Nested Rollout Policy Adaptation for optimizing vehicle selection in complex VRPs

The goal of Vehicle Routing Problems (VRP) and their variations is to transport a set of orders with the minimum number of vehicles at least cost. Most approaches are designed to solve specific problem variations independently, whereas in real world applications, different constraints are handled concurrently. This research extends solutions obtained for the traveling salesman problem with time windows to a much wider class of route planning problems in logistics. The work describes a novel approach that:  supports a heterogeneous fleet of vehicles  dynamically reduces the number of vehicles  respects individual capacity restrictions  satisfies pickup and delivery constraints  takes Hamiltonian paths (rather than cycles) The proposed approach uses Monte-Carlo Tree Search and in particular Nested Rollout Policy Adaptation. For the evaluation of the work, real data from the industry was obtained and tested and the results are reported.

Packaging as a vehicle of nutritional information for children: Enhancing children’s perceptions of healthy food

Field lab in marketing: Children consumer behaviour

Vehicle Routing Problems that Minimize the Completion Time: Heuristics, Worst-Case Analyses, and Computational Results

In the standard Vehicle Routing Problem (VRP), we route a fleet of vehicles to deliver the demands of all customers such that the total distance traveled by the fleet is minimized. In this dissertation, we study variants of the VRP that minimize the completion time, i.e., we minimize the distance of the longest route. We call it the min-max objective function. In applications such as disaster relief efforts and military operations, the objective is often to finish the delivery or the task as soon as possible, not to plan routes with the minimum total distance. Even in commercial package delivery nowadays, companies are investing in new technologies to speed up delivery instead of focusing merely on the min-sum objective. In this dissertation, we compare the min-max and the standard (min-sum) objective functions in a worst-case analysis to show that the optimal solution with respect to one objective function can be very poor with respect to the other. The results motivate the design of algorithms specifically for the min-max objective. We study variants of min-max VRPs including one problem from the literature (the min-max Multi-Depot VRP) and two new problems (the min-max Split Delivery Multi-Depot VRP with Minimum Service Requirement and the min-max Close-Enough VRP). We develop heuristics to solve these three problems. We compare the results produced by our heuristics to the best-known solutions in the literature and find that our algorithms are effective. In the case where benchmark instances are not available, we generate instances whose near-optimal solutions can be estimated based on geometry. We formulate the Vehicle Routing Problem with Drones and carry out a theoretical analysis to show the maximum benefit from using drones in addition to trucks to reduce delivery time. The speed-up ratio depends on the number of drones loaded onto one truck and the speed of the drone relative to the speed of the truck.

MILITARY VEHICLE OPTIMIZATION AND CONTROL

It is remarkable that there are no deployed military hybrid vehicles since battlefield fuel is approximately 100 times the cost of civilian fuel. In the commercial marketplace, where fuel prices are much lower, electric hybrid vehicles have become increasingly common due to their increased fuel efficiency and the associated operating cost benefit. An absence of military hybrid vehicles is not due to a lack of investment in research and development, but rather because applying hybrid vehicle architectures to a military application has unique challenges. These challenges include inconsistent duty cycles for propulsion requirements and the absence of methods to look at vehicle energy in a holistic sense. This dissertation provides a remedy to these challenges by presenting a method to quantify the benefits of a military hybrid vehicle by regarding that vehicle as a microgrid. This innovative concept allowed for the creation of an expandable multiple input numerical optimization method that was implemented for both real-time control and system design optimization. An example of each of these implementations was presented. Optimization in the loop using this new method was compared to a traditional closed loop control system and proved to be more fuel efficient. System design optimization using this method successfully illustrated battery size optimization by iterating through various electric duty cycles. By utilizing this new multiple input numerical optimization method, a holistic view of duty cycle synthesis, vehicle energy use, and vehicle design optimization can be achieved.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

Effect of Pavement-Vehicle Interaction on Highway Fuel Consumption and Emission

Vehicle fuel consumption and emission are two important effectiveness measurements of sustainable transportation development. Pavement plays an essential role in goals of fuel economy improvement and greenhouse gas (GHG) emission reduction. The main objective of this dissertation study is to experimentally investigate the effect of pavement-vehicle interaction (PVI) on vehicle fuel consumption under highway driving conditions. The goal is to provide a better understanding on the role of pavement in the green transportation initiates. Four study phases are carried out. The first phase involves a preliminary field investigation to detect the fuel consumption differences between paired flexible-rigid pavement sections with repeat measurements. The second phase continues the field investigation by a more detailed and comprehensive experimental design and independently investigates the effect of pavement type on vehicle fuel consumption. The third study phase calibrates the HDM-IV fuel consumption model with data collected in the second field phase. The purpose is to understand how pavement deflection affects vehicle fuel consumption from a mechanistic approach. The last phase applies the calibrated HDM-IV model to Florida’s interstate network and estimates the total annual fuel consumption and CO2 emissions on different scenarios. The potential annual fuel savings and emission reductions are derived based on the estimation results. Statistical results from the two field studies both show fuel savings on rigid pavement compared to flexible pavement with the test conditions specified. The savings derived from the first phase are 2.50% for the passenger car at 112km/h, and 4.04% for 18-wheel tractor-trailer at 93km/h. The savings resulted from the second phase are 2.25% and 2.22% for passenger car at 93km/h and 112km/h, and 3.57% and 3.15% for the 6-wheel medium-duty truck at 89km/h and 105km/h. All savings are statistically significant at 95% Confidence Level (C.L.). From the calibrated HDM-IV model, one unit of pavement deflection (1mm) on flexible pavement can cause an excess fuel consumption by 0.234-0.311 L/100km for the passenger car and by 1.123-1.277 L/100km for the truck. The effect is more evident at lower highway speed than at higher highway speed. From the network level estimation, approximately 40 million gallons of fuel (combined gasoline and diesel) and 0.39 million tons of CO2 emission can be saved/reduced annually if all Florida’s interstate flexible pavement are converted to rigid pavement with the same roughness levels. Moreover, each 1-mile of flexible-rigid conversion can result in a reduction of 29 thousand gallons of fuel and 258 tons of CO2 emission yearly.

Electric Vehicle Battery Charger: Wireless Power Transfer System Controlled by Magnetic Core Reactor

This paper presents a control process and frequency adjustment based on the magnetic core reactor for electric vehicle battery charger. Since few decades ago, there have been significant developments in technologies used in wireless power transfer systems, namely in battery charger. In the wireless power transfer systems is essential that the frequency of the primary circuit be equal to the frequency of the secondary circuit so there is the maximum energy transfer. The magnetic core reactor allows controlling the frequencies on both sides of the transmission and reception circuits. Also, the assembly diagrams and test results are presented.

Performance of LTE-V2V for Cooperative Awareness

The large scale development of an Intelligent Transportation System is very close. The main component of such a smart environment is the network that provides connectivity for all vehicles. Public safety is the most demanding application because requires a fast, reliable and secure communication. Although IEEE 802.11p is presently the only full wireless standard for vehicular communications, recent advancements in 3GPP LTE provide support to direct communications and the ongoing activities are also addressing the vehicle to vehicle case. This thesis focuses on the resource allocation procedures and performance of LTE-V2V. To this aim, a MATLAB simulator has been implemented and results have been obtained adopting different mobility models for both in-coverage and out-of-coverage scenarios.

Improvement of The Driver Simulator Control and Comparison Between Driver-Road-Vehicle Interaction in Real and Simulated Environment

The present doctoral thesis discusses the ways to improve the performance of driving simulator, provide objective measures for the road safety evaluation methodology based on driver’s behavior and response and investigates the drivers' adaptation to the driving assistant systems. The activities are divided into two macro areas; the driving simulation studies and on-road experiments. During the driving simulation experimentation, the classical motion cueing algorithm with logarithmic scale was implemented in the 2DOF motion cueing simulator and the motion cues were found desirable by the participants. In addition, it found out that motion stimuli could change the behaviour of the drivers in terms of depth/distance perception. During the on-road experimentations, The driver gaze behaviour was investigated to find the objective measures on the visibility of the road signs and reaction time of the drivers. The sensor infusion and the vehicle monitoring instruments were found useful for an objective assessment of the pavement condition and the drivers’ performance. In the last chapter of the thesis, the safety assessment during the use of level 1 automated driving “ACC” is discussed with the simulator and on-road experiment. The drivers’ visual behaviour was investigated in both studies with innovative classification method to find the epochs of the distraction of the drivers. The behavioural adaptation to ACC showed that drivers may divert their attention away from the driving task to engage in secondary, non-driving-related tasks.

Optimal bidding strategies of an electric vehicle aggregator in an electricity market

In recent years Electric Vehicles (EVs) are getting more importance as future transport systems, due to the increase of the concerns relevant to the greenhouse gases emission and the use fossil fuel. The management of the charging and discharging process of EVs could provide new business model for participating in the electricity markets. Moreover, vehicle to grid systems have the potential of increasing utility system flexibility. This thesis develops some models for the optimal integration of the EVs in the electricity market. In particular, the thesis focuses on the optimal bidding strategy of an EV aggregator participating to both the day ahead market and the secondary reserve market. The aggregator profit is maximized taking into account the energy balance equation, as well as the technical constraints of energy settlement, power supply and state of charge of the EVs. The results obtained by using the GAMS (General Algebraic Modelling System) environment are presented and discussed.

Design of an innovative electric vehicle simulator for Charging Systems' End-of-Line Testing

The trend related to the turnover of internal combustion engine vehicles with EVs goes by the name of electrification. The push electrification experienced in the last decade is linked to the still ongoing evolution in power electronics technology for charging systems. This is the reason why an evolution in testing strategies and testing equipment is crucial too. The project this dissertation is based on concerns the investigation of a new EV simulator design. that optimizes the structure of the testing equipment used by the company who commissioned this work. Project requirements can be summarized in the following two points: space occupation reduction and parallel charging implementation. Some components were completely redesigned, and others were substituted with equivalent ones that could perform the same tasks. In this way it was possible to reduce the space occupation of the simulator, as well as to increase the efficiency of the testing device. Moreover, the possibility of conjugating different charging simulations could be investigated by parallelly launching two testing procedures on a unique machine, properly predisposed for supporting the two charging protocols used. On the back of the results achieved in the body of this dissertation, a new design for the EV simulator was proposed. In this way, space reduction was obtained, and space occupation efficiency was improved with the proposed new design. The testing device thus resulted to be way more compact, enabling to gain in safety and productivity, along with a 25% cost reduction. Furthermore, parallel charging was implemented in the proposed new design since the conducted tests clearly showed the feasibility of parallel charging sessions. The results presented in this work can thus be implemented to build the first prototype of the new EV simulator.

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

Active Control of the Acoustic Field in a Vehicle Cabin

In this thesis, a thorough investigation on acoustic noise control systems for realistic automotive scenarios is presented. The thesis is organized in two parts dealing with the main topics treated: Active Noise Control (ANC) systems and Virtual Microphone Technique (VMT), respectively. The technology of ANC allows to increase the driver's/passenger's comfort and safety exploiting the principle of mitigating the disturbing acoustic noise by the superposition of a secondary sound wave of equal amplitude but opposite phase. Performance analyses of both FeedForwrd (FF) and FeedBack (FB) ANC systems, in experimental scenarios, are presented. Since, environmental vibration noises within a car cabin are time-varying, most of the ANC solutions are adaptive. However, in this work, an effective fixed FB ANC system is proposed. Various ANC schemes are considered and compared with each other. In order to find the best possible ANC configuration which optimizes the performance in terms of disturbing noise attenuation, a thorough research of \gls{KPI}, system parameters and experimental setups design, is carried out. In the second part of this thesis, VMT, based on the estimation of specific acoustic channels, is investigated with the aim of generating a quiet acoustic zone around a confined area, e.g., the driver's ears. Performance analysis and comparison of various estimation approaches is presented. Several measurement campaigns were performed in order to acquire a sufficient duration and number of microphone signals in a significant variety of driving scenarios and employed cars. To do this, different experimental setups were designed and their performance compared. Design guidelines are given to obtain good trade-off between accuracy performance and equipment costs. Finally, a preliminary analysis with an innovative approach based on Neural Networks (NNs) to improve the current state of the art in microphone virtualization is proposed.

Power flow forecasting and smart charging for electric vehicle charging systems with photovoltaic integration

An essential role in the global energy transition is attributed to Electric Vehicles (EVs) the energy for EV traction can be generated by renewable energy sources (RES), also at a local level through distributed power plants, such as photovoltaic (PV) systems. However, EV integration with electrical systems might not be straightforward. The intermittent RES, combined with the high and uncontrolled aggregate EV charging, require an evolution toward new planning and paradigms of energy systems. In this context, this work aims to provide a practical solution for EV charging integration in electrical systems with RES. A method for predicting the power required by an EV fleet at the charging hub (CH) is developed in this thesis. The proposed forecasting method considers the main parameters on which charging demand depends. The results of the EV charging forecasting method are deeply analyzed under different scenarios. To reduce the EV load intermittency, methods for managing the charging power of EVs are proposed. The main target was to provide Charging Management Systems (CMS) that modulate EV charging to optimize specific performance indicators such as system self-consumption, peak load reduction, and PV exploitation. Controlling the EV charging power to achieve specific optimization goals is also known as Smart Charging (SC). The proposed techniques are applied to real-world scenarios demonstrating performance improvements in using SC strategies. A viable alternative to maximize integration with intermittent RES generation is the integration of energy storage. Battery Energy Storage Systems (BESS) may be a buffer between peak load and RES production. A sizing algorithm for PV+BESS integration in EV charging hubs is provided. The sizing optimization aims to optimize the system's energy and economic performance. The results provide an overview of the optimal size that the PV+BESS plant should have to improve whole system performance in different scenarios.

Analysis and improvements of four-wire power converters for electric vehicle chargers

A robust and well-distributed backbone charging network is the priority to ensure widespread electrification of road transport, providing a driving experience similar to that of internal combustion engine vehicles. International standards set multiple technical targets for on-board and off-board electric vehicle chargers; output voltage levels, harmonic emissions, and isolation requirements strongly influence the design of power converters. Additionally, smart-grid services such as vehicle-to-grid and vehicle-to-vehicle require the implementation of bi-directional stages that inevitably increase system complexity and component count. To face these design challenges, the present thesis provides a rigorous analysis of four-leg and split-capacitor three-phase four-wire active front-end topologies focusing on the harmonic description under different modulation techniques and conditions. The resulting analytical formulation paves the way for converter performance improvements while maintaining regulatory constraints and technical requirements under control. Specifically, split-capacitor inverter current ripple was characterized as providing closed-form formulations valid for every sub-case ranging from synchronous to interleaved PWM. Outcomes are the base for a novel variable switching PWM technique capable of mediating harmonic content limitation and switching loss reduction. A similar analysis is proposed for four-leg inverters with a broad range of continuous and discontinuous PWM modulations. The general superiority of discontinuous PWM modulation in reducing switching losses and limiting harmonic emission was demonstrated. Developments are realized through a parametric description of the neutral wire inductor. Finally, a novel class of integrated isolated converter topologies is proposed aiming at the neutral wire delivery without employing extra switching components rather than the one already available in typical three-phase inverter and dual-active-bridge back-to-back configurations. The fourth leg was integrated inside the dual-active-bridge input bridge providing relevant component count savings. A novel modified single-phase-shift modulation technique was developed to ensure a seamless transition between working conditions like voltage level and power factor. Several simulations and experiments validate the outcomes.