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Fuel cell vehicles (FCVs) are considered a promising solution for reducing emissions caused by the transportation sector. An energy management strategy (EMS) is undeniably essential in increasing hydrogen economy, component lifetime, and driving range. While the existing EMSs provide a range of performance levels, they suffer from significant shortcomings in robustness, durability, and adaptability, which prohibit the FCV from reaching its full potential in the vehicle industry. After introducing the fundamental EMS problem, this review article provides a detailed description of the FCV powertrain system modeling, including typical modeling, degradation modeling, and thermal modeling, for designing an EMS. Subsequently, an in-depth analysis of various EMS evolutions, including rule-based and optimization-based, is carried out, along with a thorough review of the recent advances. Unlike similar studies, this paper mainly highlights the significance of the latest contributions, such as advanced control theories, optimization algorithms, artificial intelligence (AI), and multi-stack fuel cell systems (MFCSs). Afterward, the verification methods of EMSs are classified and summarized. Ultimately, this work illuminates future research directions and prospects from multi-disciplinary standpoints for the first time. The overarching goal of this work is to stimulate more innovative thoughts and solutions for improving the operational performance, efficiency, and safety of FCV powertrains.

The use of multi-stack fuel cells (FCs) is attracting considerable attention in electrified vehicles due to the added degrees of freedom in terms of efficiency and survivability. In a multi-stack FC hybrid electric vehicle, the power sources (FCs and the battery pack) have different energetic characteristics and their operation is influenced by the performance drifts caused by degradation. Hence, efficient power distribution for such a multi-source system is a critical issue. This paper proposes a three-layer online EMS for a recreational vehicle composed of three FCs and a battery pack. In the first layer, two online estimators are responsible for constantly updating the characteristics of each FC and the battery to be used by the power distribution algorithm. In the second layer, a rule-based method is developed to improve the calculation speed of the power distribution algorithm by deciding when it should be activated. The last layer performs the power distribution between FCs and battery using a model-free reinforcement learning (RL) algorithm called Q-learning. The proposed RL-based EMS attempts to meet the requested power while minimizing the costs of hydrogen consumption and degradation of all power sources. To justify the performance of the proposed strategy, a comprehensive benchmark with an offline EMS and two online strategies is performed under two driving cycles. In comparison with the online strategies, the proposed method based on RL reduces the defined trip cost up to 11.5 % and 13.08 % under the Real driving cycle while having a higher cost than the offline strategy by 4.78 %.

Proton Exchange Membrane Water Electrolyzers demonstrate significant potential for hydrogen production from renewable energy sources. Addressing the inherent intermittency of these sources, a modular design for the electrolyzers emerges as an essential avenue of research. This study delves into potential solutions and strategies for harnessing renewable energy efficiently to fuel these electrolyzers and presents a comparative analysis between single-stack and modular designs based on a hypothetical scenario. Using experimental data, the research projects the hydrogen output derived from solar energy in Trois-Rivières. Machine learning techniques are employed to forecast available energy from photovoltaic panel datasets. A strategic power allocation mechanism is introduced to regulate input current across each electrolyzer, aiming to optimize system performance. Experimental evaluations on a purpose-built test bench validate the conversion efficiency of the electrolyzer. Notably, the results suggest that embracing a modular design can amplify hydrogen production by over 33% annually while concurrently minimizing system degradation.

Replacing the regular two-stage inverters with a Z-source inverter (ZSI), based on single conversion, has come under attention in fuel cell (FC) hybrid electric vehicles (HEVs). This substitution is to decrease the circuit complexity, cost, and required space. However, fulfilling these goals highly depends on the selection of a suitable topology. This paper performs a multi-criteria ranking to deeply analyze the impact of different ZSI topologies on the performance of a recreational FC-HEV, composed of FC stack and lithium-ion capacitor bank. First, the dual-energy sources are embedded into the Z-source network (ZSN). Subsequently, the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) is utilized to rank different possible ZSI topologies based on four scenarios (standard, compactness, input current ripple, and compactness versus input current ripple). This analysis indicates that each scenario can have a suitable topology with respect to its requirements. Finally, the two highest-ranked ZSI topologies according to standard scenario are implemented by signal hardware-in-the-loop (HIL) to validate the effectiveness of the performed analysis. The results indicate that the use of the ZSI topologies lead to higher average efficiencies (1.98% and 4.07%), and smaller size and volume of passive components, compared to a conventional two-stage inverter.

The usage of multi-objective cost functions (MOCFs) in sizing and energy management strategy (EMS) of fuel cell hybrid electric vehicles (FCHEVs) has expanded due to the participation of multiple technological and economic disciplines. To better understand the impact of price fluctuation on the component size and EMS of an FCHEV, this article proposed a sensitivity analysis methodology. First, a two-step optimization approach that considers hydrogen consumption, system degradation, and trip cost is used to minimize a MOCF of the Can-Am Spyder electric motorcycle simulator. Then, an effect analysis is carried out for the cost-optimal results under two driving profiles to understand the link between cost variation and system performance. These simulations indicate that each might result in different system sizes and EMS compromise. After that, an online optimization EMS based on sequential quadratic programming is used on a reduced-scale hardware-in-the-loop configuration to evaluate the simulation results with varied weights. Experimental results indicate that when an adequate size is used for each pair of weights, the EMS results in a 6% decrease in the trip cost.

Decarbonized hydrogen production using renewable energy sources and water electrolysis is perceived as a promising solution for a sustainable future. The efficiency of PEMWEs relies on several multiphysical aspects and even a slight increase in their efficiency may change the future of sustainable energy routes. Hence, this paper reviews the most compelling research on increasing PEMWE efficiency, which is one of the main pillars for the advancement of this technology. Various publications, including chemical engineering, materials, mass transfer, energy transfer, electrical control, power generation, and hybrid systems, are considered. From the electrolyzer power sources (renewable energy, hybrid, power to gas), inputs (power regulation, water temperature, pressure, ambient temperature), and stack, to components design, control strategy, and new hybrid designs have come under scrutiny in this manuscript. Finally, five essential recommendations are given as the pathways for future studies on PEMWE efficiency.

An energy management strategy (EMS) has an essential role in ameliorating the efficiency and lifetime of the powertrain components in a hybrid fuel cell vehicle (HFCV). The EMS of intelligent HFCVs is equipped with advanced data-driven techniques to efficiently distribute the power flow among the power sources, which have heterogeneous energetic characteristics. Decentralized EMSs provide higher modularity (plug and play) and reliability compared to the centralized data-driven strategies. Modularity is the specification that promotes the discovery of new components in a powertrain system without the need for reconfiguration. Hence, this article puts forward a decentralized reinforcement learning (Dec-RL) framework for designing an EMS in a heavy-duty HFCV. The studied powertrain is composed of two parallel fuel cell systems (FCSs) and a battery pack. The contribution of the suggested multi-agent approach lies in the development of a fully decentralized learning strategy composed of several connected local modules. The performance of the proposed approach is investigated through several simulations and experimental tests. The results indicate the advantage of the established Dec-RL control scheme in convergence speed and optimization criteria. © 2021 SAE International Journal of Electrified Vehicles.

An energy management strategy (EMS) is responsible for distributing the power between the electrochemical power sources of a fuel cell hybrid electric vehicle (FCHEV) with a view to minimizing the hydrogen consumption and maximizing the lifetime of the system. However, the energetic characteristics of the electrochemical devices (fuel cell, battery, and supercapacitor) are time-varying due to the influence of ageing, and different ambient and operating conditions. Any drift in the characteristics of the power sources can lead to the mismanagement of an EMS. According to the literature, ignorance of health adaptation can increase the hydrogen consumption from almost 6.5%–24% depending on the EMS. Therefore, it is necessary to develop a strategy which is aware of the actual state of the components while conducting the power split. Health monitoring techniques are potential candidates to deal with the uncertainties arising from the mentioned factors. In this respect, this paper first puts forward a concise review of the general modeling techniques which are essential for developing precise health monitoring techniques and in turn EMSs. Subsequently, the utilized methods for prognosis, diagnosis, and health state tracking of each of the mentioned power sources in a FCHEV are introduced. Then, a new taxonomy for the classification of the EMSs based on their health-awareness is proposed based on which three categories of prognostic-based, diagnostic-based, and systemic EMSs are formed. Each category is thoroughly explained, and a state-of-the-art review of these health-aware EMSs is presented. Finally, future perspectives of this new line of research and development are discussed before drawing a conclusion.

The temporal changes of power and efficiency in a fuel cell (FC) stack can cause malperformance in the energy management strategy (EMS) of a FC hybrid electric vehicle. Therefore, the online estimation of these physical attributes is becoming an integral part of any EMS. This paper aims to utilize a two-step method to extract the maximum power and efficiency points of a FC system online. In this respect, an online parameter estimation technique, composed of smooth variable structure filter (SVSF) and Kalman filter (KF), is utilized in the first step to estimate the parameters of a FC semi-empirical voltage model. KF generates statistically optimal estimates for a linear, well-designed system model in the existence of Gaussian noise. However, these assumptions do not always hold in real applications and can lead to unstable estimation. A practical solution to deal with these instabilities is to enforce boundaries on the state estimates through SVSF which is based on sliding mode estimation concept. Hence, unlike the other similar studies, this paper synthesizes the robustness of SVSF with the precision of KF to enhance the characteristics estimation process of a FC stack. In the second step, the updated voltage model is utilized to extract the efficiency and power curves of the real FC system. To corroborate the potential of the proposed approach, a thorough comparison with KF, as an attested estimation method, is performed. The experimental tests on a 500-W FC stack indicate the superior performance of the SVSF-KF compared to that of KF.

The use of multiple low-power fuel cells (FCs), instead of a high-power one, in the powertrain of a FC-hybrid electric vehicle (FC-HEV) has recently received considerable attention. This is mainly due to the fact that this configuration can lead to higher efficiency, durability, and reliability. However, the added degrees of freedom require an advanced multi-agent energy management strategy (EMS) for an effective power distribution among power sources. This paper puts forward an EMS based on game theory (GT) for a multi-stack FCHEV with three FCs and a battery pack. GT is a well-approved method for characterizing the interactions in multiagent systems. Unlike the other strategies, the proposed EMS is equipped with an online identification system to constantly update the time-varying characteristics of the power sources. The performance of the suggested strategy is investigated through two case studies. Firstly, a comparative study with two other EMSs, dynamic programming (offline), and a competent rule-based strategy (online), is conducted to realize the capability of GT. Secondly, to justify the necessity of online system identification, the degradation effect of each power source on the EMS performance is examined. The carried-out studies show that the total cost (hydrogen consumption and degradation) of the proposed strategy is almost 6% better than the rule-based EMS while keeping a reasonable difference with dynamic programming. Moreover, health unawareness of power sources can increase the hydrogen consumption up to 7% in the studied system.

One of the recently suggested solutions for enhancing the fuel economy and lifetime in a fuel cell (FC) hybrid electric vehicle (HEV) is the use of a multi-stack (MS) structure for the FC system. However, to fully realize the potential of this structure, the design of an appropriate energy management strategy (EMS) is necessary. This paper aims to compare the operating cost, including hydrogen consumption and degradation of the FC, between a single-stack (SS) and an MSFC-HEV. To do so, a hierarchical EMS, composed of two layers, is devised for the MS system. In the first layer, a rule-based strategy determines how many FCs should be ON according to the requested power, battery state of charge (SOC), and FCs degradations. In the second layer, an equivalent consumption minimization strategy (ECMS) is developed to determine the output power of each activated FC according to the cost function and constraints. Regarding the SS structure, ECMS is employed for power distribution. The purpose of this strategy is to decrease fuel consumption and FC system degradation costs in both structures. The performance of the ECMS is compared with dynamic programming (DP) as a global optimization strategy for validation purposes. The obtained results using experimental data show that an FC-HEV with an MS structure reaches less hydrogen and degradation costs than an SS one.

The advanced modular powertrains are envisioned as primary part of future hybrid fuel cell vehicles (FCVs). The existing papers in the literature solely cope with the hardware side of modularity, while the software side is also vital to capitalize on the total capacity of these powertrains. Driven by this motivation, this article puts forward a comparative study of two novel decentralized convex optimization frameworks based on alternating direction method of multipliers (ADMM) to solve a multi-objective power allocation strategy (PAS) problem in a modular FCV (MFCV). The MFCV in this article is composed of two fuel cell (FC) stacks and a battery pack. Despite the existing centralized strategies for such a modular system, this manuscript proposes two decentralized PASs (Dec-PASs) based on Consensus ADMM (C-ADMM) and Proximal Jacobian ADMM (PJ-ADMM) to bridge the gap regarding the appreciation of modularity in software terms. Herein, after formulating the central PAS optimization problem, the principle of utilizing such decentralized algorithms is presented in detail. Subsequently, the performance of the proposed Dec-PASs is examined through several numerical simulations as well as experiments on a developed small-scale test bench. The obtained results illustrate that decomposition into decentralized forms enables solving the complex PAS optimization problem faster and provides modularity and flexibility. Furthermore, the proposed Dec-PASs can cope with fault and malfunction and thus augment the durability and robustness of modular powertrain systems.

Due to the growing emergence of vehicle electrification, agricultural tractor developers are launching hybrid powertrains in which energy management strategy (EMS) assumes a prominent role. This work mainly aims at developing an EMS for a plug-in hybrid electric tractor (PHET) to minimise fuel consumption and increase the operating range. The developed off-road PHET power sources are composed of a biogas-fuelled Internal Combustion Engine Generator (Bio-Gen), a photovoltaic system, and a battery pack. To control the power flow among different sources, a two-layer EMS is formulated. In this regard, initially, the farm operating mode is recognised by means of classification of a working cycle's features. Then, a control strategy based on a multi-mode fuzzy logic controller (MFLC) is employed to manage the power flow. At each sequence, the classifier identifies the farm operation condition and accordingly activates the relative mode of the MFLC to meet the requested power from the Bio-Gen. The performance of the proposed EMS has been evaluated based on three real-world typical agricultural working cycles. The results demonstrate the successful performance of the proposed intelligent EMS under farm conditions by maintaining the energy sources' operation in a high-efficiency zone which can lead to the extension of the working range and decrease fuel consumption.

This paper compares three optimal hybrid energy system configurations, namely full-active, semi-active and passive, for the particular purpose of assessing the viability of utilizing the passive architecture in a three-wheel electric vehicle composed of a fuel cell (FC) and a supercapacitor. In this respect, the characteristics of these configurations are investigated through three steps. Firstly, the mathematical model of each component is developed by employing experimental data. Subsequently, the optimal size of each topology is determined by a two-step optimization approach. This approach is based on the optimized sizing of power sources, employing a metaheuristic optimization algorithm, and optimal power flow sharing between the sources with the aim of satisfying the requested power while declining the fuel consumption and enhancing the system's lifetime. Finally, the best-achieved size of each architecture is compared in terms of trip cost, capital cost, and the system weight. The obtained results show that passive topology can reduce the trip cost by 14.8% and 6.4% compared to full-active and semi-active ones, respectively. However, the active architecture results in less degradation in the FC compared to the other two topologies. Furthermore, a validation phase is done under a real driving profile and the results are further discussed. © 2021 Elsevier Ltd

Employing semi-empirical models to estimate some characteristics of a fuel cell (FC) stack, such as power and polarization curves, is demanded for efficient design of a power allocation strategy in a FC hybrid electric vehicle. However, the multivariate nature of a FC system has made the design of an accurate model challenging. Since each semi-empirical model has its own pros and cons, this paper puts forward a data fusion approach for online characteristics estimation of a FC stack utilizing four well-known models, namely Mann, Squadrito, Amphlett, and Srinivasan. Despite the other similar techniques, the suggested one utilizes the strengths of each mentioned FC model while avoiding their drawbacks. Kalman filter is employed to identify the parameters of the models online to embrace the uncertainties caused by the alteration of operating conditions and degradation level. Considering the parameters, the output voltage given by each model as well as their covariance are computed. Then, a covariance intersection algorithm is proposed to fuse the estimated output voltages. The fusion of the models’ outputs leads to the estimation of fused characteristics curves. To underline the effectiveness of the proposed method, it is applied to four different experimental datasets extracted from three 500-W Horizon FCs. The obtained results demonstrate the superior performance of the suggested estimator in the sense of mean square error. On average, the mean square error of the data fusion method is 39.64% and 36.59% lower than other studied methods while estimating the polarization curve and power curve, respectively.

Since a proton exchange membrane (PEM) fuel cell (FC) has time-varying characteristics, its online characteristics estimation (voltage, power, internal resistance, etc.) is becoming a key step in designing an energy management strategy (EMS) for hybrid FC vehicles. In this respect, this paper proposes a new method based on Lyapunov adaptation law to estimate the linear and nonlinear parameters of a renowned PEMFC model in the literature. Unlike most of similar estimators, the suggested approach determines the maximum current, which is a nonlinear parameter, online while guaranteeing the system closed-loop stability. This parameter is normally assumed to be constant while it changes through time owing to degradation and operating conditions variation. This alteration makes the model imprecise while extracting some important characteristics, such as maximum power and polarization curve. Therefore, it needs to be regularly updated along with other parameters. To demonstrate the capability of the suggested method, a detailed comparison is provided with the well-known extended Kalman filter (EKF) as an attested nonlinear estimator. Moreover, to highlight the effectiveness of the nonlinearity consideration, a comparison with KF is performed where the nonlinear parameter is considered constant. The performed experiments on a 500-W PEMFC show that the proposed method can be over twice as accurate as EKF and KF concerning the estimation of maximum power and current while its runtime is nearly half of them.

This paper proposes a simplified proton-exchange membrane fuel cell (PEMFC) temperature model for the purpose of estimating PEMFC temperatures with high accuracy using air-cooling systems. Besides knowing that most of the existing models were designed for specific systems, the proposed model also focuses on generalizing the conventional temperature model for easy adoption by other PEMFCs. The proposed model is developed based on the first-order exponential equation to avoid the limitations of complex mechanistic temperature models. The model uses only the information available from typical commercial PEMFCs, the main inputs of which are the current, elapsed time, and ambient temperature. In addition, the PEMFC area, number of cells in the stack, and high/low operating currents were incorporated in the proposed model to ensure its generalizability and applicability to different PEMFC technologies with air-cooling systems under various ambient conditions. The required model parameters were optimized using the Harris hawks optimization method. The proposed model was validated using experiments conducted on the Horizon-500 W and NEXA-1.2 kW PEMFC systems equipped with air-cooling mechanisms under different ambient temperatures and load currents. The root mean square error of all the examined cases was less than 0.5. The proposed model is helpful for simulations, dynamic real-time controllers, and emulators because of its fast response and high accuracy.

This paper puts forward an adaptive cold start strategy for a proton exchange membrane fuel cell (PEMFC) based on maximum power mode. The proposed strategy consists of a water evacuation process after PEMFC shutdown and a self-heating process at PEMFC cold startup. To maximize the performance of the suggested strategy, an optimal operating condition for the cold start procedure is sought first. In this respect, an experimental parametric study is performed to explore the impact of fan velocity, micro-short circuit, anode pressure, and purge procedure on the PEMFC cold start performance. After laying down the proper conditions, the proposed cold start procedure is implemented on a test bench for experimental validations. The self-heating process is based on an online adaptive algorithm that maximizes the PEMFC's internal heat depending on its operating parameters' variation. In fact, this algorithm attempts to keep the current density at high levels, leading to PEMFC's performance improvement achieved by membrane hydration and temperature increase. The experimental results confirm the effectiveness of the proposed strategy, which presents a fast and cost-effective PEMFC's cold start. © 2020 IEEE.

The PEMFC maximum power is greatly influenced by subfreezing temperature and degradation phenomena. Therefore, a dependable model is required to estimate the power with respect to the variation of the operating conditions and state of health. Semi-empirical models are potent tools in this regard. Nonetheless, there is not much information about their cold environment reliability. This paper comprehensively compares the performance of some models (already tested in normal ambient temperature) in subfreezing condition to introduce the most reliable one for PEMFC cold start-up application. Firstly, seven models are compared regarding voltage losses and precision. Subsequently, the three most dependable ones are selected and experimentally compared at sub-zero temperature in terms of polarization curve estimation for three PEMFCs with different degradation levels. The results of this study indicate that the model introduced by Amphlett et al. has a superior performance compared to other ones regarding the characteristic's estimation in below-zero temperature.

The desire to reduce the power electronics related issues has turned the attentions to passive coupling of powertrain components in fuel cell hybrid electric vehicles (FCHEVs). In the passive coupling, the fuel cell (FC) stack is directly connected to an energy storage system on the DC bus as opposed to the active configuration where a DC-DC converter couples the FC stack to the DC bus. This paper compares the use of passive and active couplings in a three-wheel FCHEV to reveal their strengths and weaknesses. In this respect, a passive configuration, using a FC stack and a supercapacitor, is suggested first through formulating a sizing problem. Subsequently, the components are connected in an active configuration where an optimized fuzzy energy management strategy is used to split the power between the components. The performance of the vehicle is compared at each case in terms of capital cost and trip cost, which is composed of FC degradation and hydrogen consumption, and total cost of the system per hour. The obtained results show the superior performance of the passive configuration by 17% in terms of total hourly cost, while the active one only results in less degradation rate in the FC system. © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

This paper addresses the design of a systemic management to improve the energetic efficiency of an open cathode proton exchange membrane fuel cell (PEMFC) in a hybrid system. Unlike the other similar works, the proposed approach capitalizes on the usage of both thermal management strategy and current control to meet the requested power from the system by the minimum fuel consumption. To do so, firstly, an experimentally based 3D mapping is performed to relate the requested power form the PEMFC to its operating temperature and current. Secondly, the reference temperature which leads to gaining the demanded power by the minimum current level is determined to minimize the hydrogen consumption. Finally, the temperature control is formulated by an optimized fuzzy logic scheme to reach the determined reference temperature by acting on the cooling fan of the PEMFC system, whilst the current is being regulated by its controller. The inputs of the fuzzy controller are the PEMFC current and temperature error and the sole output is the duty factor of the fan. The proposed methodology is tested on an experimental test bench to be better evaluated in a real condition. The obtained results from the proposed systemic management indicate promising enhancement of the system efficiency compared to a commercial controller. The proposed method of this work is extendable and applicable in fuel cell hybrid electric vehicles.

Energy management strategy (EMS) has a profound influence over the performance of a fuel cell hybrid electric vehicle since it can maintain the energy sources in their high efficacy zones leading to efficiency and lifetime enhancement of the system. This paper puts forward an online multi-mode EMS to efficiently split the power among the components while embracing the effects of the driving conditions and performance degradation of the fuel cell system. In this regard, firstly, a self-organizing map (SOM) is trained to cluster the driving patterns. The SOM competitive layer in this work is composed of ten driving features as inputs and it classifies the driving patterns into three classes in the output. Subsequently, a three-mode fuzzy logic controller (FLC) is designed and optimized offline by the genetic algorithm for each driving pattern. Unlike the other similar works, the output membership function of the FLC is designed based on the online identification of the maximum power and efficiency of the fuel cell system which change over time. Finally, the SOM is utilized to recognize the driving mode at each sequence and accordingly activate the most suitable mode of the FLC to meet the requested power by efficient use of the energy sources. The performance of the proposed EMS has been validated by using the hardware-in-the-loop platform for several scenarios. The experimental results analyses indicate the promising performance of the suggested methodology in terms of ameliorating hydrogen economy and the fuel cell system lifetime.

Proton exchange membrane fuel cell (PEMFC) models are multivariate with different nonlinear elements which should be identified accurately to assure dependable modeling. Metaheuristic algorithms are perfect candidates for this purpose since they do an informed search for finding the parameters. This paper utilizes three algorithms, namely shuffled frog-leaping algorithm (SFLA), firefly optimization algorithm (FOA), and imperialist competitive algorithm (ICA) for the PEMFC model calibration. In this regard, firstly, the algorithms are employed to find the parameters of a benchmark PEMFC model by minimizing the sum of squared errors (SSE) between the measured and estimated voltage for two available case studies in the literature. After conducting 100 independent runs, the algorithms are compared in terms of the best and the worst SSEs, the variance, and standard deviation. This comparison indicates that SFLA marginally outperforms ICA and FOA regarding the best SSE in both cases while it performs 20% and twofold better than other algorithms concerning the worst SSE. Furthermore, the obtained variance and standard deviation by SFLA are much less than the other algorithms showing the precision and repeatability of this method. Finally, SFLA is used to calibrate the model for a new case study (Horizon 500-W PEMFC) with variable temperature. © 2019 Elsevier Ltd

Output power of a fuel cell (FC) stack can be controlled through operating parameters (current, temperature, etc.) and is impacted by ageing and degradation. However, designing a complete FC model which includes the whole physical phenomena is very difficult owing to its multivariate nature. Hence, online identification of a FC model, which serves as a basis for global energy management of a fuel cell vehicle (FCV), is considerably important. In this paper, two well-known recursive algorithms are compared for online estimation of a multi-input semi-empirical FC model parameters. In this respect, firstly, a semi-empirical FC model is selected to reach a satisfactory compromise between computational time and physical meaning. Subsequently, the algorithms are explained and implemented to identify the parameters of the model. Finally, experimental results achieved by the algorithms are discussed and their robustness is investigated. The ultimate results of this experimental study indicate that the employed algorithms are highly applicable in coping with the problem of FC output power alteration, due to the uncertainties caused by degradation and operation condition variations, and these results can be utilized for designing a global energy management strategy in a FCV. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Proton exchange membrane fuel cells (PEMFCs) have become the center of attention for energy conversion in many areas such as automotive industry, where they confront a high dynamic behavior resulting in their characteristics variation. In order to ensure appropriate modeling of PEMFCs, accurate parameters estimation is in demand. However, parameter estimation of PEMFC models is highly challenging due to their multivariate, nonlinear, and complex essence. This paper comprehensively reviews PEMFC models parameters estimation methods with a specific view to online identification algorithms, which are considered as the basis of global energy management strategy design, to estimate the linear and nonlinear parameters of a PEMFC model in real time. In this respect, different PEMFC models with different categories and purposes are discussed first. Subsequently, a thorough investigation of PEMFC parameter estimation methods in the literature is conducted in terms of applicability. Three potential algorithms for online applications, Recursive Least Square (RLS), Kalman filter, and extended Kalman filter (EKF), which has escaped the attention in previous works, have been then utilized to identify the parameters of two well-known semi-empirical models in the literature, Squadrito et al. and Amphlett et al. Ultimately, the achieved results and future challenges are discussed. © 2018 Elsevier B.V.