Fuel Cell Hybrid Vehicle Research Articles

Predictive Energy Management for Fuel Cell Hybrid Vehicles Considering Efficiency and Safety

Predictive Energy Management for Fuel Cell Hybrid Vehicles Considering Efficiency and Safety

Capacity configuration of fuel cell hybrid vehicles using enhanced multi-objective particle swarm optimization with competitive mechanism

A well-designed hybrid powertrain is crucial for ensuring the safe, efficient, and durable operation of fuel cell hybrid vehicles. This paper introduces a modular design approach for powertrains, utilizing a Competitive mechanism-based Multi-Objective Particle Swarm Optimization (CMOPSO) algorithm integrated with nested dynamic programming. In this approach, the upper layer employs the CMOPSO algorithm to design the powertrain system, while the lower layer optimizes power coordination for each proposed design. This two-layer optimization framework considers factors such as vehicle economy and durability. Under WLTP conditions, the capacity configuration results are a fuel cell with a rated power of 22 kW, 100 batteries in series, and 7 batteries in parallel. Furthermore, the modular approach outperforms three other algorithms in terms of solution count, diversity, and overall performance metrics. The study also highlights that the vehicle’s power demand characteristics, influenced by different driving cycles, significantly affect capacity configuration results. Sensitivity analysis reveals that both the total operating cost and manufacturing cost of the vehicle are most sensitive to variations in the fuel cell rated power.

Comparison of deep reinforcement learning-based energy management strategies for fuel cell vehicles considering economics, durability and adaptability

The energy management strategy (EMS) is the top priority to ensure the safe and efficient operation of fuel cell hybrid vehicles. Nowadays, EMSs based on deep reinforcement learning (DRL) have become a research hotspot. However, most DRL-based EMSs have not discussed the impact of algorithm hyperparameters, and have not provided a comprehensive evaluation of indicators including fuel cost, aging, and efficiency. There is a lack of a unified performance metrics for different DRL algorithms. To solve this, a comparative study of EMSs based on five DRL methods is conducted in this paper, and a multi-objective reward function that integrates hydrogen consumption, fuel cell degradation, and battery state-of-charge fluctuation is designed. First, the hyperparameters and weight coefficients of the reward function are determined based on the algorithm convergence performance in the training process and average hydrogen consumption, respectively. Then the comprehensive performance of the above-mentioned DRL-based EMSs are compared horizontally. Finally, six driving conditions are used as test sets to explore the adaptability. The results show that the TD3-based EMS has the smallest equivalent hydrogen consumption and degradation per 100 km, which are 1165 g and 0.0651% respectively. This work can provide valid guidance for researchers to use DRL in EMS.

Optimal energy management strategy based on neural network algorithm for fuel cell hybrid vehicle considering fuel cell lifetime and fuel consumption

Optimal energy management strategy based on neural network algorithm for fuel cell hybrid vehicle considering fuel cell lifetime and fuel consumption

Multi-objective optimization with Q-learning for cruise and power allocation control parameters of connected fuel cell hybrid vehicles

Fuel cell hybrid vehicles (FCHVs) are significant for achieving zero carbon emissions. Connected FCHVs can leverage traffic information to collaboratively optimize cruise and power allocation control, enhancing various performance aspects. For urban driving scenarios, this paper introduces a multi-strategy series control architecture for longitudinal cruise and power allocation control in connected FCHVs. However, particle swarm optimization (PSO) algorithms face challenges in high-dimensional decision and objective spaces when optimizing multiple strategies. Additionally, manually preset PSO parameters hinder particle evolution from dynamically adapting to unknown multi-objective spaces, thereby limiting the development of multiple performance metrics. To address this issue, this paper proposes a Q-learning multi-objective PSO (QMOPSO) algorithm. This algorithm tackles high-dimensional optimization challenges by improving population initialization distribution and subpopulation division, and enables particles to dynamically adjust exploration strategies, thereby maximizing multiple objective performances. The results indicate that compared to a control scheme optimized with PSO under predefined driving conditions, the multi-strategy series control framework optimized with the QMOPSO algorithm improves tracking stability by 50.20%, driving comfort by 1.77%, fuel economy by 6.10%, and reduces power source degradation by 2.04% in urban driving scenarios. Compared to PSO and multi-objective PSO algorithms, the QMOPSO algorithm demonstrates superior trade-offs. This research provides a collaborative optimization solution for FCHVs in connected environments.

Digital Twin-Enhanced Control for Fuel Cell and Lithium-Ion Battery Hybrid Vehicles

With the development of lithium-ion batteries and fuel cells, the application of hybrid power systems is becoming more and more widespread. To better optimize the energy management problem of fuel cell hybrid systems, the accuracy of system modeling and simulation is very important. The hybrid system is formed by connecting the battery to the fuel cell through an active topology. Digital twin technology is applicable to the mapping of physical entities to each other with high interactivity and fast optimization iterations. In this paper, a relevant model based on mathematical logic is established by collecting actual operational data; subsequently, the accuracy of the model is verified by combining relevant operating conditions and simulating the model. Subsequently, a three-dimensional visualization model of a hybrid power system-based sightseeing vehicle and its operating environment was established using digital twin technology to improve the model simulation of the fuel cell hybrid power system. At low speeds, the simulation results of the hybrid power system-based sightseeing vehicle have a small error compared with the actual running state, and the accuracy of the data related to each internal subcomponent is high. In the simple interaction between the model display vehicle and the environment, the communication state can meet the basic requirements of the digital twin model because the amount of data to be transferred is small. This study makes a preliminary attempt at digital parallelism by combining mathematical logic with visualization models and can be used as a basis for the subsequent development of more mature digital twin models.

Research on the adaptability of energy management to thermal management of PEMFCs

Abstract The stable output of power and the effective control of stack temperature play a very important role in the durability and stability of fuel cell hybrid vehicles. In actual working conditions, energy management of power system and thermal management of Proton Exchange Membrane Fuel Cells (PEMFCs) need to be coordinated with each other to jointly ensure the efficient and stable operation of vehicles, however, most of the research in these two directions is independent. In response to the research gaps mentioned above, the concept of adaptability has been proposed for the first time with the aim of combining these two systems for research. This paper takes the fuel cell hybrid vehicle as the research object and establishes the energy management system and thermal management system model based on Matlab/Simulink software. In order to investigate the adaptability of energy management strategies to the temperature control system, two representative types of rule-based and optimization-based energy management strategies (power-following strategy, PFS, and adaptive equivalent hydrogen consumption minimization strategy, A-EHMS) are designed to be numerically simulated in the driving cycle condition of the UDDS, and then combined with the temperature control system of the fuel cell to comparatively analyze the above two types of energy management strategies from the perspectives of power allocation and the impact on the thermal management system. The results show that the PFS provides better protection for the battery under the same operating conditions. And from the perspective of fuel economy and adaptability to the temperature control system, the fuel consumption of A-EHMS is reduced by 12.5% and the adaptability to the temperature control system is improved by about 13.5% compared to PFS.

Scenario-oriented adaptive ECMS using speed prediction for fuel cell vehicles in real-world driving

To exploit the energy-saving potential and optimize the battery state of charge (SOC) maintaining capability of energy management strategies for fuel cell hybrid vehicles in specific driving scenarios, this study proposes a scenario-oriented adaptive equivalent consumption minimization strategy (SA-ECMS) based on a Nanjing-oriented driving cycle (NODC) and future speeds predicted via a hybrid neural network model. The proposed strategy determines the initial value of the equivalent factor (EF) and the proportional coefficient of the adaptive increment based on the NODC. Then, it periodically adjusts the EF via local optimization process according to the predicted speed to enhance scenario-specific adaptability and energy efficiency performance. Simulation results show that the hybrid neural network model achieves an average calculation time of 0.0033 s with a root-mean-square error of 0.85 m/s for 10 s prediction horizon, outperforming existing speed prediction models. Compared with the existing SOC feedback-based ECMS, the proposed SA-ECMS effectively suppresses the battery SOC within a narrower fluctuation range of −0.12% to 0.33%, achieves a deviation of only 0.0026 from the SOC reference value, and reduces the equivalent hydrogen-fuel consumption by 2.49% to 7.06 g/km.

An adaptive fuel cell hybrid vehicle propulsion sizing model

An adaptive fuel cell hybrid vehicle propulsion sizing model

A comparative study of the machine learning-based energy management system for hydrogen fuel cell electric vehicles

The demand for better energy technologies has sparked research and development of electric and hybrid vehicles. Due to their clean, sustainable, and high energy density, fuel cell vehicles have begun to stand out above the rest. Therefore, fuel cell hybrid vehicles can compete with internal combustion engine-powered vehicles in the future. However, fuel cells face obstacles, including slow dynamics that necessitate managing their operation together favorably. To reduce an HEV's operational costs, this study analyzes the HEV energy management issue utilizing machine learning approaches, particularly reinforcement learning. This paper aims to comprehensively review the existing work on a couple of machine-learning-based energy management systems for an electric vehicle run by hydrogen fuel cell, it can be concluded that progress was evident when the Q-learning-based algorithm was utilized towards lowering the SOC battery variation of about 0.7 per unit which was the primary task, while a Deep Deterministic Policy Gradient (DDPG) based energy management system (EMS) starts operating the fuel cell at a higher efficiency rate comparatively while using the battery.

An Optimal Approach to Energy Management Control of a Fuel-Cell Vehicle

This paper presents the design of an energy management control system to improve powertrain efficiency and optimize the amount of fuel used by a hybrid fuel cell vehicle in a route-based scenario. To reach this goal, a complete tank-to-wheel model is developed under the assumption of a known scenario, the speed profile that best minimizes the energy required to complete the test is computed, and a controller able to handle the power request is designed. In particular, a Model Predictive Control architecture is used to split the power request between the primary and the secondary power source (fuel cell and supercapacitors). The effectiveness of the proposed approach is assessed through extensive simulation tests using a realistic model.

Hierarchical eco-driving control strategy for connected automated fuel cell hybrid vehicles and scenario-/hardware-in-the loop validation

A reliable and real-time eco-driving control strategy that incorporates vehicle connectivity is crucial for enhancing the fuel economy and mobility of fuel cell hybrid vehicles (FCHVs). However, the strong coupling of dynamic traffic, vehicle dynamics, and powertrain characteristics makes eco-driving challenging in terms of effectiveness and computational efficiency for co-optimizing velocity planning and energy management. This study proposes a hierarchical eco-driving predictive control framework for connected automated FCHVs, which improves comprehensive performance and is real-time applicable by incorporating an explicit dynamic traffic model (EDTM)-based velocity planning and an equivalent consumption minimization strategy (ECMS). The EDTM predicts the ego vehicle’s state of proceeding through signalized intersections in dynamic traffic scenarios. The model predictive control is employed for multi-objective velocity planning, which balances energy savings, comfort, and traffic efficiency. At the powertrain level, a multi-horizon predictive ECMS (MhP-ECMS) is designed to incorporate both the optimized velocity (short-horizon) and the EDTM-based predictive velocity (mid-horizon). Dynamic traffic Scenario- and Hardware-in-the-Loop (S/HiL) validations show that the proposed strategy can make driving smoother in vehicle-following and through signalized intersections, and markedly improve fuel economy by 5.41% compared to the baseline controller. This study helps provide valuable insight into improving the efficiency and mobility of connected vehicles.

Effect of the Degree of Hybridization and Energy Management Strategy on the Performance of a Fuel Cell/Battery Vehicle in Real-World Driving Cycles

The study utilizes open-access data to generate power demand curves for a hybrid automotive system, testing twelve configurations with three different energy management strategies and four values for the degree of hybridization (DOH), the latter representing the share of the total power of the vehicle powertrain supplied by the battery. The first control logic (Battery Main—BTM) uses mainly batteries to satisfy the power demand and fuel cells as backup, while in the other two controllers, fuel cells operate continuously (Fuel Cell Main—FCM) or within a fixed range (Fuel Cell Fixed—FCF) using batteries as backup. The results are assessed in terms of H2 consumption, overall system efficiency, and fuel cell predicted lifespan. The battery is heavily stressed in the BTM and FCF logics, while the FCM logic uses the battery only occasionally to cover load peaks. This is reflected in the battery’s State of Charge (SOC), indicating different battery stress levels between the BTM and FCF modes. The FCF logic has higher stress levels due to load demand, reducing battery lifetime. In the BTM and FCM modes, the fuel cell operates with variable power, while in the FCF mode, the fuel cell operates in a range between 90 and 105% of its rated power to ensure its lifetime. In the BTM and FCM modes, hydrogen consumption decreases at almost the same rate as the DOH increases, due to a decrease in battery capacity and a smaller amount of hydrogen being used to recharge it. In contrast, the FCF control logic results in a larger fuel consumption when the DOH decreases. In terms of FC durability, the FCF control logic performs better, with a predicted lifetime ranging from 1815 h for DOH = 0.5 to 2428 h for DOH = 0.1. The FCM logic has the worst performance, with a predicted lifetime of 800 to 808 h, being almost insensitive to the DOH variation. Simulations were performed on two different driving cycles, and similar trends were observed. Simulations taking into account fuel cell (FC) performance degradation showed an increase in hydrogen consumption of approximately 38% after 12 years. Overall, this study highlights the importance of optimizing control systems to improve the performance of fuel cell hybrid vehicles, also taking into account the component of performance degradation.

Mode-dependent Energy Management of a Dual-Source Active Switched Quasi-Z-Source Inverter for Fuel Cell Hybrid Vehicles

Mode-dependent Energy Management of a Dual-Source Active Switched Quasi-Z-Source Inverter for Fuel Cell Hybrid Vehicles

Topology Comparison and Sensitivity Analysis of Fuel Cell Hybrid Systems for Electric Vehicles

The hybrid system containing the fuel cell and energy storage sources contributes to realizing hydrogen energy in road traffic applications. It is meaningful to evaluate the different forms of system topology. In this work, the quantitative comparison of topologies and sensitivity analysis of system parameters are conducted. Firstly, the output characteristics of the fuel cell, battery, and ultracapacitor are modeled by experimental data. Secondly, the dimensions of four hybrid topologies are determined by an iterative constraint method. An energy management strategy based on dynamic programming is conducted for all topologies. Then the overall economic cost is used as an indicator for quantitative evaluation, considering the initial acquisition, hydrogen consumption, and component aging. The base-case results illustrate that the fuel/cell battery semi-active topology is the least costly one. Finally, seven parameters are analyzed to determine the system cost’s key factors. The relationship between costs and driving conditions is further studied. The rankings of the normalized parameter analysis results inspire us on how to further reduce the cost of fuel cell hybrid vehicles.

Modelling, validation and analysis of preheating strategy of fuel cell vehicle during Subzero cold start

The rapid cold start capability of fuel cell vehicles in low-temperature environments is one of the key factors restricting their commercialization. Reasonable and effective strategies are crucial to improving the low-temperature cold start capability of fuel cell vehicles. In this paper, a cold start model of fuel cell system is established, and the model is verified based on the cold start experiment of a hybrid fuel cell vehicle. The necessity of the coolant preheating strategy in the cold start is analyzed, and the important role of the coolant preheating strategy in helping the fuel cell system to start quickly at -10°C and below is demonstrated. Furthermore, the effects of air inlet temperature, air inlet flow rate, and external heater power on the cold start performance are investigated. The results show that when the air inlet temperature rises from -13°C to 65°C, the start time is shortened from 279 s to 234 s (reduced by 16.13 %). And at low air inlet temperatures, the lower flow rate of air can significantly reduce the temperature difference and maintain uniformity of temperature inside the stack, while at a higher temperature, the higher air flow rate causes the temperature of the cell stack to rise rapidly. For heating power, the start time is shortened from 421 s to 126 s (reduced by 70.07 %) when the heating power is increased from 3 kW to 11 kW. However, it will cause a larger temperature difference between the inlet and outlet of the coolant, which is not conducive to the uniform distribution of the temperature inside the stack. Thus, the start time can be shortened by increasing the air inlet temperature, air inlet flow rate, and heating power with the consideration of the temperature difference that the stack can withstand. The model developed by this study provides an effective way to analyze the fuel cell system during cold start, which is useful for the development of control strategies in the future.

A 4750 hours’ durability investigation of the PEMFC stack based on fuel cell hybrid vehicles conditions

ABSTRACT A novel protocol for testing the durability of proton exchange membrane fuel cell (PEMFC) stacks is developed using real-world road load data obtained from automobiles equipped with a PEMFC system manufactured by Weichai Power Company Limited, and the predetermined methodology is carried out for a cumulative duration of 4750 hours. The empirical results obtained from the durability test reveal that the average cell voltages (ACV) at specific current of 21 A, 105 A, 175 A, and 290 A exhibit degradation rates of 2%, 3.3%, 3.9%, and 10.9%, correspondingly. Furthermore,the polarization curve test results demonstrate ACV decay rates of 2.2% and 6.9% at 21 A and 360 A, respectively. The output voltage decay increases and the consistency of cells diminishes as the current rises. In comparison to the initial stage of the durability test, there is a slight decline in cell consistency observed toward the conclusion. In addition, a sensitivity test of the operational conditions is conducted at the 50th and 500th cycles, respectively. The findings reveal that the sensitivity to the cell consistency and output voltage remains largely stable throughout the durability test, and this suggests that the stack does not exhibit any noticeable alterations in its physicochemical characteristics.

Dual-Source High-Performance Active Switched Quasi-Z-Source Inverter for Fuel Cell Hybrid Vehicles

Dual-Source High-Performance Active Switched Quasi-Z-Source Inverter for Fuel Cell Hybrid Vehicles

Buck converter topology for fuel cell hybrid vehicles

Buck converter topology for fuel cell hybrid vehicles

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.