HMSPC: A Hybrid Mechanistic-Stochastic Physical-Continuous Model for Battery Dynamics
Bibliographic record
Abstract
Battery voltage dynamics are irregularly sampled, noise-corrupted, and strongly regime-dependent, properties that challenge standard sequential models. I propose HMSPC (Hybrid Mechanistic-Stochastic Physical-Continuous Model), a continuous-time latent variable model that addresses these challenges through two key components: a gated input-conditioned latent ODE that explicitly incorporates exogenous observations (current and temperature) into continuous-time state evolution, and a heteroscedastic observation model with uncertainty regularization that produces calibrated predictive variance. Built on the Latent ODE (Rubanova et al., 2019) framework, HMSPC replaces purely autonomous latent dynamics with a learned gating mechanism that adaptively controls how strongly operating conditions influence trajectory evolution at each integration step. Evaluated on the MIT-Stanford dataset (Severson et al., 2019) against Latent ODE and Vanilla Neural ODE (Chen et al., 2018) baselines across 5 seeds, HMSPC achieves a mean RMSE of 32.37 ± 1.34 mV compared to 58.33 ± 4.19 mV for Latent ODE, yielding a 45% reduction, alongside well-calibrated uncertainty estimates (ECE 0.078). Ablation studies confirm that input-conditioned drift and heteroscedastic noise each contribute meaningfully to both accuracy and calibration.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".