Minimizing battery wear in a hybrid energy storage system using a linear quadratic regulator
Bibliographic record
Abstract
A battery-ultracapacitor Hybrid Energy Storage System (HESS) combines the advantages of both Li-ion batteries and ultracapacitors. Li-ion batteries sustain a relatively long electric only driving range but degrade if exposed to high C-rates and large number of cycles. Ultracapacitors are robust, have a quasi infinite cycle life and can sustain highly dynamic power profiles. This paper proposes a HESS Linear Quadratic Regulator (LQR) design to mitigate issues related to battery wear and peak power demands for electric and hybrid electric vehicles. The LQR controller imposes the battery current with a bidirectional power electronic converter that interfaces the battery to the ultracapacitor. The HESS is accurately modeled using experimental battery and ultracapacitor data in conjunction with equivalent circuit models. Simulations are carried out to validate the LQR controller on a UDDS drive cycle. Reduced battery wear is quantified using a spectral analysis of the battery current, which identifies microcycles.
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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".