The current development of fuel cell hybrid electric vehicles is facing many technical challenges, evolving the power sources, the power electronic configuration, the energy management strategy, and the control techniques. Among these challenges rises the optimal sizing issue as fuel cell hybrid electric vehicle efficiency is highly dependent on the on-board energy storage system. Thus, this paper focuses on the optimal sizing of the hybrid energy storage systems by considering the energy management strategy based on frequency separation. Best solutions are computed for different load profiles through a multi-objective grey wolf optimizer. Then, the obtained results are presented and discussed.

The global need to solve pollution problems has conducted automotive engineers to promote the development and the use of electric vehicle technologies. This paper focuses on the fuel cell hybrid electric vehicle which uses a proton exchange membrane fuel cell as a main source associated to hybrid storage device: lithium ion battery and ultracapacitors. A common interest in such technology is to spread out the energy flow between its different sources in order to satisfy the power demand for any requested mission. However, the challenging task stills the optimization of this split to reduce hydrogen consumption and respect, at the same time, the system limitations such as admissible limits of storage system capacities and battery current variation. An adaptive filtering-based energy management strategy is proposed in this paper to ensure an optimum distribution of the energy between the sources taking into account dynamic and energetic constraints of each device. For more performance, a fuzzy logic system is used to adapt the frequency of separation with the system state evolution. A sliding mode control is applied to control electric characteristics (voltage and currents) in the considered hybrid power supply. Simulation results, obtained under MATLAB^®/SimPowerSystems^® for four driving cycles are presented. The proposed strategy achieved good performances by respecting the ultracapacitors state of charge while preserving the battery lifetime under various driving missions.

Fuel Cell Hybrid Electric Vehicles (FCHEVs) are promising solutions for reducing emissions, extending driving range, and reducing reliance on lithium. However, achieving optimal performance requires careful consideration of fuel cell and storage system sizing, battery aging, and energy management. This work proposes a co-design framework that simultaneously optimizes the weight, energy management, and battery lifespan of a fuel cell–battery–ultracapacitor power source. A single WLTP driving cycle is used for sizing, while battery aging is extrapolated over 1000 cycles. Multi-objective optimization is performed using both a Non-Sorted Harris Hawks Optimizer (NSHHO) and a Multi-Objective Grey Wolves Optimizer (MOGWO), targeting total system mass ( $M_{tot}$ ) and battery degradation metrics (capacity and resistance state-of-health, SOH ${}_{cap}$ and SOH ${}_{res}$ ). The results indicate that the optimal total mass ranges between 215.4–223.4 kg, with a battery SOH ${}_{cap}$ degradation of 0.091–0.0925 and SOH ${}_{res}$ degradation of 0.09673–0.09676 over 1000 WLTP cycles. The selected medium solution (OS2) achieves $M_{tot} = 217.4$ kg with $f_{H} = 0.0073$ Hz, including 56 fuel cell cells, 63 battery cells, and 59 ultracapacitor cells. Battery EOL is predicted at 10,929 WLTP cycles for capacity and 10,337 cycles for resistance, representing a 36.6% improvement compared to literature values. The energy management strategy ensures accurate power sharing: the fuel cell covers low-frequency loads, the battery handles medium-frequency power, and ultracapacitors manage high-frequency peaks. Both NSHHO and MOGWO converge to consistent Pareto-optimal solutions, confirming the effectiveness and robustness of the proposed co-design framework.

Fuel cell hybrid electric vehicles are increasingly viewed as a relevant solution for cleaner and more efficient transportation because they combine the high energy capability of fuel cells with the flexibility of electrical storage devices. In architectures integrating a proton exchange membrane fuel cell, a battery, and an ultracapacitor, the overall performance depends on both the sizing of the sources and the way power is distributed during operation. This creates a strongly coupled design problem in which energy management, source dimensions, and battery aging cannot be treated separately. This chapter presents a co-design perspective for such systems by integrating source sizing and frequency-based power allocation within a unified optimization framework. In this approach, each source contributes according to its dynamic and energetic characteristics, while battery durability is assessed through both capacity degradation and internal resistance increase. The chapter highlights how this integrated formulation reveals meaningful trade-offs between compactness, operating consistency, and long-term source preservation. More broadly, it shows that simultaneous optimization provides a more suitable design methodology than conventional staged approaches for strongly coupled FCHEV architectures.

Lithium-ion batteries are key enablers of electrified transportation, and their reliable operation requires accurate real-time state-of-charge (SOC) information. Yet, online SOC estimation remains challenging due to nonlinear battery dynamics, load variations, parameter uncertainties, and the limited availability of resting periods required for open-circuit-voltage (Voc) calibration. This chapter proposes an online SoC estimation approach for a LiFePO4 battery using only measured current and terminal voltage, without requiring prior knowledge of the initial SOC. A low-order equivalent-circuit model is first identified using the recursive least-squares method under pulse-current excitation and subsequently validated against experimental voltage measurements. The Voc–SOC relationship is then embedded as an algebraic constraint to formulate a descriptor battery model. Based on this formulation, a reduced-order unknown-input observer is developed to estimate the battery SOC while accounting for the unknown initial state and unmeasured disturbances. The observer design provides guaranteed convergence properties and avoids the need for additional sensors or direct Voc measurements during operation. Experimental investigations are conducted under both pulse-discharge conditions and the Worldwide harmonized Light vehicles Test Procedure (WLTP) profile to assess the robustness and accuracy of the proposed estimation method. The experimental results demonstrate fast convergence from different initial SoC conditions and high estimation accuracy over a wide operating range. The proposed approach therefore provides a practical and computationally efficient solution for real-time SoC monitoring in electric and hybrid electric vehicle battery management systems, particularly under dynamic operating conditions where conventional OCV-based estimation methods are difficult to apply.