Electric Vehicle Powertrain Research Articles

Novel strategies to reduce engine emissions and improve energy efficiency in hybrid vehicles

Hybrid electric vehicles (HEV) can provide a same power as gasoline and diesel-powered vehicles while also reducing fuel consumption by 40–50%. However, due to a complex system of HEV, the power control strategies are used to improve the work efficiency, fuel economy and to reduce exhaust emissions. In the present work, four parameters are studied with the main focus on the optimization of power system of a parallel hybrid vehicle. These parameters are the influence of fuzzy logic algorithm on hybrid electric vehicle power train that is acceleration performance, climbing performance, engine efficiency and exhaust gas emissions. First, the vehicle’s power train is optimized using the Advanced Vehicle Simulator (ADVISOR). Secondly, by ensuring acceleration and climbing performance, the vehicle manufacturing cost is reduced by reducing the weight of the vehicle. Thirdly, two driving road test conditions, Extra-Urban Driving Cycle (EUDC) and Urban Dynamometer Driving Schedule (UDDS) are used to enhance the fuel consumption efficiency of the vehicle. Using a fuzzy logic algorithm for random operations, the operating range of an internal combustion engine is controlled as much as possible in the most efficient range. Through the control of 121 different algorithms, the operating efficiency of the powertrain is optimized to reduce exhaust emissions and improve fuel efficiency. The results showed that the proposed strategy can reduce fuel consumption by 5%, CO by 50% and improved the operating efficiency of engine by 15%. Not only this control strategy optimizes the efficiency of powertrain, but also the efficiency of the internal combustion engine. Moreover, the motor and battery pack itself can be optimized. These hybrid vehicles can minimize the fuel demand and are the best substitute for the classic internal combustion engines.

Energy-Saving Optimization and Control of Autonomous Electric Vehicles With Considering Multiconstraints.

The energy utilization efficiency of autonomous electric vehicles is seriously affected by the longitudinal motion control performance. However, the longitudinal motion control is constrained by the driving scene. This article proposes an energy-saving optimization and control (ESOC) method to improve the energy utilization efficiency of autonomous electric vehicles. In ESOC, the constraints from the driving scene are thoroughly considered, and the autonomous driving scene constraints are mapped to the vehicle dynamics and control domain. On this basis, the efficiency self-searching method and the multiconstraint energy-saving control strategy are designed. The main ideology of the proposed ESOC is that the energy utilization efficiency of an autonomous electric vehicle can be improved by optimizing and controlling the operation point distribution of the powertrain efficiency. The experimental results demonstrate that the operation point distribution of the autonomous electric vehicle's powertrain efficiency can be well optimized by the proposed ESOC, and the energy consumption results indicate that the proposed ESOC outperforms the state-of-the-art methods.

Efficient Power Electronic Converters for Electrical Vehicle Application

Now a days we are looking for alternate sources like electric vehicles, in order to cut down the pollution from automobiles which are growing rapidly. Across the globe, governments have been tackling the concerning problem of air-polluting emissions by committing significant resources to improving air quality. Achieving the goal of air purification will require that both the private and public sectors invest in clean energy technology. It will also need a transition from conventional houses to smart houses and from conventional vehicles to electric vehicles (EVs). It will be necessary to integrate renewable energy sources (RESs) such as solar photovoltaics, wind energy systems and diverse varieties of bioenergies. In this paper, a hybrid converter is proposed and designed to realize the integration of the DC/DC converter, and DC/AC inverter together in the Battery Electric Vehicles (BEVs) power train with high performance in any operating mode, acting as a backup generator to supply emergency power directly to home.

Switched Reluctance Motors and Drive Systems for Electric Vehicle Powertrains: State of the Art Analysis and Future Trends

This paper presents a detailed literature review on switched reluctance motor (SRM) and drive systems in electric vehicle (EV) powertrains. SRMs have received increasing attention for EV applications owing to their reliable structure, fault tolerance ability and magnet free design. The main drawbacks of the SRM are torque ripple, low power density, low power factor and small extended speed range. Recent research shows that multi-stack conventional switched reluctance motors (MSCSRM) and multi-stack switched reluctance motors with a segmental rotor (MSSRM-SR) are promising alternative solutions to reduce torque ripples, increase torque density and increase power factor. Different winding configurations such as single-layer concentrated winding (SLC), single layer mutually coupled winding (SLMC), double layer concentrated winding (DLC), double layer mutually coupled winding (DLMC) and fully-pitched winding (FP) are introduced in the literature in recent years to increase average torque and to decrease torque ripples. This research analyzes winding methods and structure of the SRMs, including conventional and segmental rotors. They have been compared and assessed in detail evaluation of torque ripple reduction, torque/power density increase, noise/vibration characteristics and mechanical structure. In addition, various drive systems are fully addressed for the SRMs, including conventional drives, soft-switching drives, drives with standard inverters and drives with an integrated battery charger. In this paper, the SRM control methods are also reviewed and classified. These control methods include strategies of torque ripple reduction, fault-diagnosis, fault-tolerance techniques and sensorless control. The key contributions of this paper provide a useful basis for detailed analysis of modeling and electromechanical design, drive systems, and control techniques of the SRMs for EV applications.

Plant and Controller Optimization for Power and Energy Systems With Model Predictive Control

Abstract This article explores the optimization of plant characteristics and controller parameters for electrified mobility. Electrification of mobile transportation systems, such as automobiles and aircraft, presents the ability to improve key performance metrics such as efficiency and cost. However, the strong bidirectional coupling between electrical and thermal dynamics within new components creates integration challenges, increasing component degradation, and reducing performance. Diminishing these issues requires novel plant designs and control strategies. The electrified mobility literature provides prior studies on plant and controller optimization, known as control co-design (CCD). A void within these studies is the lack of model predictive control (MPC), recognized to manage multi-domain dynamics for electrified systems, within CCD frameworks. This article addresses this through three contributions. First, a thermo-electromechanical hybrid electric vehicle (HEV) powertrain model is developed that is suitable for both plant optimization and MPC. Second, simultaneous plant and controller optimization is performed for this multi-domain system. Third, MPC is integrated within a CCD framework using the candidate HEV powertrain model. Results indicate that optimizing both the plant and MPC parameters simultaneously can reduce physical component sizes by over 60% and key performance metric errors by over 50%.

Design and Modelling of the Powertrain of a Hybrid Fuel Cell Electric Vehicle

<div class="section abstract"><div class="htmlview paragraph">This paper presents a Fuel Cell Electric Vehicle (FCEV) powertrain development and optimization, aiming to minimize hydrogen consumption. The vehicle is a prototype that run at the Shell Eco-marathon race and its powertrain is composed by a PEM fuel cell, supercapacitors and a DC electric motor. The supercapacitors serve as an energy buffer to satisfy the load peaks requested by the electric motor, allowing a smoother (and closer to a stationary application) working condition for the fuel cell. Thus, the fuel cell can achieve higher efficiency rates and the fuel consumption is minimized.</div><div class="htmlview paragraph">Several models of the powertrain were developed using MATLAB-Simulink and then experimentally validated in laboratory and on the track. The proposed models allow to evaluate two main arrangements between fuel cell and supercapacitors: 1) through a DC/DC converter that sets the FC current to a desired value; 2) using a direct parallel connection between fuel cell and supercapacitors.</div><div class="htmlview paragraph">The results obtained with the direct parallel connection (with the appropriate sizing of the overall capacity) have highlighted a significant efficiency advantage, while the DC/DC converter insertion enables an improved control of the fuel cell current and requires a smaller capacitance.</div><div class="htmlview paragraph">Furthermore, a sizing methodology for the supercapacitors capacitance is proposed for both layouts: with the DC/DC converter it mainly depends on the energy range provided by supercapacitors to the electric motor, while in the direct parallel connection the supercapacitors sizing is outlined by concurrently evaluating the circuit’s predicted hydrogen consumption and granting the most suitable conditions to increase the fuel cell performance.</div><div class="htmlview paragraph">Finally, the results obtained from the model were validated by comparing them with experimental data obtained in the laboratory and on the track.</div></div>

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.

A comparative 3-D transient electromagnetic, thermal and powertrain study of single rotor BLPMSM and dual rotor machine for electric propelled vehicle

This article addresses the benefits of novel dual rotor machine for automobile sector. In case of traditional electric vehicle technology, the vehicle has a single rotor electric machine that can able to perform motoring or regenerative operation and can be possible according to the energy available in the battery. To overcome the above-mentioned problem, novel electric machine design topology with dual operation of motor and generator at same instances of time is proposed with improved electromagnetic, thermal and mechanical transmission losses. The design can be possible by adopting 7.2 kW claw pole alternator and 15 kW brushless permanent magnet synchronous machine as a dual rotor machine considering in-wheel concept. The performance analysis of the machine is done by using finite element analysis technique. The electromagnetic compatibility of machine is performed by three-dimensional transient solver in MagNet software. The electromagnetic analysis of machine is improved by introducing notches in the stator and permanent magnets in the rotor. Based on the electromagnetic results, thermal analysis of machine is calculated by coupling transient analysis using ThermNet software. The simulation results of machine are validated by using electric vehicle Powertrain blocks. The real-time performance of the machine is tested with real-world drive cycle using simulation software. The result substantiates the novelty of this design for improving the driving range of the vehicle for electric vehicle applications.

Multi-objective optimization design and control of plug-in hybrid electric vehicle powertrain for minimization of energy consumption, exhaust emissions and battery degradation

Plug-in hybrid electric vehicles (PHEV) offer an attractive alternative to achieve the ambitious goals set by strong policies focused on near-term air quality and fuel-efficient road transportation. This paper provides a comprehensive study regarding the PHEV’s optimum powertrain design, by means of a multi-criteria analysis carried out by the interactive adaptive-weight genetic algorithm approach. The optimization aims to simultaneously minimize the PHEV’s fuel consumption, exhaust emissions, electric powertrain size, battery state of health, charging time and costs. To achieve these objectives, several PHEV’s design parameters are optimized such as in-wheel electric motors’ torque curves, battery voltage and capacity. The drivetrain is also optimized according to the determination of the best configuration of gearbox and differential gear ratios, taking into account constructive constraints. Furthermore, the fuzzy logic controllers responsible for the engine/electric motors power-split management and gear shifting control are included in the multi-objective optimization in order to define the best membership functions, rules and respective weights. To guarantee robust solutions, the PHEV is optimized under different driving conditions, which is given by the combination of the FTP-75, HWFET and US06 driving cycles. To evaluate the optimum PHEV performance, it is also simulated under the WLTC driving cycle and a real-world driving cycle based on the Campinas city. The best trade-off configuration results in 39.57% decrease in vehicle travel cost along with 43.39% carbon monoxide (CO), 45.13% unburned hydrocarbons (HC) and 72.64% nitrogen oxides (NOx) emissions reduction under the combined driving cycle.

High Performance Electric Vehicle Powertrain Modeling, Simulation and Validation

Accurate electric vehicle (EV) powertrain modeling, simulation and validation is paramount for critical design and control decisions in high performance vehicle designs. Described in this paper is a methodology for the design and development of EV powertrain through modeling, simulation and validation on a real-world vehicle system with detailed analysis of the results. Although simulation of EV powertrains in software simulation environments plays a significant role in the design and development of EVs, validating these models on the real-world vehicle systems plays an equally important role in improving the overall vehicle reliability, safety and performance. This modeling approach leverages the use of MATLAB/Simulink software for the modeling and simulation of an EV powertrain, augmented by simultaneously validating the modeling results on a real-world vehicle which is performance tested on a chassis dynamometer. The combination of these modeling techniques and real-world validation demonstrates a methodology for a cost effective means of rapidly developing and validating high performance EV powertrains, filling the literature gaps in how these modeling methodologies can be carried out in a research framework.

Fuel cell-supercapacitor topologies benchmark for a three-wheel electric vehicle powertrain

This paper compares three optimal hybrid energy system configurations, namely full-active, semi-active and passive, for the particular purpose of assessing the viability of utilizing the passive architecture in a three-wheel electric vehicle composed of a fuel cell (FC) and a supercapacitor. In this respect, the characteristics of these configurations are investigated through three steps. Firstly, the mathematical model of each component is developed by employing experimental data. Subsequently, the optimal size of each topology is determined by a two-step optimization approach. This approach is based on the optimized sizing of power sources, employing a metaheuristic optimization algorithm, and optimal power flow sharing between the sources with the aim of satisfying the requested power while declining the fuel consumption and enhancing the system’s lifetime. Finally, the best-achieved size of each architecture is compared in terms of trip cost, capital cost, and the system weight. The obtained results show that passive topology can reduce the trip cost by 14.8% and 6.4% compared to full-active and semi-active ones, respectively. However, the active architecture results in less degradation in the FC compared to the other two topologies. Furthermore, a validation phase is done under a real driving profile and the results are further discussed.

A Design Methodology for Hybrid Electric Vehicle Powertrain Configurations With Planetary Gear Sets

Abstract Increasingly strict emission and fuel economy standards stimulate the researches on hybrid electric vehicle techniques in the automobile industry and one of the most important techniques is the design of powertrain configurations. In this paper, a theoretical design methodology for hybrid electric vehicle powertrain configurations is proposed to find the configurations with excellent performance in a large pool of configurations. There are two main parts in a powertrain configuration, power/coupling devices (engine, electric machine, wheel, and planetary gear set) and mechanical connections between these devices. Different connections will lead to configurations having different performances. This paper divides all connections in configurations into three categories and a novel matrix representation method is developed to express these kinds of connections to reflect system dynamics and physical structure of configurations. With the support of the matrix representation method, configuration selections from large pools can automatically be completed by computer and manual calculation and comparison can be avoided, which saves much energy and time. Finally, the proposed method is vigorously verified by simulations.

Enhancing the efficiency of electric vehicle’s motor using electro-mechanical continuously variable transmission

Over the last decades, significant progress has been made on the technologies of battery electric vehicle (BEV) powertrain system. As a result, the driving mileage of the latest EVs has been improved tremendously, making the ownership of BEV more viable now. Nevertheless, even with the latest progress, the driving mileage of a BEV is still generally lower as compared to the vehicle with an advanced internal combustion engine. Thus, this paper investigates the potential of increasing the mileage of a BEV further by using electro-mechanical continuously variable transmission (EMCVT) in its powertrain system. The purpose of the EMCVT is to allow the electric motor used in the BEV powertrain system to operate in its most efficient condition for a wide range of vehicle’s speeds. This condition is defined using the efficiency mapping of a typical electric motor applied in the existing BEVs. Based on this mapping, the motor’s efficiency when used with EMCVT is compared with the efficiency of the same motor when used with a single speed transmission for various vehicle speeds. In this investigation, the efficiency of the EM CVT is considered at 75% to 95%, and the BEV with EMCVT is considered to be 80 kg heavier than the BEV using a single speed transmission. The comparison between the BEV with and without EMCVT shows that the application of EMCVT allows the motor to operate in its most efficient condition for a wider range of the vehicle’s velocity. However, the improved motor’s efficiency is compromised by the losses in the EMCVT, making it challenging to achieve lower power consumption during driving. Therefore, to achieve meaningful improvement in terms of power consumption, the efficiency of the EMCVT has to be higher than 85%.

Synergistic Correction of Current Sampling Errors in Dual-Motor Powered Electric Powertrain for High Power Electric Vehicles

The current sampling errors in dual-motor group (DMG) cause more adverse effects on system performance than that in a single motor system. To solve this problem, in this article, a synergistic correction strategy for the abovementioned problems in DMG fed by double inverters is proposed. First, an orthogonal pulsewidth modulation carriers strategy is proposed to imitate the “shutdown” status of motor subsystems to obtain ascertainable current relations. Then, the current values with sampling errors in DMG at several “fixed” sampling points are derived, based on which the sampling errors in DMG are corrected. Meanwhile, the rules for the action time of active vectors and output voltage range in each inverter are analyzed for each “fixed” sampling point. Finally, the proposed strategy is verified by experimental results on a DMG system, which show that the sampling errors can be precisely estimated with the proposed strategy.

Thermally modulated lithium iron phosphate batteries for mass-market electric vehicles

The pursuit of energy density has driven electric vehicle (EV) batteries from using lithium iron phosphate (LFP) cathodes in early days to ternary layered oxides increasingly rich in nickel; however, it is impossible to forgo the LFP battery due to its unsurpassed safety, as well as its low cost and cobalt-free nature. Here we demonstrate a thermally modulated LFP battery to offer an adequate cruise range per charge that is extendable by 10 min recharge in all climates, essentially guaranteeing EVs that are free of range anxiety. Such a thermally modulated LFP battery designed to operate at a working temperature around 60 °C in any ambient condition promises to be a well-rounded powertrain for mass-market EVs. Furthermore, we reveal that the limited working time at the high temperature presents an opportunity to use graphite of low surface areas, thereby prospectively prolonging the EV lifespan to greater than two million miles. Ternary layered oxides dominate the current automobile batteries but suffer from material scarcity and operational safety. Here the authors report that, when operating at around 60 °C, a low-cost lithium iron phosphate-based battery exhibits ultra-safe, fast rechargeable and long-lasting properties.

Optimization of the powertrain of electric vehicles for a given route

The global challenge of reducing pollutant and greenhouse gas emissions has forced the development of alternatives to traditional internal combustion engine vehicles, such as electric or hybrid vehicles. Electric engines are the most efficient for delivery trucks or city buses. Their acceleration and deceleration patterns make them inefficient for the use of internal combustion engines. However, their range and purchase cost are the main factors limiting their use in these applications. The range and acquisition cost of an electric vehicle are mainly related to the energy storage system. Therefore, the optimal size of the battery pack should be considered as a design objective when its application is known. This paper presents a methodology to optimize the battery pack of an electric vehicle based on a given travel distance in a target time. Therefore, it would be applicable to delivery vehicles, buses and any vehicle whose route and travel time are known in advance. The proposed methodology allows minimizing energy consumption by determining the optimal gear ratio for a given route, setting the travel time as a target. A complete vehicle model and a multi-objective genetic algorithm are used for this purpose.

Hardware-in-the-Loop Validation of Different Power Train Topologies’ Models in Electric Vehicles: A Plug-and-Play Capability

This study presents a novel modelling methodology for electric vehicle power train. This covers both Front Wheel and all-wheel drives. The simulation is built in PSIM and verified in Typhoon's hardware in the loop (HIL) solution. HIL technology is used for real time verification. The approach is highly attractive due to characteristics of rapid prototyping which allows quick and easy adjustment in simulation in real time. Thus, avoiding the high costs associated with physical prototypes. The paper presents results (dynamic responses of various vehicle components) and the effects of adjusting various vehicle parameters. The results obtained from simulation is successfully verified in the HIL platform. The result from this study proves the robustness of the simulation and HIL model and control algorithms. This methodology saves costs and lead time to build and design a physical hardware. Additionally, the results from the modeled vehicles agree with data provided by the manufacturer. Finally, the design is simulated in a modular way such that they can be used for various propulsion setups and schemes

Model-based simulation of dynamic behaviour of electric powertrains and their limitation induced by battery current saturation

The wide-spread e-mobility revolution is asking for new software solutions capable of providing meaningful information not only for the mechanical part of the vehicle architecture, as in the past, but also for novel electric and hybrid driveline components. The objective of the paper is to provide a lightweight model for a fully electric vehicle powertrain, trying to assess the current constraints imposed by the battery to the whole driveline, and able to reflect this limitation throughout the transmission line, down to the electric motor and thus to the performance of the full vehicle. The model is suitable for real-time performances thanks to the effective computation of the powertrain via analytical solutions. Moreover, the whole system has been modelled following a model-based approach in Modelica language and is made part of the Altair activate car real-time library, in which both the electrical and mechanical parts integrate smoothly.

A Comprehensive Review of Advanced Traction Motor Control Techniques Suitable for Electric Vehicle Applications

Transport and vehicular travel are essential for socio-economic growth, as they enable the operation of cities and businesses globally. However, large percentages of the transport sector currently rely on internal combustion engine vehicles, causing an increase in greenhouse gases and air pollution. Electric vehicles provide a promising solution to the issues faced by the transport sector. As a result, optimization of the electric vehicle powertrain is a key area, with the traction motor control mechanism forming a major component of the powertrain. Consequently, this paper aims to review the novel control techniques which have been applied to the traction motor system in electric vehicles. Direct torque control and indirect field oriented control are commonly applied control techniques as they allow for advanced control of the induction and permanent magnet synchronous motors currently used in most of the electric vehicles being produced. In general, various improvements have been made to conventional direct torque control and indirect field oriented control schemes, in order to reduce ripple and improve parameter insensitivity. While efforts are still being carried out in these areas for electric vehicle applications, it was found that there has been a large emphasis placed on powertrain efficiency improvement through optimization of the traction motor control system. Efficiency improvements have recently been achieved through optimal selection of stator flux and DC link voltage values. Research into efficiency improvement is likely to continue as extended vehicle range can be achieved.

Cascade Optimisation of Battery Electric Vehicle Powertrains

Motivated by challenges in the motor manufacturing industry, a solution to reduce computation time and improve minimisation performance in the context of optimisation of battery electric vehicle powertrain is presented. We propose a cascade optimisation method that takes advantage of two different vehicle models: the proprietary YASA MATLAB® vehicle model and a Python machine learning-based vehicle model derived from the proprietary model. Gearbox type, powertrain configuration and motor parameters are included as input variables to the objective function explored in this work while constraints related to acceleration time and top speed must be met. The combination of these two models in a constrained optimisation genetic algorithm managed to both reduce the amount of computation time required and achieve more optimal target values relating to minimising vehicle total cost than either the proprietary or machine learning model alone. The coarse-to-fine approach utilised in the cascade optimisation was proven to be mainly responsible for the improved optimisation result. By using the final population of the machine learning vehicle model optimisation as the initial population of the following simulation-based minimisation, the initial time-consuming search to produce a population satisfying all domain constraints was practically eliminated. The obtained results showed that the cascade optimisation was able to reduce the computation time by 53% and still achieve a minimisation value 14% lower when compared to the YASA Vehicle Model Optimisation.