Sizing and Energy Management of a Hybrid Locomotive Based on Flywheel and Accumulators

The French National Railways Company (SNCF) is interested in the design of a hybrid locomotive based on various storage devices (accumulator, flywheel, and ultracapacitor) and fed by a diesel generator. This paper particularly deals with the integration of a flywheel device as a storage element with a reduced-power diesel generator and accumulators on the hybrid locomotive. First, a power flow model of energy-storage elements (flywheel and accumulator) is developed to achieve the design of the whole traction system. Then, two energy-management strategies based on a frequency approach are proposed. The first strategy led us to a bad exploitation of the flywheel, whereas the second strategy provides an optimal sizing of the storage device. Finally, a comparative study of the proposed structure with a flywheel and the existing structure of the locomotive (diesel generator, accumulators, and ultracapacitors) is presented

Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used

Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology

The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components

PEMFC Optimization Strategy with Auxiliary Power Source in Fuel Cell Hybrid Vehicle

one of the present-day implementation of fuel cell is acting as main power source in Fuel Cell Hybrid Vehicle (FCHV). This paper proposes some strategies to optimize the performance of Polymer Electrolyte Membrane Fuel Cell (PEMFC) implanted with auxiliary power source to construct a proper FCHV hybridization. The strategies consist of the most updated optimization method determined from three point of view i.e. Energy Storage System (ESS), hybridization topology and control system analysis. The goal of these strategies is to achieve an optimum hybridization with long lifetime, low cost, high efficiency, and hydrogen consumption rate improvement. The energy storage system strategy considers battery, supercapacitor, and high-speed flywheel as the most promising alternative auxiliary power source. The hybridization topology strategy analyzes the using of multiple storage devices injected with electronic components to bear a higher fuel economy and cost saving. The control system strategy employs nonlinear control system to optimize the ripple factor of the voltage and the current and using the AOC-EMS system to improve the hydrogen consumption rate. ECMS and BERS strategy based on Time-Triggered Controller Area Network (TTCAN) also promoted to optimize hydrogen consumption rate from recovered kinetic energy while in braking regeneration mode

Design and performance analysis of electric vehicles fed by multiple fuel cells

Recent advances in fuel cell developments have introduced them to many applications such as hybrid electric vehicles and heat/power cogenerations. They bring the advantage of clean energy and decrease the dependency on imported oil by providing fuel efficient devices in many applications such as electric vehicles. Conventional designs of hybrid fuel cell vehicles make use of a single fuel cell power source and a storage device to provide the base load and transients in various driving cycles. This thesis proposes a new configuration of multiple fuel cell power sources in hybrid fuel cell vehicles. Fuel cells are downsized in this new configuration to provide the same amount of power, which brings the advantage of a highly fuel economic design. The power control algorithm for this new configuration is presented and simulation results are studied for a case of double fuel cell power sources. Efficiency analysis for this new configuration is presented and compared with the conventional configuration. The main objective of this thesis is to achieve a higher efficiency in urban driving cycle. In conventional configurations, the fuel cell is not efficiently loaded in urban driving cycles, where small powers were required from the single fuel cell power source. Reliability analysis is also presented for this configuration

MIMO Hinf control for power source coordination - application to energy management systems of electric vehicles

International audienceThis paper deals with a control strategy used for designing energy management systems within average-power electric vehicles. The power supply system is composed of three sources, namely a fuel cell, a battery and an ultracapacitor - specialized within distinct frequency ranges - which must be coordinated in order to satisfy power demand of the vehicle's electrical motor. The three sources with their associated DC-DC converters are paralleled on a common DC-bus supplying the electrical motor. The DC-bus is required to be constant regardless of the load state thanks to the fuel cell which provides the mean power and to the other two sources - auxiliary sources - which are controlled to supply the high-frequency variations of power demand according to an H1 optimization strategy. MATLAB/ Simulink numerical simulation is used to validate the proposed strategy under real driving cycle condition proposed by IFSTTAR (Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux), and this approach is assessed against another optimal strategy that uses LQR as control design

Real-Time Power Management of A Fuel Cell/Ultracapacitor Hybrid

This thesis presents the system architecture design, system integration methodology, and real-time control of a fuel cell/ultracapacitor hybrid power system. The main objective is for the hybrid system to respond to real-world fluctuations in power without negatively impacting fuel cell life. A Proton Exchange Membrane (PEM) Fuel Cell is an electrochemical device which converts the chemical energy of pure hydrogen into electricity through a chemical reaction with oxygen. The high conversion efficiency, zero harmful emissions, high power-to-weight ratio, scalability, and low temperature operation make PEM fuel cells very attractive for stationary and portable power applications. However, fuel cells are limited in responding to fast transients in power demand, moreover power fluctuations have negative impact on fuel cell durability. This motivates the use of a supplementary energy storage device to assist the fuel cell by buffering sharp transients in power demand. The high power density, long cycle life, and efficiency of ultracapacitors make them an ideal solution for such auxiliary energy storage in a hybrid fuel cell system. The power management strategy that determines the power split between the fuel cell and ultracapacitor is key to the power following capability, long-term performance, and life-time of the fuel cell. In this thesis, a rule-based and a model predictive control strategy are designed, implemented and evaluated for power management of a fuel cell/ultracapacitor hybrid. The high-level control objectives are to respond to rapid variations in load while minimizing damaging fluctuations in fuel cell current and maintaining ultracapacitor charge (or voltage) within allowable bounds. An experimental test stand was created to evaluate the performance of the controllers. The test stand connects the fuel cell and ultracapacitor to an electronic load through two dc/dc converters, which provide two degrees of freedom, enabling independent low-level control of the DC BUS voltage and the current split between the fuel cell and ultracapacitor. Experiments show that both rule-based and model predictive power management strategies can be tuned to meet both high and low-level control objectives for a given power demand profile. However, the capability to explicitly enforce the constraints in model predictive scheme and its predictive nature in meeting power demands enables a more systematic design and results in general in smoother performance

Ultracapacitor Heavy Hybrid Vehicle: Model Predictive Control Using Future Information to Improve Fuel Consumption

This research is concerned with the improvement in the fuel economy of heavy transport vehicles through the use of high power ultracapacitors in a mild hybrid electric vehicle platform. Previous work has shown the potential for up to 15% improvement on a hybrid SUV platform, but preliminary simulations have shown the potential improvement for larger vehicles is much higher. Based on vehicle modeling information from the high fidelity, forward-looking modeling and simulation program Powertrain Systems Analysis Toolkit (PSAT), a mild parallel heavy ultracapacitor hybrid electric vehicle model is developed and validated to known vehicle performance measures. The vehicle is hybridized using a 75kW motor and small energy storage ultracapacitor pack of 56 Farads at 145 Volts. Among all hybridizing energy storage technologies, ultracapacitors pack extraordinary power capability, cycle lifetime, and ruggedness and as such are well suited to reducing the large power transients of a heavy vehicle. The control challenge is to effectively manage the very small energy buffer (a few hundred Watt-hours) the ultracapacitors provide to maximize the potential fuel economy. The optimal control technique of Dynamic Programming is first used on the vehicle model to obtain the \u27best possible\u27 fuel economy for the vehicle over the driving cycles. A variety of energy storage parameters are investigated to aid in determining the best ultracapacitor system characteristics and the resulting effects this has on the fuel economy. On a real vehicle, the Dynamic Programming method is not very useful since it is computationally demanding and requires predetermined vehicle torque demands to carry out the optimization. The Model Predictive Control (MPC) method is an optimization-based receding horizon control strategy which has shown potential as a powertrain control strategy in hybrid vehicles. An MPC strategy is developed for the hybrid vehicle based on an exponential decay torque prediction method which can achieve near-optimal fuel consumption even for very short prediction horizon lengths of a few seconds. A critical part of the MPC method which can greatly affect the overall control performance is that of the prediction model. The use of telematic based \u27future information\u27 to aid in the MPC prediction method is also investigated. Three types of future information currently obtainable from vehicle telematic technologies are speed limits, traffic conditions, and traffic signals, all of which have been incorporated to improve the vehicle fuel economy