Fuel Cell Hybrid Vehicle Research Articles

Real-time battery state of charge based power-split adaptation to different driving conditions for range extender fuel cell hybrid vehicles

A real-time applicable adaptive power split controller for fuel cell hybrid vehicles is proposed. Simulation analyses were carried-out on a high degree of hybridization (i.e., with small fuel cell size) vehicle configuration, whose design and control strategies were simultaneously optimized by exploiting a specification-independent power-split controller. The goal was to upgrade the original thermostatic controller by letting the fuel cell system increase its power share whenever the battery state of charge falls below a critical value, so as to ensure both proper functioning and adequate lifetime for the hybridizing device. This way, it was possible to adapt the optimized vehicle configuration and control strategy to different driving cycles with respect to the one considered in the design phase, also including extra-urban segments. The research outcomes confirm how the previously designed range extender fuel cell vehicle configuration can be safely used onto a variety of driving cycles, including those exhibiting more frequent high power requests. The adaptive control strategy is able to intervene avoiding that the state of charge level goes below the minimum threshold, set to 0.25 after a trial and error analysis. This will allow keeping the benefits associated to limiting fuel cell system size, which also reflects into reduced costs and solves mass production related bottlenecks. The latter aspect is of paramount importance, especially when aiming at supporting short-term market penetration of fuel cell vehicles.

Real-Time Implementation of Improved Power Frequency Approach Based Energy Management of Fuel Cell Hybrid Vehicle Considering Storage Limitations

Real-Time Implementation of Improved Power Frequency Approach Based Energy Management of Fuel Cell Hybrid Vehicle Considering Storage Limitations

Real-Time Parameter Estimation of a Fuel Cell for Remaining Useful Life Assessment

This article presents a real-time parameter estimation of a proton exchange membrane fuel cell (PEMFC). The proposed strategy estimates online the PEMFC's resistance, since it is directly correlated to its remaining useful life assessment. The estimation of the PEMFC's parameters is a difficult task to undertake due to various uncertainties, like temperature and aging, that lead to a drift in parameters and limit the performance of the overall energy system. Therefore, online system identification is essential to track online the PEMFC's time-varying parameters. Unlike other identification techniques, the proposed strategy is based on a simple yet accurate PEMFC's model and adjusts its parameters in real-time using a Lyapunov-based adaptation law, which yields guaranteed stability. Experiments are conducted on a 500-W Horizon PEMFC and results along with a comparison against the well-known Kalman filter highlight the effectiveness of the proposed approach, which is instrumental for its numerous applications, such as the energy management of hybrid fuel cell vehicles.

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.

The Effect of Fuel Cell and Battery Size on Efficiency and Cell Lifetime for an L7e Fuel Cell Hybrid Vehicle

The size of the fuel cell and battery of a Fuel Cell Hybrid Electric Vehicle (FCHEV) will heavily affect the overall performance of the vehicle, its fuel economy, driveability, and the rates of fuel cell degradation observed. An undersized fuel cell may experience accelerated ageing of the fuel cell membrane and catalyst due to excessive heat and transient loading. This work describes a multi-objective design exploration exercise of fuel cell size and battery capacity comparing hydrogen fuel consumption, fuel cell lifetime, vehicle mass and running cost. For each system design considered, an individually optimised Energy Management Strategy (EMS) has been generated using Stochastic Dynamic Programming (SDP) in order to prevent bias to the results due to the control strategy. It has been found that the objectives of fuel efficiency, lifetime and running cost are largely complimentary, but degradation and running costs are much more sensitive to design changes than fuel efficiency and therefore should be included in any optimisation. Additionally, due to the expense of the fuel cell, combined with the dominating effect of start/stop cycling degradation, the optimal design from an overall running cost perspective is slightly downsized from one which is optimised purely for high efficiency.

Current and Voltage Harmonics in the Powertrain of Electric Vehicles

Switching electronics, such as the drive inverter, generate significant voltage and current harmonics with frequencies as low as a few kilohertz which are propagated throughout the complete electric powertrain and all connected components. With amplitudes up to several volts and dozens of amperes, such harmonics can cause significant problems and lead to performance limitations or even component failures. In order to maintain the quality of the dc-link voltage and guarantee the sound interaction of all components, power electronics must therefore be equipped with filter capacitors damping these harmonics. However, the filter stages account for a large part of the overall component cost and volume. Usually, the required dc-link filters are designed independently for each component on its own without taking into account the configuration of the complete powertrain. This, however, might result in undersizing of filters and possible system failures or contrary to that to oversized and expensive components. In this study, an analytic approach for the computation of the electric harmonics generated by the drive unit of a hybrid fuel cell vehicle will be derived and validated using measurement data. Using this model, the significance of the electric harmonics for the design of the powertrain as well as the sizing process of the filter capacitors will be analyzed.

An Improved Test Method for Energy Consumption and Range of Fuel Cell Vehicles

At present, there are two main standards, ISO 23828 : 2013 and SAE J 2572–2014, which prescribe the hydrogen consumption test using the pressure method, gravimetric method, and flow method. However, these methods do not meet the test requirements for electric energy consumption and the range of plug-in hybrid fuel cell vehicles (FCVs) which are the main technical considerations in China and Europe. In this paper, a new test method for the hydrogen consumption, electric energy consumption, and range of FCVs is proposed without the use of additional hydrogen supply, measurement instruments, or energy consumption correction, which can improve the operability of the test and avoid the conversion between electric energy and hydrogen. One plug-in hybrid FCV and one nonplug-in hybrid FCV were tested using the proposed method. The results show that the new method meets the requirements of fuel economy test for FCVs with hydrogen consumption rate, electric energy consumption rate, the range for plug-in hybrid FCVs, hydrogen consumption rate, and the range for nonplug-in FCVs.

Energy Management Strategy for Fuel Cell and Battery Hybrid Vehicle Based on Fuzzy Logic

In order to improve fuel economy and enhance operating efficiency of fuel cell hybrid vehicles (FCHVs), fuzzy logic control (FLC) strategies are available and suggested for adoption. In this paper, the powertrain of a fuel cell hybrid vehicle is designed and the parameters of the motor, battery, and fuel cell are calculated. The FLC strategy and the power following control (PFC) strategy are designed for the studied FCHV. A secondary development for Advanced Vehicle Simulator (ADVISOR) is implemented based on the standard driving cycles, and a Chinese typical city driving cycle is imported. Simulation results demonstrate that the proposed FLC strategy is more valid and reasonable than the traditional PFC strategy. The proposed FLC strategy affects the vehicle characteristics significantly and contributes to better performance in four aspects: fuel economy, efficiency of battery and fuel cell system, battery state of charge (SOC), and battery life. Hence, the FLC strategy is more suitable for the energy management strategy for fuel cell and battery hybrid vehicles.

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.

Data-driven approach for short-term power demand prediction of fuel cell hybrid vehicles

Due to slow internal mass transport, the fuel cell is a typical time-delay control object in vehicular hybrid powertrain. To yield better control effect, the predictive control is considered as an effective solution, in which the short-term power demand of vehicle is a key input variable and must be predicted accurately. However, a time-phase mismatch phenomenon usually occurs in prediction results when using non-iterative direct prediction method, resulting in poor prediction accuracy. This study systematically explains the mechanism of the studied time-phase mismatch and proposes a novel iterative learning framework (ILF) to reduce it. Several machine learning algorithms are compared to select a proper learning core for ILF. The results show that prediction RMSE reduces up to 76.8% and 65.0% for the power and power change rate predictions, respectively, comparing with non-iterative prediction manner. The least-squares support vector machine as the learning core of ILF achieves the best performance within the shortest runtime. Moreover, the proposed ILF predictor has a good adaptability to various driving conditions through more validations. The proposed ILF has better predictable ability for the future data comparing with classical recurrent time-series prediction method. The proposed ILF is expected to improve the accuracy of vehicle load-status perception.

Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization

Abstract In this article, a multi-objective optimization-oriented energy management strategy is investigated for fuel cell hybrid vehicles on the basis of rule learning. The degradation of fuel cells and lithium-ion batteries are considered as the objective function and translated into the equivalent hydrogen consumption. The optimal fuel cell power sequence and state of charge trajectory, considered as the energy management input, are solved offline via the Pontryagin’s minimum principle. The K-means algorithm is employed to hierarchically cluster the optimal data set for preparation of rules extraction, and then the rules are excavated by the improved repeated incremental pruning to production error reduction algorithm and fitted by the quasi-Newton method. The simulation results highlight that the proposed rule learning-based energy management strategy can effectively save hydrogen consumption and prolong fuel cell life with real-time application potential.

A Modified MPC-Based Optimal Strategy of Power Management for Fuel Cell Hybrid Vehicles

Power management is a vital technology to increase the economy of fuel cell hybrid vehicles. To overcome the deficiency of most existing optimization approaches, in this article, a modified model predictive control-based optimal strategy of power management is proposed for fuel cell hybrid vehicles. Besides, a partial least squares-based load estimation method is utilized to avoid the assumption of conventional model predictive control. Furthermore, an alternating direction multiplier method-based quadratic programming solver is also designed to avoid the ill-condition of the quadratic programming. Finally, the reliability and effectiveness of the proposed strategy are demonstrated by the simulation and experiments.

Efficiency Upgrade of Hybrid Fuel Cell Vehicles’ Energy Management Strategies by Online Systemic Management of Fuel Cell

In this article, an approach for boosting the efficiency of energy management strategies (EMSs) in fuel cell hybrid electric vehicles using an online systemic management of the fuel cell system (FCS) is put forward. Unlike other similar works which solely determine the requested current from the FCS, this article capitalizes on simultaneous regulation of current and temperature, which have different dynamic behavior. In this regard, first, 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 three-dimensional map which relates the requested power from the stack to its optimal temperature and current. Second, 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's health adaptation unawareness can increase the hydrogen consumption up to 6.6%.

Energy Management of Fuel Cell Hybrid Vehicle Based on Partially Observable Markov Decision Process

This paper presents a nonmyopic energy management strategy (EMS) for controlling multiple energy flow in fuel cell hybrid vehicles. The control problem is solved by convex programing under a partially observable Markov decision process-based framework. We propose an average-reward approximator to estimate a long-term average cost instead of using a model to predict future power demand. Thus, the dependence between the system closed-loop performance and the model accuracy for predicting the future power demand is decoupled in the energy management design for fuel cell hybrid vehicles. The energy management scheme consists of a real-time self-learning system, an average-reward filter based on the Markov chain Monte Carlo sampling, and an action selector system through the rollout algorithm with a convex programing-based policy. The performance evaluation of the EMS is conducted via simulation studies using the data obtained from real-world driving experiments and its performance is compared with three benchmark schemes.

Power sources sizing for a fuel cell hybrid vehicle

AbstractFuel cell hybrid vehicles (FCHV) are attractive alternatives to fossil fuel based ones in terms of lowering levels of polluting emissions. This paper presents a methodology for modeling, sizing, and power management of FCHV. The aim of the study is to achieve the lowest cost of driving over different driving cycles taking into consideration the vehicle specifications and minimum drivability constraints. The tunnel dynamic programming (TDP) is used to find the optimal power split between the battery and the fuel cell to meet the power demand for driving. On the other hand, since cost is a major barrier hindering the commercialization of FCHV, Pareto front is used to determine the best combination of sizes to achieve the lowest operational cost. The analysis covers different driving cycles including a combination of the highway and urban driving cycles mimicking a realistic one. The focus on lithium titanate battery and the combination of TDP‐Pareto front are the significant contributions of this paper. Comparative results against a commercial hybrid vehicle showed that the proposed model found a more efficient design in terms of hydrogen consumption and more capable in meeting aggressive drivability constraints as the electric motor size selected is greater.

Switched Energy Management Strategy for Fuel Cell Hybrid Vehicle Based on Switch Network

Environmentally friendly and pollution-free fuel cell/lithium battery hybrid vehicles have received the attention of the community in recent years. It is imperative for fuel cell/lithium battery hybrid vehicles to use the energy management strategy (EMS) to distribute the output power of each power source to improve fuel economy and system life. In practical application, inconsistency of battery pack will lead to security hazard and capacity degradation. However, few EMS take the inconsistency of battery pack into account. Also, the current battery equalization strategy rarely discusses how to perform the equilibrium process while meeting the power demand of vehicle. To solve these issues, a novel equalization energy management strategy (EEMS) based on the switch network is proposed at first. Then, a switched energy management strategy (SEMS) that switches between the EEMS and the equivalent consumption minimization strategy (ECMS) is proposed and implemented in the fuel cell/lithium battery hybrid system to validate its effectiveness. The results show that the proposed SEMS can ameliorate the inconsistency of series lithium battery pack while meeting the power demand of vehicle’s normal operation. It can improve the safety and durability of the system and reduce the equalization time. Besides, it has good expansibility and no energy waste.

Optimal Control of Multi-Source Electric Vehicles in Real Time Using Advisory Dynamic Programming

This paper presents a novel method, advisory dynamic programming (AD-DP), for power management of a fuel cell hybrid vehicle (FCHEV). The presented method embraces a new understanding of vehicle states as driver-dependent and -independent states in time domain to define a suitable state space to be used for dynamic programming. Driver-dependent states are defined in terms of multiple characteristic parameters for vehicle speed and power demand, corporately. Driver-independent states are defined in terms of discrete values for supercapacitor's state of charge $SoC_{sc}$ . Transition costs between all states in the state space are calculated offline and tabulated in look-up tables for online implementation. A state predictive model is developed based on the transition statistics of driver-dependent states for a suitable number of driving cycles. Backward calculation of the total transition cost for the predicted horizon in state space is used to define optimal power split strategy for the powertrain. The formulation of optimal control problem, in terms of situation-based solutions related to vehicle states, enables a significant reduction of computational steps and hence addresses the main challenge of real-time applications. The algorithm is adapted in terms of optimization horizon and number of discrete states for DIS to suit the real-time application. Experimental application of AD-DP, using an emulation test-rig, is conducted over different driving cycles. The obtained results reveal an improvement in energy efficiency up to 29% compared to the adaptive rule-based method. The contribution of this paper can be identified as: first, development of a corporate definition for vehicle states, that can be further implemented in optimization-based power management methods. Second, the formulation of an adaptive DP that requires lower computational steps and hence suits real-time applications in hybrid electric vehicles.

Multi-Objective Optimization Energy Management Strategy for Fuel Cell Hybrid Vehicles Based on Rule Learning

In this paper, a rule learning-based energy management strategy is proposed to achieve optimal energy consumption economy and prolong the batteries lifetime for a fuel cell hybrid electric vehicle (FCHEV). First, the Pontryagin’s minimum principle (PMP) is used to obtain the optimal global solution. Then, K-means algorithm is adopted to simplify the optimal database composed of the optimized data based on PMP and the corresponding driving cycle features. According to the simplification data set, the improved repeated incremental pruning to produce error reduction (RIPPER) algorithm based on rule learning theory is used to learn the rules. Finally, the multiple linear regression algorithms are utilized to fit the data in the rule set. Simulation results validate that the proposed strategy can achieve more than 94% of the PMP strategy in the fuel consumption and also can prolong the batteries lifetime.

An optimized energy management strategy for fuel cell hybrid vehicles

Fuel cell hybrid vehicles are environmentally friendly and have good development prospects. Their energy management is very important, but there are currently few studies on this topic. In this paper, an optimized energy management strategy specifically applied to fuel cell hybrid vehicles is proposed. Fuel cell hybrid vehicles have some different characteristics from ordinary hybrid vehicles, which need to be considered in energy management. For the fuel cell to remain activated during driving, its output power must remain above a minimum power. In addition, it takes some time for the fuel cell to start up. These characteristics are considered in the vehicle model. The energy management strategy proposed in this paper maximizes energy use efficiency by controlling the SOC value of the battery. The SOC value of the battery is in the proper range during driving. At high speeds, fuel is consumed as a source of energy and the battery is charged to keep its SOC at a high level. When approaching the destination or when the vehicle is traveling at a low speed, the battery is discharged to reduce fuel consumption. Applying this strategy to the ECE-15 and EUDCL environments, the simulation results show that this optimized energy management strategy improves the energy efficiency of the fuel cell and enables the battery to operate at the appropriate power.

Real-time nonlinear model predictive energy management system for a fuel-cell hybrid vehicle

Proton exchange membrane fuel cell is considered as one of the most efficient sources of renewable energy. Time-varying dynamic and nonlinear equations are two major factors that make control and power optimization of fuel-cell vehicles challenging. In this paper, by using a comprehensive PEMFC vehicle model and nonlinear model predictive controller, a novel energy management method is represented. By considering both of the regenerating brake power and fuel-cell output power as the controller inputs, the steady-state equations have been derived in a nonlinear form. Also, the NMPC contains a constrained quadratic cost function which has been minimized to make the controller inputs completely optimized. Moreover, the two controller references using in this article have been achieved based on experimental FTP drive-cycle data. Finally, the optimized cost-function weighting matrices will be calculated by genetic algorithm, and then, the real-time controller inputs are applied to the vehicle model to get better results on vehicle range during an online procedure, and the online results are compared with the experimental drive-cycle data.