Fuel Cell Hybrid Vehicle Research Articles

Investigation of drive cycle simulation performance for electric, hybrid, and fuel cell powertrains of a small-sized vehicle

The powertrain simulations of electric, hybrid, and fuel cell hybrid vehicles for the determination of vehicle performance and powertrain efficiency have been studied in the last few decades. Related investigations on fuel cell hybrid vehicles mainly focus on the improvement of a specific powertrain component or vehicle performance for a specific hybrid type. Therefore, this study aims to investigate the effects of not only a specific hybrid configuration but also various electric and fuel cell hybrid powertrain alternatives comparatively including fuel cell, battery, and supercapacitor as energy sources on component efficiency via drive cycle simulations depending on the same vehicle parameters and capacity of the components. Four powertrain cases that differ from each other in terms of the electric power sources (battery, supercapacitor, and fuel cell) were modeled and simulated according to the ECE-15, JPN 10–15, and WLTP drive cycle using MATLAB® - Simulink® environment. The simulations gave the power outputs of powertrain components and component efficiencies, state of charge of the battery, hydrogen consumption, and airflow rate of the fuel cell during the drive cycle. The results showed that better battery efficiency is obtained in Case-1; better vehicle range and lower battery power output than Case-1 can be achieved in Case-2; better fuel cell efficiency is obtained in Case-3; and the lowest H2 consumption is obtained in Case-4. Thus, desired powertrain performance and powertrain efficiency can be achieved by choosing the proper powertrain component in hybrid vehicles.

Internet-Distributed Hardware-in-the-Loop Simulation Platform for Plug-In Fuel Cell Hybrid Vehicles

In order to simulate a PHEV’s dynamic characteristics with high fidelity and study the degradation process of a PHEV’s power sources in real-world driving conditions, an Internet-distributed hardware-in-the-loop (ID-HIL) simulation platform for PHEVs is established. It connects several geographically distributed hardware-in-the-loop (HIL) subsystems (including an in-loop vehicle, Cloud server, driving motor, fuel cells, and lithium battery) via the Internet to simulate the powertrain of a plug-in fuel cell hybrid vehicle (PHEV). In the proposed ID-HIL system, the in-loop vehicle without a hybrid powertrain can simulate a PHEV’s dynamic characteristics. Meanwhile, the other in-loop subsystems can work in the same way as if they were on board. Thus, the degradation process of the power sources, such as the fuel cells and lithium battery, can be studied in real-world driving conditions. A 21 km on-road driving test proves the ID-HIL’s feasibility and fidelity.

Hierarchical Optimization Based on Deep Reinforcement Learning for Connected Fuel Cell Hybrid Vehicles through Signalized Intersections

With the advantages of non-pollution and energy-saving, hydrogen fuel cell hybrid vehicles (HFCHVs) are regarded as one of the potential traveling ways in the future. The energy management of FCHVs has a huge energy-efficient potential which is combined with the Internet of Things (IOT) and auto-driving technologies. In this paper, a hierarchical joint optimization method that combines deep deterministic policy gradient and dynamic planning (DDPG-DP) for speed planning and energy management of the HFCHV is proposed for urban road driving scenarios. The results demonstrate that when the HFCHV is operating in driving scenario 1, the traveling efficiency of the DDPG-DP algorithm is 17.8% higher than that of the IDM-DP algorithm, and the hydrogen fuel consumption is reduced by 2.7%. In contrast, the difference in the traveling efficiency and fuel economy is small among the three algorithms in driving scenario 2, the number of idling/stop situations of the DDPG-DP algorithm is reduced compared with that of the IDM-DP algorithm. This will support further research for multi-objective eco-driving optimization of fuel cell hybrid vehicles.

Effects of Price Range Variation on Optimal Sizing and Energy Management Performance of a Hybrid Fuel Cell Vehicle

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.

An Improved Soft Actor-Critic-Based Energy Management Strategy of Fuel Cell Hybrid Vehicles with a Nonlinear Fuel Cell Degradation Model

An Improved Soft Actor-Critic-Based Energy Management Strategy of Fuel Cell Hybrid Vehicles with a Nonlinear Fuel Cell Degradation Model

Sizing of Fuel Cell/Supercapacitor Hybrid System based on Frequency Splitting of required Energy

Optimal power source sizing and energy management strategies are crucial in the problem of component sizing for hybrid electric vehicles fuel cell. Ensuring cost-effective sizing while meeting power demand necessitates consideration of these factors as well, thereby ensuring a good driving range, reduced energy-loss and consumption, and minimal degradation of fuel cells and batteries for hybrid power sources. The purpose of this work concerns the sizing and the modelling of a power source utilized in a fuel cell hybrid vehicle, the principal source of energy is a Proton Exchange Membrane Fuel Cell, while an Ultra-Capacitor bank serves as an auxiliary source. The sizing algorithm initiates by computing the power demand, which is determined by the mechanical characteristics of the vehicle. This calculation involves considering the instantaneous speed of the chosen drive cycle and the instantaneous road gradient. Subsequently, the algorithm proceeds to determine the mechanical power needed by the motor. In this article, a frequency splitting approach is employed to determine the power distribution between the SC and the fuel-cell for Worldwide Harmonised Light Vehicles Test Procedure (WLTP) driving cycles. The fuel cell operates effectively at low frequencies, whereas the supercapacitor provides power at high frequencies. The efficiencies of every power transformer, including the motor, gearbox, differential, and DC-DC converters, are considered in our work. The data analysis is conducted using the MATLAB software environment. The obtained results demonstrated that the approach outlined in this research article offers a more efficient sizing and energy management between sources in terms of simplicity and adherence to operational conditions of the fuel cell and supercapacitor.

Online adaptive energy management strategy for fuel cell hybrid vehicles based on improved cluster and regression learner

An effective energy management strategy (EMS) is essential to ensure the safe and efficient operation of the fuel cell hybrid vehicles. In this paper, an online adaptive EMS is proposed for the fuel cell hybrid vehicles to minimize hydrogen consumption and adjust the strategy according to the driving conditions. Driving pattern recognition is realized by the improved k-means cluster approach which combines multiple k-means clusters with specific distances to serve as the classifier. In each driving pattern, separate machine learning (ML) models are trained to obtain the energy management regression learner. Comparison experiments are performed to determine the optimal ML model and input parameters. The effectiveness of the proposed EMS is evaluated using two compound test driving cycles. Results show that the proposed method achieves the lowest fuel consumption compared to the other five algorithms considered. Remarkably, it reduces hydrogen consumption by up to 5.66% when compared to commonly used methods.

Toyota Mirai: Powertrain Model and Assessment of the Energy Management

Toyota Mirai is a widely known fuel cell hybrid vehicle and its powertrain represents a benchmark for engineers and researchers. This work presents a lumped-parameter low-order model of Toyota Mirai powertrain and an assessment of the energy management focused on fuel economy and battery and fuel cell degradation. In addition, a rule-based strategy is proposed to reproduce the behavior of Toyota Mirai energy management strategy. The powertrain model and the proposed strategy are validated against experimental data. The assessment of the energy management is performed by comparing the Toyota Mirai strategy with optimal strategies obtained offline via dynamic programming. Different optimal strategies are computed by including the demand of the fuel cell and the battery in the cost function. Simulation results show a high correlation between the proposed model and strategy compared to the experimental data. Comparison of Toyota Mirai strategy with the optimal strategies reveals a small margin for improvement in fuel economy and higher margins in terms of demand of the fuel cell and battery.

Simulation and optimization of hydrogen consumption in a fuel cell/battery hybrid vehicle

This paper presents a physics-based mathematical model for a fuel cell hybrid vehicle system. The performance design of fuel cell hybrid electric vehicles (FCHEVs) is an area of interest in transportation applications. FCHEV is a combination of a proton exchange membrane fuel cell (PEMFC), with a battery and associated DC/DC and DC/AC converters. Suitable batteries are used in FCHEV acts as a backup system with efficient energy management. The battery is designed for fast power transfer during transient response and constant performance without hydrogen. The power management schemes discussed are consistent with the most common modern power management techniques used in fuel cell vehicle applications, including: the state engine control strategies, rule-based fuzzy logic strategies, classical proportional integral control strategies, frequency fuzzy logic decoupling/control minimization strategies. Therefore, we tested and evaluated the reliability of the model using the MATLAB/Simulink environment, with mixed results.

Degradation-oriented real-time power control of fuel cell hybrid vehicles under uncertain driving conditions

This work provides a real-time power allocation algorithm to address uncertain actual driving situations for fuel cell hybrid vehicles. To predict the vehicle speed under nondeterministic driving conditions, a fusion prediction model is developed based on the advantages of the Markov chain and neural network. The optimal power splitting decision in each receding horizon is then solved using the Pontryagin's minimum principle (PMP) method, considering fuel consumption, State of Charge (SOC), and performance degradation. A degradation model of electrochemical active surface area (ECSA) based on Pt catalyst dissolution was developed. Then the effect of the energy management algorithm on fuel cell degradation was evaluated using the degradation model. Compared with the two conventional real-time power splitting strategies, the approach suggested in this research can better reduce the fuel consumption and maintain the stability of battery SOC with a lower fluctuation while taking into account the degradation of the fuel cell.

Maximizing Driving Range for Fuel Cell Range Extender Vehicles With Fixed Energy Storage Costs

Industry and researchers are investigating both battery electric vehicles (BEVs) and fuel cell hybrid vehicles (FCHVs) for the future of sustainable passenger vehicle technology. While BEVs have clear efficiency advantages, FCHVs have key benefits in terms of refueling time and energy density. This article first proposes the concept of a fuel cell range extender vehicle (FCREV) that uses whole-day driving prediction (WDDP) control, which uses driver destination inputs to determine whether the planned driving trips that day will exceed the useable battery energy capacity. If so, the fuel cell is turned on at the start of the day. The benefit of WDDP control is that a smaller, lower cost fuel cell can be used to greatly extend the driving range since the fuel cell can charge the battery during both driving and parked periods of the day. Furthermore, this research proposes a fast analytical optimization algorithm for designing a WDDP-FCREV to maximize the range on a given drive cycle for a set cost. The results show that an optimized WDDP-FCREV can greatly exceed the range of the same-cost BEV, by 105%–150% for no H2 refueling and by 150%–250% when H2 refueling is allowed every 4 h.

Optimization algorithm applied to extended range fuel cell hybrid vehicles. Contribution to road transport decarbonization

Optimization algorithm applied to extended range fuel cell hybrid vehicles. Contribution to road transport decarbonization

Eco-driving policy for connected and automated fuel cell hybrid vehicles platoon in dynamic traffic scenarios

Cooperation of platooning vehicles enabled by eco-driving and connected and automated technology has shown considerable potential in energy saving and traffic efficiency improvement. However, affected by the impacts of the randomly changed states of traffic vehicles and the uncertainty of future road conditions, the adoption of eco-driving for the connected and automated fuel cell vehicle (CAFCHV) platoon in a dynamic traffic environment will encounter significant challenges. Additionally, improper inter-vehicle spacing can significantly affect operating safety and energy efficiency. To address this problem, a centralized variable spacing control strategy that adaptively adjusts with velocity and slope for the CAFCHV platoon is designed to calculate the optimal speed and promote energy allocation efficiency. In the upper control layer, gradient-based model prediction control (GRAMPC) is leveraged to calculate the optimal speed of the platoon, where the velocity of each vehicle can be regulated via its own and the surrounding vehicle's state and relative position. In the bottom layer, each vehicle in the platoon solves an MPC-based energy management strategy (EMS) to achieve power allocation in various power sources. Compared with the adaptive cruise control (ACC) driving manner, the simulation results regulated by the Planning manner display a considerable improvement in terms of the global cost, hydrogen consumption, battery degradation, and SOC by 1.36%, 1.48%, 2.47%, and 1.56%, respectively.

Energetic and Exergetic Performance Investigation of Different Topologies for Hybrid Fuel Cell Vehicles

The use of green energy has increased day by day. Environmentally friendly hybrid electric vehicles with low CO2 emissions have gained public attention. However, most battery electric vehicles are still having range problems, and emission values of hybrid electric vehicles are still not at the desired levels. Thus, fuel cell vehicles have gained some attention as good alternatives. The primary energy source for these vehicles is fuel cells, which are used in conjunction with batteries and supercapacitors to increase system performance. The combination of fuel cell + battery, fuel cell + supercapacitor, and fuel cell + battery + supercapacitor systems are currently the most popular topologies in this technology. The performance of these topologies is related to the overall energy efficiency and exergy efficiency of the vehicle model. Energy and exergy are two basic terminologies used to determine system performance and quality. Exergy determines the thermodynamic losses that cannot be determined using energy formulations alone. Thereby, it is very important to use both terminologies together to examine the performance of topologies and to determine any system losses. For this purpose, in this study, fuel cell + battery, fuel cell + supercapacitor, and fuel cell + battery + supercapacitor topologies were prepared and applied for Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test Cycle (HWFET), New European Driving Cycle (NEDC), and Federal Test Procedure (FTP) driving cycles. Comparisons of these topologies in fuel consumption, power performance, and energy and exergy efficiencies were performed for the driving cycles. Also, energy flow, during the driving cycle, has showed and interpreted for the fuel cell vehicle that is designed and analyzed.

Modeling and simulation of vehicle integrated thermal management system for a fuel cell hybrid vehicle

The high-efficiency thermal management is one of the major challenges for fuel cell vehicles due to the diversity and complexity of the components and systems. Thermal management systems and corresponding control strategies can affect the components’ performance, durability and reliability, the vehicle’s driving performance, fuel economy and occupant thermal comfort. The main objective of this study is to perform multi-case simulation analysis of the fuel cell vehicle thermal management system through modeling to derive the operating mechanism and thermal performance analysis of each subsystem and integrated system. To achieve this purpose, an integrated thermal management system model with different control strategies was proposed based on a fuel cell vehicle prototype, powered by a 65 kW proton exchange membrane fuel cell stack and a Li-ion battery with a capacity of 15 kWh, which considers the cooling of the driving system, fuel cell stack, battery and cabin. The results showed that the critical temperature of each subsystem could be controlled within a reasonable range even in the battery charging mode, where the fuel cell generated about 136.6 % more thermal power than the discharging mode to charge the battery. Moreover, in the series structure of the radiators and condenser, the average temperatures of the inlet air of the motor and the subsequent fuel cell radiator were respectively 3.9 °C and 7 °C higher than the ambient temperature (35 °C), thus reducing the impact of the radiator fan. The proposed model may provide the contribution in the design of integrated solutions for thermal management systems for fuel cell hybrid vehicles under high temperature scenarios, including control strategies development and system-level coupling analysis.

Deep reinforcement learning based energy management strategy for range extend fuel cell hybrid electric vehicle

To meet the power and long-range driving requirements of the vehicle, this paper presents a dual mode operation scheme for a range extend fuel cell hybrid vehicle for the first time, with an in-depth study of the pure electric mode and the range extend mode. The deep deterministic policy gradient algorithm is a well-known deep reinforcement learning algorithm that can solve complex nonlinear problems. To achieve the optimal power distribution among energy sources in the two modes, a dual deep deterministic policy gradient algorithm framework is proposed for the first time in this paper. In addition, a pervious action guidance mechanism is proposed to enable networks to approximate the action value function more efficiently in training. The training results show that the adopted previous action guidance mechanism helps to improve the learning convergence and exploration ability. The validation results show that the proposed strategy improves the operating economy by about 30% compared to the rule-based strategy, reduces the average fuel cell output fluctuation to less than 100 W, and reduces the fuel cell lifetime loss greatly. It is hoped that the proposed new structure, patterns, and energy management strategy will provide more ideas for scholars in future research.

An extreme gradient boosting-based thermal management strategy for proton exchange membrane fuel cell stacks

The working temperature is one of the critical factors for proton exchange membrane fuel cell (PEMFC) stacks, as it directly influences the performance and working life of PEMFCs. Due to the inherent nonlinearity of the PEMFC stack thermal management system model, current thermal management strategies (TMSs) generally face with the drawbacks of lower accuracy and robustness. In this research, a novel machine learning (ML) algorithm, i.e. the extreme gradient boosting (Xgboost) algorithm is applied to the TMS of a PEMFC stack to control the inlet and outlet temperatures of the PEMFC stack, where the proton exchange membrane (PEM) water content is also considered and kept at a reasonable level. In order to evaluate the effectiveness of the proposed strategy, a fuel cell hybrid vehicle (FCHV) model from Autonomie software is selected and the proposed TMS is tested and compared to other strategies under a mixed driving cycle for the PEMFC stack. Results show that the proposed Xgboost-based TMS presents the best control performance, which reduces the maximum deviation of the PEMFC stack temperature and the variation range of the PEM water content compared to other ML-based TMSs and the fuzzy logic-based TMS.

Application of Solid Oxide Fuel Cells on Hybrid Electric Vehicles Operating in Fleet

Solid oxide fuel cells (SOFCs) are well suited to be used with different fuels, including methane and biomethane. Therefore, it may be useful to study their possible application on board hybrid electric vehicles and exploit the fuel cell system, which is characterized by high efficiency, and allow the use of biomethane as a renewable green energy source. Furthermore, there is not yet a consolidated hydrogen distribution network for automotive use, while biomethane would make it possible to take advantage of the existing distribution network and infrastructures of methane. SOFC technology is well suited to be used on vehicles operating in fleets, with a consistent and known mission through the working days, which helps to mitigate SOFCs known limitations such as slow transients and long ignition times. In this work, a model of a fuel cell hybrid vehicle equipped with a SOFC is presented and then used for the sizing of a door-to-door waste collection vehicle. After that, a case study has been carried out considering such a vehicle working on a real-world, door-to-door waste collection mission profile (maximum around 10h/days shift for 7days/week), showing the entire potential of this architecture in terms of environmental impact.

Retracted: Multienergy Management Strategy of Fuel Cell Hybrid Vehicle Based on Distributed Parameter Model

Retracted: Multienergy Management Strategy of Fuel Cell Hybrid Vehicle Based on Distributed Parameter Model

Operating Cost Comparison of a Single-Stack and a Multi-Stack Hybrid Fuel Cell Vehicle Through an Online Hierarchical Strategy

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.