Performance Analysis and Simulation of Electric Vehicles

1. Introduction

As electric vehicles offer a cleaner, more efficient, and more cost-effective mode of transport, already being a choice for the future of the transport sector, the analysis of the performance of electric vehicles (EV) is a dynamic research area that must come up with pertinent solutions and with optimization strategies for functional and operational aspects (energy efficiency, autonomy, powertrain performance, and overall vehicle dynamics) [1,2]. Energy efficiency is crucial for expanding the range of electric vehicles, with necessary research involving the optimization of powertrain components, including the electric motor, battery, and power electronics.

At the same time, it must be emphasized that the energy efficiency of an EV is also determined/linked to the efficiency of the battery thermal management system. It is well known that efficient battery management systems (BMSs) are essential for monitoring and optimizing the battery’s operational performance [3], ensuring safety by reducing fire risks, and last but not least, extending the battery’s lifespan and operation within the required parameters.

The particular design and dynamics characteristics of electric vehicles must be taken into account through studies on vehicle maneuverability, stability and comfort, aerodynamics, and chassis design [4,5,6,7]. All of these research directions and topics have a direct and important role in improving the performance of electric vehicles.

Due to their development and accuracy, a multitude of modeling and simulation tools are currently widely used in engineering to model and analyze the performance of electric vehicles. These simulations help to immediately understand the impact of different parameters on the overall performance and efficiency of the electric vehicle through dynamic modeling processes, simulating standard driving cycles and performing complex simulations related to components/subsystems/propulsion systems, aerodynamic efficiency, and thermal dynamics. It can be stated that engineering modeling and simulation processes are the engine of the study for the continuous improvement of electric vehicles, making them more efficient, reliable, and suitable as a solution for large-scale adoption in the field of transportation.

Some of these previously presented aspects have been addressed and presented as results of research in this Special Issue, in areas related to the performance of hybrid electric vehicles, operational safety by reducing/eliminating fire risks due to batteries, optimization by increasing transmission efficiency, control and command of power flow, and analysis of thermal management systems of electric vehicle batteries.

2. An Overview of the Published Articles

One of the most significant challenges in the development of HEV performance is the complexity of the hybrid control system, which must know when to operate the electric motor and optimal power delivery. In addition, gear shifting becomes a difficult problem in optimization, a problem that plays a key role in the energy efficiency of the propulsion system. In this regard, Filho, R.H.Q. et al. proposed the implementation and use of Artificial Intelligence tools (Contribution 1). A genetic algorithm (GA) was used as a machine learning-based control strategy to determine the torque split and the engaged gear for each driving condition of an MHEV operation, with the aim of optimizing the fuel consumption. A quasi-static vehicle model was developed in Matlab/Simulink and tested for the FTP75 and HWFET driving cycles. The simulation results indicate that the control decisions made using the genetic algorithm are qualitatively consistent for all operating conditions, with the potential to be used as a control strategy outside the simulation environment.

The challenges related to the power and efficiency of electric motors (as an integral part of the powertrain of an electric vehicle) require the development of multi-speed transmissions for electric commercial vehicles. In this regard, Kim, J. et al. (in Contribution 2), present the development possibilities in domains that were approached through the modeling and computer simulation methods of a four-speed transmission with a synchronizer. A transmission shift map was developed and the verification of the increase in power and efficiency was carried out by comparing the proposed solution with the existing product on the automotive market.

Another approach related to the performance of electric vehicles is also related to the reduction/elimination of the risk of fire caused by overheating the electric vehicle battery or by a short circuit due to road accidents. The efficiency of using auxetic structures to dissipate the impact between the electric vehicle chassis and the battery case was analyzed in order to reduce the risk of battery damage and maintain the safety of the vehicle occupants (Contribution 3). The modeling of the resistance structure was based on a particular shape based on a re-entrant auxetic model, and the simulations were performed at an impact velocity of 10 m/s with a rigid pole. The results obtained by Scurtu, Szabo, and Gheres highlighted the fact that, by using auxetic structures in the construction of the battery case, the effect of the impact can be mitigated, with a decrease in the number of damaged cells of up to 35.2%.

The aim of Pavković, D. et al.’s work was to design, develop, and analyze the effectiveness of a vibration damping system for the belt transmission in the front accessory drive of a mild hybrid powertrain, which was analyzed in Contribution 4 using computer simulation methods. The simulation results highlighted the attenuation of vibrations related to the operation of the belt through active control techniques (vibration magnitude reduced by three to five times during the engine starting phase), with a positive effect on a 30% gain in acceleration during vehicle launch.

Specific performance modeling and simulation techniques have also been applied to electric aerial vehicles, by Krznar, M. et al. in Contribution 5. This topic was approached by designing a control system and verifying the proposed solution by simulating a hybrid electric propulsion topology suitable for power flow control in unmanned aerial vehicles (UAVs). The general control system features a proportional–integral–derivative (PID) feedback control of the thermal thruster rotation speed using an estimator, and the voltage and current of the active rectifier of the BLDC generator are controlled by proportional–integral (PI) feedback controllers, augmented by feed-forward load compensators based on the estimator. The general design of the control system was based on the choice and use of an optimal damping criterion, which gave the analytical expressions for the control and estimator parameters.

The dynamic growth of electric vehicle use in recent years has led to a potential increase in the risk of fire and hazards associated with high-energy batteries used in the construction of electric vehicles (Li-ion technology), and this has been considered by Brzezinska D. and Bryant P. Contribution 6 presents the general fire risks for electric vehicles and possible protection strategies against fires due to the faulty operation/exploitation of batteries. Through analysis methods and computer simulation processes, CFD simulations were performed to predict smoke dispersion and temperature distribution during an EV fire in an indoor parking lot. The presented case study demonstrates how the use of these tools predicts the conditions for the safe evacuation of people and the conditions for firefighting in the event of a fire caused by electric vehicles.

Starting from the premise presented above, namely that one of the important systems in the construction of an electric vehicle is the battery thermal management system (with the role of optimizing the operation of the battery in terms of performance and lifespan), Contribution 7 critically analyzes the studies and research carried out to date related to the type, design, and operating principles of battery thermal management systems (with an emphasis on cooling technologies). The advantages and disadvantages of individual components and the functional constructive solutions of existing BTMs were extensively investigated based on the adaptability of these systems to different Li-ion battery shapes. The study provides necessary and important information and the authors (Buidin T.I.C. and Mariasiu F.) propose future research directions for those interested in this topic, in order to increase the efficiency of battery thermal management systems and the overall efficiency of the electric vehicle.

3. Conclusions

The approaches in the research carried out and published in the Special Issue “Performance Analysis and Simulation of Electric Vehicles” showcases a wide array of innovative approaches, each addressing various challenges in the development of electric vehicles (EVs). These diverse methodologies encompass advanced simulation techniques, battery management systems, powertrain optimization, and energy efficiency improvements. For instance, several studies utilized finite element analysis (FEA) to model the structural behavior of EV batteries, while others employed machine learning algorithms and computer simulation models to predict energy consumption and battery performance and optimize charging cycles, contributing to the overall reliability and performance of EVs (battery and hybrid).

Looking ahead, advancements in autonomous driving systems and their implications for EV performance and safety are present promising areas for future research. Continued interdisciplinary collaboration will be essential to address these emerging challenges and drive the next generation of electric vehicle technologies.

The editor extends their heartfelt gratitude to all contributing authors, whose efforts have significantly enriched this Special Issue. The success of this publication is evident from the impressive number of views, downloads, and citations it has garnered, reflecting its impact and relevance in the scientific community. This collective work not only highlights the complexity of EV technology, but also proposes practical solutions to advance the field, paving the way for future innovations in electric mobility.