system uses forecast data, energy requirement predictions, and real-time battery status as feedback to control charging operations. The controller continuously monitors conditions and adjusts charging accordingly, preventing unnecessary charging when energy availability is already sufficient or when forecasts indicate future energy surplus.
3Ease of operation
If the system responds reactively to energy needs, then operational simplicity is maintained, but response time deteriorates when rapid charging is required
Solution Approach 1:
The system performs preliminary charging based on forecast data and predicted energy needs, proactively preparing energy reserves before they are required. This automated advance charging eliminates the need for complex real-time decision-making while ensuring rapid response capability when energy is needed, as the battery is already charged or charging in advance.
Solution Approach 2:
The system autonomously manages charging operations using forecast data and energy requirement predictions without requiring manual intervention. The controller automatically determines charge start times, selects energy sources, and manages switching operations, maintaining operational simplicity while achieving fast response times through proactive charge management.
Data Source
AI summary
This invention provides an energy storage device manager, a system comprising the energy storage device manager, computer-readable media configured for providing the energy storage device manager, and methods of using the energy storage device manager. The energy storage device manager can optionally control charge buses and/or load buses to modulate the state of charge of an energy storage device. The energy storage device manager can optionally be configured with a plurality of modes that target different states of charge. The plurality of modes can optionally comprise a maintain mode which targets a nominal (e.g. 50%) charge state and a high-charge mode that targets a state of charge greater than the maintain mode. The plurality of modes can optionally further include an in-use mode which targets a state of charge greater than the maintain mode, and turns on a load bus that is turned off in the preparation mode. The energy storage device manager can optionally be configured to determine a charge start time to execute the preparation mode. The energy storage device manager can optionally be configured to determine the charge start time based on forecast data (e.g. power prediction forecast determined based on weather forecast).