A techno-economic evaluation of changing from conventional vehicles to range extended electric vehicles

Transportation accounts for 22% of greenhouse gas emissions in the UK, and increases to 25% in Northern Ireland. Surface transport carbon dioxide emissions, consisting of road and rail, are dominated by cars. Demand for mobility is rising rapidly and vehicle numbers are expected to more than double by 2050. Car manufacturers are working towards reducing their carbon footprint through improving fuel efficiency and controlling exhaust emissions. Fuel efficiency is now a key consideration of consumers purchasing a new vehicle. While measures have been taken to help to reduce pollutants, in the future, alternative technologies will have to be used in the transportation industry to achieve sustainability. There are currently many alternatives to the market leader, the internal combustion engine. These alternatives include hydrogen fuel cell vehicles and electric vehicles, a term which is widely used to cover battery electric vehicles, plug-in hybrid electric vehicles and extended-range electric vehicles. This study draws direct comparisons measuring the differing performance in terms of fuel consumption, carbon emissions and range of a typical family saloon car using different fuel types. These comparisons will then be analysed to see what effect switching from a conventionally fuelled vehicle to a range extended electric vehicle would have not only on the end user, but also the UK government.

Electric vehicles and opportunities to displace tailgate emissions using Smart Charging controls

Under the European Union Renewable Energy Directive each Member State is mandated to ensure that 10% of transport energy (excluding aviation and marine transport) comes from renewable sources by 2020. The Irish Government intends to achieve this target with a number of policies including ensuring that 10% of all vehicles in the transport fleet are powered by electricity by 2020. This paper investigates the impact of the 10% electric vehicle target in Ireland in 2020 using a dynamic programming based long term generation expansion planning model. The model developed optimizes power dispatch using hourly electricity demand curves up to 2020, while incorporating generator characteristics and certain operational requirements such as energy not served and loss of load probability while satisfying constraints on environmental emissions, fuel availability and generator operational and maintenance costs. Two distinct scenarios are analysed based on a peak and off-peak charging regimes in order to simulate the effects of the electric vehicles charging in 2020. The importance and influence of the charging regimes on the amount of energy used and tailgate emissions displaced is then determined.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Novel decentralised formation control for unmanned vehicles

This paper proposes a new methodology for solving the unmanned multi-vehicle formation control problem. It employs a unique “extension-decomposition-aggregation” scheme to transform the overall complex formation control problem to a group of sub-problems which work via boundary interactions. The H∞ robust control strategy is applied to design the decentralised formation controllers to reject the interactions and work jointly to maintain the stability of the overall formation. Simulation studies have been performed to verify its performance and effectiveness.

A co-operative formation-based collision avoidance approach for a group of autonomous vehicles (Special Issue on Adaptive Control and Signal Processing in Marine System)

This paper employs a unique decentralised cooperative control method to realise a formation-based collision avoidance strategy for a group of autonomous vehicles. In this approach, the vehicles' role in the formation and their alert and danger areas are first defined, and the formation-based intra-group and external collision avoidance methods are then proposed to translate the collision avoidance problem into the formation stability problem. The extension–decomposition–aggregation formation control method is next employed to stabilise the original and modified formations, whilst manoeuvring, and subsequently solve their collision avoidance problem indirectly. Simulation study verifies the feasibility and effectiveness of the intra-group and external collision avoidance strategy. It is demonstrated that both formation control and collision avoidance problems can be simultaneously solved if the stability of the expanded formation including external obstacles can be satisfied.

Accessory minerals and potentially toxic elements in Tanzanian vermiculites with respect to agricultural applications

The study assessed accessory minerals and metals in Tanzanian vermiculites with respect to their potential suitability for agricultural applications. Mineral and chemical analyses were involved. Pot experiments were also conducted to assess plant uptake of metals from soil with vermiculites. Fibrous sepiolite and amphiboles were minerals of health concern found in some samples. The sepiolite fibers had aspect ratios similar to those of asbestos minerals, which cause respiratory disorders and lung cancer when inhaled and thus pose a potential health risk to animals and humans. The amphibole fibers were thicker than 10 μm and are unlikely to be inhaled. Chromium (Cr) and nickel (Ni) concentrations in some samples were greater than the limits permitted in agricultural soils, but the elements are not highly plant available and do not inhibit the uptake of essential macronutrients. Heating vermiculites at 400-600° C enhanced extractability of Cr and Ni and should preferably be avoided. © Taylor & Francis Group, LLC.

Mineralogical and chemical characterization of some vermiculites from the mozambique belt of tanzania for agricultural use

Vermiculite minerals are locally available in the Mozambique Belt of Tanzania but are not currently commercially exploited. In part this may be due to lack of any precise characterization. This study was carried out as a first step to assess the suitability of these vermiculites for crop production by characterization of their mineralogical and chemical compositions. X-ray diffraction and scanning electron microscopy combined with an energydispersive X-ray system were used to establish the mineralogy. Electron microprobe analysis and inductively coupled plasma-mass spectrometry were used to study the chemical compositions and to identify any possible issues related to chemical composition that might affect their use if applied as soil conditioners. The samples were characterized as vermiculites and hydrobiotites with a wide variety of accessory minerals. Accessory minerals that might be of some concern are galena, fibrous amphiboles and sepiolite. The total levels of Ni in all vermiculites, and Cr in some, were also found to be high relative to common European standards and this might limit their potential as soil conditioners. It is clear that a field assessment of the bioavailability of various elements would be necessary before decisions relating to potential agricultural use could be made. © 2009 The Mineralogical Society.

A Reliability Assessment Approach For Integrated Transportation and Electrical Power Systems Incorporating Electric Vehicles

With the increasing utilization of electric vehicles (EVs), transportation systems and electrical power systems are becoming increasingly coupled. However, the interaction between these two kinds of systems are not well captured, especially from the perspective of transportation systems. This paper studies the reliability of integrated transportation and electrical power system (ITES). A bidirectional EV charging control strategy is first demonstrated to model the interaction between the two systems. Thereafter, a simplified transportation system model is developed, whose high efficiency makes the reliability assessment of the ITES realizable with an acceptable accuracy. Novel transportation system reliability indices are then defined from the view point of EV’s driver. Based on the charging control model and the transportation simulation method, a daily periodic quasi sequential reliability assessment method is proposed for the ITES system. Case studies based on RBTS system demonstrate that bidirectional charging controls of EVs will benefit the reliability of power systems, while decrease the reliability of EVs travelling. Also, the optimal control strategy can be obtained based on the proposed method. Finally, case studies are performed based on a large scale test system to verify the practicability of the proposed method.

Battery modelling methods for electric vehicles - a review

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are rapidly gaining popularity as a means of de-carbonization in the transport sector in tackling sustainable energy supply and environment pollution problems. To build a proper battery model is essential in predicting battery behaviour under various operating conditions for avoiding unsafe battery operations and developing proper controlling algorithms and maintenance strategies. This paper presents a comprehensive review of battery modelling methods. In particular, the mechanism and characteristics of Li-ion batteries are presented, and different modelling methods are discussed. Considering that equivalent electric circuit models (EECMs) are the most widely used, a detailed analysis of the modelling procedure is presented.

A Review of Indirect Bridge Monitoring Using Passing Vehicles

Indirect bridge monitoring methods, using the responses measured from vehicles passing over bridges, are under development for about a decade. A major advantage of these methods is that they use sensors mounted on the vehicle, no sensors or data acquisition system needs to be installed on the bridge. Most of the proposed methods are based on the identification of dynamic characteristics of the bridge from responses measured on the vehicle, such as natural frequency, mode shapes, and damping. In addition, some of the methods seek to directly detect bridge damage based on the interaction between the vehicle and bridge. This paper presents a critical review of indirect methods for bridge monitoring and provides discussion and recommendations on the challenges to be overcome for successful implementation in practice.