Battery SOH Management With Dynamic Cost-Based Measure Timing
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Solution Overview
Problem
Existing methods for managing secondary battery deterioration do not adequately consider dynamically fluctuating costs, leading to potentially suboptimal measures and timing for reuse or other interventions.
Innovation Solution
A battery deterioration degree management system that calculates costs and predicts the state of health (SOH) to determine measures like rebalancing, rebuilding, or diversion, optimizing economic effects and implementation timing based on fluctuating costs and market prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If measures are implemented without considering dynamically fluctuating costs, then implementation simplicity is maintained, but economic effect is reduced
Solution Approach 1:
The system dynamically adjusts measure selection and timing based on real-time cost fluctuations. The management device continuously monitors cost data and SOH predictions, adapting the optimal measure (rebalancing, rebuilding, or diversion) as cost conditions change, rather than using static decision rules.
Solution Approach 2:
The system implements feedback loops where cost data and SOH measurements are continuously fed back to the management device. This feedback enables the system to recalculate optimal measures based on updated cost information, ensuring decisions reflect current economic conditions.
2Ease of operation
If measures are implemented without considering timing based on fluctuating costs, then decision-making simplicity is maintained, but implementation timing optimization is lost
Solution Approach 1:
The system performs preliminary calculations of optimal measure timing based on predicted SOH trajectories and current cost data. By anticipating future SOH values and cost fluctuations, the system prepares advance recommendations for when measures should be implemented, optimizing timing before actual deterioration reaches critical levels.
Solution Approach 2:
The implementation timing is dynamically determined based on real-time cost fluctuations and SOH predictions. The system continuously updates the optimal timing recommendation as cost conditions change, allowing flexible adjustment of when measures should be implemented to maximize economic effect.
3Device complexity
If static cost assumptions are used, then calculation complexity is reduced, but accuracy of economic effect assessment deteriorates
Solution Approach 1:
The system uses dynamic cost data that reflects real-time market conditions and operational costs. Rather than relying on static cost assumptions, the management device continuously updates cost parameters based on current data, improving the accuracy of economic effect assessments for different measure options.
Data Source
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AI summary
An object of the invention is to provide a technique capable of appropriately selecting a measure with a high economic effect and an implementation timing thereof as a measure to be implemented when a secondary battery deteriorates. A battery deterioration degree management system according to the invention calculates a cost necessary for implementing a measure against a decrease of a state of health of a battery based on cost data describing a cost along with implementation of the measure, and determines the measure having a high economic effect and an implementation timing thereof based on the cost (see FIG. 4).