Hybrid Energy Storage System With Active Power-Mix Control in a Dual-Chemistry Battery Pack for Light Electric Vehicles
Bibliographic record
Abstract
This paper demonstrates a hybrid energy storage system (HESS), comprised of lithium-ion (LI) and lead-acid (PbA) batteries, for a utility light electric vehicle. While LI batteries have superior energy density, lower internal resistance, and longer lifetime than PbA batteries, the module cost is typically three times higher. The objective of this paper is to design an HESS that: 1) is cost competitive with a PbA single energy storage system (SESS) and 2) maintains most of the performance benefits of a conventional LI SESS. This is done by minimizing the Peukert effect and thus increasing the usable energy of the PbA battery. The proposed modular multiphase dc-dc converter achieves stable interleaving operation, and the traditional noninverting buck-boost converter is modified to allow intermodule balancing. A simple power-mix algorithm with active intramodule state-of-charge balancing is proposed, and the modular hybrid battery system is demonstrated experimentally with low-cost embedded hardware. The cost and the performance of the HESS are assessed side by side with PbA and LI SESS configurations. The HESS has a total projected cost midway between the SESS PbA cost and the SESS Li cost, while providing 23% efficiency (range/kWh) increases over the SESS PbA vehicle.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".