Efficiency upgrade of hybrid fuel cell vehicles' energy management strategies by online systemic management of fuel cell

This paper puts forward an approach for boosting the efficiency of energy management strategies (EMSs) in fuel cell hybrid electric vehicles (FCHEVs) using an online systemic management of the fuel cell system (FCS). Unlike other similar works which solely determine the requested current from the FCS, this work capitalizes on simultaneous regulation of current and temperature, which have different dynamic behavior. In this regard, firstly, an online systemic management scheme is developed to guarantee the supply of the requested power from the stack with the highest efficiency. This scheme is based on an updatable 3D map which relates the requested power from the stack to its optimal temperature and current. Secondly, two different EMSs are used to distribute the power between the FCS and battery. The EMSs' constraints are constantly updated by the online model to embrace the stack performance drifts owing to degradation and operating conditions variation. Finally, the effect of integrating the developed online systemic management into the EMSs' design is experimentally scrutinized under two standard driving cycles and indicated that up to 3.7% efficiency enhancement can be reached by employing such a systemic approach. Moreover, FCS health adaptation unawareness can increase the hydrogen consumption up to 6.6%

An adaptive state machine based energy management strategy for a multi-stack fuel cell hybrid electric vehicle

This paper aims at designing an online energy management strategy (EMS) for a multi-stack fuel cell hybrid electric vehicle (FCHEV) to enhance the fuel economy as well as the fuel cell stacks (FCSs) lifetime. In this respect, a two-layer strategy is proposed to share the power among four FCSs and a battery pack. The first layer (local to each FCS) is held solely responsible for constantly determining the real maximum power and efficiency of each stack since the operating conditions variation and ageing noticeably influence stacks' performance. This layer is composed of a FCS semi-empirical model and a Kalman filter. The utilized filter updates the FCS model parameters to compensate for the FCSs' performance drifts. The second layer (global management) is held accountable for splitting the power among components. This layer uses two inputs per each FCS, updated maximum power and efficiency, as well as the battery state of charge (SOC) and powertrain demanded power to perform the power sharing. The proposed EMS, called adaptive state machine strategy, employs the first two inputs to sort the FCSs out and the other inputs to do the power allocation. The ultimate results of the suggested strategy are compared with two commonly used power sharing methods, namely Daisy Chain and Equal Distribution. The results of the suggested EMS indicate promising improvement in the overall performance of the system. The performance validation is conducted on a developed test bench by means of hardware-in-the-loop (HIL) technique

Investigation of the battery degradation impact on the energy management of a fuel cell hybrid electric vehicle

This paper studies the influence of battery degradation over the performance of a fuel cell hybrid electric vehicle (FCHEV). For this purpose, an optimized fuzzy strategy based on the costs of battery and fuel cell degradations as well as fuel consumption and battery recharging is employed. Simulations are done by two driving cycles for three scenarios based on battery state of health (SOH) and validity of feedback signal. Simulation results prove that battery aging has a considerable impact on the total cost of a FCHEV. Moreover, tuning of the EMS parameters according to the battery SOH decreases the defined cost

Power allocation strategy based on decentralized convex optimization in modular fuel cell systems for vehicular applications

Recently, modular powertrains have come under attentions in fuel cell vehicles to increase the reliability and efficiency of the system. However, modularity consists of hardware and software, and the existing powertrains only deal with the hardware side. To benefit from the full potential of modularity, the software side, which is related to the design of a suitable decentralized power allocation strategy (PAS), also needs to be taken into consideration. In the present study, a novel decentralized convex optimization (DCO) framework based on auxiliary problem principle (APP) is suggested to solve a multi-objective PAS problem in a modular fuel cell vehicle (MFCV). The suggested decentralized APP (D-APP) is leveraged for accelerating the computational time of solving the complex problem. Moreover, it enhances the durability and the robustness of the modular powertrain system as it can deal with the malfunction of the power sources. Herein, the operational principle of the suggested D-APP for the PAS problem is elaborated. Moreover, a small-scale test bench based on a light-duty electric vehicle is developed and several simulations and experimental validations are performed to verify the advantages of the proposed strategy compared to the existing centralized ones

Online modeling of a fuel cell system for an energy management strategy design

An energy management strategy (EMS) efficiently splits the power among different sources in a hybrid fuel cell vehicle (HFCV). Most of the existing EMSs are based on static maps while a proton exchange membrane fuel cell (PEMFC) has time-varying characteristics, which can cause mismanagement in the operation of a HFCV. This paper proposes a framework for the online parameters identification of a PMEFC model while the vehicle is under operation. This identification process can be conveniently integrated into an EMS loop, regardless of the EMS type. To do so, Kalman filter (KF) is utilized to extract the parameters of a PEMFC model online. Unlike the other similar papers, special attention is given to the initialization of KF in this work. In this regard, an optimization algorithm, shuffled frog-leaping algorithm (SFLA), is employed for the initialization of the KF. The SFLA is first used offline to find the right initial values for the PEMFC model parameters using the available polarization curve. Subsequently, it tunes the covariance matrices of the KF by utilizing the initial values obtained from the first step. Finally, the tuned KF is employed online to update the parameters. The ultimate results show good accuracy and convergence improvement in the PEMFC characteristics estimation. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Online energy management of a hybrid fuel cell vehicle considering the performance variation of the power sources

This study investigates the impact of battery and fuel cell (FC) degradation on energy management of a FC hybrid electric vehicle. In this respect, an online energy management strategy (EMS) is proposed considering simultaneous online adaptation of battery and FC models. The EMS is based on quadratic programming which is integrated into an online battery and proton exchange membrane FC (PEMFC) parameters identification. Considering the battery and PEMFC states of health, three scenarios have been considered for the EMS purpose, and the performance of the proposed EMS has been examined under two driving cycles. Numerous test scenarios using standard driving cycles reveal that the ageing of battery and PEMFC has a considerable impact on the hydrogen consumption. Moreover, the proposed EMS can successfully tackle the model uncertainties owing to the performance drifts of the power sources at the mentioned scenarios

Dynamic semiempirical PEMFC model for prognostics and fault diagnosis

This article introduces a dynamic semiempirical model that predicts the degradation of a proton exchange membrane fuel cell (PEMFC) by introducing time-based terms in the model. The concentration voltage drop is calculated using a new statistical equation based on the load current and working time, whereas the ohmic and activation voltage drops are updated using time-based equations borrowed from the existing literature. Furthermore, the developed model calculates the membrane water content in the PEMFC, which indicates the membrane hydration state and indirectly diagnoses the flooding and drying faults. Moreover, the model parameters are optimized using a recently developed butterfly optimization algorithm. The model is simple and has a short runtime; therefore, it is suitable for monitoring. Voltage degradation under various loading currents was observed for long working hours. The obtained results indicate a significant degradation in PEMFC performance. Therefore, the proposed model is also useful for prognostics and fault diagnosis

A novel online energy management strategy for multi fuel cell systems

This paper addresses the design of an energy management strategy (EMS) for a multi-stack fuel cell system (MFCS). In this regard, firstly, two power allocation strategies, namely Daisy Chain and Equal Distribution have been developed and compared in characteristics terms. Subsequently, a novel adaptive strategy is proposed to split the power between the fuel cells and the battery by utilizing the demanded power, state of charge (SOC) of the battery, maximum power and efficiency point of each fuel cell. In a MFCS, each fuel cell shows variable performances in different operating conditions depending on its specific ageing, material, and external factors. The purpose of this study is to ensure an equal level of degradation for each fuel cell and to make them operate in an efficient zone, with the assistance of an online identification method as well as an adaptive power strategy. Simulations have been conducted in Matlab-Simulink environment. In this work, a mechanistic fuel cell model is employed to imitate the behaviour of a real MFCS and a semi-empirical model, coupled with an adaptive recursive least square (ARLS) to predict the maximum power (MP) and maximum efficiency (ME). The results of the proposed strategy show noticeable improvements in the fuel economy

An Adaptive State Machine Based Energy Management Strategy for a Multi-Stack Fuel Cell Hybrid Electric Vehicle

This paper aims at designing an online energy management strategy (EMS) for a multi-stack fuel cell hybrid electric vehicle (FCHEV) to enhance the fuel economy as well as the fuel cell stacks (FCSs) lifetime. In this respect, a two-layer strategy is proposed to share the power among four FCSs and a battery pack. The first layer (local to each FCS) is held solely responsible for constantly determining the real maximum power and efficiency of each stack since the operating conditions variation and ageing noticeably influence stacks' performance. This layer is composed of a FCS semi-empirical model and a Kalman filter. The utilized filter updates the FCS model parameters to compensate for the FCSs' performance drifts. The second layer (global management) is held accountable for splitting the power among components. This layer uses two inputs per each FCS, updated maximum power and efficiency, as well as the battery state of charge (SOC) and powertrain demanded power to perform the power sharing. The proposed EMS, called adaptive state machine strategy, employs the first two inputs to sort the FCSs out and the other inputs to do the power allocation. The ultimate results of the suggested strategy are compared with two commonly used power sharing methods, namely Daisy Chain and Equal Distribution. The results of the suggested EMS indicate promising improvement in the overall performance of the system. The performance validation is conducted on a developed test bench by means of hardware-in-the-loop (HIL) technique