Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory

Abstract

This paper proposes a practical solution to address the energy management issue in fuel cell hybrid electric vehicles (FCHEVs). This solution revolves around a powertrain system that contains a fuel cell (FC) as the main supply, a photovoltaic cell (PC) as the secondary energy source, and a battery bank (Batt) as backup storage to compensate for the FC’s low response rate. The energy in this hybrid powertrain system alternated between the designated elements and the load via a DC bus, and to maintain a stable output voltage, the DC link was adjusted using a nonlinear approach that is based on the flatness theory and the nonlinear autoregressive moving average (NARMA-L2) neuro-controller. As for the current regulation loops, the sliding mode technique was employed to attain the high dynamic of the reference signals produced by the energy manager loop. To validate the accuracy of the proposed energy management approach (EMA), a test bench was equipped with digital, electronic circuits and a dSPACE DS-1104 unit. This experimental bench contained a fuel cell emulator FC of 1200 W and 46 A, lithium-ion batteries of 24 V, and a solar source capable of 400 W. The obtained results, indeed, attested to the validity of the approach used, yielding a notable performance during multiple charge variations. This ultimately demonstrated that the management approach enhanced the efficiency of the hybrid powertrain.

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

“…Detaching the evaluation procedure from a physics-based model means neglecting the uncertainty or fluctuations in measured data, on which the model relies to accurately prompt the outcome [37]. Moreover, implementing an ANN-based controller allows for improvement of the robustness of the BMS controller against external disturbances, with related impacts that are contained to within 0.33% [38].…”

“…Upon closer examination of the bibliographic contributions, it became apparent that a significant portion addressed cross-topic research. For instance, references [38][39][40][41][42][43][44] explore the overlap between BMS and PQ, focusing on their combined effects on the grid. This underscores the importance of interdisciplinary collaboration, as the application of AI extends beyond the primary area of interest to adjacent topics.…”