A Comprehensive Review of Topologies and Energy Management Methods for Hybrid Energy Storage Systems in Electric Vehicle Applications
Bibliographic record
Abstract
Hybrid energy storage systems (HESSs), combining batteries and supercapacitors (SCs), have emerged as a promising solution to address the conflicting demands of high energy density, power density, and cycle life in electric vehicles (EVs). This paper presents a comprehensive and up-to-date review of power converter topologies and energy management strategies (EMSs) for HESS integration in EVs. First, the paper systematically classifies converter architectures into dual-stage, single stage, and quasi-stage topologies to analyze their operational principles, control flexibility, efficiency, and suitability for modern EVapplications. Detailed comparisons of semi-active, fully active, multi-input, and reconfigurable dual-stage converters, as well as advanced single-stage multi-source inverter (MSI) configurations along with quasi-single-stage and quasi-dual-stage topologies, are provided. Next, the EMSs applicable to HESSs in EVs are reviewed and categorized into rule-based, optimization based, machine learning (ML)-based, and hybrid methodologies. Recent advances in ML, particularly reinforcement learning (RL) algorithms, are critically examined for their potential in enabling adaptive, model-free energy management under complex and uncertain driving conditions. Comprehensive tables and comparative analyses highlight the strengths, limitations, and validation methods of leading techniques. Finally, the paper identifies key trends, technical challenges, and research gaps, and discusses future research directions to guide the co-design of converter topologies and intelligent EMSs for next-generation EVs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".