Battery Allocation Control Using Deterioration Change Prediction
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Solution Overview
Problem
Existing battery management systems fail to accurately predict secondary battery deterioration due to neglecting the influence of past usage history and future battery load, leading to inefficient management and reduced cruising distance in electric vehicles.
Innovation Solution
A battery management system that includes a usage history acquisition unit, deterioration estimation unit, battery load acquisition unit, and deterioration change degree identification unit to assess current and future deterioration states, allowing for optimal association of secondary batteries with future use modes to minimize deterioration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only predetermined target time point prediction is used without considering past usage history, then the prediction process is simple, but the accuracy of deterioration prediction is insufficient
Solution Approach 1:
The system performs preliminary acquisition and storage of usage history information before deterioration prediction is needed. By pre-collecting data on battery usage patterns, temperature conditions, and charge/discharge cycles, the system prepares the necessary historical context in advance, enabling more accurate predictions when allocation decisions are made without adding complexity to the real-time decision process
Solution Approach 2:
The system establishes a feedback loop where actual deterioration results from previously allocated battery-vehicle combinations are fed back into the usage history database. This feedback mechanism continuously refines the prediction model by comparing predicted deterioration with actual outcomes, improving future prediction accuracy while maintaining a manageable system structure through iterative learning
2Reliability
If allocation is changed without updated deterioration prediction, then management operations are simple, but the deterioration prediction does not reflect current battery state
Solution Approach 1:
The system transitions from static predetermined-time prediction to dynamic on-demand prediction triggered by allocation changes. When a battery-vehicle allocation is modified, the system dynamically recalculates deterioration predictions based on updated usage history and new allocation conditions, ensuring reliability without requiring continuous predictions that would reduce management efficiency
Solution Approach 2:
The system changes the prediction parameter from fixed time-point estimation to flexible condition-based estimation. By adjusting the prediction to reflect actual usage patterns accumulated in the usage history and specific allocation conditions, the system maintains reliable deterioration assessments that adapt to changing battery states and allocation scenarios
3Duration of action of stationary object
If secondary batteries with larger deterioration prediction are assigned to high load vehicles, then short-term performance is maintained, but long-term battery life is reduced
Solution Approach 1:
The system performs preliminary deterioration prediction and usage history analysis before making allocation decisions. By evaluating both the predicted deterioration at target time points and the accumulated usage history, the system proactively assigns batteries to vehicles in a way that balances immediate performance needs with long-term durability, preventing premature battery failure before it occurs
Solution Approach 2:
The system changes the allocation decision parameter from solely performance-based to a composite parameter including deterioration prediction, usage history, and vehicle load characteristics. This multi-parameter approach enables optimization of both battery life and cruising distance by matching battery condition with appropriate vehicle requirements
Data Source
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AI summary
A battery management system (1) includes a usage history acquisition unit (51a), a deterioration estimation unit (51b), a battery load acquisition unit (51c), a deterioration change degree identification unit (51d), and a battery management unit (51e). The usage history acquisition unit (51a) acquires usage history information. The deterioration estimation unit (51 b) estimates a current deterioration state and a deterioration factor of the secondary battery based on the usage history information. The battery load acquisition unit (51c) acquires battery load information indicating a future battery load. The deterioration change degree identification unit (51d) identifies a deterioration change degree by using the current deterioration state, the deterioration factor and the battery load information of the secondary battery. The battery management unit (51e) uses the deterioration change degree to associate secondary batteries with future use modes so that an evaluation value indicating a degree of deterioration of the secondary battery in use devices becomes a minimum value.