Coupled energy management algorithm for MESS in urban EV
Bibliographic record
Abstract
Multi‐source energy storage systems (MESSs) have been gaining prominence in electric vehicles (EVs) research area. Energy‐ and power‐flow control of on‐board MESS and its integration are essential to the performance of urban EVs. Development of an energy management system (EMS) is an important issue with significant influence on the EV range and capabilities. In this study, an innovative coupled energy management algorithm is presented, applied to a fully decoupled MESS containing batteries and supercapacitors (SCs). The proposed energy management algorithm uses an original online filtering technique coupled to a fuzzy logic controller (FLC). The main advantages of the coupled approach and filtering are identified and discussed. The online filtering technique is placed inside the control loop, allowing the decoupling of the frequency of the battery power reference signal given by the FLC. The control loop as well as the EMS were previously simulated in MATLAB/Simulink™ for an urban EV. Furthermore, the coupled EMS has been validated through power‐level reduced‐scale hardware‐in‐the‐loop (HIL) simulations. The experimental results show the effectiveness of the proposed coupled energy management algorithm. As a result of this development, the proposed EMS is effective in controlling the power‐flows with battery lifetime improvement and optimisation in EV performance.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".