Enhanced energy management of dual-stage hybrid energy storage systems with a novel adaptive robust control algorithm
Bibliographic record
Abstract
This paper presents an indirect adaptive robust control algorithm for a nonlinear hybrid energy storage system (NHESS) that can be used in electric vehicles. The NHESS consists of a fuel cell as a primary source and an ultra-capacitor as an additional energy source. A neural network approximation strategy (Indirect Adaptive Robust RBF Neural Network IAR-RBFNN) estimates state and unknown functions. The IAR-RBFNN for the NHESS is resilient and robust, subject to bounded but unknown disturbances that can affect the fuel-cell and ultra-capacitor currents and the output voltage of a DC bus. The proposed controller switches between the two power converters to track the ideal current levels and help regulate the DC-bus voltage to higher levels to improve efficiency. To overcome the effect of the disturbances, the proposed controller contains a robustifying term, and the adaptation laws are guaranteed to be ultimately bounded using an e-modification approach. Additionally, the approximation capacity of radial basis function neural network (RBFNN) systems is employed to estimate the entire system dynamics, unlike only estimation disturbance or a few system parameters. The performance of the proposed control strategy is further evaluated against other reported studies in the literature in terms of several performance indicators. Quantitative results demonstrate that the proposed controller achieves RMSE values of 0.13 Ω for I F C , 0.25 Ω for I P B , and 2.5 V for V D C , significantly outperforming the ATSMC and ORAT2F methods which recorded higher RMSEs of 0.41 Ω , 0.65 Ω , and 3.51 V, respectively. The results reveal that the proposed controller outperforms the benchmarking control strategies regarding the tracking performance for the fuel-cell and ultra-capacitor ideal currents. • A novel robust controller for energy management of hybrid electric vehicles. • IAR-RBFNN used for output voltage regulation of DC Bus of Fuel Cell HEVs. • Improved efficiency of DC-bus voltage of hybrid FC+UC energy system. • Adaptive estimation of the unknown model and external disturbances.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".