The novel application of optimization and charge blended energy management control for component downsizing within a plug-in hybrid electric vehicle

The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%–9% being realizable

Study of low acceleration space transportation systems. Volume I - Summary Phase II STUDY report

Technical feasibility of electric-nuclear propulsion system for manned Mars missio

Event-Driven Network Model for Space Mission Optimization with High-Thrust and Low-Thrust Spacecraft

Numerous high-thrust and low-thrust space propulsion technologies have been developed in the recent years with the goal of expanding space exploration capabilities; however, designing and optimizing a multi-mission campaign with both high-thrust and low-thrust propulsion options are challenging due to the coupling between logistics mission design and trajectory evaluation. Specifically, this computational burden arises because the deliverable mass fraction (i.e., final-to-initial mass ratio) and time of flight for low-thrust trajectories can can vary with the payload mass; thus, these trajectory metrics cannot be evaluated separately from the campaign-level mission design. To tackle this challenge, this paper develops a novel event-driven space logistics network optimization approach using mixed-integer linear programming for space campaign design. An example case of optimally designing a cislunar propellant supply chain to support multiple lunar surface access missions is used to demonstrate this new space logistics framework. The results are compared with an existing stochastic combinatorial formulation developed for incorporating low-thrust propulsion into space logistics design; our new approach provides superior results in terms of cost as well as utilization of the vehicle fleet. The event-driven space logistics network optimization method developed in this paper can trade off cost, time, and technology in an automated manner to optimally design space mission campaigns.Comment: 38 pages; 11 figures; Journal of Spacecraft and Rockets (Accepted); previous version presented at the AAS/AIAA Astrodynamics Specialist Conference, 201

New ICE concept for hybrid application

Due to increasingly strict regulations on automobile CO2 emission around the world, this thesis focuses on the development of the control strategies of a plug-in series hybrid electric vehicle (HEV) with the goal of minimizing CO2 emission. The thesis consists of three parts. The first target is to set up an electric vehicle (EV) model, which is the base of a plug-in series hybrid electric vehicle. The electric machine and battery are sized, and range capability and energy consumption are evaluated for a vehicle running in EV mode. The second objective is the assessment of the reference performance of the Range Extender (R-EX) architecture through the dynamic programming (DP) function in MATLAB, in terms of minimizing CO2 emissions in the charge-sustaining condition. The third one is the development of the rule based control strategy through the analysis of the DP results by rules extraction. In this thesis, a B-segment hatchback passenger car is modelled. The simulations were carried out along seven standard driving cycles that were developed to model different road conditions. This thesis also evaluates the effect of different values of auxiliary power on the electric range, energy consumption and thresholds of the rule-based control strategy. A sensitivity analysis of the carbon intensity of electricity is performed from a worldwide perspective. Finally, the minimum values of CO2 emission and the optimal engine operating points over different driving cycles are obtained from the dynamic programming; two flow charts of the proposed rule-based control strategies are derived, which are implementable for an electrical control unit to determine the power split between different energy sources

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

Predictive energy-efficient motion trajectory optimization of electric vehicles

This work uses a combination of existing and novel methods to optimize the motion trajectory of an electric vehicle in order to improve the energy efficiency and other criteria for a predefined route. The optimization uses a single combined cost function incorporating energy efficiency, travel safety, physical feasibility, and other criteria. Another focus is the optimal behavior beyond the regular optimization horizon

Journey predictive energy management strategy for a plug-in hybrid electric vehicle

The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonisation of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialisation. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Further, recent studies suggest the use of \intelligent transport" infrastructure to include a predictive element to the energy management strategy to achieve reductions in emissions. The thesis addresses the problem of determining the links between component-sizing, real-world usage and energy management strategies for a PHEV. The objective is to develop an integrated framework in which the advantages of predictive energy management can be realised by component downsizing for a PHEV. The study is spilt into three sections. The first part presents the framework by which the predictive element can be included into the PHEV's energy management strategy. Second part describes the development of the PHEV component models and the various energy management strategies which control the split in energy used between the engine and the battery. In this section a new control strategy is presented which integrates the predictive element proposed in the first part. Finally, in the third section an optimisation framework is presented by which the size of the components within the PHEV are reduced due to the lower energy demands of the new proposed energy management strategy. The first part of the study presents a framework by which the energy consumption of a vehicle may be predicted over a route. The proposed energy prediction framework employs a neural network and was used o_-line for estimating the real-world energy consumption of the vehicle so that it can be later integrated within the vehicles energy management control system. Experimental results show an accuracy within 20%-30% when comparing predicted and measured energy consumptions for over 800 different real-world EV journeys … [cont.]

A survey of differential flatness-based control applied to renewable energy sources

Conference ProceedingsThis paper presents an overview of various methods used to minimize the fluctuating impacts of power generated from renewable energy sources. Several sources are considered in the study (biomass, wind, solar, hydro and geothermal). Different control methods applied to their control are cited, alongside some previous applications. Hence, it further elaborates on the adoptive control principles, of which includes; Load ballast control, dummy load control, proportional integral and derivative (PID) control, proportional integral (PI) control, pulse-width modulation (PWM) control, buck converter control, boost converter control, pitch angle control, valve control, the rate of river flow at turbine, bidirectional diffuser-augmented control and differential flatnessbased controller. These control operations in renewable energy power generation are mainly based on a steady-state linear control approach. However, the flatness based control principle has the ability to resolve the complex control problem of renewable energy systems while exploiting their linear properties. Using their flatness properties, feedback control is easily achieved which allows for optimal/steady output of the system components. This review paper highlights the benefits that range from better control techniques for renewable energy systems to established robust grid (or standalone generations) connections that can bring immense benefits to their operation and maintenance costs

Electrified Powertrains for a Sustainable Mobility: Topologies, Design and Integrated Energy Management Strategies

This Special Issue was intended to contribute to the sustainable mobility agenda through enhanced scientific and multi-disciplinary knowledge to investigate concerns and real possibilities in the achievement of a greener mobility and to support the debate between industry and academic researchers, providing an interesting overview on new needs and investigation topics required for future developments