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

Fuel-efficient driving strategies

This thesis is concerned with fuel-efficient driving strategies for vehicles driving on roads with varying topography, as well as estimation of road grade\ua0and vehicle mass for vehicles utilizing such strategies. A framework referred\ua0to as speed profile optimization (SPO), is introduced for reducing the fuel\ua0or energy consumption of single vehicles (equipped with either combustion\ua0or electric engines) and platoons of several vehicles. Using the SPO-based\ua0methods, average reductions of 11.5% in fuel consumption for single trucks,\ua07.5 to 12.6% energy savings in electric vehicles, and 15.8 to 17.4% average\ua0fuel consumption reductions for platoons of trucks were obtained. Moreover,\ua0SPO-based methods were shown to achieve higher savings compared to\ua0the commonly used methods for fuel-efficient driving. Furthermore, it was\ua0demonstrated that the simulations are sufficiently accurate to be transferred\ua0to real trucks. In the SPO-based methods, the optimized speed profiles were\ua0generated using a genetic algorithm for which it was demonstrated, in a\ua0discretized case, that it is able to produce speed profiles whose fuel consumption\ua0is within 2% of the theoretical optimum.A feedforward neural network (FFNN) approach, with a simple feedback\ua0mechanism, is introduced and evaluated in simulations, for simultaneous estimation of the road grade and vehicle mass. The FFNN provided road grade\ua0estimates with root mean square (RMS) error of around 0.10 to 0.14 degrees,\ua0as well as vehicle mass estimates with an average RMS error of 1%, relative\ua0to the actual value. The estimates obtained with the FFNN outperform road\ua0grade and mass estimates obtained with other approaches

Dual-layered Multi-Objective Genetic Algorithms (D-MOGA): A Robust Solution for Modern Engine Development and Calibrations

Heavy-duty (HD) diesel engines are the primary propulsion systems used within the freight transportation sector and are subjected to stringent emissions regulations. The primary objective of this study is to develop a robust calibration technique for HD engine optimization in order to meet current and future regulated emissions standards during certification cycles and off-cycle vocation activities. Recently, California - Air Resources Board (C-ARB) has also shown interests in controlling off-cycle emissions from vehicles operating in California by funding projects such as the Ultra-Low NOx study by Sharp et. al [1]. Moreover, there is a major push for the complex real-world driving emissions testing protocol as the confirmatory and certification testing procedure in Europe and Asia through the United Nations - Economic Commission for Europe (UN-ECE) and International Organization for Standardization (ISO). This calls for more advanced and innovative approaches to optimize engine operation to meet the regulated certification levels.;A robust engine calibration technique was developed using dual-layered multi-objective genetic algorithms (D-MOGA) to determine necessary engine control parameter settings. The study focused on reducing fuel consumption and lowering oxides of nitrogen (NOx) emissions, while simultaneously increasing exhaust temperatures for thermal management of exhaust after-treatment system. The study also focused on using D-MOGA to develop a calibration routine that simultaneously calibrates engine control parameters for transient certification cycles and vocational drayage operation. Several objective functions and alternate selection techniques for D-MOGA were analyzed to improve the optimality of the D-MOGA results.;The Low-NOx calibration for the Federal Test Procedure (FTP) which was obtained using the simple desirability approach was validated in the engine dynamometer test cell over the FTP and near-dock test cycles. In addition, the 2010 emissions compliant calibration was baselined for performance and emissions over the FTP and custom developed low-load Near-Dock engine dynamometer test cycles. Performance and emissions of the baseline calibrations showed a 63% increase in engine-out brake-specific NOx emissions and a proportionate 77% decrease in engine-out soot emissions over the Near-Dock cycle as compared to the FTP cycle. Engine dynamometer validation results of the Low-NOx FTP cycle calibration developed using D-MOGA, showed a 17% increase brake-specific NOx emissions over the FTP cycle, compared to the baseline calibrations. However, a 50% decrease in engine-out soot emissions and substantial increase in exhaust temperature were observed with no penalties on fuel consumption.;The tools developed in this study can play a role in meeting current and future regulations as well as bridging the gap between emissions during certification and real-world engine operations and eventually could play a vital role in meeting the National Ambient Air Quality Standards (NAAQS) in areas such as the port of Los Angeles, California in the South Coast Air Basin

Sim-heuristics low-carbon technologies’ selection framework for reducing costs and carbon emissions of heavy goods vehicles

UK logistics fleets face increasing competitive pressures due to volatile fuel prices and the small profit margins in the industry. By reducing fuel consumption, operational costs and carbon emissions can be reduced. While there are a number of technologies that can reduce fuel consumption, it is often difficult for logistics companies to identify which would be the most beneficial to adopt over the medium and long terms. With a myriad of possible technology combinations, optimising the vehicle specification for specific duty cycles requires a robust decision-making framework. This paper combines simulated truck and delivery routes with a metaheuristic evolutionary algorithm to select the optimal combination of low-carbon technologies that minimise the greenhouse gas emissions of long-haul heavy goods vehicles during their lifetime cost. The framework presented is applicable to other vehicles, including road haulage, waste collection fleets and buses by using tailored parameters in the heuristics model

Advanced methodology for the optimal sizing of the energy storage system in a hybrid electric refuse collector vehicle using real routes

This paper presents a new methodology for optimal sizing of the energy storage system ( ESS ), with the aim of being used in the design process of a hybrid electric (HE) refuse collector vehicle ( RCV ). This methodology has, as the main element, to model a multi-objective optimisation problem that considers the specific energy of a basic cell of lithium polymer ( Li – Po ) battery and the cost of manufacture. Furthermore, optimal space solutions are determined from a multi-objective genetic algorithm that considers linear inequalities and limits in the decision variables. Subsequently, it is proposed to employ optimal space solutions for sizing the energy storage system, based on the energy required by the drive cycle of a conventional refuse collector vehicle. In addition, it is proposed to discard elements of optimal space solutions for sizing the energy storage system so as to achieve the highest fuel economy in the hybrid electric refuse collector vehicle design phase.Postprint (published version

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMS are structured, presenting a new taxonomy for dynamic optimization-based EMS from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMS, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously

Energy minimization for an electric bus using a genetic algorithm

Background and methods: This paper addresses, in simulation, energy minimization of an autonomous electric minibus operating in an urban environment. Two different case studies have been considered, each involving a total of 10 different 2?km bus routes and two different average speeds. In the proposed method,\ua0the minibus follows an optimized speed profile, generated using a genetic algorithm. Results: In the first case study the vehicle was able to reduce its energy consumption by around 7 to 12% relative to a baseline case in which it maintains a constant speed between stops, with short acceleration and deceleration phases. In the second case study, involving mass variation (passengers entering and alighting) it was demonstrated that the number of round trips that can be completed on a single battery charge is increased by around 10% using the proposed method

Fuel-Efficient Driving Strategies for Heavy-Duty Vehicles: A Platooning Approach Based on Speed Profile Optimization

A method for reducing the fuel consumption of a platoon of heavy-duty vehicles (HDVs) is described and evaluated in simulations for homogeneous and heterogeneous platoons. The method, which is based on speed profile optimization and is referred to as P-SPO, was applied to a set of road profiles of 10 km length, resulting in fuel reduction of 15.8% for a homogeneous platoon and between 16.8% and 17.4% for heterogeneous platoons of different mass configurations, relative to the combination of standard cruise control (for the lead vehicle) and adaptive cruise control (for the follower vehicle). In a direct comparison with MPC-based approaches, it was found that P-SPO outperforms the fuel savings of such methods by around 3 percentage points for the entire platoon, in similar settings. In P-SPO, unlike most common platooning approaches, each vehicle within the platoon receives its own optimized speed profile, thus eliminating the intervehicle distance control problem. Moreover, the P-SPO approach requires only a simple vehicle controller, rather than the two-layer control architecture used in MPC-based approaches