Research Project: Taşıtlarda 2025-2030 Regülasyonlarına Uyum Amacıyla Sera Gazı ve Zararlı Egzos Emisyonlarının Azaltılması İçin Teknolojilerin Geliştirilmesi
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Contributors
Funders
ID
TB.00511
Authors
Erkey, Can
Faculty Member
Publications
Real-time optimal hierarchical energy and power management system for fuel cell-battery hybrid electric vehicles
(Pergamon-Elsevier Science Ltd, 2025) Tümer, Beril; Arkun, Yaman; Tümer, Beril; Yildiz, Deniz Sanli; Graduate School of Sciences and Engineering; Department of Chemical and Biological Engineering; Yes; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING; College of Engineering
The transportation sector is a major contributor to global greenhouse gas emissions, with heavy-duty vehicles (HDVs) accounting for a significant share. Hydrogen fuel cell technology, particularly Proton Exchange Membrane Fuel Cells (PEMFCs), offers a promising zero-emission solution for HDVs due to their high efficiency and environmental benefits. However, standalone PEMFC systems face challenges in dynamic response and energy recovery. To overcome these limitations, Fuel Cell Hybrid Electric Vehicles (FCHEVs) integrate PEMFCs with batteries. This hybridization requires a robust Energy Management System (EMS) for optimal power distribution. The main objective is to minimize the hydrogen consumption while satisfying the power demand and the battery state-of-charge (SOC) constraints in the presence of fuel cell and battery degradation over time. This study presents a hierarchical optimal energy and power management system for FCHEVs with multiple PEMFC stacks and a battery. The upper layer of the hierarchy consists of a dual-rate economic model predictive controller which optimally splits the total power demand between the 'slow' fuel cell system and the 'fast' battery considering total hydrogen consumption. The second-layer controller then distributes the power demand allocated to the fuel cell among the individual stacks, taking into account the hydrogen consumption and degradation of individual stacks. The fast transient power demands which cannot be met by the fuel cell stacks are identified and allocated to the battery control system. A mechanistic dynamic PEMFC model is combined with a battery model to support the proposed hierarchical control strategy. Simulation results show that the proposed method consistently achieves lower hydrogen consumption than two benchmark strategies-rule-based control and SOC trajectory control.
Water and thermal management in PEM fuel cells using feasible humidity plots and model predictive controllers
(Elsevier , 2025) Arkun, Yaman; Tümer, Beril; Tümer, Beril; Yildiz, Deniz Sanli; Department of Chemical and Biological Engineering; Yes; College of Engineering
Water and thermal management are critical for the performance, efficiency and longevity of PEM fuel cells (PEMFCs). Effective water and thermal management require the design of control systems that can maintain the water balance and temperature at stable and optimal levels. In this paper, we consider a stack of PEM fuel cells integrated with a water recovery and cooling system. A mechanistic dynamic model is developed to be able to predict the water content and temperature in response to the fuel cell inputs. Water management uses a cascade arrangement of a supervisory Model Predictive Controller (MPC) and local anode and cathode PID humidity controllers to balance the membrane water content. Thermal management consists of a separate MPC controller to regulate the fuel stack temperature. One novelty of this work lies in identifying and utilizing the feasible region for the relative humidities of the anode and cathode when controlling the membrane water content. We introduce the feasible humidity plots (FHP) which define the feasible values for the anode and cathode relative humidities for a given fuel cell design and its operating conditions. This useful information helps to assign the setpoint values to the local PID humidity controllers of the water management system. It is shown by simulations that the water and thermal management MPC controllers work in tandem and successfully track the desired setpoint changes in humidity and temperature while rejecting external disturbances such as load changes. In addition, the control system is robust against modeling errors and possible model-plant mismatch introduced by fuel cell aging.
Real-time optimal power sharing in multi-stack fuel cells
(Elsevier, 2025) Tümer, Beril; Arkun, Yaman; Tümer, Beril; Şanlı Yıldız, Deniz; Department of Chemical and Biological Engineering; Yes; College of Engineering
This paper presents a real-time optimization strategy for power allocation between two fuel cell stacks, maximizing overall efficiency while minimizing hydrogen consumption. The proposed method accounts for stack degradation, characterized by a time-varying electron transfer coefficient (α), estimated in real-time using RLS-Kalman filtering from voltage measurements. The strategy also considers hydrogen crossover effects, which impact fuel efficiency and utilization. The optimization approach was evaluated against two conventional strategies—equal distribution and daisy chain—demonstrating superior performance across various operating scenarios. A new efficiency-based daisy chain algorithm was introduced and compared with the classical power-based method, further highlighting the benefits of the optimization framework. The real-time formulation enables on-the-fly parameter estimation and model updates, making it adaptable to multiple stacks and various objective functions. This approach provides a robust and scalable solution for fuel cell power management under degradation, aging, and other adverse conditions.
