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Electric (Hybrid) Vehicles: Optimization Techniques, Control Systems and Powertrain Modeling - selected papers from conference EVER2021

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 (30 September 2021) | Viewed by 24336

Special Issue Editors

Prof. Dr. Omar Hegazy

E-Mail Website
Guest Editor
Mobility, Logistics and Automotive Technology Research Center, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Interests: advanced power electronics with WBG (SiC &GaN); powertrains; electric vehicles; connectivity & IoT; digital twin; V2X systems; BMS; co-design optimization; smart DC and AC microgrids
Special Issues, Collections and Topics in MDPI journals
Dr. Steven Wilkins

E-Mail Website
Guest Editor
1. Senior Research Scientist, Powertrains Department, TNO, Automotive Campus 30, NL-5708 JZ Helmond, The Netherlands
2. Assistant Professor, EPE Group, Technische Universiteit Eindhoven (TU/e), Postbus 513, 5600 MB Eindhoven, The Netherlands
Interests: modelling and simulation of electrified powertains; energy management strategies; energy storage systems; battery management systems; state estimation
Special Issues, Collections and Topics in MDPI journals
Dr. Mohamed El Baghdadi

E-Mail Website
Guest Editor
Senior Researcher and Vehicle Drivetrain Expert & Leader of Vehicle Team at MOBI-Mobility, Logistics and Automotive Technology Research Center, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
Interests: power electronics; electric machines; electric and (plug-in) hybrid vehicles; (wireless) charging and power supply; power management strategies; control systems; optimization techniques; modelling & simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, there is a strong trend towards sustainable energy and e-mobility solutions in order to significantly reduce greenhouse emissions. Thus, hybrid electric vehicles (HEVs), plug-in electric vehicles (PHEVs) and electric vehicles (EVs), with their connectivity, have recently received a growing amount of interest to provide sustainable solutions towards ecological and energy-efficient systems.

In light of this growing trend, optimization techniques (such as co-design optimization approaches and powertrain control design optimization), modelling approaches and Digital Twins, Artificial Intelligence (AI) and Machine learning algorithms and powertrain components (i.e., power electronics: inverters, DCDC converters and chargers; batteries; electric motors; cooling systems) play a key role in the future developments and generations of vehicle drivetrains towards energy-efficient vehicles powertrains and low total cost of ownership (TCO).

The goal of this Special Edition is to bring the recent ideas and insights of the worldwide research community and of experts together into a common platform to present and discuss the recent advances in powertrain design optimization, powertrain components, powertrain modeling and Digital Twins, connectivity and energy management strategies.

Prospective authors are invited to submit original contributions/articles for review and for possible publication in this SI. Topics of interest include (but are not limited to):

  • Design and co-design optimization techniques for vehicles powertrains;
  • Modular powertrain platforms;
  • Charging management strategies;
  • Energy management strategies;
  • New modeling approaches and scalability including real-time modeling, Digital Twins of powertrain and its components;
  • Electrical energy and power storage systems;
  • Emerging power electronics systems based on SiC and GaN technologies ;
  • New E/E architectures for vehicles systems;
  • Battery management systems (BMS);
  • Vehicle-to-grid (V2G), vehicle-to-home (V2H) and grid-to-vehicle (G2V) technologies;
  • Reliability of power electronics converters.
Prof. Dr. Omar Hegazy
Dr. Steven Wilkins
Dr. Mohamed El Baghdadi
Guest Editors

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Published Papers (6 papers)

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Research

17 pages, 4541 KiB  
Article
Trajectory Tracking Control of Intelligent Electric Vehicles Based on the Adaptive Spiral Sliding Mode
by Yanxin Nie, Minglu Zhang and Xiaojun Zhang
Appl. Sci. 2021, 11(24), 11739; https://doi.org/10.3390/app112411739 - 10 Dec 2021
Cited by 9 | Viewed by 2458
Abstract
Aiming at the multi-objective control problem of the tracking effect and vehicle stability in the process of intelligent vehicle trajectory tracking, a coordinated control strategy of the trajectory tracking and stability of intelligent electric vehicles is proposed based on the hierarchical control theory. [...] Read more.
Aiming at the multi-objective control problem of the tracking effect and vehicle stability in the process of intelligent vehicle trajectory tracking, a coordinated control strategy of the trajectory tracking and stability of intelligent electric vehicles is proposed based on the hierarchical control theory. The vehicle dynamics model and trajectory tracking model are established. In order to tackle the chattering problem in the traditional sliding mode controller, an Adaptive Spiral Sliding Mode controller is designed by taking the derivative of the controller as the upper controller, which is intended to reduce the heading deviation and lateral deviation in the trajectory tracking process whilst ensuring the stability of the vehicle itself. In the lower controller, a four-wheel tire force optimal distribution method is designed. According to the requirements of the upper controller, combined with the yaw stability of the vehicle, the directional control distribution of the four-wheel tire force is realized. A joint simulation model was built based on CarSim and Simulink, and simulation experiments were performed. The results show that the proposed control strategy can effectively control the heading deviation and lateral deviation in the vehicle trajectory tracking while ensuring the lateral stability of the vehicle. Full article
(This article belongs to the Special Issue Electric (Hybrid) Vehicles: Optimization Techniques, Control Systems and Powertrain Modeling - selected papers from conference EVER2021)
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22 pages, 7870 KiB  
Article
Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning
by Benedikt Danquah, Stefan Riedmaier, Yasin Meral and Markus Lienkamp
Appl. Sci. 2021, 11(5), 1983; https://doi.org/10.3390/app11051983 - 24 Feb 2021
Cited by 2 | Viewed by 2843
Abstract
The modelling and simulation process in the automotive domain is transforming. Increasing system complexity and variant diversity, especially in new electric powertrain systems, lead to complex, modular simulations that depend on virtual vehicle development, testing and approval. Consequently, the emerging key requirements for [...] Read more.
The modelling and simulation process in the automotive domain is transforming. Increasing system complexity and variant diversity, especially in new electric powertrain systems, lead to complex, modular simulations that depend on virtual vehicle development, testing and approval. Consequently, the emerging key requirements for automotive validation involve a precise reliability quantification across a large application domain. Validation is unable to meet these requirements because its results provide little information, uncertainties are neglected, the model reliability cannot be easily extrapolated and the resulting application domain is small. In order to address these insufficiencies, this paper develops a statistical validation framework for dynamic systems with changing parameter configurations, thus enabling a flexible validation of complex total vehicle simulations including powertrain modelling. It uses non-deterministic models to consider input uncertainties, applies uncertainty learning to predict inherent model uncertainties and enables precise reliability quantification of arbitrary system parameter configurations to form a large application domain. The paper explains the framework with real-world data from a prototype electric vehicle on a dynamometer, validates it with additional tests and compares it to conventional validation methods. It is published as an open-source document. With the validation information from the framework and the knowledge deduced from the real-world problem, the paper solves its key requirements and offers recommendations on how to efficiently revise models with the framework’s validation results. Full article
(This article belongs to the Special Issue Electric (Hybrid) Vehicles: Optimization Techniques, Control Systems and Powertrain Modeling - selected papers from conference EVER2021)
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24 pages, 18452 KiB  
Article
Adaptive MPC Based Real-Time Energy Management Strategy of the Electric Sanitation Vehicles
by Hao Wang, Hongwen He, Jianwei Li, Yunfei Bai, Yuhua Chang and Beizhan Yan
Appl. Sci. 2021, 11(2), 498; https://doi.org/10.3390/app11020498 - 6 Jan 2021
Cited by 1 | Viewed by 2045
Abstract
Electric sanitation vehicles have increasingly been applied to cleaning work due to the requirement of air pollution control. The power distribution and energy management strategy (EMS) influence the vehicle’s performance a lot both in the aspects of cleaning effect and electricity consumption. Aiming [...] Read more.
Electric sanitation vehicles have increasingly been applied to cleaning work due to the requirement of air pollution control. The power distribution and energy management strategy (EMS) influence the vehicle’s performance a lot both in the aspects of cleaning effect and electricity consumption. Aiming to improve energy economy and ensure clean tasks, first, the electricity consumption percentages of the vehicle onboard devices are analyzed and the main contributors are clarified, and the power requirement model of the working motor is built based on experimental data. Second, a universal modeling method of garbage distribution on the road surface is proposed, which implements a nonlinear autoregressive neural network as the predictor. Third, an adaptive model predictive control (AMPC)-based EMS is proposed and verified. The results show the AMPC method can accurately predict the garbage density and the proposed EMS can approximate the energy consumption of the DP-based EMS with little deviation. Compared to conventional rule-based EMS, the AMPC-based EMS achieved a 15.5% decrease in energy consumption as well as a 14.6% decrease in working time. Full article
(This article belongs to the Special Issue Electric (Hybrid) Vehicles: Optimization Techniques, Control Systems and Powertrain Modeling - selected papers from conference EVER2021)
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21 pages, 6802 KiB  
Article
Optimal Design and Control of a Two-Speed Planetary Gear Automatic Transmission for Electric Vehicle
by Wei Huang, Jianfeng Huang and Chengliang Yin
Appl. Sci. 2020, 10(18), 6612; https://doi.org/10.3390/app10186612 - 22 Sep 2020
Cited by 14 | Viewed by 9219
Abstract
Multispeed transmissions are helpful for improvement of the economy and drivability of electric vehicles (EVs). In this paper, we propose a two-speed transmission based on dual planetary gear mechanism, in which shifts are realized by torque transfer between two brakes located on ring [...] Read more.
Multispeed transmissions are helpful for improvement of the economy and drivability of electric vehicles (EVs). In this paper, we propose a two-speed transmission based on dual planetary gear mechanism, in which shifts are realized by torque transfer between two brakes located on ring gears. To synthesize the dynamic and economic performances of the vehicle, a multiobjective optimization problem is constructed for gear ratio optimization and Pareto-optimal solutions of gear ratio combinations are obtained by Nondominated sorting genetic algorithm-II (NSGA-II). In particular, the minimum electric energy consumption of the EV is calculated with a fast Dynamic Programming (DP) in each iteration. Following this, a constant-output-torque control (COTC) scheme is adopted for the torque phase and inertia phase of gearshift process to ensure constant output torque on the wheel. To enhance transient responses, the feedforward–feedback controller structure is applied and a disturbance observer is integrated to improve robustness. Simulation results demonstrate that the two-speed transmission has much better performance in terms of acceleration time and energy economy compared to the fixed-ratio transmission, and the proposed gearshift control method is able to achieve fast and smooth gear shift robustly while maintaining constant output torque. Full article
(This article belongs to the Special Issue Electric (Hybrid) Vehicles: Optimization Techniques, Control Systems and Powertrain Modeling - selected papers from conference EVER2021)
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26 pages, 11545 KiB  
Article
Experimental Implementation of Power-Split Control Strategies in a Versatile Hardware-in-the-Loop Laboratory Test Bench for Hybrid Electric Vehicles Equipped with Electrical Variable Transmission
by Majid Vafaeipour, Mohamed El Baghdadi, Florian Verbelen, Peter Sergeant, Joeri Van Mierlo and Omar Hegazy
Appl. Sci. 2020, 10(12), 4253; https://doi.org/10.3390/app10124253 - 21 Jun 2020
Cited by 14 | Viewed by 3951
Abstract
The energy management strategy (EMS) or power management strategy (PMS) unit is the core of power sharing control in the hybridization of automotive drivetrains in hybrid electric vehicles (HEVs). Once a new topology and its corresponding EMS are virtually designed, they require undertaking [...] Read more.
The energy management strategy (EMS) or power management strategy (PMS) unit is the core of power sharing control in the hybridization of automotive drivetrains in hybrid electric vehicles (HEVs). Once a new topology and its corresponding EMS are virtually designed, they require undertaking different stages of experimental verifications toward guaranteeing their real-world applicability. The present paper focuses on a new and less-extensively studied topology of such vehicles, HEVs equipped with an electrical variable transmission (EVT) and assessed the controllability validation through hardware-in-the-loop (HiL) implementations versus model-in-the-loop (MiL) simulations. To this end, first, the corresponding modeling of the vehicle components in the presence of optimized control strategies were performed to obtain the MiL simulation results. Subsequently, an innovative versatile HiL test bench including real prototyped components of the topology was introduced and the corresponding experimental implementations were performed. The results obtained from the MiL and HiL examinations were analyzed and statistically compared for a full input driving cycle. The verification results indicate robust and accurate actuation of the components using the applied EMSs under real-time test conditions. Full article
(This article belongs to the Special Issue Electric (Hybrid) Vehicles: Optimization Techniques, Control Systems and Powertrain Modeling - selected papers from conference EVER2021)
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20 pages, 8999 KiB  
Article
A Novel Energy Optimization Control Strategy for Electric Drive System Based on Current Angle
by Jianjun Hu, Ying Yang, Meixia Jia, Yongjie Guan and Tao Peng
Appl. Sci. 2020, 10(11), 3778; https://doi.org/10.3390/app10113778 - 29 May 2020
Cited by 7 | Viewed by 2674
Abstract
The combination of permanent magnet synchronous motor (PMSM) and inverter is the key electric drive system (EDS) of electric vehicles (EVs), and its overall efficiency seriously affects the energy consumption of EVs. In order to further improve the efficiency of PMSM-inverter, the influence [...] Read more.
The combination of permanent magnet synchronous motor (PMSM) and inverter is the key electric drive system (EDS) of electric vehicles (EVs), and its overall efficiency seriously affects the energy consumption of EVs. In order to further improve the efficiency of PMSM-inverter, the influence of a special control object current angle β on EDS was studied and the general rule between β and EDS efficiency was obtained in this paper. Then, the golden section search (GSS) method was used to obtain optimal β and its corresponding stator current is, which can realize EDS working in optimal efficiency in the whole EDS working area. On this basis, an overall efficiency optimization control strategy for EDS based on the current angle β look-up table was proposed in this paper. To verify the effectiveness of the proposed control strategy, simulation considering iron loss and copper loss of motor and inverter loss was completed, which showed that compared with traditional control, the control strategy proposed in this paper can effectively improve the working efficiency of EDS under steady state and transient state. Full article
(This article belongs to the Special Issue Electric (Hybrid) Vehicles: Optimization Techniques, Control Systems and Powertrain Modeling - selected papers from conference EVER2021)
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