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Optimal Design and Control of Thermal Hybrid Powertrains

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 17430

Special Issue Editors


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Guest Editor
Department of Energy “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: mechanical engineering; automotive engineering; combustion engines; hybrid drives

Special Issue Information

Dear Colleagues,

The even more stringent emission regulations together with the increasing concern regarding environmental issues have encouraged the automotive industry to largely invest on R&D to define innovative solutions for the attainment of green vehicles characterized by low CO2 and pollutant emissions. Powertrain electrification is definitely bound to play a significant role in achieving such a purpose, and possible options would be represented by full electric vehicles (FEVs) and (plug-in) hybrid electric vehicles ((P)HEVs) as opposed to conventional powertrains. Despite the possibility offered by FEVs in theoretically winning out on-road tank-to-wheel emissions, the high costs, mainly related to the batteries, together with the compromises on performance and range limitations, mainly to be ascribed to the current lack of infrastructure, are likely to represent strong road-blocks that might negatively affect customer acceptance. Therefore, hybrid architectures featuring both electric and conventional powertrain components are bound to represent an optimal compromise in the short–medium-term scenario.

(P)HEVs offer improved fuel economy and lower emissions than conventional vehicles as well as the possibility of increasing the driving range and of taking advantage of the existing fuel infrastructures with respect to FEVs. However, the exploitation of the full potential of (P)HEVs requires a dedicated design to cope with the constraints deriving from the different driving missions and the requirements from the customer together with sound and flexible control solutions that are to be tailored on the considered power-units.

This Special Issue encourages researchers working in this field to share their latest developments in optimal design and control of (P)HEVs for road vehicles. Specific topics of interest for publication include but are not limited to:

  • Dedicated powertrain technologies and architectures to: minimize (P)HEV purchase cost; make TCO attractive for customers; optimize range in pure electrics; maximize electric mode span and enhance customer experience; and achieve combined sizing and energy management;
  • Energy recovery from heat and other sources in (P)HEVs;
  • Tools, systems, and components for thermal management on (P)HEVs in order to enhance efficiency so as to minimize energy needs and lower thermal requirements on components;
  • Advance and adapt the integration of functionalities tailored to (P)HEVs missions, using internal and extended data (e.g., data sharing with vehicles ahead) and applying innovative methods to accurately predict and optimize energy use (such as advanced rule-based methods, including deterministic and artificial intelligence; advanced optimization methods, including global optimal and real-time near-optimal; predictive techniques for real-time strategies; and machine learning, big data, and cloud computing for energy management).

Prof. Daniela Anna Misul
Prof. Dr. Ezio Spessa
Guest Editors

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Keywords

  • hybrid
  • energy management
  • emissions
  • control solutions

Published Papers (7 papers)

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Research

17 pages, 3145 KiB  
Article
Model-Based Analysis of Different Equivalent Consumption Minimization Strategies for a Plug-In Hybrid Electric Vehicle
by Stefan Geng, Thomas Schulte and Jürgen Maas
Appl. Sci. 2022, 12(6), 2905; https://doi.org/10.3390/app12062905 - 11 Mar 2022
Cited by 6 | Viewed by 2042
Abstract
Plug-in hybrid electric vehicles (PHEVs) are developed to reduce fuel consumption and the emission of carbon dioxide. Common powertrain configurations of PHEVs (i.e., the configuration of the combustion engine, electric motor, and transmission) can be operated either in series, parallel, or power split [...] Read more.
Plug-in hybrid electric vehicles (PHEVs) are developed to reduce fuel consumption and the emission of carbon dioxide. Common powertrain configurations of PHEVs (i.e., the configuration of the combustion engine, electric motor, and transmission) can be operated either in series, parallel, or power split hybrid mode, whereas powertrain configurations with multimode transmissions enable switching between those modes during vehicle operation. Hence, depending on the current operation state of the vehicle, the most appropriate mode in terms efficiency can be selected. This, however, requires an operating strategy, which controls the mode selection as well as the torque distribution between the combustion engine and electric motor with the aim of optimal battery depletion and minimal fuel consumption. A well-known approach is the equivalent consumption minimization strategy (ECMS). It can be applied by using optimizations based on a prediction of the future driving behavior. Since the outcome of the ECMS depends on the quality of this prediction, it is crucial to know how accurate the predictions must be in order to obtain acceptable results. In this contribution, various prediction methods and real-time capable ECMS implementations are analyzed and compared in terms of the achievable fuel economy. The basis for the analysis is a holistic model of a state-of-the-art PHEV powertrain configuration, comprising the multimode transmission, corresponding powertrain components, and representative real-world driving data. Full article
(This article belongs to the Special Issue Optimal Design and Control of Thermal Hybrid Powertrains)
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20 pages, 1947 KiB  
Article
Data-Driven Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric and Connected Vehicles
by Wilson Pérez, Punit Tulpule, Shawn Midlam-Mohler and Giorgio Rizzoni
Appl. Sci. 2022, 12(5), 2705; https://doi.org/10.3390/app12052705 - 5 Mar 2022
Cited by 5 | Viewed by 1838
Abstract
Advanced energy management strategies (EMS) are used to control the power flow through a vehicle’s powertrain. However, the cost of high-power computational hardware and lack of a priori knowledge of future road conditions poses difficult challenges for engineers attempting to implement globally optimal [...] Read more.
Advanced energy management strategies (EMS) are used to control the power flow through a vehicle’s powertrain. However, the cost of high-power computational hardware and lack of a priori knowledge of future road conditions poses difficult challenges for engineers attempting to implement globally optimal frameworks. One solution is to use advanced driver assistance systems (ADAS) and connectivity to obtain a prediction of future road conditions. This paper presents a look-ahead predictive EMS which combines approximate dynamic programming (ADP) methods and an adaptive equivalent consumption minimization strategy (A-ECMS) to obtain a near-optimal solution for a future prediction horizon. ECMS is highly sensitive to the equivalence factor (EF), making it necessary to adapt during a trip to account for disturbances. A novel adaptation method is presented in this work which uses an artificial neural network to learn the nonlinear relationship between a speed and the state of charge (SOC) trajectory prediction obtained from ADP to estimate the corresponding EF. A traffic uncertainty analysis demonstrates an approximately 10% fuel economy (FE) improvement over traditional A-ECMS. Using a data-driven adaptation method for A-ECMS informed by a dynamic programming (DP) based prediction results in an EMS capable of online implementation. Full article
(This article belongs to the Special Issue Optimal Design and Control of Thermal Hybrid Powertrains)
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17 pages, 13833 KiB  
Article
Energy Management Strategy for Hybrid Multimode Powertrains: Influence of Inertial Properties and Road Inclination
by Antonio Tota, Enrico Galvagno, Luca Dimauro, Alessandro Vigliani and Mauro Velardocchia
Appl. Sci. 2021, 11(24), 11752; https://doi.org/10.3390/app112411752 - 10 Dec 2021
Cited by 6 | Viewed by 1811
Abstract
Multimode hybrid powertrains have captured the attention of automotive OEMs for their flexible nature and ability to provide better and optimized efficiency levels. However, the presence of multiple actuators, with different efficiency and dynamic characteristics, increases the problem complexity for minimizing the overall [...] Read more.
Multimode hybrid powertrains have captured the attention of automotive OEMs for their flexible nature and ability to provide better and optimized efficiency levels. However, the presence of multiple actuators, with different efficiency and dynamic characteristics, increases the problem complexity for minimizing the overall power losses in each powertrain operating condition. The paper aims at providing a methodology to select the powertrain mode and set the reference torques and angular speeds for each actuator, based on the power-weighted efficiency concept. The power-weighted efficiency is formulated to normalize the efficiency contribution from each power source and to include the inertial properties of the powertrain components as well as the vehicle motion resistance forces. The approach, valid for a wide category of multimode powertrain architectures, is then applied to the specific case of a two-mode hybrid system where the engagement of one of the two clutches enables an Input Split or Compound Split operative mode. The simulation results obtained with the procedure prove to be promising in avoiding excessive accelerations, drift of powertrain components, and in managing the power flow for uphill and downhill vehicle conditions. Full article
(This article belongs to the Special Issue Optimal Design and Control of Thermal Hybrid Powertrains)
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17 pages, 4109 KiB  
Article
Experimental Comparison of Direct and Active Throttle Control of a 7 kW Turboelectric Power System for Unmanned Aircraft
by Johnathan Burgess, Timothy Runnels, Joshua Johnsen, Joshua Drake and Kurt Rouser
Appl. Sci. 2021, 11(22), 10608; https://doi.org/10.3390/app112210608 - 11 Nov 2021
Cited by 1 | Viewed by 2531
Abstract
This article compares direct turbine throttle control and active turbine throttle control for a turboelectric system; the featured turboprop is rated for 7 kW of shaft output power. The powerplant is intended for applications in unmanned aerial systems and requires a control system [...] Read more.
This article compares direct turbine throttle control and active turbine throttle control for a turboelectric system; the featured turboprop is rated for 7 kW of shaft output power. The powerplant is intended for applications in unmanned aerial systems and requires a control system to produce different amounts of power for varying mission legs. The most straightforward control scheme explored is direct turbine control, which is characterized by the pilot controlling the throttle of the turbine engine. In contrast, active control is characterized by the turbine reacting to the power demanded by the electric motors or battery recharge cycle. The transient response to electric loads of a small-scale turboelectric system is essential in identifying and characterizing such a system’s safe operational parameters. This paper directly compares the turbogenerator’s transient behavior to varying electric loads and categorizes its dynamic response. A proportional, integral, and derivative (PID) control algorithm was utilized as an active throttle controller through a microcontroller with battery power augmentation for the turboelectric system. This controller manages the turbine’s throttle reactions in response to any electric load when applied or altered. By comparing the system’s response with and without the controller, the authors provide a method to safely minimize the response time of the active throttle controller for use in the real-world environment of unmanned aircraft. Full article
(This article belongs to the Special Issue Optimal Design and Control of Thermal Hybrid Powertrains)
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34 pages, 16108 KiB  
Article
A Model for the Estimation of the Residual Driving Range of Battery Electric Vehicles Including Battery Ageing, Thermal Effects and Auxiliaries
by Gianmatteo Cannavacciuolo, Claudio Maino, Daniela Anna Misul and Ezio Spessa
Appl. Sci. 2021, 11(19), 9316; https://doi.org/10.3390/app11199316 - 8 Oct 2021
Cited by 4 | Viewed by 1884
Abstract
Sustainable mobility has recently become a priority of research for on-road vehicles. Shifting towards vehicle electrification is one of the most promising solutions concerning the reduction in pollutant emissions and greenhouse gases, especially for urban areas. Nevertheless, battery electric vehicles might carry substantial [...] Read more.
Sustainable mobility has recently become a priority of research for on-road vehicles. Shifting towards vehicle electrification is one of the most promising solutions concerning the reduction in pollutant emissions and greenhouse gases, especially for urban areas. Nevertheless, battery electric vehicles might carry substantial limitations compared with other technologies. Specifically, the electric range could be highly affected by the ageing process, non-optimal thermal management of the battery and cabin conditioning. In this paper, a model for the estimation of the residual range of electric vehicles is proposed accounting for the influence of battery state of health, battery pack temperature, power consumption of the main vehicle auxiliaries, and battery pre-heating on the residual driving range. The results of the model application to an L7 battery electric vehicle highlighted that the electric range can be highly affected by several factors related to real-world driving conditions and can consistently differ from nominal values. Full article
(This article belongs to the Special Issue Optimal Design and Control of Thermal Hybrid Powertrains)
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15 pages, 2973 KiB  
Article
Exploitation of a Particle Swarm Optimization Algorithm for Designing a Lightweight Parallel Hybrid Electric Vehicle
by Matteo Spano, Pier Giuseppe Anselma, Daniela Anna Misul and Giovanni Belingardi
Appl. Sci. 2021, 11(15), 6833; https://doi.org/10.3390/app11156833 - 25 Jul 2021
Cited by 12 | Viewed by 2232
Abstract
The dramatic global climate change has driven governments to drastically tackle pollutant emissions. In the transportation field, one of the technological responses has been powertrain electrification for passengers’ cars. Nevertheless, the large amount of possible powertrain designs does not help the development of [...] Read more.
The dramatic global climate change has driven governments to drastically tackle pollutant emissions. In the transportation field, one of the technological responses has been powertrain electrification for passengers’ cars. Nevertheless, the large amount of possible powertrain designs does not help the development of an exhaustive sizing process. In this research, a multi-objective particle swarm optimization algorithm is proposed to find the optimal layout of a parallel P2 hybrid electric vehicle powertrain with the aim of maximizing fuel economy capability and minimizing production cost. A dynamic programming-based algorithm is used to ensure the optimal vehicle-level energy management. The results show that diverse powertrain layouts may be suggested when different weights are assigned to the sizing targets related to fuel economy and production cost, respectively. Particularly, upsizing the power sources and increasing the number of gears might be advised to enhance HEV fuel economy capability through the efficient exploitation of the internal combustion engine (ICE) operation. On the other hand, reduction of the HEV production cost could be achieved by downsizing the power sources and limiting the number of gears with respect to conventional ICE-powered vehicles thanks to the interaction between ICE and electric motor. Full article
(This article belongs to the Special Issue Optimal Design and Control of Thermal Hybrid Powertrains)
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22 pages, 7636 KiB  
Article
Energy Management Optimization of a Dual Motor Lithium Ion Capacitors-Based Hybrid Super Sport Car
by Alessandro Franceschi, Nicolò Cavina, Riccardo Parenti, Maurizio Reggiani and Enrico Corti
Appl. Sci. 2021, 11(2), 885; https://doi.org/10.3390/app11020885 - 19 Jan 2021
Cited by 2 | Viewed by 2934
Abstract
Nowadays, hybrid electric vehicles represent one of the main solutions for the reduction of greenhouse gases in the automotive sector. Alongside the reduction of CO2, hybrid electric vehicles serve as a strong alternative on drivability and performance to conventional internal combustion [...] Read more.
Nowadays, hybrid electric vehicles represent one of the main solutions for the reduction of greenhouse gases in the automotive sector. Alongside the reduction of CO2, hybrid electric vehicles serve as a strong alternative on drivability and performance to conventional internal combustion engine-based vehicles. Vehicles exist with various missions; super sport cars usually aim to reach peak performance and to guarantee a great driving experience to the driver, but great attention must also be paid to fuel consumption. According to the vehicle mission, hybrid electric vehicles can differ in the powertrain configuration and the choice of the energy storage system. Manufacturers have recently started to work on Lithium-Ion Capacitors (LiC) -based hybrid vehicles. This paper discusses the usage of a control-oriented vehicle and powertrain model to analyze the performance of a dual motor LiC-based hybrid V12 vehicle by Automobili Lamborghini. P3–P4 and P2–P4 parallel hybrid configurations have been selected and compared since they allow to fully exploit the potential of the LiC storage system characterized by high power. The validated model has been used to develop control strategies aimed at fuel economy and CO2 reduction, and in particular, both Rule Based Strategies (RBS) and Equivalent Consumption Minimization Strategies (ECMS) are presented in the paper. A critical comparison between the various powertrain configurations is carried out, keeping into account the peculiarities of the LiC technology and evaluating the performance of the different control approaches. Full article
(This article belongs to the Special Issue Optimal Design and Control of Thermal Hybrid Powertrains)
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