Browsing by Author "Othaganont, Pongpun"
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Item Open Access Multi-objective optimisation for battery electric vehicle powertrain topologies(Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2016-10-06) Othaganont, Pongpun; Assadian, Francis; Auger, Daniel J.Electric vehicles are becoming more popular in the market. To be competitive, manufacturers need to produce vehicles with a low energy consumption, a good range and an acceptable driving performance. These are dependent on the choice of components and the topology in which they are used. In a conventional gasoline vehicle, the powertrain topology is constrained to a few well-understood layouts; these typically consist of a single engine driving one axle or both axles through a multi-ratio gearbox. With electric vehicles, there is more flexibility, and the design space is relatively unexplored. In this paper, we evaluate several different topologies as follows: a traditional topology using a single electric motor driving a single axle with a fixed gear ratio; a topology using separate motors for the front axle and the rear axle, each with its own fixed gear ratio; a topology using in-wheel motors on a single axle; a four-wheel-drive topology using in-wheel motors on both axes. Multi-objective optimisation techniques are used to find the optimal component sizing for a given requirement set and to investigate the trade-offs between the energy consumption, the powertrain cost and the acceleration performance. The paper concludes with a discussion of the relative merits of the different topologies and their applicability to real-world passenger cars.Item Open Access Optimisation methods for battery electric vehicle powertrain.(2017-07) Othaganont, Pongpun; Auger, Daniel J.; Whidborne, James F.The battery electric vehicle (BEV) is considered to be one of the solutions for reducing greenhouse gasses and an alternative means of transportation. However, some current limitations such as higher powertrain costs, limited driving range and negative perceptions of that range, have reduced BEVs’ popularity. This thesis aims to improve the tank-to-wheel energy consumption of the BEV by presenting possible powertrain architectures and developing new tools for powertrain analysis. The study has two main objectives; the first is to evaluate different possible powertrain topologies. The selected topologies include the single-motor single-axle, the double-motor double-axle, the in-wheel-motor single-axle and the in-wheel-motor double-axle. Models of these powertrains have been modified from the Quasi-Static toolbox, using vehicle parameters from the Nissan Leaf and subject to state assumptions. The multi-objective optimisation method has been applied to establish the costs/benefits of energy consumption, acceleration performance and powertrain cost. The results show that each topology presents its own benefits as the in-wheel types are good at energy efficiency and drivability, while the cost of the powertrain is the major drawback. The non-in-wheel-motor vehicle provides sufficient energy efficiency and driveability with lower powertrain cost. The second objective is to evaluate a possible alternative tool for BEV powertrain modelling and optimisation. The tool consists of four methodologies: sensitivity analysis, differential flatness, the Chebfun computational tool and the multi-disciplinary optimisation method. The study presents a possible alternative optimisation tool which may perhaps benefit the designer. This new tool may not be as convenient as the previous one; however, the new tool may give the designer greater understanding and insight into the BEV powertrain.Item Open Access Sensitivity analyses for cross-coupled parameters in automotive powertrain optimization(MDPI, 2014-06-17) Othaganont, Pongpun; Assadian, Francis; Auger, Daniel J.When vehicle manufacturers are developing new hybrid and electric vehicles, modeling and simulation are frequently used to predict the performance of the new vehicles from an early stage in the product lifecycle. Typically, models are used to predict the range, performance and energy consumption of their future planned production vehicle; they also allow the designer to optimize a vehicle’s configuration. Another use for the models is in performing sensitivity analysis, which helps us understand which parameters have the most influence on model predictions and real-world behaviors. There are various techniques for sensitivity analysis, some are numerical, but the greatest insights are obtained analytically with sensitivity defined in terms of partial derivatives. Existing methods in the literature give us a useful, quantified measure of parameter sensitivity, a first-order effect, but they do not consider second-order effects. Second-order effects could give us additional insights: for example, a first order analysis might tell us that a limiting factor is the efficiency of the vehicle’s prime-mover; our new second order analysis will tell us how quickly the efficiency of the powertrain will become of greater significance. In this paper, we develop a method based on formal optimization mathematics for rapid second-order sensitivity analyses and illustrate these through a case study on a C-segment electric vehicle.