Comparative cost-based analysis of a novel plug-in hybrid electric vehicle with conventional and hybrid electric vehicles

© 2015 Universiti Malaysia Pahang. Hybrid electric vehicles provide higher fuel efficiency and lower emissions through the combination of the conventional internal combustion engine with electric machines. This paper analyzes and compares two types of hybrid electric powertrain with a conventional vehicle powertrain to study the lifetime costs of these vehicles. The novelty of the University of Technology Sydney plug-in hybrid electric vehicle (UTS PHEV) arises through a special power-splitting device and energy management strategy. The UTS PHEV and comparative powertrains are studied through numerical simulations to determine fuel consumption for the proposed low and high congestion drive cycles. Satisfactory results are achieved in terms of fuel economy, the all-electric range and electrical energy consumption for the UTS PHEV powertrain, providing significant improvement over the alternative powertrains. The analysis of these vehicles is extended to include a cost-based analysis of each powertrain in order to estimate the total lifetime costs at different fuel prices. The results obtained from this analysis demonstrate that whilst the conventional powertrain is cheaper in terms of purchase and maintenance costs, both alternative configurations are more cost-effective overall as the average price of fuel increases

A Comparative Fuel Analysis of a Novel HEV with Conventional Vehicle

© 2017 IEEE. Improvements in fuel economy have always been a dominating driver of vehicle engineering. With some exceptions, benefits attained from hybrid powertrains to transient power delivery has not been the emphasis of research and development efforts. Developing cities around the world would realise significant benefits from improvements to fuel economy, which is outlined in this research by assessing the benefits of a novel HEV architecture. These benefits are compared to a conventional ICE-powered vehicle equivalent, which has an advantage in terms lower upfront costs. The commercial success of HEV implementation, therefore, is determined by its price comparison to conventional vehicles and payback over a number of years of use. This becomes especially important in regions of low-middle income, where the market is much more price-sensitive. The fuel economy of a conventional vehicle and mild hybrid electric vehicle are compared in this paper. This analysis includes vehicle modelling and simulation. Fuel economy is assessed and referenced with standard drive cycles provided by the U.S Environmental Protection Agency. Results demonstrate the benefits of a lower ongoing cost for the HEV architecture

Impact of Low and High Congestion Traffic Patterns on a Mild-HEV Performance

Copyright © 2017 SAE International. Driven by stricter mandatory regulations on fuel economy improvement and emissions reduction, market penetration of electrified vehicles will increase in the next ten years. Within this growth, mild hybrid vehicles will become a leading sector. The high cost of hybrid electric vehicles (HEV) has somewhat limited their widespread adoption, especially in developing countries. Conversely, it is these countries that would benefit most from the environmental benefits of HEV technology. Compared to a full hybrid, plug-in hybrid, or electric vehicle, a mild hybrid system stands out due to its maximum benefit/cost ratio. As part of our ongoing project to develop a mild hybrid system for developing markets, we have previously investigated improvements in drive performance and efficiency using optimal gearshift strategies, as well as the incorporation of high power density supercapacitors. In this paper, the fuel and emissions of a baseline conventional vehicle and mild hybrid electric vehicle (MHEV) are compared. The objective of this analysis is to compare the fuel economy and Greenhouse Gas (GHG) emissions of the baseline and MHEV models, using low and high-density traffic patterns chosen for their similarity to traffic density profiles of our target markets. Results demonstrate the benefits of a lower ongoing cost for the HEV architecture. These advantages include torque-hole filling between gear changes, increased fuel efficiency and performance

A toolbox for multi-objective optimisation of low carbon powertrain topologies

Stricter regulations and evolving environmental concerns have been exerting ever-increasing pressure on the automotive industry to produce low carbon vehicles that reduce emissions. As a result, increasing numbers of alternative powertrain architectures have been released into the marketplace to address this need. However, with a myriad of possible alternative powertrain configurations, which is the most appropriate type for a given vehicle class and duty cycle? To that end, comparative analyses of powertrain configurations have been widely carried out in literature; though such analyses only considered limited types of powertrain architectures at a time. Collating the results from these literature often produced findings that were discontinuous, which made it difficult for drawing conclusions when comparing multiple types of powertrains. The aim of this research is to propose a novel methodology that can be used by practitioners to improve the methods for comparative analyses of different types of powertrain architectures. Contrary to what has been done so far, the proposed methodology combines an optimisation algorithm with a Modular Powertrain Structure that facilitates the simultaneous approach to optimising multiple types of powertrain architectures. The contribution to science is two-folds; presenting a methodology to simultaneously select a powertrain architecture and optimise its component sizes for a given cost function, and demonstrating the use of multi-objective optimisation for identifying trade-offs between cost functions by powertrain architecture selection. Based on the results, the sizing of the powertrain components were influenced by the power and energy requirements of the drivecycle, whereas the powertrain architecture selection was mainly driven by the autonomy range requirements, vehicle mass constraints, CO2 emissions, and powertrain costs. For multi-objective optimisation, the creation of a 3-dimentional Pareto front showed multiple solution points for the different powertrain architectures, which was inherent from the ability of the methodology to concurrently evaluate those architectures. A diverging trend was observed on this front with the increase in the autonomy range, driven primarily by variation in powertrain cost per kilometre. Additionally, there appeared to be a trade-off in terms of electric powertrain sizing between CO2 emissions and lowest mass. This was more evident at lower autonomy ranges, where the battery efficiency was a deciding factor for CO2 emissions. The results have demonstrated the contribution of the proposed methodology in the area of multi-objective powertrain architecture optimisation, thus addressing the aims of this research

An Optimization Approach for Energy Efficient Coordination Control of Vehicles in Merging Highways

Environmental concerns along with stronger governmental regulations regarding automotive fuel-economy and greenhouse-gas emissions are contributing to the push for development of more sustainable transportation technologies. Furthermore, the widespread use of the automobile gives rise to other issues such as traffic congestion and increasing traffic accidents. Consequently, two main goals of new technologies are the reduction of vehicle fuel consumption and emissions and the reduction of traffic congestion. While an extensive list of published work addresses the problem of fuel consumption reduction by optimizing the vehicle powertrain operations, particularly in the case of hybrid electric vehicles (HEV), approaches like eco-driving and traffic coordination have been studied more recently as alternative methods that can, in addition, address the problem of traffic congestion and traffic accidents reduction. This dissertation builds on some of those approaches, with particular emphasis on autonomous vehicle coordination control. In this direction, the objective is to derive an optimization approach for energy efficient and safe coordination control of vehicles in merging highways. Most of the current optimization-based centralized approaches to this problem are solved numerically, at the expense of a high computational load which limits their potential for real-time implementation. In addition, closed-form solutions, which are desired to facilitate traffic analysis and the development of approaches to address interconnected merging/intersection points and achieve further traffic improvements at the road-network level, are very limited in the literature. In this dissertation, through the application of the Pontryagin’s minimum principle, a closed-form solution is obtained which allows the implementation of a real-time centralized optimal control for fleets of vehicles. The results of applying the proposed framework show that the system can reduce the fuel consumption by up to 50% and the travel time by an average of 6.9% with respect to a scenario with not coordination strategy. By integrating the traffic coordination scheme with in-vehicle energy management, a two level optimization system is achieved which allows assessing the benefits of integrating hybrid electric vehicles into the road network. Regarding in-vehicle energy optimization, four methods are developed to improve the tuning process of the equivalent consumption optimization strategy (ECMS). First, two model predictive control (MPC)-based strategies are implemented and the results show improvements in the efficiency obtained with the standard ECMS implementation. On the other hand, the research efforts focus in performing analysis of the engine and electric motor operating points which can lead to the optimal tuning of the ECMS with reduced iterations. Two approaches are evaluated and even though the results in fuel economy are slightly worse than those for the standard ECMS, they show potential to significantly reduce the tuning time of the ECMS. Additionally, the benefits of having less aggressive driving profiles on different powertrain technologies such as conventional, plug-in hybrid and electric vehicles are studied

Intelligent Control Algorithm for Energy Management System of Light Electric Vehicles

A state-based logic control algorithm was developed to coordinate a multi-source energy management system (EMS) for light electric vehicles (LEVs), such as scooters. This work was undertaken in view of the increasing importance of hybrid electric vehicles (HEVs) in many rapidly developing Asian countries. The multiple energy sources in this investigation were batteries, fuel cells (FC) and super-capacitors (SCs). Since each resource has its own advantages and disadvantages, a combination of the resources provides a more reliable and powerful energy model for hybrid electric vehicles (HEV).An algorithm was developed to manage the switching of the multiple energy resources efficiently. The performance of the proposed model in terms of vehicle acceleration and load power was measured against the ECE-47 test drive cycle. The sources of energy changeover were examined at 50% of thebattery state of charge (SOC) or under heavy load conditions. The results showed a close match of the model to the test cycle under both normal and heavy load cycle conditions. The feasibility of the proposed intelligent controlling algorithm for the EMSof light electric vehicles was thus verified. This study could contribute huge benefit to the manufacturers and research institutions involved in lightelectric vehicle

Design and evaluation of a predictive powertrain control system for a plug-in hybrid electric vehicle to improve the fuel economy and the emissions

Taghavipour, A., Azad, N. L., & McPhee, J. Design and evaluation of a predictive powertrain control system for a plug-in hybrid electric vehicle to improve the fuel economy and the emissions. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 229(5), 624–640. Copyright © 2014 SAGE. Reprinted by permission of SAGE Publications. https://dx.doi.org/10.1177/0954407014547925In this article, a power management scheme for a plug-in power-split hybrid electric vehicle is designed on the basis of the model predictive control concept of charge depletion plus charge sustenance strategy and the blended-mode strategy. The commands of model predictive control are applied to the powertrain components through appropriate low-level controllers: standard proportional–integral controllers for electric machines, and sliding-mode controllers for engine torque control. Minimization of the engine emissions is a key factor for designing the engine’s low-level controller. Applying this control scheme to a validated high-fidelity model of a plug-in hybrid electric vehicle, developed in the MapleSim environment with a chemistry-based Lithium-ion battery model, results in considerable improvements in the fuel economy and the emissions performance.NSERCToyotaMaplesoft Industrial Research Chair progra

Comparative fuel economy, cost and emissions analysis of a novel mild hybrid and conventional vehicles

© IMechE 2017. Mild hybrid vehicles have been explored as a potential pathway to reduce vehicle emissions cost-effectively. The use of manual transmissions to develop novel hybrid vehicles provides an alternate route to producing low cost electrified powertrains. In this paper, a comparative analysis examining a conventional vehicle and a mild hybrid electric vehicle is presented. The analysis considers fuel economy, capital and ongoing costs and environmental emissions, and includes developmental analysis and simulation using mathematical models. Vehicle emissions (nitrogen oxides, carbon monoxide and hydrocarbons) and fuel economy are computed, analysed and compared using a number of alternative driving cycles and their weighted combination. Different driver styles are also evaluated. Studying the relationship between the fuel economy and driveability, where driveability is addressed using fuel-economical gear shift strategies. Our simulation suggests the hybrid concept presented can deliver fuel economy gains of between 5 and 10%, as compared to the conventional powertrain

Design and control of the energy management system of a smart vehicle

This thesis demonstrates the design of two high efficiency controllers, one non-predictive and the other predictive, that can be used in both parallel and power-split connected plug-in hybrid electric vehicles. Simulation models of three different commercially available vehicles are developed from measured data for necessary testing and comparisons of developed controllers. Results prove that developed controllers perform better than the existing controllers in terms of efficiency, fuel consumption, and emissions

Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet