A driving cycle is a time series of a vehicle’s speed, reflecting its movement in real road conditions. In addition to certification and comparative research, driving cycles are used in the virtual design of drive systems and embedded control algorithms, traffic management and intelligent road transport (traffic engineering). This study aimed to develop an adaptive driving cycle for a known route to optimize the energy consumption of an electric vehicle and improve the driving range. A novel distance-based adaptive driving cycle method was developed. The proposed algorithm uses the segmentation and iterative synthesis procedures of Markov chains. Energy consumption during driving is monitored on an ongoing basis using Gaussian process regression, and speed and acceleration are corrected adaptively to maintain the planned energy consumption. This paper presents the results of studies of simulated driving cycles and the performance of the algorithm when applied to the real recorded driving cycles of an electric vehicle.
Time signals recorded by the on-board diagnostic system (OBD), describing the manner of vehicle’s movement in actual road conditions show non-stationarity and non-linearity, as well as statistical multiscalarity. In practice, it means that the analysis of registered time series requires modelling of non-linear phenomena. The aim of this study was to examine the nature of the vehicle speed profile in actual road conditions with the method of multifractal analysis. A number of studies indicates that the driving tests applied for many years have not been representative for the actual operating conditions of vehicles. For both the new Worldwide Harmonised Light duty Test Cycle (WLTC), a worldwide harmonised procedure of light vehicle testing, as well as in actual urban driving conditions along the measuring route, being subject to empirical research, confirmation of strong multifractal properties of the recorded vehicle speed time series have been obtained.
Simulation methods commonly used throughout the design and verification process of various types of motor vehicles require development of naturalistic driving cycles. Optimization of parameters, testing and gradual increase in the degree of autonomy of vehicles is not possible based on standard driving cycles. Ensuring representativeness of synthesized time series based on collected databases requires algorithms using techniques based on stochastic and statistical models. A synthesis technique combining the MCMC method and multifractal analysis has been proposed and verified. The method allows simple determination of the speed profile compared to classic frequency analysis.
Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl IntroductionFuelling hydrogen internal combustion engines (HYICE) with hydrogen is presently the subject matter of numerous research & development works. According to paper [5] it is a temporary solution before projected fuel cells are implemented, which aims to prepare for and put into operation hydrogen storage and distribution infrastructure. Mainly spark-ignition engines are adapted for hydrogen fuelling but it is also possible to adjust self-ignition engines for hydrogen fuelling.Hydrogen supply IC engine fuel should be considered depending on the type of diesel cycle:The use of hydrogen alone or as an addition to gasoline or 1.LPG and methane in spark-ignition engines; The use of hydrogen as an addition to diesel oil in self-ignition 2.engines. Hydrogen in spark-ignition engines.An analysis of the impact of hydrogen used as basic fuel [3, 4] proved that:it is possible to achieve efficiency at a level similar or higher -than in case of a conventional engine fuelled with gasoline with limited engine power; high emission of nitrogen oxides in exhaust (fuel contains no -carbon compounds producing toxic substances).An analysis of impact of use of hydrogen as an addition to hydrocarbon fuel [1,8,13,15] proved that: it is possible to achieve efficiency similar to that of a conven--tional engine fuelled with gasoline with slightly limited engine power, CO and HC emissions decrease, whereas the emission of NO -X increases and thermal efficiency grows when poor mixtures are used.Hydrogen IC engines are based on the technology of spark ignition piston engines and after some modifications may be used fuelled both with conventional fuels as well as with hydrogen [5]. In the papers [3,4], authors refer to pre-ignition hydrogen as one of the main problems in applying hydrogen in piston engines with spark ignition. According to the authors the basic causes for pre-ignition include: low energy of hydrogen ignition (0,02 mJ), -wide range of combustion limits 4%-75% v/v, -small critical distance for flame propagation. -As regards the effects of pre-ignition the authors point to:lower efficiency of engine, -engine roughness work, -possibility that flame moves to the inlet duct. -Because small gasoline engines operate with a slightly richer mixture and do not have a catalytic reactor, their fuel consumption and emissions are very high. When gasoline engines are fuelled with hydrogen only, emission of NO X increases and the flame often retreats to the inlet system [8].KruczyńsKi s, ŚlęzAK m, Gis W, OrlińsKi P. Evaluation of the impact of combustion hydrogen addition on operating properties of self-ignition engine. Eksploatacja i Niezawodnosc -maintenance and reliability 2016; 18 (3): 343-347, http://dx.doi.org/10.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.