Hybrid Electric Vehicle Applications Research Articles

Bi-directional DC-AC using BLDC motor for electric and hybrid electric vehicles applications with reduced number of switches

Electric scooters often have a single design and are difficult to produce at a greater level than other forms of transportation. A possible cut electric scooter is built with the optimization design of the vehicle combustible wealth and the pollution characteristics in order to arrange to a correct solution by resolving each and every challenge. The engines' electrical systems are comprised of lead acid batteries. For the sake of a straightforward simulation procedure, the engine operates in two states ON and OFF while the vehicle's speed is regulated in three ranges roughly corresponding to high, medium, and low. The planetary speed ratio and the final drive speed ratio are gathered in the computation method's general outline. Utilizing simulation MATLAB, the findings of optimization strategies are resolved. This technique improves the fuel plenty and discharge quality of the possibility cut electric automobiles.

Use of Lithium-ion batteries in environmentally friendly vehicles

The concern for the environment and energy has led to a significant increase in research on electric vehicles (EVs) and hybrid electric vehicles (HEVs). The main challenges that need to be addressed to popularize these advanced vehicles are related to the battery. Since the early 1990s, we have been actively promoting research and development on batteries for environmentally friendly vehicle applications. Initially, the focus was on lithium-ion batteries as a potential solution to the critical battery issue mentioned earlier. Through extensive theoretical studies and numerous experimental demonstrations, we have successfully demonstrated that the potential of lithium-ion batteries exists and can be realized. This has opened up new possibilities and values that cannot be achieved with conventional batteries. This article aims to clarify the specific performance requirements of advanced batteries for EVs or HEVs, with a particular emphasis on the critical aspects of thermal design and construction for ensuring system stability. Furthermore, it highlights the significant improvements made in the power output of lithium-ion batteries for their application in HEVs.

A Magnetic-Geared Machine With Improved Magnetic Circuit Symmetry for Hybrid Electric Vehicle Applications

Magnetic-geared machine (MGM) has been widely investigated as the power split device for hybrid electric vehicles with the inherently high-integration of multi-mechanical ports. It is revealed that the magnetic circuits of armature winding in MGM are essentially asymmetrical for different phases under the magnetic field modulation effect. This inevitably results in unbalanced phase back electromotive force (EMF) waveforms, severe torque ripples, acoustic noise, and difficulty for precise control. To alleviate the asymmetry problem, a generic winding design model is presented for MGMs in this paper, which allows for consideration of all potential winding configurations. Firstly, a novel back EMF harmonic factor is proposed to account for both the harmonic content and asymmetric level in MGM. The genetic algorithm (GA) is further employed to optimize the winding in terms of minimizing the back EMF harmonic factor, armature magnetomotive force harmonic contents, and phase winding resistance. The proposed winding layout exhibits superior filtering capability for three-phase asymmetric magnetic field harmonics. As compared with the conventional integral-slot distributed winding, the MGM with the proposed winding exhibits improved back EMF waveform symmetry, suppressed torque ripple, and reduced core losses. Finally, an experimental prototype is manufactured to validate the effectiveness of asymmetric magnetic field suppression for MGMs. The results show the investigated winding design method is an effective solution to the asymmetric issue of MGMs, paving the way to research opportunities for further improvements.

The Optimization of PEM Fuel-Cell Operating Parameters with the Design of a Multiport High-Gain DC–DC Converter for Hybrid Electric Vehicle Application

The fossil fuel crisis is a major concern across the globe, and fossil fuels are being exhausted day by day. It is essential to promptly change from fossil fuels to renewable energy resources for transportation applications as they make a major contribution to fossil fuel consumption. Among the available energy resources, a fuel cell is the most affordable for transportation applications because of such advantages as moderate operating temperature, high energy density, and scalable size. It is a challenging task to optimize PEMFC operating parameters for the enhancement of performance. This paper provides a detailed study on the optimization of PEMFC operating parameters using a multilayer feed-forward neural network, a genetic algorithm, and the design of a multiport high-gain DC–DC converter for hybrid electric vehicle application, which is capable of handling both a 6 kW PEMFC and an 80 AH 12 V heavy-duty battery. To trace the maximum power from the PEMFC, the most recent SFO-based MPPT control technique is implemented in this research work. Initially, a multilayer feed-forward neural network is trained using a back-propagation algorithm with experimental data. Then, the optimization phase is separately carried out in a neural-power software environment using a genetic algorithm (GA). The simulation study was carried out using the MATLAB/R2022a platform to verify the converter performance along with the SFO-based MPPT controller. To validate the real-time test bench results, a 0.2 kW prototype model was constructed in the laboratory, and the results were verified.

Thermal behaviour of large scale lithium-ion battery for electric vehicle and hybrid electric vehicle application: a review

Thermal behaviour of large scale lithium-ion battery for electric vehicle and hybrid electric vehicle application: a review

Thermal behaviours of large scale lithium-ion battery for electric vehicle and hybrid electric vehicle application: a review

Thermal behaviours of large scale lithium-ion battery for electric vehicle and hybrid electric vehicle application: a review

Investigation of Five-Phase Flux-Switching Permanent Magnet Machines for EV and HEV Applications

As a member of stator permanent magnet (PM) brushless machines, flux-switching (FS) PM machine features outstanding advantages, e.g., robust rotor structure, easy temperature management, high torque density, etc. In this paper, the five-phase FSPM machines for electric vehicle (EV) and hybrid electric vehicle (HEV) applications are investigated. Firstly, the working principle of such machine is analyzed by air-gap field modulation theory. Then, the feasible slot/pole combination is discussed, considering flux leakage, back electro-magnetic force (EMF) quality and unbalanced magnetic force (UMF). Besides, in order to further choose the recommendable slot/pole combination, the torque performances are studied and compared by taking the 20-stators-slot FSPM machines as examples, such as rated torque, overload capability, flux weakening capability and fault tolerance capability. Further, in order to optimize the slot/pole combination which suffers from severe UMF, two different techniques for UMF reduction are proposed, including chamfering both in stator and rotor side. Finally, finite element analysis is employed to verify the analytical analyses. Meanwhile, a 20/18 prototyped machine is manufactured and tested. It is found that the experimental results verify the effectiveness of the analytical analyses.

Improving accuracy in state of health estimation for lithium batteries using gradient-based optimization: Case study in electric vehicle applications.

Significant improvements in battery performance, cost reduction, and energy density have been made since the advancements of lithium-ion batteries. These advancements have accelerated the development of electric vehicles (EVs). The safety and effectiveness of EVs depend on accurate measurement and prediction of the state of health (SOH) of lithium-ion batteries; however, this process is uncertain. In this study, our primary goal is to enhance the accuracy of SOH estimation by reducing uncertainties in state of charge (SOC) estimation and measurements. To achieve this, we propose a novel method that utilizes the gradient-based optimizer (GBO) to evaluate the SOH of lithium batteries. The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. We evaluated our method against four robust algorithms, namely particle swarm optimization-least square support vector regression (PSO-LSSV), BCRLS-multiple weighted dual extended Kalman filtering (BCRLS-MWDEKF), Total least square (TLS), and approximate weighted total least squares (AWTLS) in hybrid electric vehicle (HEV) and electric vehicle (EV) applications. Our method consistently outperformed the alternatives, with the GBO achieving the lowest maximum error. In EV scenarios, GBO exhibited maximum errors ranging from 0.65% to 1.57% and mean errors ranging from 0.21% to 0.57%. Similarly, in HEV scenarios, GBO demonstrated maximum errors ranging from 0.81% to 3.21% and mean errors ranging from 0.39% to 1.03%. Furthermore, our method showcased superior predictive performance, with low values for mean squared error (MSE) (<1.8130e-04), root mean squared error (RMSE) (<1.35%), and mean absolute percentage error (MAPE) (<1.4).

Improved Federal Test Procedure (FTP75) driving cycle performance for PMSM‐fed hybrid electric vehicles using artificial neural network

SummaryThe sensorless speed control of permanent magnet synchronous motor (PMSM) is gaining popularity in hybrid electric vehicle (HEV) applications leading to its enhanced safety, reliability, and cost savings. Speed control using vector control for PMSM‐fed HEV requires the speed encoder. When the speed sensor information fails, the inverter must ensure power delivery to the PMSM continuously by estimating the speed; this mode of operation is referred as limp‐home mode in HEV. In this paper, a speed sensorless scheme has been proposed for PMSM‐based HEV during limp‐home mode operation. This paper presents a model reference adaptive system (MRAS) speed estimator based on an adaptive neural network controller (NNC) for speed estimation of PMSM. In the HEV application, in case of speed/position encoder failure, the speed of the PMSM can be estimated by stator flux using stator current.The proposed method employs stator currents in the reference model to eliminate the DC drift problem. Furthermore, the NNC is employed in the adaptation mechanism to improve the Federal Test Procedure (FTP75) driving cycle performance. The performance of the proposed control scheme has been validated with dSPACE 1104 R &amp; D rapid development controller using vector control for the PMSM during variable speed and torque, including the zero‐speed applications

Enhanced Integrator with Drift Elimination for Accurate Flux Estimation in Sensorless Controlled Interior PMSM for High-Performance Full Speed Range Hybrid Electric Vehicles Applications

Interior Permanent Magnet Synchronous Motor (IPMSM) motion-sensorless speed control necessitates precise knowledge of rotor flux, speed, and position. Due to numerous non-ideal aspects, such as converter nonlinearities, detection errors, integral initial value, and parameter mismatches, the conventional first-order integrator’s estimated rotor flux experiences a DC offset (Doff). Low-pass filters (LPF) with a constant cut-off frequency yield accurate estimates only in the medium- and high-speed range; however, at the low-speed area, both magnitude and phase estimates are inaccurate. The presented technique resolves the aforementioned issue for a broad speed range. In order to achieve precise flux estimation, this article presents an improved technique of flux estimator with two distinct drift mitigation strategies for the motion-sensorless field-oriented control (FOC) system of IPMSM. Using the orthogonality of the α- and β-axes, the proposed drift elimination system can estimate drift in different situations while maintaining a high level of dynamic performance. The stator flux linkage (SFL) computation in the synchronous coordinate is established from the estimation of the rotating shaft’s permanent magnetic flux linkage orientation and the statistical equations model of the SFL. By comparing the calculated SFL vector to the SFL vector derived from the stator winding voltage and currents integral model with a drift PI compensation loop, a feedback loop is formed to neutralize integral drift, and the rotational speed and position of an IPMSM is estimated utilizing the vector product of the two flux linkages in a phase-locked loop. Theoretical interpretation is presented, and Matlab Simulink simulations, as well as experimental outcomes, consistently demonstrate that the suggested estimation techniques can eliminate the phenomenon of flux drift.

Fuel Cell Fed Electrical Vehicle Performance Analysis with Enriched Switched Parameter Cuk Converter

Background: This study aims to evaluate the performance of a 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC) fed Electric Vehicle (EV) using an Enriched Switched Parameter Cuk (ESPC) converter and an Elman Back Propagation (EBP) maximum power point tracking algorithm (MPPT). The acceptance of fuel cell-fed EVs in modern society is critical to the development of a pollution-free environment. One of the significant contributors to excessive pollution is transportation on public roads using internal combustion engines powered by crude oil as their primary energy source. Objective: This study identifies suitable high voltage gain DC-DC converters with minimum duty cycle operation for fuel cell-fed electric vehicle systems and develops an intelligent MPPT controller for hybrid electric vehicle applications. Methods: In this study, MATLAB/Simulink environment is used to design a 1.26 kW PEMFC powered electric vehicle. To integrate PEMFC to BLDC motor, an Enriched Switched Parameter Cuk converter is built with a high static converter voltage gain. Results: The effectiveness and performance of the fuel cell-fed EV system are investigated using perturb and observe method and Elman Back Propagation MPPT approaches for various fuel cell input temperature conditions and intervals. Conclusion: This study discusses the use of low-voltage fuel cell sources with power electronic converters that are available for various high gains in the literature. The proposed ESPC is designed to reduce stress on power converter components and is intended for low-voltage FC-fed electric vehicle applications.

Sliding mode controller for multiphase bidirectional flyback converter topology in hybrid electric vehicle applications

Day by day, the power electronics (PE) application is increasing in hybrid electric vehicles (EV). The power interfacing between the DC link drive and battery is the main issue of EVs. The best choice for this application is the bidirectional flyback converter (BFC). This can be used in a different interface, like load from the grid to the battery, external Genset to the battery, and battery to the load. In this work, a Multiphase BFC Topology is developed for Hybrid EVs. It includes the design of the Sliding mode controller (SMC), which offers superior performance to the existing controller. Due to its separated nature and smaller machinery, it is most favoured in high-voltage applications. The suggested work is executed in MATLAB/Simulink environment. The proposed method is compared with the current controllers like GA-PID, Fuzzy logic controller (FLC). From the experimental outcomes, it is observed that the proposed SMC technique gives better converter performance.

Structural study of atomically precise doped Au38−xAgx NCs@ZIF-8 electrode material for energy storage application

Fast-charging storage devices have attained attention in recent years due to their prospective wide range of applications in microelectronic gadgets and hybrid electric vehicles. In the pursuit of new efficient high-capacity electrodes, the implication of atomically precise metal nanoclusters (NCs) in the field of supercapacitors is rare. Herein, structurally distorted atomically precise doped Au38−xAgx NCs protected by 2,4-dimethylbenzenethiolate (2,4-DMBT) were synthesized by doping Ag atoms to the parent monometallic Au38 NCs. A general strategy to integrate structurally distorted atomically precise doped Au38−xAgx NCs with ZIF-8 (Au38−xAgx NCs@ZIF-8) was employed for methodical electrochemical and physicochemical studies of intrinsic energy storage mechanisms. The structural changes of doped Au38−xAgx NCs@ZIF-8 were systematically revealed and the effect of heteroatom doping, synergistic effect, reduced HOMO-LUMO gap (HLG) of highly distorted doped Au38−xAgx NCs@ZIF-8 resulted in enhanced electronic transfer kinetics, ultimately improving specific capacitance 2.2 times higher than the parent monometallic Au38 NCs. Doped Au38−xAgx NCs@ZIF-8 based hybrid supercapacitor (HSC) provides a high energy density of 14.75 Wh kg−1 and power density of 2212.8 W kg−1. The development of structurally distorted atomically precise doped Au38−xAgx NCs@ZIF-8 electrode material can pave the ways for doped metal nanoclusters for next-generation energy storage devices.

Development of an Intelligent Control Strategy for a Hybrid Energy System Integrated with the HEV Drive

Efficient and accurate performance of a control system plays a major role in developing an energy-efficient intelligent control system (ICS) for the Grid-connected Hybrid Electric Vehicle (HEV) applications. The ICS constitutes the control of hybrid energy split among the renewable sources (PV and Fuel Cell), HEV drive control is achieved by controlling the grid-connected inverter and bi-directional DC–DC converter. MPPT is designed in ICS to extract the maximum amount of power from solar PV. RESS control provides the maximum amount of extracted energy from PV and FC. In this proposed system, a bi-directional DCDC converter along with the storage system is designed to transfer energy in forward and reverse directions from source to storage element and storage element to load end in the process of charging and discharging of the battery which constitutes the bidirectional energy transfer. In addition to these control operations, ICS provides HEV drive control by controlling the grid-connected inverter. The operation of the developed ICS is compared with the conventional controllers and the results show that the proposed system with ICS is highly efficient than conventional controllers. The proposed system is tested with a three-phase load and HEV drive, both speed and torque controls are performed for the grid-connected HEV drive, and the system is tested with the different standard drive cycles.

High voltage‐gain and wide‐range input DC‐DC converter using single switch suitable for fuel‐cell hybrid vehicles

SummaryIn this paper, a new high voltage‐gain DC‐DC converter with wide‐range input is presented suitable for hybrid electric vehicle applications. The proposed structure acts as an interface converter between the fuel cell and DC link. The main benefits of the proposed topology consist of low voltage stress across semiconductors, flexibility of duty cycle changes without voltage fluctuations at the output side, having only one switch, and simple controls. Using a power switch with lower resistance on state (Rds‐on) reduces the total losses of the proposed topology. The proposed converter is analyzed in both CCM and DCM operating modes. Finally, a laboratory prototype at operating 50 kHz with 135 W is built and tested.

Estimation Strategy of RUL Calculation in the Case of Crack in the Magnets of PMM Used in HEV Application

Knowing the importance of assuring their reliability and availability, prognosis and remaining useful life calculation (RUL) concepts are highly suggested to be applied in critical applications such as hybrid electric vehicles (HEV). In the electrical propulsion system of HEVs, the electrical machine is one of the most critical elements considering its cost and function. Most electrical machines used in HEVs are permanent magnet machines (PMM). Most severe faults in PMM that affect its normal operation are the result of demagnetization. However, applying prognosis to a real prototype to detect the presence of mechanical defects such as cracks in the magnet of PMM and calculating the RUL of this defective element are challenging. In this paper, we are going to take advantage of a finite element model already built for the PMM in the healthy state and the state where cracks of different depths are integrated into the magnet. After that, relevant vital parameters that are affected when this type of fault persists in the machine are collected. Then, prognosis is applied to detect the presence of the crack in one piece of magnet in the electrical machine. Following this, the RUL calculation is performed to predict the remaining time before the crack propagates and a total fracture occurs in the magnet. The method used to execute the prognosis is the hidden Markov model (HMM). The RUL calculation will be performed using Paris equation, being the most important equation that models the growth and propagation of cracks

Comprehensive investigation on Lithium batteries for electric and hybrid-electric unmanned aerial vehicle applications

Comprehensive investigation on Lithium batteries for electric and hybrid-electric unmanned aerial vehicle applications

Adaptive Nonlinear Control of Salient-Pole PMSM for Hybrid Electric Vehicle Applications: Theory and Experiments

This research work deals with the problem of controlling a salient-pole permanent-magnet synchronous motor (SP-PMSM) used in hybrid electric vehicles. An adaptive nonlinear controller based on the backstepping technique is developed to meet the following requirements: control of the reference vehicle speed in the presence of load variation and changes in the internal motor parameters while keeping the reliability and stability of the vehicle. The complexity of the control problem lies on the system nonlinearity, instability and the problem of inaccessibility to measure all the internal parameters, such as inertia, friction and load variation. For this issue, an adaptive backstepping regulator is developed to estimate these parameters. On the basis of formal analysis and simulation, as well as test results, it is clearly shown that the designed controller achieves all the goals, namely robustness and reliability of the controller, stability of the system and speed control, considering the uncertainty parameters’ measurements.

Review of Artificial Intelligent Algorithms for Engine Performance, Control, and Diagnosis

This paper reviews the artificial intelligent algorithms in engine management. This study provides a clear image of the current state of affairs for the past 15 years and provides fresh insights and improvements for future directions in the field of engine management. The scope of this paper comprises three main aspects to be discussed, namely, engine performance, engine control, and engine diagnosis. The first is associated with the need to control the basic characteristics that prove that the engine is working properly, namely, emission control and fuel economy. Engine control refers to the ability to identify and fulfill the requirements derived from performance, emissions, and durability. In this part, hybrid electric vehicle (HEV) application and transient operations are discussed. Lastly, engine diagnosis entails assessment techniques that can be used to identify problems in the engine and solve them accordingly. In this part, misfire detection, knock detection, and intake system leakage will be evaluated. In engine performance, neural network algorithms provide efficient results in terms of emission control and fuel economy as the requirements are easily achievable. However, when it comes to engine control and diagnosis, the fuzzy logic rule with its strong robustness and neural networks algorithms are limited in efficiency due to the complex nature of the processes and the presence of big data, for instance, in HEVs in engine control. That has brought forward the usage of reinforcement learning and novel machine learning algorithms in recent years to maximize efficiency in engine control and engine diagnosis, as highlighted in the following part. The PRISMA methodology was used to justify the reference selection in this review.

Three-Phase Dual-Winding Multitasked PMSM Machine Using Double Layer Concentrated Winding for HEV Application

This paper proposes a three-phase dual-winding permanent magnet synchronous machine (PMSM) providing multitasked operation for accessory drive systems in hybrid electric vehicles (HEV). The novelty of this research lies in the machine having special slots and pole combinations with specific configuration of two sets of double-layer concentrated group winding separated by the unwound tooth between the phase groups formed on the same stator to achieve both the motoring and generator operation simultaneously as well as independently. These operations are accomplished by incorporating motor and generator winding separately on the same stator of a single PMSM machine. This single machine is particularly designed to eliminate the mutual coupling between these two sets of windings such that the load variation on the generator does not affect the mechanical output power of the motor. To confirm this multitasking, the electromagnetic design of the machine has been presented, and its operating modes are analyzed. The performance is compared using finite element analysis with that of a conventional PMSM machine having a double-layer winding configuration.