Abstract:Pengembangan konsep kendaraan Low-Cost Green Car (LCGC) dan zero emission menjadi fokus penelitian pada beberapa negara. Hal ini dilatarbelakangi cadangan bahan bakar minyak yang terus menurun dan bahaya pencemaran lingkungan. Motor listrik merupakan satu – satunya penggerak utama yang dapat diaplikasikan pada mobil listrik yang mengusung kedua konsep tersebut. Pada generasi awal pengembangan mobil listrik, motor DC menjadi solusi yang paling sesuai dikarenakan kemudahan pengoperasiannya dan pengaturan kecepat… Show more
“…Electrical modeling involves modeling the stator current and rotor flux in the dq rotating frame. The electrical modeling is expressed by state space equations [21]. The modeling of the rotor speed in IM is shown by Equation (1).…”
The control of electric motors, particularly three-phase induction motors, has developed rapidly due to their application in industry. Indirect Field Oriented Control (IFOC) is one of the most widely used control systems due to its ease of application. IFOC controls a three-phase induction motor in the same way as a DC motor. However, IFOC requires a Sliding Mode Control (SMC) controller with Lyapunov stability theory to ensure robustness and stability. In exceptional conditions, such as low-speed settings, the SMC-based IFOC requires unique sets to operate with a steady-state error (Ess) at a speed response of less than 2%. Other parameters to be considered are rise time and electromagnetic torque response at low speeds. The addition of the boundary layer of the hyperbolic tangent function to a first-order SMC can increase induction motor (IM) control up to 175 rpm with a value of Ess = 1.96% compared to the saturation and signum functions, which are only capable of a reference speed of 300 rpm in no-load conditions with a value of Ess = 2% for the saturation function and 1.94% for the signum function. SMC with the hyperbolic tangent function boundary layer performs best under load conditions. The rising time value does not significantly differ under no-load or torque-load conditions between the SMC with the saturation, hyperbolic tangent function boundary layers and without the boundary layer. Adding a boundary layer with the hyperbolic tangent function can reduce ripple significantly compared to the saturation function under no-load or load conditions.
“…Electrical modeling involves modeling the stator current and rotor flux in the dq rotating frame. The electrical modeling is expressed by state space equations [21]. The modeling of the rotor speed in IM is shown by Equation (1).…”
The control of electric motors, particularly three-phase induction motors, has developed rapidly due to their application in industry. Indirect Field Oriented Control (IFOC) is one of the most widely used control systems due to its ease of application. IFOC controls a three-phase induction motor in the same way as a DC motor. However, IFOC requires a Sliding Mode Control (SMC) controller with Lyapunov stability theory to ensure robustness and stability. In exceptional conditions, such as low-speed settings, the SMC-based IFOC requires unique sets to operate with a steady-state error (Ess) at a speed response of less than 2%. Other parameters to be considered are rise time and electromagnetic torque response at low speeds. The addition of the boundary layer of the hyperbolic tangent function to a first-order SMC can increase induction motor (IM) control up to 175 rpm with a value of Ess = 1.96% compared to the saturation and signum functions, which are only capable of a reference speed of 300 rpm in no-load conditions with a value of Ess = 2% for the saturation function and 1.94% for the signum function. SMC with the hyperbolic tangent function boundary layer performs best under load conditions. The rising time value does not significantly differ under no-load or torque-load conditions between the SMC with the saturation, hyperbolic tangent function boundary layers and without the boundary layer. Adding a boundary layer with the hyperbolic tangent function can reduce ripple significantly compared to the saturation function under no-load or load conditions.
In the roadmaps of the automotive industry, the electric vehicle (EV) is regarded as a crucial technology for the future of automotive power systems. The EV has become a top priority of major global car manufacturers and is expected to disrupt the road transportation sector. In Malaysia and Indonesia, EVs just started as an important force. However, in Malaysia, the lack of EV infrastructure, along with its strong dependency on fossil fuels, poses an enormous challenge. The situation is very similar in Indonesia. Indonesia has huge potential as Southeast Asia’s largest vehicle market and a major nickel producer, an important EV battery ingredient. Therefore, this article addresses several critical issues in implementing EVs in Malaysia and Indonesia. In preparing this review, we have thoroughly selected very important EV keywords that are frequently asked. We have also interviewed some prominent figures in the field of EV to address the most critical aspects worth including in the paper. In doing so, we plan to provide content that will be beneficial not only to the academic world but also to the automotive industry in general. Firstly, a summary of the EV adoption scenario in each country was presented. Afterwards, the types of EVs and battery capacities available in both countries were explained. The next section focused on the adoption rate of EVs, followed by the discussion of EVs charging infrastructure. In addition to that, issues pertaining to vehicle tax credit were also addressed. The opportunities and challenges of EV were then addressed in depth before concluding remarks were given.
“…Rules nyquist sampling is used because if the signal is less than 2 times the maximum frequency of the signal to be sampled, then there will be aliasing effects. Aliasing is an effect where the resulting signal has a different frequency from the original signal [2].…”
Technological developments in the world have no boundaries. One of them is Speech Recognition. At first, words spoken by humans cannot be recognized by computers. To be recognizable, the word is processed using a specific method. Linear Predictive Coding Method (LPC) is a method used in this research to extract the characteristics of speech. The result of the LPC method is the LPC coefficient which is the number of LPC orders plus 1. The LPC coefficient is processed using Fast Fourier Transform (FFT) 512 to simplify the process of speech recognition. The results are then trained using Backpropagation Neural Network (BPNN) to recognize the spoken word. Speech recognition on the program is implemented as an animated object motion controller on the computer. The end result of this research is animated objects move in accordance with the spoken word. The optimal BPNN structure in this research is to use traingda training function, number of nodes 3, learning rate 0.05, epoch 1000, performance goal 0,00001. This structure can produce the smallest MSE value that is 0,000009957. So, this structure can recognize new words with 100% accuracy for trained data, 80% for the same respondents with trained data and reach 67.5% for new respondents.
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