Hybrid Energy Storage System for Hybrid and Electric Vehicles: Review and a New Control Strategy
Bibliographic record
Abstract
Onboard energy storage system (ESS) plays a major role for vehicle electrification. The performance of hybrid electric vehicle (HEV), plug-in HEV (PHEV), extended range electric vehicle (EREV), fuel cell vehicle (FCV), and electric vehicles (EV) heavily depends upon their ESS technology. The ESS must be able to store sufficient energy for adequate pure electric range, provide adequate peak power for needed vehicle performance under various driving cycles, absorb energy efficiently during regenerative breaking, and have long operation life and low costs. At present, pure battery based ESS often cannot effectively meet all these requirements due to many trade-offs. In order to improve the overall performance of ESS, integration of two (or more) energy sources have been studied to best utilize the unique characteristics of each, leading to a hybrid energy storage system (HESS). Hybridization of high-energy batteries and ultracapacitors with complementary characteristics present a common choice today. In this paper, the necessity and superiority of a HESS are illustrated considering system performance, efficiency, costs, functional life, and temperature requirements. Three major types of battery-ultracapacitor HESS, passive, semi-active and fully active, are presented. Various HESS control strategies proposed in the past are then reviewed, including rules or reference curves and tables based control, fuzzy logic control, and closed-loop control. Building upon these review and analyses, a novel control strategy based on signal separation using sparse coding is proposed at the end.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".