Hybrid lead-acid/lithium-ion energy storage system with power-mix control for light electric vehicles
Bibliographic record
Abstract
The performance versus cost tradeoffs of a fully electric, hybrid energy storage system (HESS), using lithium-ion (LI) and lead-acid (PbA) batteries, are explored in this work for a light electric vehicle (LEV). While LI batteries typically have higher energy density, lower internal resistance and longer lifetime than PbA batteries, the module cost of LI batteries are typically three to five times the cost of PbA batteries. The objective is to design a HESS that 1) is cost-competitive with a PbA single energy storage system (SESS) and 2) maintains most of the performance benefits of a lithium SESS. A modular HESS architecture with a bi-directional dc-dc converter and controller is proposed, and a power-mix algorithm with active inter-chemistry battery state-of-charge (SOC) balancing is presented, simulated, and verified experimentally. The batteries, converter and control algorithm are modeled in MATLAB, and the effects of total ESS energy, vehicle loading, depth-of-discharge (DOD), and speed are explored. The cost and performance of the HESS are assessed side-by-side with PbA and LI single energy storage system (SESS) configurations of comparable total energy, using expected vehicle range as the performance metric. The experimental HESS has a total projected cost midway between the SESS PbA cost and the SESS Li cost, while providing 17% range and 22.5% efficiency increase over the SESS PbA vehicle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".