Review of Electrical Motor Drives for Electric Vehicle Applications

Abstract

EVs (Electric Vehicles) have been rejuvenated over the last decades while the motor drive technologies are still evolving. This paper provides a review of electrical motor drive technologies used in EV applications, with a performance comparison of candidate machines and their drive topologies. EV applications demand high efficiency, high torque density, high reliability, and wide speed range while reducing weight, complexity, total costs and environmental impact. In the literature, DC (Direct Current) motors, IMs (Induction Motors) and PM (Permanent Magnet) motors can be generally found in marketplace whilst RMs (Reluctance Motors) have been researched for some time and are nearing commercial availability. This paper evaluates the performance of these four main types of electrical motor drives for EV propulsion applications using analytical methods. PM motors may offer the best performance in terms of torque density and compactness but the cost is the highest (primarily dominated by rare-earth permanent magnets), limiting their widespread application in mass production EVs. DC motors have their own merits but suffer from limited power density and necessity for maintenance. Induction motor drives are a mature and proven technology. In particular, squirrel-cage IMs are robust, reliable and inexpensive, striking a balance between system cost and complexity, power density and extended speed range. Reluctance motors can provide a good torque density and cost effective EV drive solutions. Their drawbacks can also be overcome by the use of power electronic converters and advanced control strategies. Induction and reluctance motor drives are well suited for cost sensitive mass production EV applications. Looking to the future, increased hybridization may be a way forward in industry which combines attractive features of different electrical machines and control algorithms and still offer much promise in performance and total cost. At last, reliability study on EVs requires historical information and driving patterns, demanding research expertise in eco-sociology, human behaviors as well as human-machine interface.

Wearable Soft Technologies for Haptic Sensing and Feedback

“…Very high efficiency and high torque motors are available, designed for the rapidly expanding market of electric mobility (electric scooters, bicycles and cars). [ 159–161 ] Clutches are widely used in robotics and on exoskeletons to lock joints in position and reduce energy consumption. Lightweight and soft ES clutches have been demonstrated on the ankle [ 142 ] and the elbow.…”

Multiple Sensor Fail-Operational Architecture for Electric Vehicle Powertrain Control System

“…The SoC estimation method can be divided into eight types [9], of which Coulomb counting and electrical model-based estimation is a relatively mature and widely used algorithm. Th most common SoC estimation method is to use Coulomb counting indicated in Equation (12), where SoC( ) is the initial value of SoC, is the rated capacity, is the curren on the DC bus, and is the chemical loss of the battery itself. With the measured output current, the battery capacity can be obtained by accumu lating the electric charge.…”

“…Among them, BMS and MCU are responsible for generating the vehicle propulsion like the traditional internal combustion engine system. The main function of BMS is to monitor and calculate the state of charge (SoC), state of health (SoH), and state of power (SoP) of the battery pack, equalize the battery cell voltage [4][5][6][7][8], and further ensure its safety through relying on the DC bus current sensor, the battery cell voltage sensor, and the temperature sensor [9][10][11], while Bukhari shows that the MCU performs the related propulsion control based on the battery status, phase current sensor, rotor position sensor, and temperature sensor [12]. Both BMS and MCU systems strongly rely on the signals of the sensors, and any fault may cause both systems the unexpected behaviors [13].…”

State of charge estimation of lithium-ion batteries using adaptive neuro fuzzy inference system

“…They have a wide range of caracteristics. Each application places different demands on the motor and hence many different technologies are appropriate [1]. Energy storage systems (ESS) are categorized to different methods.…”