Control and Optimization of Fuel Cell Based Powertrain for Automotive Applications

Fuel cell powered electric vehicles, with fast-refueling time, high energy density, and zero CO2 emissions, are becoming a promising solution for future fossil-free transportation. However, the relatively slow dynamic response and the inability of recovering the regenerative energy make vehicles solely powered by fuel cells not an immediately attractive solution. Instead, hybrid vehicles powered by fuel cells combined with energy buffers such as batteries and supercapacitors could be of more interest. Due to the unique characteristics of each energy buffer, the vehicle performance may vary with the hybrid energy storage system configuration. This thesis performs a comprehensive study on various energy storage configurations for applications in fuel cell hybrid electric vehicles. This thesis first examines the fuel cell/supercapacitor passive hybrid configuration where the fuel cell and supercapacitor share the same DC-link voltage. The power distribution between them is inherently determined by their internal resistances. Therefore, the DC-link voltage varies and depends on the vehicle power demand. In this work, a fuel cell/supercapacitor passive hybrid powertrain is first modeled and evaluated. Simulation results show that the energy efficiency is 53%–71% during propulsion and 84%–94% during braking, respectively. Moreover, a 3 kW lab-scale fuel cell/supercapacitor passive hybrid system is designed and investigated. Experimental results show that the fuel cell takes time to respond to a load change, while the supercapacitor provides the transient power, which makes it possible to downsize the fuel cell.Since the passive configuration loses the active controllability, this thesis further considers a fully-active fuel cell/supercapacitor system to improve the controllability of the power distribution. This configuration requires a boost converter for the fuel cell and a buck-boost converter for the supercapacitor. In this work, an adaptive power split method is used to smooth the fuel cell current and prevent the supercapacitor from exceeding its lower and upper charge limits. The cut-off frequency of the low-pass filter is adaptively controlled by the spectrum area ratio. Experimental results show that the supercapacitor state-of-charge is effectively controlled within the desired range. Moreover, a load disturbance compensator is proposed and demonstrated to improve the control performance such that the DC-link voltage fluctuation caused by the load current variation is significantly reduced.This thesis also investigates the cost-effectiveness of different energy buffers hybridized with fuel cells in various trucking applications. First, a chance-constraint co-design optimization problem is formulated. Convex modeling steps are presented to show that the problem can be decomposed and solved using convex programming. Results show that the power rating of the electric machine can be dramatically reduced when the delivered power is satisfied in a probabilistic sense. Moreover, the hybridization of fuel cells with lithium-ion batteries results in the lowest cost while the vehicle using lithium-ion capacitors as the energy buffer can carry the heaviest payload. This work also develops a robust co-design optimization framework considering the uncertainties in parameters (e.g., vehicle movement) and design decision variables (e.g., scaling factors of fuel cells and batteries). Results show that these uncertainties might propagate to uncertainties in state variables (e.g., battery energy) and optimization variables (e.g., battery power), leading to a larger battery capacity and therefore a higher total cost in robust optimal solutions. In summary, this thesis performs a comprehensive study on control and optimization of fuel cell based powertrains for automotive applications. This will provide a guidance on component selection and sizing, as well as powertrain system configuration and optimization for design of fuel cell powered electric vehicles

Studies on Utilizing the Three Famous International Index Systems to Evaluate Scientific Research Level of Higher Learning Institutions

Science Citation Index (SCI), The Engineering Index (EI) and Index to Scientific & Technical Proceeding (ISTP) are widely accepted and used to evaluate the scientific research level of higher learning institutions by many country's science and technology field currently. After research, we point out the blemishes in this method and put forward the problems that need to be noticed, and then, under current conditions, bring forward brand-new standard and method to estimate research level, efficiency, fund exploitation and so on. One shouldn't over-emphasize the total amount of papers collected in SCI, EI & ISTP when evaluating the scientific research level of higher learning institutions, whereas using ‘comprehensive factor’ analysis method can make it more scientific and efficient

Adaptive Backstepping-based H∞ Robust controller for Photovoltaic Grid-connected Inverter

To improve the robustness and stability of the photovoltaic grid-connected inverter system, a nonlinear backstepping-based H∞ controller is proposed. A generic dynamical model of grid-connected inverters is built with the consideration of uncertain parameters and external disturbances that cannot be accurately measured. According to this, the backstepping H∞ controller is designed by combining techniques of adaptive backstepping control and L2-gain robust control. The Lyapunov function is used to design the backstepping controller, and the dissipative inequality is recursively designed. The storage functions of the DC capacitor voltage and grid current are constructed, respectively, and the nonlinear H∞ controller and the parameter update law are obtained. Experimental results show that the proposed controller has the advantage of strong robustness to parameter variations and external disturbances. The proposed controller can also accurately track the references to meet the requirements of high-performance control of grid-connected inverters

Energy Efficiency Comparison of Hybrid Powertrain Systems for Fuel-Cell-Based Electric Vehicles

Fuel cell electric vehicles have great superiorities in endurance mileage, charging speed and climate tolerance compared to battery electric vehicles. However, a supercapacitor or battery bank is required to maintain a fast-dynamic response, which leads to several hybridization structures for fuel-cell-based electric vehicles due to the unique characteristics of each device, and their performances are also differing. The purpose of this paper is to provide a comprehensive comparison of hybrid powertrain systems for three types of powertrains: fuel cell/supercapacitor passive hybrid, fuel cell/supercapacitor semi-active hybrid, and fuel cell/battery semi-active hybrid. Each powertrain component model is developed from the real components wherever possible, and Honda FCX Clarity fuel cell vehicle is studied as the benchmark. The powertrain energy efficiency under Worldwide harmonized Light vehicles Test Cycle (WLTC) is analyzed and evaluated. The simulation results show that three powertrains have the same energy consumption, and fuel cell/supercapacitor passive hybrid powertrain increases the system efficiency by 2% and 4% in propulsion and regenerative braking, respectively. By contrast, the other two powertrain topologies have similar performance in terms of energy efficiency

Design and experimental verification of a fuel cell/supercapacitor passive configuration for a light vehicle

The fuel cell/supercapacitor passive configuration without using any DC/DC converters is promising in auto-motive applications as it can downsize the fuel cell stack, maintain the peak power capability, improve the system efficiency, and remove the need of additional control. This paper presents the design and characterization of a fuel cell/supercapacitor passive hybrid system for a 60 V light vehicle. A detailed design procedure for the passive hybrid test platform is presented with each component modelled and experimentally verified. The voltage error of the fuel cell and the supercapacitor model in the steady state is within 2% and 3%, respectively. Experimental results also validate the function of the passive configuration under conditions of a step load and a drive cycle. The simulation model of the passive hybrid system matches the measurements when a step load current is applied. The supercapacitor provides the transient current due to its smaller resistance while the fuel cell handles the steady state current, which makes it possible to downsize the fuel cell stack. For the drive cycle examined in this paper, the fuel cell stack can be downsized to one third of the load peak power

LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization

Photovoltaic (PV) output is susceptible to meteorological factors, resulting in intermittency and randomness of power generation. Accurate prediction of PV power output can not only reduce the impact of PV power generation on the grid but also provide a reference for grid dispatching. Therefore, this paper proposes an LSTM-attention-embedding model based on Bayesian optimization to predict the day-ahead PV power output. The statistical features at multiple time scales, combined features, time features and wind speed categorical features are explored for PV related meteorological factors. A deep learning model is constructed based on an LSTM block and an embedding block with the connection of a merge layer. The LSTM block is used to memorize and attend the historical information, and the embedding block is used to encode the categorical features. Then, an output block is used to output the prediction results, and a residual connection is also included in the model to mitigate the gradient transfer. Bayesian optimization is used to select the optimal combined features. The effectiveness of the proposed model is verified on two actual PV power plants in one area of China. The comparative experimental results show that the performance of the proposed model has been significantly improved compared to LSTM neural networks, BPNN, SVR model and persistence model

Joint Component Sizing and Energy Management for Fuel Cell Hybrid Electric Trucks

This paper proposes a cost-effective way to design and operate fuel cell hybrid electric trucks (FCHETs) where a chance-constrained optimization is formulated. The aim of the introduced problem is to minimize a summation of component cost and operational cost with consideration of fuel cell (FC)degradation and cycle life of energy buffer. We propose to decompose the problem into two sub-problems that are solved by sequential convex programming. The delivered power satisfies a cumulative distribution function of the wheel power demand, while the truck can still traverse driving cycles with a similar speed and travel time without delivering unnecessarily high power. This allows to downsize powertrain components, includingelectric machine, FC and energy buffer. A case study considering different energy buffer technologies, including supercapacitor (SC), lithium-ion battery (LiB), and lithium-ion capacitor (LiC) is investigated in a set of trucking applications, i.e. urban delivery, regional delivery, construction, and long-haul. Results show that the power rating of the electric machine is drastically reduced when the delivered power is satisfied in a probabilistic sense. Moreover, the configuration with LiB as the energy buffer has the lowest expense but the truck with LiC can carry more payload