Electric Vehicle's Range and State of Charge Estimations using AutoML
Bibliographic record
Abstract
This paper examines the potential of AutoML for predicting the range and State of Charge (SOC) of Electric Vehicles (EVs). Unlike traditional SOC estimation methods, such as Coulomb counting, Equivalent Circuit Models (ECM), or Machine Learning (ML)-based approaches, Range Estimation Algorithms (REA) consider route-specific factors to offer more precise battery depletion predictions. However, ML-based REAs can be complex and time-consuming to train, necessitating a deep understanding of Artificial Neural Networks (NN) architecture and optimization strategies. AutoML addresses this issue by automating the selection of the optimal NN architecture, hyper- parameters, and data preprocessing techniques, making it more accessible for those with limited expertise to develop effective ML models. Our study centers on constructing SOC estimation and range estimation models using the AutoML library AutoGluon, developed by Amazon Web Services (AWS). Our findings indicate that while SOC estimation alone has limitations in predicting an EV's remaining range, REAs are specifically designed to overcome this challenge by building on SOC estimation to accurately forecast the remaining distance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".