Citation: Laurén, M.; Goswami, G.;
Tupitsina, A.; Jaiswal, S.; Lindh, T.;
Sopanen, J. General-Purpose and
Scalable Internal-Combustion Engine
Model for Energy-Efficiency Studies.
Machines 2022, 10, 26. https://
doi.org/10.3390/machines10010026
Academic Editor: Francesco
Castellani
Received: 16 November 2021
Accepted: 27 December 2021
Published: 30 December 2021
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machines
Article
General-Purpose and Scalable Internal-Combustion Engine
Model for Energy-Efficiency Studies
Mika Laurén
1,
* , Giota Goswami
2
, Anna Tupitsina
3
, Suraj Jaiswal
2
, Tuomo Lindh
3
and Jussi Sopanen
2
1
Department of Mechanical Engineering, Turku University of Applied Sciences, 20520 Turku, Finland
2
Department of Mechanical Engineering, Lappeenranta University of Technology,
53850 Lappeenranta, Finland; giota.goswami@lut.fi (G.G.); suraj.jaiswal@lut.fi (S.J.); jussi.sopanen@lut.fi (J.S.)
3
Department of Electrical Engineering, Lappeenranta University of Technology, 53850 Lappeenranta, Finland;
anna.tupitsina@lut.fi (A.T.); tuomo.lindh@lut.fi (T.L.)
* Correspondence: mika.lauren@turkuamk.fi; Tel.: +358-44-907-2058
Abstract: Hybrid powertrains that combine electric machines and internal-combustion engines offer
substantial opportunities to increase the energy efficiency and minimize the exhaust emissions of
vehicles and nonroad working machines. Due to the wide range of applications of such powertrains,
simulation tools are used to evaluate and compare the energy efficiency of hybrid powertrains for
application-specific working cycles in virtual environments. Therefore, the accurate modeling of the
powertrain components of a hybrid system is important. This paper presents an agile calculation
tool that can generate realistic fuel consumption data of a scalable diesel engine. This method
utilizes a simple efficiency model of the combustion and crank train friction model to generate the
fuel consumption map in the operating area of a typical diesel engine. The model parameters are
calibrated to produce accurate fuel consumption data in the initial phase of system-level simulations.
The proposed method is also validated by using three real engine datasets, and the comparison of
results is presented.
Keywords: internal combustion engine; modelling; powertrain developing; nonroad mobile machinery
1. Introduction
Increasingly tightening emission regulations and energy-efficiency demands are key
drivers for the electrification of transportation and nonroad mobile machines. Hybrid elec-
trical vehicles (HEV) provide the potential to reduce fuel consumption and consequently
diminish CO
2
emissions, since the workload of the machine is distributed between an
internal-combustion engine (ICE) and electrical energy storage. HEV powertrain compo-
nent designs can be studied through the simulation models of different vehicles and mobile
machines. In energy-efficiency studies, accurate fuel consumption of the ICE in static
loading condition is needed. Therefore, special attention should be paid to the efficiency
and specific fuel consumption maps of diesel engine operation to aid in the estimation of
fuel consumption in the system’s dynamic simulation [1].
The topology selection and dimensioning of hybrid powertrain components of offroad
vehicles, such as agricultural tractors [2,3], different loaders [4], and forest machines [5],
is a challenging task. The varying working operational profiles and demands of auxiliary
devices of these vehicles make it difficult to evaluate the performance, fuel consumption,
and feasibility of different control strategies of the vehicle under design. Simulators
that can be used to evaluate and study these characteristics were introduced in previous
studies [6]. Such simulation models are capable of simulating standard work cycles, testing
powertrain components such as combustion engines, energy storage, and electrical drives,
and changing the topology of the vehicle.
In typical combustion engine models, many parameters are determined on the basis of
measurements. However, if efficiency maps and specific fuel consumption maps are avail-
able, a relatively simple simulation model can be created without relying on measurements.
Machines 2022, 10, 26. https://doi.org/10.3390/machines10010026 https://www.mdpi.com/journal/machines