Load Scheduling in Multi-Battery Devices Using Internal Resistance
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Solution Overview
Problem
Existing electronic devices with multiple battery modules inefficiently manage power loads, as they typically treat all batteries as a single entity, failing to account for varying battery properties and load characteristics, leading to excessive energy wastage and reduced battery life.
Innovation Solution
A load scheduling module that monitors battery parameters and assigns loads based on the type and characteristics of both the batteries and the loads, using algorithms such as greedy and threshold scheduling to optimize energy usage by distributing high-power and low-power loads across batteries with varying internal resistances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If all loads are assigned to a single battery until exhaustion, then device operation continues without interruption, but energy wastage increases and battery life is reduced
Solution Approach 1:
The system changes the parameter of battery selection by considering internal resistance values and load power requirements together. Instead of using a simple exhaustion-based assignment, the system dynamically selects batteries based on matching internal resistance characteristics to load demands, thereby reducing energy wastage while managing complexity through parameter-based decision making.
Solution Approach 2:
The load scheduling system transitions from a static assignment approach to a dynamic one where battery selection changes based on real-time conditions. The system continuously monitors battery internal resistance and adjusts load assignment accordingly, allowing optimal energy utilization while adapting to changing system states without requiring complex centralized control.
2Adaptability or versatility
If loads are continuously divided equally between battery modules, then state of charge remains balanced, but varying battery properties and load characteristics are not taken into account
Solution Approach 1:
The system applies local quality by matching specific battery internal resistance characteristics to specific load power requirements. Instead of treating all batteries uniformly, the system identifies which battery's internal resistance best suits each load's power demand, thereby utilizing varying battery properties effectively while improving energy efficiency through localized optimization.
Solution Approach 2:
The system segments the load scheduling decision into distinct categories based on power requirements (high-power vs. low-power loads) and matches them to batteries with appropriate internal resistance characteristics. This segmentation allows the system to take advantage of individual battery properties without requiring complex continuous optimization, improving both adaptability and energy efficiency.
3Loss of energy
If high-power loads are assigned to batteries with high internal resistance, then device operation is maintained, but energy wastage increases
Solution Approach 1:
The system changes the selection parameter from simple state-of-charge monitoring to internal resistance characterization. By measuring and utilizing internal resistance values, the system can accurately predict energy wastage for different battery-load combinations and make reliable selections that minimize energy loss while maintaining operational reliability.
Solution Approach 2:
The system implements feedback by continuously monitoring battery internal resistance and using this information to adjust load assignment decisions. This feedback mechanism ensures that high-power loads are consistently directed to batteries with lower internal resistance, reducing energy wastage while maintaining reliable operation through data-driven decision making.
4Use of energy by moving object
If multiple battery modules are used, then energy capacity is increased, but management complexity and inefficiency increase
Solution Approach 1:
The system simplifies multi-battery management by changing the focus from tracking individual battery states to measuring and utilizing internal resistance as a key parameter. This single parameter provides sufficient information to make optimal load assignment decisions, reducing management complexity while improving overall energy consumption efficiency across multiple battery modules.
Solution Approach 2:
The system enables batteries to effectively 'self-service' by allowing their internal resistance characteristics to naturally guide load assignment. Batteries with lower internal resistance automatically become preferred for high-power loads without requiring complex centralized control algorithms, thereby reducing management complexity while optimizing energy usage across the multi-battery system.
Data Source
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AI summary
Various embodiments provide techniques and devices for scheduling power loads in devices having multiple batteries. Loads are characterized based on the power required to serve them. Loads are then assigned to batteries in response to the type of load and relative monitored characteristics of the batteries. The monitored battery characteristics can change over time. In some embodiments, stored profile information of the batteries can also be used in scheduling loads. In further embodiments, estimated workloads can also be used to schedule loads.