Influence of a system “vehicle – driver – road – environment” on the energy efficiency of the vehicles with electric drive

The purpose of this paper is to present the results of an investigation as to the interconnection between main exterior factors which can influence the power consumption during the vehicle movement in the conditions of real operation. According to the results of theoretic researches, there was determined an influence of every factor on the power consumption during vehicle movement in the modes typical for Lutsk city. There was established a contribution of the factors into the total power consumption on micro and macro levels. As a result of the study it was evaluated that an influence of a driver on a power consumption is situated within 50…80 %, an influence of an air resistance is up to 10 %, an influence of a longitudinal profile and a road resistance varies within 20…35 %. According to the results of experiments, there were determined the bus driving modes in urban conditions, and according to their results, there was built an average graph of bus movement in Lutsk city. There was made a mathematic modelling of electric vehicle movement, along with that there was taken into account the most probable range of change of the exterior factors, namely vehicle acceleration, road resistance, air resistance. It was proved that while speed is growing, the influence of road resistance and of air resistance is growing up and has a parabolic character, along with that the contribution of a driver is decreasing. The contribution of the study consists in that, There were proposed the coefficients of taking into consideration the influence of exterior factors on the power consumption by the vehicle and there was built a mathematic model for their determination. These coefficients of taking into consideration the influence of exterior factors on the power consumption give a possibility to evaluate the critical influences and to make an operative decision about the minimization of power consumption as for some specific vehicles, and for an enterprise. Further researches will focus on the plotting of telemetric means of informing, in a mode of real time, of the drivers of the vehicles, of the controllers of an enterprise about the exterior influences, that will give a possibility to make the appropriative decisions instantly. Besides, the given results can be used in order to determine the level of qualification of a driver, the state of road pavement, will give a possibility to find some more rational layout of bus stops, traffic lights, to optimize the routes of vehicles movement

A Generic Prediction Approach for Optimal Control of Electrified Vehicles Using Artificial Intelligence

n order to further increase the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. Therefore, a generic prediction approach is worked out in this paper, which enables a robust prediction of all traction torque-relevant variables for such strategies. It is intended to be useful for various types of electrification; however, the focus of this work is to the application in hybrid electric vehicles. In contrast to other approaches, no additional information (e.g., telemetry data) is required and thus a reliable prediction is guaranteed at all times. In particular, approaches from the fields of stochastics and artificial intelligence have proven to be effective for such purposes. Within the scope of this work, both so-called Markov Chains and Neural Networks are applied to predict real driving profiles within a required time horizon. Therefore, at first, a detailed analysis of the driver-specific ride characteristics is performed to ensure that real-world operation is represented appropriately. Next, the two models are implemented and the calibration is further discussed. The subsequent direct comparison of the two approaches is performed based on the described methodology, which includes both quantitative and qualitative analyses. Hereby, the quality of the predictions is evaluated using Root Mean Squared Error (RMSE) calculations as well as analyses in time domain. Based on the presented results, an appropriate approach is finally recommended

Operating cycle representations for road vehicles

This thesis discusses different ways to represent road transport operations mathematically. The intention is to make more realistic predictions of longitudinal performance measures for road vehicles, such as the CO2 emissions. It is argued that a driver and vehicle independent description of relevant transport operations increase the chance that a predicted measure later coincides with the actual measure from the vehicle in its real-world application. This allows for fair comparisons between vehicle designs and, by extension, effective product development. Three different levels of representation are introduced, each with its own purpose and application. The first representation, called the bird\u27s eye view, is a broad, high-level description with few details. It can be used to give a rough picture of the collection of all transport operations that a vehicle executes during its lifetime. It is primarily useful as a classification system to compare different applications and assess their similarity. The second representation, called the stochastic operating cycle (sOC) format, is a statistical, mid-level description with a moderate amount of detail. It can be used to give a comprehensive statistical picture of transport operations, either individually or as a collection. It is primarily useful to measure and reproduce variation in operating conditions, as it describes the physical properties of the road as stochastic processes subject to a hierarchical structure.The third representation, called the deterministic operating cycle (dOC) format, is a physical, low-level description with a great amount of detail. It describes individual operations and contains information about the road, the weather, the traffic and the mission. It is primarily useful as input to dynamic simulations of longitudinal vehicle dynamics.Furthermore, it is discussed how to build a modular, dynamic simulation model that can use data from the dOC format to predict energy usage. At the top level, the complete model has individual modules for the operating cycle, the driver and the vehicle. These share information only through the same interfaces as in reality but have no components in common otherwise and can therefore be modelled separately. Implementations are briefly presented for each module, after which the complete model is showcased in a numerical example.The thesis ends with a discussion, some conclusions, and an outlook on possible ways to continue

Analyse de la consommation énergétique de véhicules électriques

RÉSUMÉ La majorité des analyses de consommation énergétique, autant au niveau des véhicules conventionnels que des véhicules électriques, se basent sur des cycles de conduite. Les cycles de conduite permettent de représenter différents comportements de conduite dans diverse conditions. Or, l’une des plus grandes lacunes dans l’élaboration des cycles de conduite est la faible disponibilité de données réelles. Les cycles de conduite présents dans la littérature ont pour limitations le nombre de journées d’enregistrements, le nombre de véhicules utilisés ainsi que le nombre de chauffeurs différents qui participent à l’étude.----------ABSTRACT Most of the studies on energy consumption estimation for conventional and electric vehicles are based on driving cycles. These driving cycles aim to represent typical driving behaviors in different situations. One of the biggest gaps in driving cycle elaborations is the limited availability of realistic data. Most of the studies reveal that the main limitations are the amounts of recording days and the number of different vehicles or drivers