Fuel Cell Hybrid Electric Vehicle Research Articles

Guided control for plug-in fuel cell hybrid electric vehicles via vehicle to traffic communication

A guided proportional integral controller-based Pontryagin’s minimum principle (PI-PMP) energy management control strategy based on dynamic traffic information for plug-in fuel cell vehicles is proposed in this paper. Combined with the real-world traffic flow data of high-way driving scenarios, an improved equivalent consumption minimization strategy (ECMS) based on dichotomy is used to quickly search for the optimal initial costate value and reference battery state of charge (SOC) trajectory. A horizon velocity predictor based on artificial neural networks (ANNs) is used to achieve short-term velocity prediction, and a PI-PMP control strategy is adopted to realize SOC trajectory following. Simultaneously, the sensitivity of the costate in ECMS and PI-PMP is analyzed in depth. The simulation results of three scenarios with no/static/dynamic traffic flow information show that the guided PI-PMP energy management strategy based on dynamic traffic flow information has a significant energy-saving effect and real-time optimization potential.

Hydrogen Fuel Cell Hybrid Electric Vehicles: the Impact of Commercial Vehicle Fleets on the Penetration of Renewable Energy Sources

The transport sector is one of the most significant contributors to greenhouse gas (GHG) emissions, with the road segment accounting for large part of the emissions and requiring immediate action to address the increasing issues of climate change. To this aim, the international community is promoting technological alternatives to fossil fuel-based powertrains, thus favoring the increase in electric vehicle (EVs) penetration. However, to be sustainable the electrification of mobility requires a large share of energy produced by renewable sources to support the decarbonization process. The simultaneous increase in volatility for both the energy demand (due to EVs diffusion) and production (due to the Renewable Energy Sources RES growth), has a significant impact on the reliability of the electricity grid infrastructure. The growth of RES penetration brings an energy production increase that may not synchronized with the demand evolution, possibly leading to a grid imbalance. To this aim, the storing potential of green hydrogen, produced from RES during energy surplus periods, could allow better exploitation of the rising installed RES capacity. The proposed study aims to analyze the benefits that can arise from introducing Fuel Cell Hybrid Electric Vehicles (FCHEVs) in a commercial electric fleet in terms of environmental and grid resilience perspectives. FCHEVs are electric vehicles propelled by a hybrid powertrain, coupling electric battery packs and a fuel cell stack as energy sources; here, hydrogen is used as an energy vector stored in high-pressurized tanks. Starting from a reference scenario of a fleet composed of 10 EVs, the number of FCHEVs is progressively increased to assess their impact on the fleet performance. A set of mission scenarios for the commercial vehicles is obtained through the Markov Chain method, starting from accurate GPS data. These different scenarios are used in a numerical model to optimally plan the fleet scheduling and their configuration in terms of vehicles associated with specific missions. Results show a reduction of up to 57 % in grid fluctuations (estimated through its standard deviation) and a decrease from 77 % to 82 % in CO2 emissions concerning traditional vehicles increasing the FCHEV penetration considering the current Italian energy scenario.

Fuel cell plug-in hybrid electric vehicle thermal management strategy in parking time for low-temperature environments

This work presents a thermal management strategy for fuel cell hybrid electric vehicles under low-temperature conditions. Protecting the fuel cell system (FCS) and battery pack from such operating conditions prevent their degradation and performance decay. Thus, an approach similar to the keep-warm strategy is proposed to avoid the well-known issues related to the thaw-at-start methods, as well as promote a rapid startup after long parking periods. Particularly, the present work proposes a strategic synergy between the battery pack recharging and thermal management during the parking phase, so as to minimize the fuel consumption, while featuring vehicle to grid functionality and rapid startup of the vehicle when resuming driving. Such thermal management strategy was evaluated via simulation analyses, considering the transient thermal dynamics of the FCS and battery pack under low-temperature conditions and the temperature control strategy. The results outlined how proper exploitation of plugin functionalities, beyond introducing innovative vehicle-to-grid (V2G) concepts to suitably link driving to parking phases energy management, also allows for low-temperature safe thermal management of involved electrochemical devices. Finally, an economic analysis demonstrated that the proposed plugin-based vehicle to grid scenario presents a cost-saving potential of up to 56 % with respect to non V2G charge-sustaining one.

Fuel Cell Hybrid Electric Vehicles: A Review of Topologies and Energy Management Strategies

With the development of the global economy, the automobile industry is also developing constantly. In recent years, due to the shortage of environmental energy and other problems, seeking clean energy as the power source of vehicles to replace traditional fossil energy could be one of the measures to reduce environmental pollution. Among them, fuel cell hybrid electric vehicles (FCHEVs) have been widely studied by researchers for their advantages of high energy efficiency, environmental protection, and long driving range. This paper first introduces the topology of common FCHEVs and then classifies and introduces the latest energy management strategies (EMSs) for FCHEVs. Finally, the future trends of EMSs for FCHEVs are discussed. This paper can be useful in helping researchers better understand the recent research progress of EMSs for FCHEVs.

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

To increase the fuel efficiency of fuel cells, lengthen their useful lives, and fulfil the demands for high energy and high power during the operation of hybrid electric vehicles. This paper's goal is to thoroughly examine the distributed parameter model-based multienergy management technique of fuel cell hybrid electric vehicles. Firstly, the simplified model of a hybrid system is constructed according to the distributed parameter model, and the fuel cell model, unidirectional DC/DC converter, and battery are described in detail. The multienergy management of hybrid electric vehicles based on improved deep Q-learning is adopted, the multienergy management strategy based on deep Q-learning is designed, so as to reduce fuel consumption and improve the working efficiency of fuel cells, optimize the energy distribution of lithium batteries and fuel cells, and adopt the experience playback mechanism of summation tree structure in the process of strategy training to complete the multienergy management of hybrid electric vehicles. The strategy described in this study can successfully increase the overall power performance of fuel cell hybrid vehicles, extend the battery's service life, and increase fuel economy, according to simulation results, which has a certain practical value.

Simulation Analysis and Control of Multi-energy System for Fuel Cell Hybrid Electric Vehicle Based on Wavelet Transform.

In order to address the issue of multi-energy system fuel cells having a short life cycle and low fuel efficiency, a Fuel Cell Hybrid Vehicle was developed. The goal of this research is to use wavelet transformation to simulate, evaluate, and regulate the multi-energy system of a fuel cell hybrid car. To begin, a hybrid model of the fuel cell and an overall dynamic model of the fuel cell, as well as a DC/DC converter model, are constructed in accordance with the simulation environment. Second, the hybrid vehicle system's power information is successfully captured, and the power signal acquired is processed using the wavelet transform. The fuel cell power control and the composite power supply's power allocation module are independently input into the hybrid system's low frequency and high frequency power requirements. PI control is used to regulate the power of the storage device in the hybrid power system, as well as the power settings of the output fuel cell and supercapacitor. The simulation results show that the power battery fluctuation range of the hybrid vehicle multi-energy system based on the wavelet transform proposed in this paper is significantly smaller than that of other methods, and the entire process operates at low power points. The results of the experiments suggest that the strategies given in this study can successfully extend the life of fuel cells while also lowering the overall fuel efficiency of the vehicle system.

Energy Management Strategy for Fuel Cell/Battery/Ultracapacitor Hybrid Electric Vehicles Using Deep Reinforcement Learning With Action Trimming

As for fuel cell hybrid electric vehicle equipped with battery (BAT) and ultracapacitor (UC), its dynamic topology structure is complex and different characteristics of three power sources induce challenges in energy management for fuel economy, power sources lifespan, and dynamic performance of the vehicle. In this paper, an energy management strategy (EMS) based on a hierarchical power splitting structure and deep reinforcement learning (DRL) is proposed. In the higher layer strategy of the proposed EMS, the UC is employed to supply peak power and recover braking energy through the adaptive filter based on fuzzy control. Then, the integrated DRL and equivalent consumption minimization strategy framework is proposed to optimize the power allocation of fuel cell (FC) and BAT in the lower layer, to ensure the highly efficient operation of FC and reduce hydrogen consumption. And the action trimming based on heuristic technique is proposed to further restrain the adverse effect of sudden peak power on FC lifespan. The simulation results show the proposed EMS can make the output of FC smoother, improve its working efficiency to alleviate the stress of BAT, and increase by 14.8% compared with the Q-learning strategy in fuel economy under WLTP driving cycle. Meanwhile, the obtained results under UDDSHDV show fuel economy of the proposed EMS can reach dynamic programming (DP) benchmark level of 89.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Novel enhancement of energy management in fuel cell hybrid electric vehicle by an advanced dynamic model predictive control

In this paper, an Advanced Dynamic Model Predictive Control (AMPC) based on a Nonlinear Model Predictive Control (NMPC) framework with a multi-objective cost function driven by dynamic weights is proposed to improve the energy performance of fuel cell hybrid electric vehicles whilst prolonging their component lifetime. By the use of dynamic weights, the cost function is effectively formulated as the combination of fuel consumption, rate of change of fuel cell power, battery power, the fuel cell efficiency, state of charge of the battery, and their temperatures. In order to enhance the adaptability of the AMPC, a Fuzzy Cognitive Map (FCM) is then newly designed to regulate online the dynamic weights to adjust the importance of each cost component according to the conditions prevailing during driving. A comparative study between the proposed AMPC, a constant weight based NMPC and a conventional NMPC having cost function with fewer objectives has been carried out by means of simulation using a FCHEV model from the simulation tool ADVISOR to illustrate the efficacy of the proposed AMPC.

Energy management strategy for fuel cell hybrid vehicles considering optimal performance of multiple energy sources combined with ant colony algorithm

AbstractTo compensate for the slow dynamic response of fuel cell system (FCS) in fuel cell hybrid electric vehicles (FCHEVs), battery and supercapacitor are widely used as auxiliary supply energy sources in FCHEVs. How to reasonably allocate the required power among the three energy sources, reduce the fuel consumption and the SOC fluctuations of auxiliary energy sources becomes a tough problem to be solved. In this paper, we propose an energy management strategy (EMS) based on fuzzy control, considering the multiple constraints of hydrogen consumption and auxiliary energy SOC fluctuations of FCHEV. In this paper, the ant colony algorithm (ACA) is used for the first time to optimize the membership functions parameters of the dual fuzzy controller. The results obtained are verified in Matlab/Simulink. The proposed ACA optimized EMS can effectively reduce the equivalent hydrogen consumption and the SOC fluctuation of battery and supercapacitor by proving in four typical driving conditions.

Comparative study of energy management systems for a hybrid fuel cell electric vehicle - A novel mutative fuzzy logic controller to prolong fuel cell lifetime

Hybrid fuel cell battery electric vehicles require complex energy management systems (EMS) in order to operate effectively. Poor EMS can result in a hybrid system that has low efficiency and a high rate of degradation of the fuel cell and battery pack. Many different types of EMS have been reported in the literature, such as equivalent consumption minimisation strategy and fuzzy logic controllers, which typically focus on a single objective optimisations, such as minimisation of H2 usage. Different vehicle and system specifications make the comparison of EMSs difficult and can often lead to misleading claims about system performance. This paper aims to compare different EMSs, against a range of performance metrics such as charge sustaining ability and fuel cell degradation, using a common modelling framework developed in MATLAB/Simulink - the Electric Vehicle Simulation tool-Kit (EV-SimKit). A novel fuzzy logic controller is also presented which mutates the output membership function depending on fuel cell degradation to prolong fuel cell lifetime – the Mutative Fuzzy Logic Controller (MFLC). It was found that while certain EMSs may perform well at reducing H2 consumption, this may have a significant impact on fuel cell degradation, dramatically reducing the fuel cell lifetime. How the behaviour of common EMS results in fuel cell degradation is also explored. Finally, by mutating the fuzzy logic membership functions, the MFLC was predicted to extend fuel cell lifetime by up to 32.8%.

Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming

Fuel cell hybrid electric vehicles have attracted a large amount of attention in recent years owing to their advantages of zero emissions, high efficiency and low noise. To improve the fuel economy and system durability of vehicles, this paper proposes an energy management strategy optimization method for fuel cell hybrid electric vehicles based on dynamic programming. Rule-based and dynamic-programming-based strategies are developed based on building a fuel cell/battery hybrid system model. The rule-based strategy is improved with a power distribution scheme of dynamic programming strategy to improve the fuel economy of the vehicle. Furthermore, a limit on the rate of change of the output power of the fuel cell system is added to the rule-based strategy to avoid large load changes to improve the durability of the fuel cell. The simulation results show that the equivalent 100 km hydrogen consumption of the strategy based on the dynamic programming optimization rules is reduced by 6.46% compared with that before the improvement, and by limiting the rate of change of the output power of the fuel cell system, the times of large load changes are reduced. Therefore, the strategy based on the dynamic programming optimization rules effectively improves the fuel economy and system durability of vehicles.

Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle

The driving trip pattern is of great significance in hydrogen consumption and battery Longevity of the plug-in fuel cell hybrid electric vehicles (PFCHEV). However, the traditional energy management strategy failed to consider the uncertainty of driving patterns. To overcome this drawback, a deep Q-learning network based trip pattern adaptive (DQN-TPA) battery longevity-conscious strategy is proposed in this study. To begin with, the trip pattern recognition based Learning Vector Quantization Neural Network is devised for pattern identification, and the adaptive-equivalent consumption minimizes strategy (A-ECMS) is conducted to improve the hydrogen consumption. Then, a TPA longevity-conscious strategy is developed and compared with the conventional multi-criteria (MC) optimization strategy to investigate the discrepancy brought by the pattern adaptation. And finally, in combination with the above efforts, an improved DQN-TPA based battery longevity-conscious strategy has been established accordingly. The advances are confirmed by the validation results that, the A-ECMS makes an 11.76% promotion in fuel economy by taking the deviation among different driving patterns into concern. The TPA strategy shows more adaptiveness than the MC optimization strategy in which, the effective Ah-throughput is 5.17% lower than MC-based while keeping the same economy. Further improvement can be achieved by the modified DQN-TPA based approach by remedying the imperfection of TPA-based recognition delay and performing the economy and durability conscious actions with 5.87% further reduction of effective Ah-throughput without observably sacrificing the fuel economy. Furthermore, the effectiveness and adaptiveness of the proposed strategy are validated by the Hardware-in-the-Loop experiments. Both the numerical validation and semi-physical validation results indicate that the DQN-TPA based approach made it possible to develop the battery longevity-conscious strategy capable of significantly adapting various driving patterns and improving the hydrogen consumption and battery durability performance of the PFCHEV.

A new cost-minimizing power-allocating strategy for the hybrid electric bus with fuel cell/battery health-aware control

The two primary challenges preventing the commercialization of fuel cell hybrid electric vehicles (FCHEV) are their high cost and limited lifespan. Improper use usage can could also hasten the breakdown of proton exchange membrane fuel cell (PEMFC). This paper proposes a new cost-minimizing power-allocating technique with fuel cell/battery health-aware control to optimize the economic potential of fuel cell/battery hybrid buses. The proposed framework quantifies the fuel cell (FC) deterioration of the whole working zone in a real hybrid electric bus using a long short-term memory network (LSTM), which reduces the time required to get the key lifetime parameters. A new FC lifespan model is embedded into the control framework, together with a battery aging model, to balance hydrogen consumption and energy source durability. In addition, in the speed prediction step, an enhanced online Markov prediction approach with stochastic disturbances is presented to increase the forecast accuracy for future disturbances. Finally, comparative analysis is used to verify the efficacy of the suggested approach, which shows that when compared to the benchmark method, the proposed strategy may extend the FC lifetime and lower operating costs by 5.04% and 3.76%, respectively.

Chance-constrained robust co-design optimization for fuel cell hybrid electric trucks

The co-design optimization that simultaneously couples embodiment design and control design is widely applied in fuel cell hybrid electric vehicles. However, due to imperfect manufacture process, modeling simplification and uncertain parameters during vehicle operation, the optimal results obtained from a deterministic co-design optimization might not be robust to variations of parameters and optimization variables. This paper introduces a chance-constrained robust co-design optimization framework, where the chance constraint firstly translates into a deterministic constraint. The robust objective is computed as a function of the second-order approximated mean and inequality constraints are computed by shifting 3 times of their standard deviations inside of deterministic bounds. The vehicle movement in long-haul trucking application is considered as an uncertain parameter and the propagation of uncertainties to state variables are also illustrated with considerations of uncertainties in design decision variables. A deterministic and stochastic co-design problem are formulated and decomposed into two steps, i.e. electric machine sizing and sizing of fuel cell and battery as well as the energy management. A case study of a fuel cell hybrid electric long-haul truck indicates the importance of the robust approach in the joint component sizing and energy management. The uncertainties of the truck movement results in uncertainties of the battery energy and power, leading to a bigger battery capacity. The energy capacity is around 2.34 times higher than that without considering uncertainties.

Ageing-aware load following control for composite-cost optimal energy management of fuel cell hybrid electric vehicle

This paper aims to explore the viability of a novel frequency-driven load following control (LFC) as the dedicated energy management unit (EMU) of a commercial-grade fuel cell hybrid electric vehicle (FCHEV). The proposed EMU is envisioned to facilitate reliable performance under uncertain driving conditions, with mutual awareness towards hydrogen consumption and sources ageing. For that, a concept of composite-cost is introduced to establish an informed trade-off among sources ageing and hydrogen consumption. Thereafter, an adaptive framework is developed to tune the load following rule in an online manner subject to instantaneous minimization of the composite-cost. The conceptual application of proposed EMU is demonstrated in the simulation environment with parameters similar to those of a commercial vehicle. Two driving environments with distinct characteristics are extensively studied: a harsh US06 urban/suburban driving cycle and Artemis-Motorway cycle with an overall higher speed. The practical applicability of the proposed method is also validated via experimental test-bench with scaled-down hybrid power system. Detailed statistical analysis exhibits that the online-tuned variant of LFC with minimization preference given to the composite-cost provides the most appropriate trade-off by decreasing the composite-cost by 1.59%, hydrogen consumption by 0.35 g, and extending battery lifespan by 0.006%.

Predictive-ECMS based degradation protective control strategy for a fuel cell hybrid electric vehicle considering uphill condition

The fuel economy and powertrain durability of fuel cell hybrid electrical vehicles (FCHEV) are sensitively affected by the load changing brought by the traffic, and most of the current power management strategies (PMS) are devoted to reducing the cost of hydrogen consumption and degradation. However, the variation in terrain information brings more severe load fluctuation, which is scarcely considered among the currently studied PMS. Since the terrain information tends to be predictable with the development of the intelligent transportation system, this paper develops a hierarchical PMS which integrates the terrain information. Firstly, a predictive equivalent consumption minimizes strategy of the inner layer is developed for providing power distribution decisions. Then, a fuel cell longevity-conscious strategy which devises a dynamic constraint on reference fuel cell output power is devised in the external layer. Finally, a heuristic battery SoC trajectory planning approach is used to guide the operation mode of the powertrain on the ramp, and the influence of the signal error is fully discussed. The effectiveness of this study is validated by the hardware-in-loop experiment, validation results show the strategy can improve 4.3% fuel economy, the erosion of the durability of the powertrain can be mitigated while the uphill condition.

Multi-objective energy management strategy for fuel cell hybrid electric vehicle based on stochastic model predictive control

The fuel cell hybrid electric vehicle (FCHEV) has earned great interest among the automotive industry in recent years. However, the power allocation strategy between proton exchange membrane fuel cell (PEMFC) and lithium-ion battery remains a technological challenge. To conquer this problem, a multi-objective predictive energy management strategy (EMS) based on model predictive control (MPC) is proposed in this paper, combined with velocity forecast and driving pattern recognition. The comparative study is conducted to reveal the interaction between each optimization objectives. Simulation results illustrate that the proposed EMS could maintain SOC around reference, reduce fuel consumption by 6.67%, and avoid PEMFC degradation which caused by frequent start-off and rapid load change.

Robust control for techno‐economic efficient energy management of fuel cell hybrid electric vehicles

The design of an efficient techno-economic autonomous fuel cell hybrid electric vehicle (FCHEV) is a crucial challenge. This paper investigates the design of a near optimal PI controller for an automated FCHEV, where autonomy is expressed as efficient and robust tracking of a given reference speed trajectory without driver's intervention. An impartial comparison is introduced to illustrate the effectiveness of the proposed metaheuristic-based optimal controllers in enhancing the system dynamic performance. The comprehensive optimization performance indicator is considered as a function of the vehicle dynamic characteristics while determining the optimal controller gains. In this paper, the proposed effective up-to-date metaheuristic techniques are the grey wolf optimization (GWO) as well as the artificial bee colony (ABC). Using MATLABTM/Simulink, numerical simulations clearly illustrate the efficiency of near-optimal gains in the optimized tuning methodologies and the fixed manual one in realizing adequate velocity tracking. The simulation results demonstrate the superiority of both ABC and GWO rather than the manual controller for driving cycles of high acceleration and deceleration levels. In absence of these latter, the manual defined gain controller is considered sufficient. Through a comprehensive sensitivity analysis, the robustness of both metaheuristic-based controllers is verified under diverse driving cycles of different operation features and nature. Despite GWO results in better dynamic characteristics, the ABC provides more economical feature with about 1.5% compared to manual system in extra urban driving cycle. However, manual-controller has the minimum fuel cost under the United States driving cycle developed by the environmental protection agency as a New York city cycle (US EPA NYCC) and urban driving cycle (ECE). Ecologically, electric vehicles have an environmentally friendly effect especially when driven with green hydrogen. Autonomous vehicles, involving velocity control systems, would raise car share and provide more comfort.

Bi-level convex optimization of eco-driving for connected Fuel Cell Hybrid Electric Vehicles through signalized intersections

Eco-driving for connected Fuel Cell Hybrid Electric Vehicles (FCHEVs) is a coupled problem of speed planning and energy management. To reduce the computational burden, bi-level optimization decouples and hierarchically solves the upper-level and lower-level subproblems. This paper proposes a bi-level convex approach for eco-driving of a connected FCHEV proceeding through multiple signalized intersections. On the upper level, the non-linear traffic light constraints are transformed into time-varying linear state constraints and the cost function becomes quadratic after using the average speed. On the lower level, model convexification is carried out for the fuel cell system and battery. Then the upper-level speed planning and lower-level energy management are sequentially solved by the MOSEK solver and the Alternating Direction Method of Multipliers (ADMM) algorithm. The results show that the proposed bi-level convex approach greatly reduces the computational cost while maintaining high energy efficiency, with only 6.59% computational time and almost the same fuel economy compared to the bi-level Dynamic Programming (DP) method.

Optimal energy management of fuel cell hybrid electric vehicle based on model predictive control and on-line mass estimation

Energy management strategies with prediction message show great potential in optimizing control objectives for fuel cell hybrid vehicles. This paper presents a novelty model-predictive-control based energy management framework for fuel cell commercial vehicle, in which the vehicle mass is firstly introduced as a variable parameter for controller. A vehicle mass identification model is established and embed in the framework based on recursive least square algorithm, and the influence of variable algorithm parameters on estimator is evaluated. Then the effects of mass varying on vehicle performance is talked about in detail. In addition, the performance for different drive cycles on different loaded is also given. The simulation results show that the fuel consumption is positively correlated with the mass identification error, and the designed controller can reduce the additional fuel consumption to around 0.1%, which is much batter to the deterministic parameters scheme. Finally, the shortcomings of the designed controller are given. This paper provides a reliable theoretical basis to address varying-mass vehicle energy management problems.