Fuel Economy Comparative Analysis of Conventional and Ultracapacitors-Based, Parallel Hybrid Electric Powertrains for a Transit Bus

Due to Internal Combustion Engines’ (ICE) significant share in global energy demand, primarily through the transportation sector, great efforts are invested in research for solutions that will increase the fuel economy of ICE-powered vehicles. The main objective of the study presented in this paper has been to perform a comparative study of a conventional and a parallel hybrid electric transit bus employing an ultracapacitors-based energy accumulator. A high-fidelity simulation model of the vehicle has been designed in the AMESim multi-domain system analysis software. The conventional powertrain model has been calibrated using data obtained during an acquisition experiment conducted in real-world traffic conditions on a transit bus. This data also served as the basis for defining the driving cycle on which the numerical analyses will be conducted. A simple, sub-optimal control law has been implemented in the hybrid powertrain simulation model. Also, an advanced energy management law based on Dynamic Programming has been derived to assess the ultimate fuel economy improvement potential of the hybrid solution and to make design decisions. Initial study shows that considerable fuel consumption reduction in excess of 30% could be achieved by implementing a regenerative hybrid system employing an ultracapacitorbased accumulator

Dynamic Programming Study of a Hybrid Electric Powertrain System for a Transit Bus

Great research efforts are invested in the quest for solutions that will increase the fuel economy of ICE-powered vehicles. The main objective of the study presented in this paper has been to analyze the ultimate fuel economy improvement potential of a transit bus by implementing a hybrid electric powertrain system utilizing an ultracapacitors-based accumulator. A simulation model of the vehicle has been calibrated by analyzing data obtained during an experiment conducted in real-world traffic conditions on a Belgrade transit bus. Apart from serving as the input for the powertrain parameters identification procedures, the acquired data also served as the basis for defining the driving cycle that will be used in numerical simulation studies. A Dynamic Programming optimization procedure has been applied on the hybrid powertrain system model in order to assess the ultimate fuel economy improvement potential. The optimization procedure has been executed for various hybrid powertrain parameters and component sizes, allowing the optimal choice of design decisions, in particular the energy accumulator size. Initial study shows that considerable fuel consumption reduction in excess of 30% can be achieved

Fuel Economy Comparative Analysis of Conventional and Ultracapacitors-Based, Parallel Hybrid Electric Powertrains for a Transit Bus

Due to Internal Combustion Engines’ (ICE) significant share in global energy demand, primarily through the transportation sector, great efforts are invested in research for solutions that will increase the fuel economy of ICE-powered vehicles. The main objective of the study presented in this paper has been to perform a comparative study of a conventional and a parallel hybrid electric transit bus employing an ultracapacitors-based energy accumulator. A high-fidelity simulation model of the vehicle has been designed in the AMESim multi-domain system analysis software. The conventional powertrain model has been calibrated using data obtained during an acquisition experiment conducted in realworld traffic conditions on a transit bus. This data also served as the basis for defining the driving cycle on which the numerical analyses will be conducted. A simple, sub-optimal control law has been implemented in the hybrid powertrain simulation model. Also, an advanced energy management law based on Dynamic Programming has been derived to assess the ultimate fuel economy improvement potential of the hybrid solution and to make design decisions. Initial study shows that considerable fuel consumption reduction in excess of 30% could be achieved by implementing a regenerative hybrid system employing an ultracapacitor-based accumulator

Stationary Test Plan Optimisation Using Slow Dynamic Slope Engine Screening

Requirements regarding high fuel efficiency, low pollutants and CO2 emission impact of the internal combustion (IC) engine results in high control calibration complexity. Modern IC engines are equipped with numerous electronically controlled subsystems, whose usage leads to almost exponential growth of stationary operating points that need to be evaluated and optimised. In that perspective, the methodology for fast pre-knowledge acquisition of examined system is presented through the application of Slow Dynamic Slope experiments – SDS. Continual slow change of a control parameter excites the system, in such a way, that allow obtaining of an approximately stationary operating regime, without the time-consuming operating point settling period. By analysing stationary-based approximation results of Slow Dynamic Slope experiments, conducted within the IC engine global operation domain (engine speed and load), certain zones could be identified. Within those zones, increased number of stationary tests is desirable in order to provide a more precise approximative model of observed engine output parameters. In this way, relatively fast dynamic SDS experiments could be used to optimise the stationary-based test plan leading to overall time savings dedicated to IC engine testing

A Neural Network-Based Control Algorithm for a Hydraulic Hybrid Powertrain System

Significant research efforts are invested in the quest for solutions that will increase the fuel economy and reduce the environmental impacts of ICE-powered vehicles. The main objective of the study presented in this paper has been to analyze and assess the performance of a control methodology for a parallel hydraulic hybrid powertrain system of a transit bus. A simulation model of the vehicle has been calibrated by analyzing data obtained during an experiment conducted in real-world traffic conditions aboard a Belgrade transit bus. A Dynamic Programming optimization procedure has been applied on the calibrated powertrain model and an optimal configuration that minimizes the fuel consumption has been selected. A Neural Network-based, implementable control algorithm has then been formed through a machine learning process involving data from the optimal, nonimplementable Dynamic Programming-based control. Several Neural Network configurations have been tested to obtain the best fuel economy for the range of conditions encountered during normal transit bus operation. It has been shown that a considerable fuel consumption reduction on the order of 30% could be achieved by implementing such a system and calibration method

A Neural Network-based Control Algorithm for a Hydraulic Hybrid Powertrain System

Significant research efforts are invested in the quest for solutions that will increase the fuel economy and reduce the environmental impacts of ICE-powered vehicles. The main objective of the study presented in this paper has been to analyze and assess the performance of a control methodology for a parallel hydraulic hybrid powertrain system of a transit bus. A simulation model of the vehicle has been calibrated by analyzing data obtained during an experiment conducted in real-world traffic conditions aboard a Belgrade transit bus. A Dynamic Programming optimization procedure has been applied on the calibrated powertrain model and an optimal configuration that minimizes the fuel consumption has been selected. A Neural Network-based, implementable control algorithm has then been formed through a machine learning process involving data from the optimal, non-implementable Dynamic Programming-based control. Several Neural Network configurations have been tested to obtain the best fuel economy for the range of conditions encountered during normal transit bus operation. It has been shown that a considerable fuel consumption reduction on the order of 30% could be achieved by implementing such a system and calibration method

Development Of Continuously Variable Intake Manifold For Formula Student Racing Engine

After several years of research and development of Formula Student’s air mass flow restricted racing engine at the Internal Combustion Engines Department of the Faculty of Mechanical Engineering, University of Belgrade, the design process of a new intake manifold for the 2014 competition season was set off. Through several seasons, the intake manifolds of the YAMAHA YZF-R6 high performance engine evolved from a dual volume, into a single volume concept and finally to the continuously variable intake manifold (CVIM) design. Comparative analysis of data obtained during in-laboratory engine testing and data logged from ECU during the races gave some guidelines in the design of CVIM. The main goal of this research is increasing the number of engine operating points with resonant supercharging. The Ricardo WAVE engine mathematical model is improved and particular attention is dedicated to the approximation of the adopted CVIM concept using Helmholtz Resonance Theory. This paper describes the correlation between optimal intake runner length and manifold volume over engine speed at wide open throttle as well as their influence on volumetric efficiency and engine effective parameters

Ignition Timing Map Calibration Based on Nonlinear Dynamic System Identification Using NARX Neural Network

The main topic of this paper is presentation of methodology for process identification and mathematical modeling of a nonlinear dynamic system, such as an IC engine, based on the experimental data acquired during base engine calibration in terms of ignition timing. With the introduction of certain assumption, mathematical model generated in this way could be used for verification of potentially optimal look-up tables and for look-up tables smoothing. For this type of time-series modeling, nonlinear autoregressive network with exogenous inputs will be used and full-factorial sweep of limited set of neural network parameters will be analyzed. Guidelines for mathematical model formation, verification and idea of stationary-based engine calibration will be briefly outlined. Comparison between measured and modeled engine torque will be shown alongside with instructions for further research on this topic