A weighted ensemble approach for interpretable state of health estimation of lithium-ion batteries based on generalized additive models
Bibliographic record
Abstract
Accurate state of health (SOH) estimation for lithium-ion batteries is crucial for predicting lifespan, ensuring safety, and optimizing energy storage performance. Despite various data-driven SOH estimation methods, individual models lack robustness and generalization across diverse battery conditions. Ensemble learning has emerged as a promising approach to address these limitations, but it faces challenges such as high computational costs, limited interpretability, and balancing diversity with accuracy. To address these challenges, this study proposes an interpretable weighted ensemble of generalized additive models (GAMs) for SOH estimation by simultaneously considering global and local accuracy optimization and enhancement. To capture time-dependent signal characteristics, data of current, voltage, and temperature are segmented by state of charge (SOC) levels. Features from each SOC segment are used to build individual GAMs, capturing the relationship between specific signal features and SOH. To balance model diversity and accuracy, a novel convex optimization model is proposed to automatically determine the weights of GAMs by achieving a unique balance of interpretability, accuracy, and robustness. Compared to existing data-driven and ensemble methods, the proposed approach achieves superior estimation accuracy and enhanced interpretability by revealing the transparent relationships between signal features across SOC stages and SOH for reliable industrial applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".