Battery SOH Prediction Using Multi-Mechanism Degradation Models
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Solution Overview
Problem
Existing SOH prediction techniques for batteries only consider the loss of lithium inventory (LLI) and fail to account for additional factors during cycle experiments and actual battery use, leading to inaccurate predictions.
Innovation Solution
A battery state prediction model that includes first, second, and third prediction models to predict loss of lithium, active material in the cathode, and active material in the anode, respectively, using acceleration factors, cation mixing, particle cracking, and volume changes to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing SOH prediction techniques only consider loss of lithium inventory (LLI), then the prediction model is simple, but the prediction accuracy is insufficient because additional factors during cycle experiments and actual battery use are not considered
Solution Approach 1:
The patent segments the SOH prediction model into three distinct prediction models: a first prediction model for loss of lithium inventory (LLI), a second prediction model for loss of active material in cathode (LAMc), and a third prediction model for loss of active material in anode (LAMa). Each model focuses on a specific degradation mechanism, allowing comprehensive prediction while maintaining manageable complexity through modular structure.
Solution Approach 2:
The patent introduces multiple degradation parameters beyond traditional LLI, including LAMc (loss of active material in cathode) and LAMa (loss of active material in anode). By changing the parameters considered in the prediction model to include these additional factors, the system achieves more accurate SOH prediction that reflects actual battery degradation during cycle experiments and use.
2Measurement precision
If multiple prediction models considering LLI, LAMc, and LAMa are integrated, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent merges three separate prediction models (first model for LLI, second model for LAMc, third model for LAMa) into an integrated battery state prediction system. The processor combines outputs from all three models to determine overall battery state, achieving comprehensive prediction accuracy by consolidating multiple specialized models into a unified system.
3Reliability
If the prediction model considers degradation factors during cycle experiments and actual use, then the model reflects real battery behavior, but the model becomes more complex compared to storage-only models
Solution Approach 1:
The patent applies local quality by creating specialized prediction models for different degradation mechanisms: the first prediction model specifically addresses LLI, the second model specifically addresses LAMc in cathode, and the third model specifically addresses LAMa in anode. Each model is optimized for its specific degradation type, allowing the system to accurately capture local degradation characteristics while maintaining overall system reliability.
Data Source
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AI summary
A battery state prediction apparatus according to an embodiment disclosed herein includes a communication interface configured to receive battery data from a battery and at least one process configured to generate a battery state prediction model comprising a first prediction model that predicts a loss of lithium, a second prediction model that predicts a loss of an active material in a cathode, and a third prediction model that predicts a loss of an active material in an anode and input the battery data to the battery state prediction model to obtain battery state data.