Battery Life Estimation Using Supercycle Model for Partial Cycles
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Solution Overview
Problem
Conventional battery life estimation methods fail to accurately predict battery lifetime in microgrids due to irregular charge and discharge patterns, particularly partial cycles, and do not account for real-time power management to minimize energy costs.
Innovation Solution
A computer-implemented method that defines a supercycle as a time window between two consecutive full charges, treating it as a single discharge unit to assess the impact of partial cycles on battery life, and allocates power between energy storage devices and the grid to maximize battery lifetime by minimizing energy costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional battery life estimation methods are used that assume full charge before each discharge event, then the estimation process is simple, but the accuracy of battery life prediction deteriorates in microgrid applications with partial charge cycles
Solution Approach 1:
The patent segments the battery operation into discrete charge and discharge events, tracking each event's depth of discharge (DoD) and state of charge (SOC) changes. By dividing the continuous operation into manageable segments, the system can accurately accumulate the impact of partial cycles without requiring complex continuous modeling, thus improving accuracy while maintaining reasonable complexity
Solution Approach 2:
The patent introduces a new dimension of tracking by maintaining a running record of cumulative DoD and SOC across multiple partial cycles. This additional dimensional tracking allows the system to account for the cumulative effect of partial charges without fundamentally changing the estimation approach, resolving the contradiction between accuracy and complexity
2Productivity
If real-time battery life estimation is performed during ongoing discharge events, then the power management can be optimized in real-time, but the computational complexity and data update requirements increase
Solution Approach 1:
The patent performs preliminary calculations by pre-establishing the relationship between DoD, SOC, and their cumulative effects on battery life. This preliminary framework allows real-time estimation during discharge events to rely on pre-computed relationships rather than complex real-time simulations, thus improving productivity while controlling complexity
Solution Approach 2:
The system implements feedback by continuously updating the cumulative DoD and SOC records during discharge events and using this updated information to adjust life estimates in real-time. This feedback mechanism enables adaptive power management without requiring overly complex computational models, as it builds on the established preliminary framework
3Duration of action of stationary object
If the battery is kept idle to achieve maximum lifetime, then the battery life is maximized, but the energy cost for the customer increases
Solution Approach 1:
The patent applies partial action by allowing controlled discharge events that do not fully deplete the battery, instead using partial discharges that optimize the balance between lifetime extension and energy cost. This partial utilization strategy prevents the extreme of keeping the battery completely idle while still achieving significant lifetime extension, thus resolving the contradiction between duration and energy loss
Solution Approach 2:
The system dynamically changes operational parameters (discharge depth, charge timing) based on real-time estimation of battery life and energy costs. By adjusting these parameters optimally rather than maintaining fixed idle or full-discharge modes, the system achieves both extended lifetime and reduced energy costs, resolving the contradiction between duration and energy loss
Data Source
AI summary
A management framework is disclosed that achieves maximum energy storage device lifetime based on energy storage device life estimation and the price of energy. The management framework includes a battery life estimation from a supercycle model, for a time window between two consecutive full charges of a battery, which allows assessing a worst case scenario impact of all partial cycles within a supercycle on the battery life. The battery life estimation then considers each supercycle as a single discharge unit instead of treating each individual discharge period separately.