Electric vehicle capacity forecasting model with application to load levelling

There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

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.

Can the scaling behavior of electric conductivity be used to probe the self-organizational changes in solution with respect to the ionic liquid structure? The case of [C8MIM][NTf2]

Electrical conductivity of the supercooled ionic liquid [C8MIM][NTf2], determined as a function of temperature and pressure, highlights strong differences in its ionic transport behavior between low and high temperature regions. To date, the crossover effect which is very well known for low molecular van der Waals liquids has been rarely described for classical ionic liquids. This finding highlights that the thermal fluctuations could be dominant mechanisms driving the dramatic slowing down of ion motions near Tg. An alternative way to analyze separately low and high temperature dc-conductivity data using a density scaling approach was then proposed. Based on which a common value of the scaling exponent [gamma] = 2.4 was obtained, indicating that the applied density scaling is insensitive to the crossover effect. By comparing the scaling exponent [gamma] reported herein along with literature data for other ionic liquids, it appears that [gamma] decreases by increasing the alkyl chain length on the 1-alkyl-3-methylimidazolium-based ionic liquids. This observation may be related to changes in the interaction between ions in solution driven by an increase in the van der Waals type interaction by increasing the alkyl chain length on the cation. This effect may be related to changes in the ionic liquid nanostructural organization with the alkyl chain length on the cation as previously reported in the literature based on molecular dynamic simulations. In other words, the calculated scaling exponent [gamma] may be then used as a key parameter to probe the interaction and/or self-organizational changes in solution with respect to the ionic liquid structure.

Energy Saving - Another Perspective for Parameter Optimization of P and PI Controllers

Modern control methods like optimal control and model predictive control (MPC) provide a framework for simultaneous regulation of the tracking performance and limiting the control energy, thus have been widely deployed in industrial applications. Yet, due to its simplicity and robustness, the conventional P (Proportional) and PI (Proportional–Integral) control are still the most common methods used in many engineering systems, such as electric power systems, automotive, and Heating, Ventilation and Air Conditioning (HVAC) for buildings, where energy efficiency and energy saving are the critical issues to be addressed. Yet, little has been done so far to explore the effect of its parameter tuning on both the system performance and control energy consumption, and how these two objectives are correlated within the P and PI control framework. In this paper, the P and PI controllers are designed with a simultaneous consideration of these two aspects. Two case studies are investigated in detail, including the control of Voltage Source Converters (VSCs) for transmitting offshore wind power to onshore AC grid through High Voltage DC links, and the control of HVAC systems. Results reveal that there exists a better trade-off between the tracking performance and the control energy through a proper choice of the P and PI controller parameters.

The effect of heating temperature on the properties of vermiculites from Tanzania with respect to potential agronomic applications

This study was carried out to assess the properties of vermiculites from Tanzania with respect to the temperature used to expand them. Vermiculites from five locations in the Mozambique Belt of Tanzania were sampled and heated at 15, 200, 400, 600 and 800 °C in a muffle furnace. Palabora Europe Ltd provided one sample for comparison from their South Africa deposit which provides vermiculite used worldwide as a soil amendment. Water release characteristic, cation exchange capacity, pH, mass loss, and bulk density were among the properties assessed. All six vermiculites responded differently on heating, and had a significant variation in their agronomic properties. Water release characteristic varied with the degree of exfoliation and phase composition. Although vermiculites from Tanzania expanded on heating, their capacity to retain plant available water was relatively low as compared to vermiculite from Palabora. Disintegration on heating and the presence of a high amount of iron could be among the factors affecting their water release characteristic. Loss of hydroxyl water was higher in vermiculites than in hydrobiotites. Dehydroxylation enhanced the availability of exchangeable K⁺ and reduced significantly the cation exchange capacity of vermiculites. The optimum exchangeable K⁺ was obtained on heating at a temperature of 600 °C. The pH was unaffected by heating to a temperature of less than 600 °C. At higher temperature, the pH increased in some samples and was accompanied by substantial amounts of exchangeable Mg²⁺. Thus, it was concluded that initial characterization of vermiculites is essential prior to potential agricultural applications in order to optimize their agronomic potential. © 2008 Elsevier B.V. All rights reserved.

A Parametric Study Of A Turbogenerator On A Diesel-Electric Hybrid Bus

The fuel consumption of automotive vehicles has become a prime consideration to manufacturers and operators as fuel prices continue to rise steadily, and legislation governing toxic emissions becomes ever more strict. This is particularly true for bus operators as government fuel subsidies are cut or removed.

In an effort to reduce the fuel consumption of a diesel-electric hybrid bus, an exhaust recovery turbogenerator has been selected from a wide ranging literature review as the most appropriate method of recovering some of the wasted heat in the exhaust line. This paper examines the effect on fuel consumption of a turbogenerator applied to a 2.4-litre diesel engine.

A validated one-dimensional engine model created using Ricardo WAVE was used as a baseline, and was modified in subsequent models to include a turbogenerator downstream, and in series with, the turbocharger turbine. A fuel consumption map of the modified engine was produced, and an in-house simulation tool was then used to examine the fuel economy benefit delivered by the turbogenerator on a bus operating on various drive-cycles.

A parametric study is presented which examined the performance of turbogenerators of various size and power output. The operating strategy of the turbogenerator was also discussed with a view to maximising turbine efficiency at each operating point.

The performance of the existing turbocharger on the hybrid bus was also investigated; both the compressor and turbine were optimised and the subsequent benefits to the fuel consumption of the vehicle were shown.

The final configuration is then presented and the overall improvement in fuel economy of the hybrid bus was determined over various drive-cycles.

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.

Synchronous Islanding Control in the Presence of Hydro-electric Generation

This paper introduces a novel load sharing algorithm to enable island synchronization. The system model used for development is based on an actual system for which historical measurement and fault data is available and is used to refine and test the algorithms performance and validity. The electrical system modelled is selected due to its high-level of hydroelectric generation and its history of islanding events. The process of developing the load sharing algorithm includes a number of steps. Firstly, the development of a simulation model to represent the case study accurately - this is validated by way of matching system behavior based on data from historical island events. Next, a generic island simulation is used to develop the load sharing algorithm. The algorithm is then tested against the validated simulation model representing the case study area selected. Finally, a laboratory setup is described which is used as validation method for the novel load sharing algorithm.