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

Practice and Innovations in Sustainable Transport

The book continues with an experimental analysis conducted to obtain accurate and complete information about electric vehicles in different traffic situations and road conditions. For the experimental analysis in this study, three different electric vehicles from the Edinburgh College leasing program were equipped and tracked to obtain over 50 GPS and energy consumption data for short distance journeys in the Edinburgh area and long-range tests between Edinburgh and Bristol. In the following section, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation is proposed to accurately estimate state of charge (SOC), which is vital for safe operation and efficient management of lithium-ion batteries. A coupled-inductor DC-DC converter with a high voltage gain is proposed in the following section to match the voltage of a fuel cell stack to a DC link bus. Finally, the book presents a review of the different approaches that have been proposed by various authors to mitigate the impact of electric buses and electric taxis on the future smart grid

Private car transport and the 10% RES-T target - quantifying the contribution of EVs and biofuels

In 2008, renewable energy accounted for less than 1% of final energy consumption in the Irish transport sector. In order to increase this share to 10% by 2020 as required under EU directive 2009/28/EC, the Irish government has introduced two specific measures: 10% of the transport fleet is to be powered by electricity by 2020, and an obligation on road transport fuel suppliers that biofuels account for a certain portion of their fuel sales. This study forecasts the impact of these existing measures towards meeting the 10% RES-T target by 2020, focussing on private car transport. The methodology presented is derived from a forecast of private car fuel demand based on a technological stock model of Ireland’s fleet. This paper demonstrates the use of this as a tool firstly as an energy forecasting technique and secondly as a method for evaluating the effects of policy measures on the technological composition and consequent renewable energy demand and related CO2emissions of private cars. Technological scenarios examined in this light are electric vehicles, compressed natural gas vehicles and biofuel blendin

Supervisory control of complex propulsion subsystems

Modern gasoline and diesel combustion engines are equipped with several subsystems with the goal to reduce fuel consumption and pollutant exhaust emissions. Subsystem synergies could be harnessed using the supervisory control approach. Look-ahead information can be used to potentially optimise power-train control for real time implementation. This thesis delves upon modelling the exhaust emissions from a combustion engine and developing a combined equivalent objective metric to propose a supervisory controller that uses look-ahead information with the objective to reduce fuel consumed and exhaust emissions. In the first part of the thesis, the focus is on diesel engine application control for emissions and fuel consumption reduction.\ua0Model of exhaust emissions in a diesel engine obtained from a combination of nominal engine operation and deviations are evaluated for transient drive cycles.\ua0The look ahead information as a trajectory of vehicle speed and load over time is considered.\ua0The supervisory controller considers a discrete control action set over the first segment of the trip ahead.\ua0The cost to optimise is defined and pre-computed off-line for a discrete set of operating conditions.\ua0A full factorial optimisation carried out off-line is stored on board the vehicle and applied in real-time.\ua0In a first proposal, the subsystem control of the after-treatment system comprising the lean NOx trap and the selective reduction catalyst is considered.\ua0As a next iteration, the combustion engine is added to the control problem.\ua0Simulation comparison of the controllers with the baseline controller offers a 1 % total fuel equivalent cost improvement while offering the flexibility to tailor the controller for different cost objective. In the second part of the thesis, the focus is on cold-start emissions control for modern gasoline engines.\ua0Emissions occurring when the engine is started until the catalyst is sufficiently warm, contribute to a significant proportion of tailpipe pollutant emissions.\ua0Electrically heated catalyst (EHC) in the three way catalyst (TWC) is a promising technology to reduce cold-start emissions where the catalyst can be warmed up prior to engine start and continued after start.\ua0A simulation framework for the engine, TWC with EHC with focus on modeling the thermal and chemical interactions during cold-start was developed.\ua0An evaluation framework with a proposed equivalent emissions approach was developed considering the challenges associated with cold-start emission control.\ua0An equivalent emission optimal post-heating time for the EHC is proposed that adapts to information which is available in a real-time on-line implementation.\ua0The proposed controller falls short of just 1 % equivalent emissions compared to the optimal case

Development of a neural network-based energy management system for a plug-in hybrid electric vehicle

The high potential of Artificial Intelligence (AI) techniques for effectively solving complex parameterization tasks also makes them extremely attractive for the design of the Energy Management Systems (EMS) of Hybrid Electric Vehicles (HEVs). In this framework, this paper aims to design an EMS through the exploitation of deep learning techniques, which allow high non-linear relationships among the data characterizing the problem to be described. In particular, the deep learning model was designed employing two different Recurrent Neural Networks (RNNs). First, a previously developed digital twin of a state-of-the-art plug-in HEV was used to generate a wide portfolio of Real Driving Emissions (RDE) compliant vehicle missions and traffic scenarios. Then, the AI models were trained off-line to achieve CO2 emissions minimization providing the optimal solutions given by a global optimization control algorithm, namely Dynamic Programming (DP). The proposed methodology has been tested on a virtual test rig and it has been proven capable of achieving significant improvements in terms of fuel economy for both charge-sustaining and charge-depleting strategies, with reductions of about 4% and 5% respectively if compared to the baseline Rule-Based (RB) strategy

Modelling the interaction between the energy system and road freight in Norway

By soft-linking models for transport demand, vehicle turnover and energy generation and use, we show how such models can complement each other and become more relevant and reliable policy support tools. A freight demand model is used to project commodity flows onto the 2050 horizon. An energy system model is used to map the relationships between energy prices, fiscal incentives, and optimal vehicle technologies. A stock-flow vehicle fleet model is used to calculate the time lag between innovation affecting new vehicles and the penetration of novel technology into the fleet. By running the latter two models in an iterative loop, we predict the flow of new vehicles with more or less decarbonized powertrains, contingent upon energy prices and fiscal incentives, while also obtaining a well-founded and more realistic assessment of the time needed for radical CO2 mitigation. The methodology is illustrated through a scenario developed for Norway.Modelling the interaction between the energy system and road freight in NorwaypublishedVersio

Automotive technology status and projections. Volume 2: Assessment report

Current and advanced conventional engines, advanced alternative engines, advanced power train components, and other energy conserving automobile modifications which could be implemented by the end of this century are examined. Topics covered include gas turbine engines, Stirling engines, advanced automatic transmissions, alternative fuels, and metal and ceramic technology. Critical problems are examined and areas for future research are indicated

Development of a Comprehensive 0-1D Powertrain and Vehicle Model for the Analysis of an Innovative 48 V Mild-Hybrid Diesel Passenger Car

L'abstract è presente nell'allegato / the abstract is in the attachmen

Modular Supply Network Optimization of Renewable Ammonia and Methanol Co-production

To reduce the use of fossil fuels and other carbonaceous fuels, renewable energy sources such as solar, wind, geothermal energy have been suggested to be promising alternative energy that guarantee sustainable and clean environment. However, the availability of renewable energy has been limited due to its dependence on weather and geographical location. This challenge is intended to be solved by the utilization of the renewable energy in the production of chemical energy carriers. Hydrogen has been proposed as a potential renewable energy carrier, however, its chemical instability and high liquefaction energy makes researchers seek for other alternative energy carriers. Ammonia and methanol can serve as promising alternative energy carriers due to their chemical stability at room temperature, low liquefaction energy, high energy value. The co-production of these high energy dense energy carriers offers economic and environmental advantages since their synthesis involve the direct utilization of CO2 and common unit operations. This problem report aims to review the optimization of the co-production of methanol and ammonia from renewable energy. Form this review, research challenges and opportunities are identified in the following areas: (i) optimization of methanol and ammonia co-production under renewable and demand uncertainty, (ii) impacts of the modular exponent on the feasibility of co-production of ammonia and methanol, and (iii) development of modern computational tools for systems-based analysis