Battery Scheduling Module for Cell Balancing and Energy Loss Reduction
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Solution Overview
Problem
Existing battery management systems face challenges in extending battery cell operation time, maintaining voltage balance, and minimizing energy overhead in large-scale battery systems, particularly due to variations in battery cell characteristics and potential cell failures.
Innovation Solution
A battery management system with a scheduling module that dynamically partitions battery cells into groups based on state of charge and recovery efficiency, using a recursive least-squares algorithm to estimate load demand and adjust scheduling ratios to optimize charge, discharge, and rest activities, while minimizing energy loss and preventing deep-discharge and overcharge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cells with higher state-of-charge are used to charge cells with lower state-of-charge, then cell balancing is achieved, but energy loss increases due to DC to AC and vice versa conversion
Solution Approach 1:
The patent extracts the energy management function from the inverter by implementing cell balancing through direct DC connections between battery cells. The scheduling module selectively connects cells in series strings to balance energy distribution without requiring AC conversion, thereby eliminating the energy losses associated with DC-AC-DC conversion while maintaining cell balancing functionality.
Solution Approach 2:
The patent introduces a scheduling module as an intermediary component that manages cell connections and energy distribution. This module acts as a mediator between battery cells, enabling direct energy transfer and balancing through controlled series connections, replacing the need for inverter-based balancing and reducing energy conversion losses.
2Duration of action of moving object
If a scheduling framework operates to extend battery cell operation-time, then operation-time is extended, but device complexity increases
Solution Approach 1:
The patent segments the battery system into multiple series strings with individually manageable cells. The scheduling module divides the battery pack into discrete controllable units, allowing independent management of charge, discharge, and rest activities for each cell or string. This segmentation enables extended operation-time through flexible scheduling while keeping the control architecture manageable through modular organization.
Solution Approach 2:
The patent implements dynamic scheduling that adapts cell activities based on real-time conditions. The scheduling module dynamically adjusts charge, discharge, and rest activities according to cell state-of-charge, load demands, and recovery efficiency. This dynamic approach extends operation-time by optimizing cell utilization while maintaining reasonable complexity through event-driven control logic.
3Loss of energy
If the number of battery cells in the subset is calculated by dividing estimated load demand by recovery rate, then energy overhead is minimized, but measurement precision requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where the scheduling module continuously monitors cell state-of-charge, voltage, and recovery efficiency. The system uses this feedback to refine load demand estimates and adjust the number of active cells dynamically. This feedback loop reduces energy overhead by optimizing cell utilization while compensating for measurement uncertainties through iterative adjustment and adaptive scheduling.
Data Source
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AI summary
Effective scheduling of battery charge and discharge activities, by making the most of battery characteristics, can extend the battery pack's operation-time and lifetime. A system and method for scheduling battery activities is disclosed. This framework dynamically adapts battery activities to load demands and to the condition of individual battery cells, thereby extending the battery pack's operation-time and making them robust to anomalous voltage imbalances. The scheduling framework includes two components. An adaptive filter estimates the upcoming load demand. Based on the estimated load demand, a scheduler can determine the number of parallel-connected battery cells to be discharged. The scheduler also effectively partitions the battery cells in a pack, allowing the battery cells to be simultaneously charged and discharged in coordination with a reconfigurable battery circuit.