Electric Vehicle Powertrain Research Articles

Active torsional vibration suppression of hybrid electric vehicle drive system based on optimal harmonic current instruction calculation method

Torsional vibrations in the drive system of a hybrid electric vehicle powered by an engine are generated by cyclic torque fluctuations. These fluctuations stem from the combustion torque and the reciprocating inertial torque of the cylinder in the reciprocating piston engine. To address this issue, a real-time engine transient torque model that considers the thermodynamic characteristics of each component is established. A method was designed to identify the torsional vibration of a powertrain based on the engine torque, electric motor torque, and powertrain model. Building on this foundation, a method was proposed to calculate the optimal harmonic current instruction of the electric motor to mitigate torsional vibration in the Hybrid electric vehicles (HEV) powertrain. Furthermore, a proportional integral-resonance (PIR) controller was designed for the injection of an electric motor reference harmonic current. Finally, a method was proposed for suppressing the torsional vibration in HEV drive systems that incorporate active harmonic current injection. The simulation and test results indicate that the proposed method can effectively diminish the amplitudes of the drive system's 2nd, 4th, 6th, and 8th harmonic torque, ensuring stable operation of the powertrain.

MULTILEVEL INVERTER WITH REDUCED SWITCHES FOR ELECTRICAL VEHICLE

This project focuses on the development of an innovative multilevel inverter designed specifically for electric vehicles (EVs), aiming to reduce complexity and enhance efficiency by minimizing the number of switches in the inverter topology. The conventional multilevel inverter configurations often involve a high count of switches, leading to increased costs, intricate designs, and elevated power losses. In contrast, our approach seeks to optimize the inverter's performance by employing advanced control strategies and modulation techniques while significantly reducing the number of switches. This reduction not only simplifies the design but also lowers production costs and maintenance efforts. Moreover, the project emphasizes the compact integration of the inverter into the EV powertrain, addressing the size and weight constraints of electric vehicles. Through thorough simulations and practical experiments, the proposed multilevel inverter's performance will be evaluated in terms of efficiency, power quality, and reliability. The anticipated outcomes aim to contribute to the advancement of power electronics for sustainable transportation, providing a more efficient, cost- effective, and compact solution tailored to the unique requirements of electric mobility. This research has the potential to significantly impact the field, fostering further innovations in power electronics for clean and efficient transportation solutions .Key Words: Electric vehicle, Inverters, Switches, Efficiency.

Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy

The actual performance of driving motors in the electric vehicle (EV) powertrain depends not only on the electromagnetic design of the motor itself but also on the driving condition of the vehicle. The traditional motor optimization method at the rated point is difficult to deal with because of the mismatch between its high-efficiency area and the actual operation area. This paper systematically proposes an optimal design method for driving motors for EVs, considering the driving conditions and control strategy to improve motor efficiency and passengers’ riding comfort. It uses cluster analysis to identify representative points and related energy weights to consider motors’ comprehensive performance in different driving cycles. Three typical operation conditions are selected to implement the proposed optimization process. In the design process, by using the sensitivity analysis method, the significance of the structural parameters is effectively evaluated. Moreover, the semianalytical efficiency model and torque model of permanent magnet driving motors based on finite element analysis results are deduced to consider the influence of magnetic saturation, space harmonics, and cross-coupling between d-axis and q-axis magnetic fields. Based on the driving system demands of an A0 class pure EV, the whole optimization design is divided into four steps and three scales, including the motor scale, control scale, and system scale. By using the multi-objective optimization method, Pareto optimality of motor efficiency and torque ripple is achieved under the city driving cycle and highway driving cycle. Compared to the optimization only at the rated condition, the proportion of motor sweet region increased about 1.25 times and 3.5 times by the proposed system-scale optimization under two driving cycles, respectively. Finally, the effectiveness of the proposed optimization method is verified by the prototype experiments.

An integrated tribodynamic model for investigation of efficiency, durability and NVH attributes of gear mesh in electric vehicle powertrains

An integrated gear tribodynamics model is proposed for the study of EV powertrains’ performance. The model considers the transient effects of lubrication regimes, non-Newtonian shear thinning, inlet shear heating, deformation states of asperities in mixed regime of lubrication and contact temperature using a set of analytical routines, which are computationally efficient. The proposed gear tribodynamics model provides a breakdown of the interdependency of these attributes and studies their impact on the performance of gear contacts. The results indicate that up to 30% of the contact load can be carried by asperities, of which 80% undergo elastoplastic deformation. In addition, the contribution of lubricant to contact stiffness can be greater than that of surface asperities by an order of magnitude.

Optimization Approaches for Cost and Lifetime Improvements of Lithium-Ion Batteries in Electric Vehicle Powertrains

With the increasing adoption of electric vehicles (EVs), optimizing lithium-ion battery capacity is critical for overall powertrain performance. Recent studies have optimized battery capacity in isolation without considering interactions with other powertrain components. Furthermore, even when the battery is considered within the full powertrain, most works have only modeled the electrical behavior without examining thermal or ageing dynamics. However, this fails to capture systemic impacts on overall performance. This study takes a holistic approach to investigate the effects of battery capacity optimization on convergence of the full EV powertrain. A battery multiphysics model was developed in MATLAB/Simulink, incorporating experimental data on electrical, thermal, and ageing dynamics and interactions with other components. The model was evaluated using real-world WLTP and Artemis driving cycles to simulate realistic conditions lacking in prior works. The findings reveal significant impacts of battery optimization on total powertrain performance unaccounted for in previous isolated studies. By adopting a system-level perspective and realistic driving cycles, this work provides enhanced understanding of interdependent trade-offs to inform integrated EV design.

(Invited) NiO/ β-(Al x Ga1-x )2O3 /Ga2O3 Heterojunction Lateral Rectifiers with Reverse Breakdown Voltage > 7kV

There is significant recent interest in development of Ga2O3 power devices due to their capability for high temperature operation, reduced on-state and switching losses due to lower on-resistance for high voltage devices, and potentially higher frequency switching capability. These are targeted for renewable energy transmission systems, electric vehicle (EV) traction inverter and motor control systems, fast charging stations and more electric aircraft. Since the efficiency of EV powertrain inverters is partially determined by the efficiency of the switching transistors, it is of interest to examine ultra-wide-bandgap semiconductor electronics. The successful development of these power transistors is expected to significantly increase the longevity of a battery charge and the resultant cost of an EV.In this work, NiO/ β-(Al x Ga1-x )2O3 /Ga2O3 heterojunction lateral geometry rectifiers with diameter 50-100 µm exhibited maximum reverse breakdown voltages >7kV, showing the advantage of increasing the bandgap using the β-(Al x Ga1-x )2O3 alloy. This Si-doped alloy layer was grown by Metal Organic Chemical Vapor Deposition with an Al composition of ~20%. On state resistances were in the range 50-2180 Ω.cm2, leading to power figures-of-merit up to 0.72 MW.cm-2. The forward turn-on voltage was in the range 2.3-2.5 V, with maximum on/off ratios >700 when switching from 5V forward to reverse biases up to -100V. Transmission line measurements showed the specific contact resistance was 0.12 Ω.cm2. The breakdown voltage is among the highest reported for any lateral geometry Ga2O3-based device. Figure 1

Evaluation of the energy efficiency of electric vehicle drivetrains under urban operating conditions

In electric vehicles, as in hybrids vehicles, a very important factor affecting the energy efficiency of the powertrain is the ability to use the regenerative braking energy. Depending on the settings available in electric vehicles, the driver can choose different modes of operation: switch off the regenerative braking mode altogether, select the intensity of regenerative braking, or leave the control system in automatic mode. The last mode is often the only one available on eclectic vehicles, so the driver cannot decide whether to switch off or increase intensity of the regenerative braking. This paper presents a new method for evaluating the energy efficiency of electric vehicle powertrains under urban operating conditions. The presented method uses a procedure for mapping the operating conditions allowing to determine the reference level of energy consumption in relation to those recorded during the identifica-tion tests. Identification tests were carried out in the Tri-City area using electric vehicles of different purposes and operating parameters. Performed tests allowed to evaluate the regenerative braking efficiency of tested vehicle, which varies over a relatively wide range, for vehicle A from 33% to 77%, for vehicle B from 27% to 55% and for vehicle C from 36% to 58%. It can be concluded that one of the main factors determining the regenerative braking efficiency is the level of state of charge of the accumu-lator and the management algorithm used by the vehicle for controlling this parameter.

Artificial Intelligence Technique-Based EV Powertrain Condition Monitoring and Fault Diagnosis: A Review

Electric powertrain used in electric vehicles (EVs), which is constituted by motor, transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review contributes to comprehensively summarizing artificial intelligence (AI)-based/AI-supported approaches in EV powertrain condition monitoring and fault diagnosis that can be used in EV applications. The application of AI on PE in EV is a new attempt, which can solve many issues with better performance than traditional methods, and even achieve functions that the conventional methods cannot achieve. This paper thoroughly discusses the motivation, advantages, limitations, and challenges associated with AI-supported methods through case summaries, classification, comparisons, and quantitative analyses between conventional and AI-based approaches. Furthermore, the review concludes by proposing forward-looking future trends in this field.

Balanced Charging Algorithm for CHB in an EV Powertrain

The scientific literature acknowledges cascaded H-bridge (CHB) converters as a viable alternative to two-level inverters in electric vehicle (EV) powertrain applications. In the context of an electric vehicle engine connected to a DC charger, this study introduces a state of charge (SOC)-governed method for charging li-ion battery modules using a cascaded H-bridge converter. The key strength of this algorithm lies in its ability to achieve balanced charging of battery modules across all three-phase submodules while simultaneously controlling the DC charger, eliminating the need for an additional intermediate converter. Moreover, the algorithm is highly customizable, allowing adaptation to various configurations involving different numbers of submodules per phase. Simulative and experimental results are presented to demonstrate the effectiveness of the proposed charging algorithm, validating its practical application.

Design of the electric vehicle powertrain systems based on μ-analysis

Aiming at the oscillation in the electric vehicle powertrain systems, a control strategy based on the structural singular value [Formula: see text] is proposed. To solve the problem of robustness under parameter uncertainty, the traditional [Formula: see text]-synthesis needs to obtain all-pass characteristics through the weight function in the nominal system, which reduces the perturbation suppression ability of the system. Unlike the previous use of [Formula: see text]-analysis as a system performance criterion, this paper starts from the essence of parameter uncertainty problems and designs controllers based on the relationship between parameter uncertainty and nominal systems, using structural singular value [Formula: see text] as a performance constraint. The result shows that the [Formula: see text]-analysis not only takes into account the robustness and disturbance rejection ability but also reduces the controller order. In addition, the oscillation suppression effect in the presence of transmission backlash is verified, and the [Formula: see text]-analysis can suppress the oscillation better than the [Formula: see text]-synthesis.

Automation program for optimum design of electric vehicle powertrain systems based on artificial neural network

Many studies have been conducted on various powertrain systems, such as multi-motor, multi-speed, or both, to enhance the energy efficiency and dynamic performance of electric vehicles (EVs). This study developed an automated design program to obtain the optimal design of EVs for various powertrain systems. The program consists of an EV simulation and artificial neural network (ANN) modeling and optimization tools. The EV simulation tool employs an integrated EV model that can analyze the efficiency and performance of various powertrain systems in a single environment. The ANN modeling and optimization tool first constructs an ANN model, and then performs optimization using the ANN model to address excessive computational efforts arising from the multi-objective genetic algorithm. This study verified the developed program by conducting analysis and optimization of five powertrain systems with the same EV specifications. A multi-objective optimization problem was formulated by considering the design variables as the torque distribution between the motors and gear shifting patterns and ratios of the transmission, and the objectives as the energy consumption and acceleration time. A comparison of the optimization results among the five powertrain systems quantitatively showed the positive effects of the multi-motor and multi-speed powertrain systems. Furthermore, the ANN-based multi-objective optimization process allowed for the efficient determination of the optimum design solutions for the proposed EV powertrain systems. Consequently, these results demonstrated the effectiveness of the automation program in rapid decision-making on EV powertrain system configurations, satisfying each designer’s requirements.

Optimization of the Lifetime and Cost of a PMSM in an Electric Vehicle Drive Train

This study focuses on optimizing the lifetime and cost of an electric vehicle powertrain by optimizing the motor’s geometrical parameters and the bus voltage while considering the battery’s sizing. We employ the WLTP driving cycle to evaluate the powertrain’s performance and use finite element and analytical modeling to consider electromagnetic, thermal, and aging behaviors. Our research investigates the interplay between the battery and motor, exploring how varying the motor geometry and parameters affects the powertrain’s overall lifetime and cost. Our findings will contribute to developing more efficient and cost-effective electric powertrains.

Aging-aware optimal power management control and component sizing of a fuel cell hybrid electric vehicle powertrain

The current study presents a comprehensive approach for optimizing the power distribution control and design of a Fuel Cell Hybrid Electric Vehicle (FCHEV) equipped with a Battery-Ultracapacitor Hybrid Energy Storage System (HESS) using a multi-objective evolutionary algorithm called interactive adaptive-weight genetic algorithm (i-AWGA). The method aims to maximize the vehicle’s driving range and the lifetimes of the fuel cell stack and battery while minimizing hydrogen fuel consumption and HESS size. The energy management strategy involves fuzzy logic controllers to distribute the power demand between the fuel cell and HESS and between the battery and ultracapacitor pack. Under the combined standardized cycle in which the optimization was developed, the optimized FCHEV configuration achieved a driving range of 444 km, hydrogen consumption of 0.9009 kg/100 km. Furthermore, the optimal configuration demonstrated robustness in real-world driving conditions, exhibiting improved energy efficiency, driving autonomy, and power sources lifespan. A cost–benefit analysis was also carried out, in which the optimized configuration was evaluated in terms of cost of ownership, achieving 31.28 US$/km, which means the substantial reduction of up to 63.59% in the invested cost-to-autonomy ratio as compared against other electrified vehicle powertrain topologies. Overall, this study offers a promising approach for designing efficient, cost-effective, and environmentally friendly FCHEVs with improved performance and durability.

ENERGY CONSUMPTION ANALYSIS FOR AN EV POWERTRAIN USING THREE BLDC IDENTICAL MOTORS

Using more than one motor in an EV (Electric Vehicle) powertrain enhances the vehicle’s torque, acceleration, and speed capabilities. A more tractive force produced by the electric powertrain requires more electric current from the battery. In modern EVs, mechanical power production can be approached to the wheels reducing the mechanical losses in the transmissions. It is the case with multiple-motor powertrains. Several advantages regarding vehicle control, physical performance, and energy efficiency improvement are obtained. In the current study, a BLDC (brushless dc) motor is investigated from the efficiency point of view to build a multiple-motor powertrain. A new methodology uses analytic calculations, simulations, and physical measurements. A powertrain with three identical motors results in a new vehicle configuration. Each motor's energy consumption improvements are analyzed following a normalized testing procedure. The results are compared with those of a single-motor powertrain.

Robust Rotor Temperature Estimation of Permanent Magnet Motors for Electric Vehicles

As the mainstream powertrain of electric vehicles, permanent magnet motors are facing the challenge of durability and thermal failure. Therefore, the real-time rotor temperature monitoring plays a critical role, however, it is hard to online measure with sensors. To this end, a rotor temperature estimation method based on the lumped-parameter thermal networks and dual H infinity filters is proposed. Firstly, the lumped-parameter thermal network of three nodes, such as the stator, rotor and bearing, is numerically formulated to determine the power loss. Accordingly, the discretized state-space expressions are specified for the time-step iterative solution. Then, to address the uncertainty of model parameters, the dual H infinity filters are used in the rotor temperature estimation process. Finally, the simulation and experimental tests are performed to validate the effectiveness and the real-time executability of the proposed method. The test results show that the proposed method can well track the actual temperature tendency with estimation errors of less than 7.5 °C. Compared with the existing methods, the worst-case estimation accuracy has been improved by at least 25 %; besides, the proposed method presents good robustness against the parameter uncertainty; meanwhile, the higher estimation convergence is made in the face of huge model deviations.

Optimal sizing and energy management of a novel dual-motor powertrain for electric vehicles

Optimal sizing and energy management of a novel dual-motor powertrain for electric vehicles

Modelling and Simulation of Hybrid Electric Vehicle Based on MATLAB/ Simulink

Abstract: Due to the more vigorous regulations on carbon gas emissions and fuel economy, Fuel Cell Electric Vehicles (FCEV) are becoming more popular in the automobile industry. This paper presents a neural network based Maximum Power Point Tracking (MPPT) controller for 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC), supplying electric vehicle powertrain through a high voltage-gain DC-DC boost converter. The proposed neural network MPPT controller uses Radial Basis Function Network (RBFN) algorithm for tracking the Maximum Power Point (MPP) of the PEMFC. High switching frequency and high voltage gain DC-DC converters are essential for the propulsion of FCEV. In order to attain high voltage gain, a three-phase high voltage gain Interleaved Boost Converter (IBC) is also designed for FCEV system. The interleaving technique reduces the input current ripple and voltage stress on the power semiconductor devices. The performance analysis of the FCEV system with RBFN based MPPT controller is compared with the Fuzzy Logic Controller (FLC) in MATLAB/Simulink platform.

Analysis and Control of an Input-Parallel Output-Series Connected Buck-Boost DC–DC Converter for Electric Vehicle Powertrains

Many electric vehicle powertrains use a boost type dc-dc converter to step-up the battery voltage for optimization and improving vehicle performance. The converter allows variation of inverter dc-link voltage to further reduce losses. By focusing on boost operation only, previously proposed drivetrains have not fully utilized the benefits of variable dc-link. This paper describes a new non-isolated bidirectional dc-dc converter capable of voltage step-down as well as high-gain voltage step-up operation. Such wide voltage range operation can lead to improved efficiency of electric vehicle drivetrains. The proposed converter comprises two non-inverting buck-boost modules, configured to operate in various optimized modes depending on the load requirement. Compared to existing bidirectional buck-boost converters, the proposed converter offers lower switch stress, reduced converter size and better efficiency. The operation of the converter in different modes is described in detail, followed by small-signal modeling and an outline of the control scheme. Experimental results obtained in an all-SiC 5 kW prototype show stable operation while the converter emulates the behaviour of the dc-dc stage in a variable dc-link EV powertrain under the ECE 15 drive cycle. The prototype demonstrated peak efficiencies of more than 98.5% and 98% for voltage step-up and step-down operations, respectively.

Integrated motor drive for vehicle electrification: a step toward a more sustainable and efficient transportation system

The transportation sector is a big source of greenhouse gas (GHG) emissions. It is responsible for about 20% of all carbon dioxide (CO2) emissions in the world, and most of those emissions come from road transportation. The development of greater efficiency vehicles with much reduced fuel consumption and GHG emissions is critical for vehicle electrification and a sustainable transportation system. Electric vehicles (EVs) offer higher energy efficiency than internal combustion engines and emit no CO2 during operation. For future vehicle electrification, cost-effective electric powertrain options are required. The current state of development of two major components of the EV powertrain: motors and drives, is introduced in this editorial.

Commuting-pattern-oriented stochastic optimization of electric powertrains for revealing contributions of topology modifications to the powertrain energy efficiency

The performance of an electric vehicle (EV) powertrain is greatly influenced by the design scheme, control strategy, and driving conditions. Owing to uncertainties in driving conditions and the mutual correlation between design scheme and control strategy, identifying the optimal topology and component parameters of EV powertrains for improving the energy efficiency of EVs remains a challenging task. Oriented to the commuting patterns of individuals, this paper proposes a stochastic optimization method for identifying the superior powertrain design that optimizes the expected energy economy of the EV powertrain and reduces the energy economy variability in random driving conditions. This paper also introduces a machine-learning solution to the complex mutual restrictions among design variables, control strategies, and driving conditions, which determines the feasible design space and powertrain performance. Suggested by massive validations, the stochastic optimization method reduces the expectation and deviation of the energy consumption of EV powertrains up to 12.0% and 5.6%, compared with deterministic optimization methods. Additionally, by comparative evaluation among stochastic optimal design results of different topologies, contributions of topology modifications to the energy efficiency of EV powertrains are clarified, and the superior powertrain topology is identified, which improves 17.1% expected energy economy compared with the current popular topology.