Torque Analysis of 2 KW BLDC (Brushless Direct Current) Motor with Speed Variations in Electric Cars E-Falco

The motor releases torque and power to drive an electric car by carrying the load from a start position until it travels at the desired speed. The KMLI E-Falco electric car uses a BLDC type electric motor with a power capacity of 2 kW. To find out the amount of torque of a 2 kW BLDC motor when driving with variations in speed, it can be done by manual calculations using the torque equation and doing a dynotest test. The dynotest results show that the motor torque at the speed: 1 km/h is 1 Nm, 10 km/h is 131 Nm, 13 km/h is 228 Nm, 20 km/h is 225 Nm, 30 km/h is 219 Nm, 40 km / h is 188 Nm, 50 km / hour is 145 Nm, 60 km / h is 113 Nm, and 70 km / h is 85 Nm. From the results of the dynotest, it shows that the peak torque occurs at a speed of 13 km / h at 228 Nm. Racing software installed in the controller can increase the motor torque by four times at a speed variation of 13-70 km/h based on the results of the dynotest above. Keywords: motor, BLDC, torque, speed, acceleration.

Scaling data PLC sebagai pengontrol motor stepper penggerak ulir extruder

Scaling data PLC untuk penggerak motor stepper pada sistem extruder memengaruhi bentuk produk yang dihasilkan saat proses ekstrusi melalui kecepatan putar dan torsi motor. Produk hasil cetakan akan gagal jika kecepatan putar motor stepper terlalu cepat atau lambat karena pengaruh torsi motor yang bekerja. Dibutuhkan pembatasan kecepatan putar motor stepper menjadi beraturan untuk menghindari kegagalan proses ekstrusi. Tujuan penelitian ini adalah mendesain scaling setpoint dan kecepatan putar motor (rpm) beserta torsi motor (Nm) untuk kontrol torsi motor melalui kecepatan putar motor stepper. Metode yang digunakan adalah eksperimen kuantitatif data scaling dengan menggunakan persamaan matematis scaling setpoint, kecepatan putar motor (rpm) dan torsi motor (Nm). Data hasil didapatkan melalui pengujian simulasi persamaan matematis scaling pada PLC dengan sampel input periode pulsa setpoint antara 100us sampai 1000us. Hasil pengujian dengan daya motor 24Watt menunjukkan kecepatan putar motor stepper antara 49,3rpm sampai 9,4rpm berbanding terbalik dengan torsi motor stepper antara 0,49Nm sampai 2,55Nm. Pada setpoint 800us didapatkan hasil scaling setpoint 820us nilai error sebesar 2,5%, cukup ideal diaplikasikan dengan kecepatan putar 11,4rpm serta torsi 2,1Nm untuk menjalankan extruder dimensi kecil. PLC data scaling for stepper motor drive in extruder system affects the shape of product produced during extrusion process through motor rotational speed and torque. Printed product will fail if rotational speed of stepper motor is too fast or slow due the working torque influence of the motor. It is necessary to limit rotational speed of stepper motor to be regular to avoid failure of extrusion process. The purpose of this research is design scaling setpoint and motor rotational speed (rpm) along with motor torque (Nm) to control motor torque through stepper motor rotational speed. Method used is quantitative experimental data scaling using mathematical equations of scaling setpoint, motor rotational speed (rpm) and motor torque (Nm). Result data is obtained by simulation testing the scaling mathematical equation on PLC with input samples of the setpoint pulse period between 100us to 1000us. Test results with 24Watt motor power show that stepper motor rotational speed is between 49.3rpm to 9.4rpm and inversely proportional to stepper motor torque between 0.49Nm until 2.55Nm. At 800us setpoint, the 820us setpoint scaling results in error value of 2.5%, which is ideal for application with rotational speed of 11.4rpm and torque of 2.1Nm to run small-dimensional extruder.

Efficiency Evaluation of Electric Bicycle Power Transmission Systems

In this study, a method for estimating the efficiency of electric bicycle power train systems consisting of typical components, such as an electric motor, gears, sprockets, and chains is presented. In order to calculate the efficiency of a power train system, the relationship between the drive motor torque and the road-load that is exerted on the rear wheel was derived, considering kinematic inertia effects and friction losses between power transmission elements. Among the factors that influence efficiency, it was found that friction losses play a dominant role, while the effects of inertia are insignificant. The factors that influence the efficiency of electric bicycles due to friction losses, such as the transmission efficiency of the chain system and the bearing in the sprocket and wheel, were quantified. To validate the proposed efficiency calculation procedure, an experimental electric bicycle was used, in which the driving torque and road-load could be quantitatively assessed, and the actual efficiency was measured on a chassis dynamometer. It is shown that for a given motor torque, a measured and estimated dynamometer torque obtained by the proposed method exhibits a good correlation, and the transmission efficiency of each component was quantified. This method provides a practical and accurate means to calculate the drive train efficiency of electric bicycles at the design stage to improve the efficiency of electric bicycles.

An Isolated Power Factor Corrected Cuk Converter with Integrated Magnetics for Brushless DC Ceiling Fan Applications

The usage of BLDC motors in the low-power range is increasing rapidly in home appliances such as ceiling fans. This has necessitated the development of reliable, compact and efficient AC-DC power supplies for motor drive circuitry. This paper presents a power supply design consisting of an AC-DC isolated PFC Cuk converter with integrated magnetics that supplies a single-shunt voltage source inverter for the sensorless drive of the BLDC fan motor. The proposed power supply design is comprised of an integrated magnetics structure in which the two inductors and the transformer windings share the same core. The zero input and output ripple current conditions have been derived from the reluctance model of the magnetic assembly. Smooth operation of the motor by minimizing the motor torque ripples is evident from the results. The Cuk converter operates in continuous conduction mode (CCM), employing the current multiplier method. The CCM-based current multiplier control loop ensures a near-unity power factor as well as low total harmonic distortion in the supply current. The current loop also provides over-current protection, enhancing reliability of the system. Moreover, the speed of the BLDC motor is controlled by the field oriented control (FOC) algorithm, which enables direct operation with alternate energy sources such as batteries and solar photovoltaic panels. The performance of the proposed supply is validated: motor torque ripple is reduced to only 2.14% while maintaining 0.999 power factor and only 4.72% THD at full load. Failure modes analysis has also been performed through software simulations, using the PLECS simulation environment. Due to the reliable power supply design with low ripples, it is well suited for low-power BLDC motors in home appliances and small power tools, in addition to ceiling fans.

Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction

As worldwide vehicle CO2 emission regulations have been becoming more stringent, electric vehicles are regarded as one of the main development trends for the future automotive industry. Compared to conventional internal combustion engines, electric vehicles can generate a wider variety of longitudinal behaviors based on their high-performance motors and regenerative braking systems. The longitudinal behavior of a vehicle affects the driver’s driving satisfaction. Notably, each driver has their own driving style and as such demands a different performance for the vehicle. Therefore, personalization studies have been conducted in attempts to reduce the individual driving heterogeneity and thus improve driving satisfaction. In this respect, this paper first investigates a quantitative characterization of individual driving styles and then proposes a personalization algorithm of accelerating behavior of electric vehicles. The quantitative characterization determines the statistical expected value of the personal accelerating features. The accelerating features include physical values that can express acceleration behaviors and display different tendencies depending on the driving style. The quantified features are applied to calculate the correction factors for the target torque of the traction motor controller of electric vehicles. This driver-specific correction provides satisfactory propulsion performance for each driver. The proposed algorithm was validated through simulations. The results show that the proposed motor torque adjustment can reproduce different acceleration behaviors for an identical accelerator pedal input.

Toward next-generation learned robot manipulation

The ever-changing nature of human environments presents great challenges to robot manipulation. Objects that robots must manipulate vary in shape, weight, and configuration. Important properties of the robot, such as surface friction and motor torque constants, also vary over time. Before robot manipulators can work gracefully in homes and businesses, they must be adaptive to such variations. This survey summarizes types of variations that robots may encounter in human environments and categorizes, compares, and contrasts the ways in which learning has been applied to manipulation problems through the lens of adaptability. Promising avenues for future research are proposed at the end.