Performance Analysis of Hybrid and Full Electrical Vehicles Equipped with Continuously Variable Transmissions

The main aim of this paper is to study the potential impacts in hybrid and full electrical vehicles performance by utilising continuously variable transmissions. This is achieved by two stages. First, for Electrical Vehicles (EVs), modelling and analysing the powertrain of a generic electric vehicle is developed using Matlab/Simulink-QSS Toolkit, with and without a transmission system of varying levels of complexity. Predicted results are compared for a typical electrical vehicle in three cases: without a gearbox, with a Continuously Variable Transmission (CVT), and with a conventional stepped gearbox. Second, for Hybrid Electrical Vehicles (HEVs), a twin epicyclic power split transmission model is used. Computer programmes for the analysis of epicyclic transmission based on a matrix method are developed and used. Two vehicle models are built-up; namely: traditional ICE vehicle, and HEV with a twin epicyclic gearbox. Predictions for both stages are made over the New European Driving Cycle (NEDC).The simulations show that the twin epicyclic offers substantial improvements of reduction in energy consumption in HEVs. The results also show that it is possible to improve overall performance and energy consumption levels using a continuously variable ratio gearbox in EVs

Fuel Economy of a Current Hybrid London Bus and Fuel Cell Bus Application Evaluation

London has over 8,500 buses in operation, carrying six million passengers on 700 routes each day. In central London the majority of the bus fleet has been replaced by diesel-electric hybrid buses. In this study, we will investigate the degree of energy efficiency via practical on-road bus performance recordings, forming a foundation for future improvements to diesel and fuel cell hybrid bus design. Research at UCL has investigated the design and performance of the ENVIRO 400H model bus on various different routes in London, obtaining a wide range of data for real world performance. This data includes information on routes, usage, energy consumption and passenger count profiling. Analysis has been conducted on the efficiency of the propulsion system over all the data sets. This knowledge can be used as the basis for developing computer modelling capabilities to in the future to optimize the system performance. The key components in the propulsion system are the diesel engine, generator, converter, battery bank, and traction motor. The energy management strategy has been analysed for different operating conditions and will be discussed in this paper. It was concluded that the system performance varied, with a number of patterns emerging with regards to the engine load and battery State of Charge for providing the propulsion power requirements. The operation strategies employed have been analysed to give a detailed understanding of the operation of the diesel-electric hybrid propulsion system under real-world operation

Techno-economic analysis of hybrid adiabatic compressed-air and biomass gasification energy storage systems for power generation through modelling and simulation

Energy storage has gained an increasing attention as a technology to smoothen out the variations associated with renewable energy power sources and adapt them into a dispatchable product to meet variable demand loads. An energy storage system can be a hybrid or stand alone. There is a rising interest for hybrid energy storage systems cited close to local consumers which is able to exploit the amount of local renewable sources on site, to provide demand side flexibility and also help to decarbonize the heating sector. The thesis is based on modelling and simulation of overall thermodynamic performance and economic analysis of an integrated hybrid energy storage system consisting of adiabatic compressed air energy storage (A-CAES), biomass gasification system with a wood dryer coupled to a syngas-diesel fuelled electric generator for the dual production of electricity and low temperature hot water for domestic use. The first part of the research work involves the modelling of the latent heat (LH) thermal energy storage (TES) for the A-CAES component. Implicit finite difference technique was applied to discretize the energy equations of the heat transfer fluid and phase change material and the resulting equations solved using a developed Matlab computer code. The developed model of the LH TES was validated using experiment measurement from literature and its performance assessed using charging rate, energy efficiency and exergy efficiency. The second part consists of modelling of biomass gasification through a developed Matlab computer code. Kinetic free stoichiometric equilibrium modelling approach was adopted. The developed model showed good agreement with two different experimental measurements. Predictions that can be done with the model include syngas yield, temperature profiles of the pyrolysis, oxidation and reduction zones respectively including syngas yield, carbon conversion efficiency and lower calorific value of the syngas. In the third part, thermodynamic modelling of the overall novel integrated system is developed. It combines the models of different components of the integrated system earlier developed. The system designed for a maximum capacity of 1.3 MW is to utilize the high syngas temperature from the biomass gasifier and the relatively hot dual fuel engine (DFE) exhaust temperature to heat up the compressed air from the A-CAES component during the charging and discharging modes, respectively. Also, the heat contained in the DFE jacket water is recovered to produce low temperature hot water for domestic hot water use. Key output parameters to assess the performance of the hybrid systems are total system efficiency (TSE), round trip efficiency (RTE) of the A-CAES, electrical efficiency, effective electrical efficiency, and exergy efficiency for the system. Furthermore, exergy destruction modelling is done to ascertain and quantify the main sources of exergy destruction in the systems components. Finally, an economic feasibility of the overall system is presented using the electricity and heat demand data of Hull Humber region as a case study. The results of this study reveals that it is technically possible to deploy the proposed system in a distributed generation to generate dispatchable wind power and hot water for domestic use. The total energy and exergy efficiency of the system is about 37.12% and 28.54%, respectively. The electrical and effective electrical efficiency are 29.3 and 32.7 %, respectively. In addition, the round trip efficiency of the A-CAES component of the system is found to be about 88.6% which is higher than that of a standalone A-CAES system, thus demonstrating the advantage of the system to recover more stored wind electricity than in conventional A-CAES system. However, the TSE of the system is less than that of a conventional A-CAES system but comparable to similar hybrid configurations. The exergy destruction of the hybrid system components is highest in the biomass gasifier followed by the DFE and the least exergy destruction occurs in the HAD. Furthermore, economic analysis results show that the system is not profitable for commercial power generation unless a 70% of the total investment cost is waived in the form of subsidy. Expectedly, the cost of electricity (COE) of £0.19 per kWh is more than the range of the mean electricity tariff for a medium user home in the UK including taxes which is £0.15 per kWh. With a subsidy of 70%, the system becomes profitable with a positive NPV value of £137,387.2 and COE of £0.10 per kWh at the baseline real discount rate of 10%. The main contribution of the thesis is that it provides an intergraded realistic tool that can simulate the future performance (thermodynamic and economic) of a hybrid energy storage system, which can aid a potential investor to make informed decision on the profitability and financial outlays for the investmen

Hybrid performance modelling of opportunistic networks

We demonstrate the modelling of opportunistic networks using the process algebra stochastic HYPE. Network traffic is modelled as continuous flows, contact between nodes in the network is modelled stochastically, and instantaneous decisions are modelled as discrete events. Our model describes a network of stationary video sensors with a mobile ferry which collects data from the sensors and delivers it to the base station. We consider different mobility models and different buffer sizes for the ferries. This case study illustrates the flexibility and expressive power of stochastic HYPE. We also discuss the software that enables us to describe stochastic HYPE models and simulate them.Comment: In Proceedings QAPL 2012, arXiv:1207.055

Model based fault diagnosis for hybrid systems : application on chemical processes

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless, this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering

Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance

Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner. Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''. The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few. This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage. The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling

Patch-based Hybrid Modelling of Spatially Distributed Systems by Using Stochastic HYPE - ZebraNet as an Example

Individual-based hybrid modelling of spatially distributed systems is usually expensive. Here, we consider a hybrid system in which mobile agents spread over the space and interact with each other when in close proximity. An individual-based model for this system needs to capture the spatial attributes of every agent and monitor the interaction between each pair of them. As a result, the cost of simulating this model grows exponentially as the number of agents increases. For this reason, a patch-based model with more abstraction but better scalability is advantageous. In a patch-based model, instead of representing each agent separately, we model the agents in a patch as an aggregation. This property significantly enhances the scalability of the model. In this paper, we convert an individual-based model for a spatially distributed network system for wild-life monitoring, ZebraNet, to a patch-based stochastic HYPE model with accurate performance evaluation. We show the ease and expressiveness of stochastic HYPE for patch-based modelling of hybrid systems. Moreover, a mean-field analytical model is proposed as the fluid flow approximation of the stochastic HYPE model, which can be used to investigate the average behaviour of the modelled system over an infinite number of simulation runs of the stochastic HYPE model.Comment: In Proceedings QAPL 2014, arXiv:1406.156

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested