Advanced vector control for PMBLDC motors in light electric vehicles: performance comparison with conventional FOC

In this study, a bidirectional DC-to-DC quadratic converter (BDQC) is designed and developed for the motoring and regenerative braking (RB) of a permanent magnet brushless DC (PMBLDC) motor for a light electric vehicle (LEV) application. A PMBLDC motor is deemed more suitable for an electric vehicle (EV) due to its high efficiency and torque density. In the present work, a BDQC of 1 kW is designed to drive a 1.1 HP PMBLDC motor through a conventional voltage source inverter (VSI). An EV’s load cycle is emulated using a highly inertial load driven by a PMBLDC motor during the converter’s boost mode operation. RB is a crucial factor in extending the driving range of EVs by efficiently utilizing battery power. The converter operates in buck mode during RB, and simultaneously, the back electromotive force (EMF) of the PMBLDC machine is boosted by the self-inductance of the PMBLDC motor and the VSI. The braking technique used in this work eliminates the traditional drawback of RB in buck mode, as the power is extracted even when the motor’s back EMF is lower than the battery’s voltage. The control strategy has been implemented using the TMS320F28335 DSP controller for a developed converter prototype of the converter and driving the PMBLDC motor. The experimental results are compared to the simulation results, and a good alignment has been found.

The use of brushless DC (BLDC) motors in electric vehicles have been rapidly increasing. In this study performance analysis was made for BLDC motors used in wheel applications in light electrical vehicles on issues such as efficiency, speed, and power density. In this study, two types of BLDC motors, axial flux (AF) and hub (outer rotor-radial flux) motor were simulated, optimized, and experimented. Both of them were studied considering multi-parameter objectives mainly, stator yoke flux density, rotor yoke flux density, air gap flux density, efficiency, torque/weight, and speed. The credibility of the models performed has been confirmed by comparing the results of simulation and measurement on rated values 10 kW, 72V (Axial) and 1 kW, 50V (Hub) 3 phase, star connected motors. To optimize the motor design comprehensively, a genetic algorithm (GA) has been realized for each motor. The design, optimization, and realization of the study are given in detail pointing out some important data on BLDC motors.

This work explores the application of switched reluctance motor (SRM) for light electric vehicle (LEV). A three phase, 12/8 SRM is used in LEV with proper control. The flux and torque profiles of the SRM, are experimentally achieved. A current control method using an improved self-tuning fuzzy logic controller (STFLC) is designed and implemented in the MATLAB®/Simulink platform. Self-tuning of the fuzzy logic controller (FLC) is achieved by a gain factor. The fuzzy rules for tuning the gain factor and for the control of SRM, are applied for the acceptable and efficient operation of SRM for LEV. A comparison in speed response between the conventional proportional integral (PI), FLC and STFLC, is performed and analyzed in detail.

Light Electric Vehicles (LEV) have the potential to provide a sustainable mobility solution for daily commuting. LEV encompass a diverse range, from e-bikes to microcars, altogether offering energy-efficient options compared to traditional cars. Despite their environmental benefits, LEV are not much established yet. The work reported here aimed to identify user requirements and perceived use barriers of LEV regarding the use case of daily commuting with individuals who habitually use a car for this purpose. The study involved an online survey (n = 51) and an online user workshop (n = 8). The results of the survey indicate that requirements arising from typical characteristics of the commuting route are met by various LEV classes. However, looking at further user wishes that were widely expressed in the sample, such as protection against adverse weather conditions, LEV models that have a cabin appear more likely to meet the requirements. The workshop further highlighted a preference for more "car-like" LEV. Interestingly, the use phase of getting started with LEV evoked more controversy than the phase of use itself, and the “E” aspect within “LEV” (electric drive) clearly dominated the discussion, compared to the “L” aspect (small and lightweight construction). Though the discussion implied that the status quo with the car acts as a strong anchor in the users’ appraisal of the LEV, participants expressed an openness to LEV, given that charging facilities are accessible and cost considerations are addressed. Users are open to adopting LEV, provided that the pricing is competitive and reflects the perceived trade-offs in vehicle features. This research sheds light on user perspectives, emphasizing the importance of addressing barriers to enhance the integration of LEV into daily commuting practices.

EV's are eco-friendly automobiles. Electric vehicles are powered, driven and propelled by electric motors, unlike traditional fossil fuel-driven IC engines. The electric motor derives energy from rechargeable batteries and fuel cells (Kumara, 2017). The performance of the electric motors used in EV's must be increased by conducting different analysis (Sandeep, 2019). This work provides stability analysis of PMBLDC motor. This project aims at increasing efficiency of PMBLDC motors contributing in R&D section of EV industry. Finite element analysis (FEA) of 48V and 72V PMBLDC motor is used to compare the torque, speed, power and efficiency of the motor (Fasil et al., 2016). The results prove that 72V PMBLDC motor has superior characteristics suiting the need of electric scooter application. By replacing the electrical components of 48V PMBLDC motor with 72V PMBLDC in the same housing of 48V motor, the results obtained with stability analysis is expected to be better than 48V PMBLDC motor.

A digital signal processor (DSP)-based probabilistic fuzzy neural network (PFNN) control is proposed in this study to control an in-wheel motor drive using a six-phase permanent magnet synchronous motor (PMSM) for light electric vehicle (LEV). First, the dynamics of LEV and in-wheel motor drive system with lumped uncertainty are described in detail. Then, a feedback linearisation control is designed to control the in-wheel motor drive system. Moreover, a non-linear disturbance observer (NDO) is applied to estimate the lumped uncertainty for the designed feedback linearisation control. However, the system response is degraded by the existed observer error. In order to achieve the required control performance of LEV, the PFNN control is developed for the control of the in-wheel motor drive system. The network structure and its on-line learning algorithm using delta adaptation law of the PFNN are derived. Moreover, a 32-bit fixed-point DSP, TMS320F2812, is adopted for the implementation of the proposed intelligent controlled drive system. Finally, some experimental results are illustrated to show the validity of the proposed PFNN control for in-wheel motor drive system.

The efficiency of permanent magnet synchronous hub motors (PMSHM) used in light electric vehicles (EVs) is lower than that used in commercial EVs. Therefore, in this study a high-efficiency radial-flux outer-rotor PMSHM was designed for light EVs. The high-efficiency motor will contribute to the reduction of the power consumption demand from the batteries of EVs, the longer life of the batteries and the longer uninterrupted operation of the system. The optimization objectives, such as motor sizing, number of slots and poles, air gap length, material selection, stator winding structure, stator slot shape, magnet thickness, and cutting method for stator sheets were considered to ensure high efficiency and low cogging torque. In this study, three validation stages were followed; electromagnetic analyzes with FEM, analytical calculations, and finally experimental validation. First, the design parameters of the motor were determined based on the analyses results obtained using ANSYS Maxwell software, and then validated both with the analytical calculations and experimental results. The comparison results show that the design data of the motor at the rated speed agree well with the analytical calculations and test results. After obtaining the optimized motor design, the motor was installed on a prototype electric car for the road test. During the test drive, the motor performed successfully and operated compatibly with the rest of the electric vehicle systems such as the motor driver and the battery.

One of the growing innovations in the electric vehicle market concerns light electric vehicles (LEVs), promoted at local and national level by many initiatives, such as the European project ELVITEN, involving six cities, which is analysed in the present paper in relation to the Genoa pilot case study. In Italy, LEVs have been increasingly successful, as the number of their registrations shows (+76% in 2019 compared to 2018). In this context, the city of Genoa, where a considerable fleet of mopeds and motorcycles (214,499 in its metropolitan area in 2018) circulates, lends itself well to the experimentation of two-wheeled LEVs. The monitoring of the use of LEVs within the framework of the ELVITEN project has shown that the average daily round trips recorded in the metropolitan area of Genoa are equal to 15–20 km, thus reinforcing the idea that LEVs represent a valid alternative to Internal Combustion Engine (ICE) private vehicles. Moreover, the characteristics of the travel monitored and the users’ feedback highlight that the question of range anxiety is less present than expected. Finally, and contrary to our expectations, the data analysis indicates that the use of LEVs in Genoa during two months of Covid-19 pandemic lockdown—March and April 2020—shows a decrease of 21%, while the average decrease recorded by the six cities globally considered is 51%.

Road accidents in Israel were responsible for 23% of injuries in pediatric population between 2013 and 2017. In recent years, the massive entrance of e-bike and other light electric vehicles (called collectively LEV) into the roads is significantly changing the epidemiology of road accidents among children. The study aims to describe the causes, injury types, and other epidemiological characteristics of children injured in road accident and compare injuries of LEV to regular bicycles and other light non-electric vehicles (called collectively LNEV). This retrospective study included all referrals to pediatric emergency department due to road accidents, from April 2015 through March 2017. The details of the accidents and injuries were retrieved, and the subjects' characteristics were compared based on vehicle type. A total of 1531 children met the inclusion criteria. The study found that LEV road accidents among children cause more severe injuries than other LNEV in terms of injury severity score (ISS) (mean ISS 5.8 ± 4.9 vs. 4.7 ± 3.6, P = 0.001), head and neck injuries (18.7% vs. 12.9%, respectively, P < 0.05), lower extremities (36.5% vs. 23.9%, P = 0.001), and multisystem injuries (58.6% vs. 31.8%, P < 0.001). The findings of the current study suggest that the use of LEVs is changing the epidemiology of road accidents, which requires adjustments in accident and injury prevention strategies.Conclusion: The study results should encourage authorities to provide appropriate community-based programs to promote helmet use, introduce mandatory training and licensing program for LEV riders, and enhance enforcement. What is Known: • Road accidents are the leading cause of death among children and young adults (5 to 29 years). • In recent years, there is a massive entrance of e-bikes and other light electric vehicles on the roads. What is New: • LEV (light electric vehicle) accidents among children impose more severe injuries than other LNEVs (light non-electric vehicles) in terms of ISS, hospitalizations, and multisystem injuries. • The use of LEV is changing the epidemiology of road accidents. This change requires enhancing accident and injury prevention strategies.

An improved analytical method is implemented for the optimal control of turn-on angle and turn-off angle for low inductance region of the non-linear inductance profile of switched reluctance motor (SRM) for light electric vehicle (LEV). The optimal commutation algorithm is defined using the non-linear inductance model in the region of low inductance. The method includes the back electromotive force (EMF) of SRM whereas the classical method omits the use. The performance is improved. Because of the extremely nonlinear torque characteristics of SRM, the control of three-phase SRM for LEV is implemented using the sliding mode control (SMC) strategy. The effectiveness of the SMC control using the commutation algorithm is verified on a developed experimental prototype.

The increase exigency of mobility, and consequent the fall down effect on the global pollution due to the use of chemical propellant propulsion has rise the attention of the international community on the pollution free alternative. The paper addresses the main components of modern electric vehicles and the design of a light electric vehicle (LEV) for middle range use. Industrial considerations about the exigency of dedicated infrastructure for introducing electric vehicles in large scales are reported with reference to the MES-DEA experience. A detailed description of the main characteristic of the fuel cell suited for LEV application are given. Experimental results of a full test of compliance for a PEM fuel cell optimized for LEV application are reported .

For the transformation of the mobility sector, small and light electric vehicles (LEV) show great promise, owing to their efficiency and low vehicle weight resulting in low energy consumption and lower greenhouse gas emissions per driven kilometer. The presented study focuses on the theoretical potential of substitutability of passenger car trips in Germany by varied LEVs based on the “Mobilität in Deutschland 2017” (“Mobility in Germany 2017”) dataset, for the year 2030. A detailed approach for identifying substitutable car trips was developed, reflecting age, trip purpose, number of passengers, and other decision criteria. By conducting a life cycle assessment of the considered LEVs and passenger cars, potential emission savings were analyzed. In the considered baseline scenario, it is found that emissions could be reduced by 44 % with 50% of passenger car mileage being substituted by LEVs. This study, thereby, gives way to further research on LEVs, and would urge both policy makers and general users to steer towards comprehensive measures that encourage a switch from cars to LEVs.

This paper presents a double-stator permanent magnet brushless DC machine (DS-PMBLDC) which is proposed to be used in light electric vehicles and to replace a typical motor for electric vehicles in future. However, to fulfill the limitation of motor performance at different conditions, electric vehicles require their own specific motor design. Thus, a modular type of motor which can easily replace the electrical motor based on requirements for the electric driving system in power train of an electric vehicle was proposed and discussed in this paper. The operating principle of the proposed machine is reported. Concentrated winding is adopted for the stators of a 9-slot 8-pole DS-PMBLDC machine. The cogging torque, back-EMF, air-gap flux density, torque and power characteristic have been analyzed using 2-dimensional Finite-Element Analysis (2D-FEA). Experimental and simulation results are compared and discussed. Theoretical analysis of the proposed machine show an efficiency of 80% and 75% efficiency in motoring and generating mode respectively. The 2D-FEA simulation results are in good agreement with the measurement results.

Abstract: In this paper, an effective method to achieve accurate and efficient torque control of an interior permanent magnet synchronous motor (IPMSM) in electric vehicles, based on low-resolution Hall-effect sensors, is proposed. The high-resolution rotor position is estimated by a proportional integral (PI) regulator using the deviation between actual output power and reference output power. This method can compensate for the Hall position sensor mounting error, and estimate rotor position continuously and accurately. The permanent magnetic flux linkage is also estimated based on a current PI controller. Other important parameters, such as the d-axis and q-axis inductances, stator resistance, and energy loss, are measured offline by experiments. The measured parameters are saved as lookup tables which cover the entire current operating range at different current levels. Based on these accurate parameters, a maximum torque per ampere (MTPA) control strategy, combined with the feedforward parameter iteration method, can be achieved for accurate and efficient torque control. The effectiveness of the proposed method is verified by both simulation and experimental results.

Electric vehicle (EV) motor and common traction motor are operation in different states, the insulation aging model based fixed working conditions are no longer applicable. The secondary development of motor system is carried in ADVISOR to get a new motor thermal model with which to analysis the lifetime of winding insulation. In addition, SIMULINK model is established to decouple motor currents in ADVISOR. ANSYS is carried to simulate the motor temperature rise using the obtained decouple currents, two results of models are in good agreement which verifies the validity of the redevelopment thermal model. Combined with the electric ageing model of winding insulation, the electro-thermal aging model in the EV motor is proposed based actual operation states, and insulation lifetime is calculated which provides theory basis for the analysis of electro-thermal aging model in EV motors.