ArticleScientific reports2025
Research on energy-saving adaptive optimization of hybrid electric vehicle based on improved dynamic programming and control rule extraction.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing reasonable energy management strategy(EMS) to coordinate the working status of various power sources is the key to ascendancy the energy-saving advantages of power-split hybrid electric bus. However, few optimal control strategies have been conducted to utilize operating condition information to achieve approximate online global optimization of energy-saving algorithms, and maximize energy-saving potential. Aiming at the problem, a real-time adaptive EMS based on improved DP algorithm and control rule extraction is proposed for a power-split HEB in this paper. An improved DP algorithm based on secant method for solve the lack of theoretical basis for determining weight coefficients is constructed. The optimal control rules are extracted through outlier detection and graphical method. And an online adaptive algorithm within Relevance Vector Machine and particle swarm optimization algorithm is performed to achieve online adaptive optimal effect. Finally, the simulation and hardware-in-the-loop test are conducted. Simulation results validate that control algorithm proposed in this paper is close to DP results, while achieving the 14.6% oil saving compared with logic threshold control. Additionally, the control algorithm proposed in this paper also demonstrates good real-time performance. The main contribution of this paper is to explore a novel way to fully exert the fuel economy potential and adaptability of power-split HEB in various cycles.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.