Search Results (162)

The utilization of electric vehicles (EVs) has grown significantly and continuously in recent years, encouraging the creation of new implementation opportunities. The battery electric vehicle (BEV) charging system can be effectively used during peak load periods, for voltage regulation, and for the improvement of power system stability within the smart grid. It provides an efficient bidirectional interface for charging the battery from the grid and discharging the battery into the grid. These two operation modes are referred to as grid-to-vehicle (G2V) and vehicle-to-grid (V2G), respectively. The management of power flow in both directions is highly complex and sensitive, which requires employing a robust control scheme. In this paper, an Integral Fast Terminal Synergetic Control Scheme (IFTSC) is designed to control the BEV charger system through accurately tracking the required current and voltage in both G2V and V2G system modes. Moreover, the Black-Winged Kite Algorithm is introduced to select the optimal gains of the proposed IFTS control scheme. The system stability is checked using the Lyapunov stability method. Comprehensive simulations using MATLAB/Simulink are conducted to assess the safety and efficacy of the suggested optimal IFTSC in comparison with IFTSC, optimal integral synergetic, and conventional PID controllers. Furthermore, processor-in-the-loop (PIL) co-simulation is carried out for the studied system using the C2000 launchxl-f28379d digital signal processing (DSP) board to confirm the practicability and effectiveness of the proposed OIFTS. The analysis of the obtained quantitative comparison proves that the proposed optimal IFTSC provides higher control performance under several critical testing scenarios. Full article

Convolutional neural networks (CNNs) are widely used in geotechnical engineering. Real-time detection in complex geological environments, combined with the strict power constraints of embedded devices, makes Field-Programmable Gate Array (FPGA) platforms ideal for accelerating CNNs. Conventional parallelization strategies in FPGA-based accelerators often result in imbalanced resource utilization and computational inefficiency due to varying kernel sizes. To address this issue, we propose a customized heterogeneous hybrid parallel strategy and refine the bit-splitting approach for Digital Signal Processor (DSP) resources, improving timing performance and reducing Look-Up Table (LUT) consumption. Using this strategy, we deploy the lightweight YOLOv5n network on an FPGA platform, creating a high-speed, low-power subsurface geotechnical defect-detection system. A layer-wise quantization strategy reduces the model size with negligible mean average precision (mAP) loss. Operating at 300 MHz, the system reduces LUT usage by 33%, achieves a peak throughput of 328.25 GOPs in convolutional layers, and an overall throughput of 157.04 GOPs, with a power consumption of 9.4 W and energy efficiency of 16.7 GOPs/W. This implementation demonstrates more balanced performance improvements than existing solutions. Full article

This work devised an on-device learning approach to self-calibrate Micro-Electro-Mechanical Systems-based Inertial Measurement Units (MEMS-IMUs), integrating a digital signal processor (DSP), an accelerometer, and a gyroscope in the same package. The accelerometer and gyroscope stream their data in real time to the DSP, which runs artificial intelligence (AI) workloads. The real-time sensor data are subject to errors, such as time-varying bias and thermal stress. To compensate for these drifts, the traditional calibration method based on a linear model is applicable, and unfortunately, it does not work with nonlinear errors. The algorithm devised by this study to reduce such errors adopts Radial Basis Function Neural Networks (RBF-NNs). This method does not rely on the classical adoption of the backpropagation algorithm. Due to its low complexity, it is deployable using kibyte memory and in software runs on the DSP, thus performing interleaved in-sensor learning and inference by itself. This avoids using any off-package computing processor. The learning process is performed periodically to achieve consistent sensor recalibration over time. The devised solution was implemented in both 32-bit floating-point data representation and 16-bit quantized integer version. Both of these were deployed into the Intelligent Sensor Processing Unit (ISPU), integrated into the LSM6DSO16IS Inertial Measurement Unit (IMU), which is a programmable 5–10 MHz DSP on which the programmer can compile and execute AI models. It integrates 32 KiB of program RAM and 8 KiB of data RAM. No permanent memory is integrated into the package. The two (fp32 and int16) RBF-NN models occupied less than 21 KiB out of the 40 available, working in real-time and independently in the sensor package. The models, respectively, compensated between 46% and 95% of the accelerometer measurement error and between 32% and 88% of the gyroscope measurement error. Finally, it has also been used for attitude estimation of a micro aerial vehicle (MAV), achieving an error of only 2.84°. Full article

This paper proposes a novel maximum power point tracking (MPPT) strategy for renewable energy systems using Input Impedance Control (I²C) in power electronic converters, combined with an adaptive nonlinear controller. Unlike conventional voltage- or current-based methods, the I²C-MPPT approach leverages the maximum power transfer theorem by dynamically matching the converter’s equivalent input impedance to the source’s internal impedance. The adaptive nonlinear controller, designed using the Lyapunov stability theory, estimates system uncertainties and provides superior performance compared to traditional Proportional–Integral (PI) controllers. The proposed approach is validated through both simulations in MATLAB and experimental implementation using a Digital Signal Processor (DSP)-based controller. Practical results confirm the controller’s effectiveness in maintaining maximum power transfer under dynamic variations in source parameters, demonstrating improved settling time and robust operation. These findings underscore the potential of the I²C approach for enhancing the efficiency and reliability of renewable energy systems. Full article

Due to their improved performance and advantages for the environment, fuel cell hybrid electric cars, or FCEVs, have garnered a lot of attention. Establishing an energy management strategy (EMS) for fuel cell electric vehicles (FCEVs) is essential for optimizing power distribution among various energy sources. This method addresses concerns regarding hydrogen utilization and efficiency. The Arithmetic Optimization Algorithm is employed in the proposed energy management system to enhance the strategy of maximizing external energy, leading to decreased hydrogen consumption and increased system efficiency. The performance of the proposed EMS is evaluated using the Federal Test Procedure (FTP-75) to replicate city driving situations and is compared with existing algorithms through a comparison co-simulation. The co-simulation findings indicate that the suggested EMS surpasses current approaches in reducing fuel consumption, potentially decreasing it by 59.28%. The proposed energy management strategy demonstrates an 8.43% improvement in system efficiency. This enhancement may reduce dependence on fossil fuels and mitigate the adverse environmental effects associated with automobile emissions. To assess the feasibility and effectiveness of the proposed EMS, the system is tested within a Processor-in-the-Loop (PIL) co-simulation environment using the C2000 launchxl-f28379d Digital Signal Processing (DSP) board. Full article

Accurate and reliable estimation of the state of charge (SOC) of lithium-ion batteries is essential for the performance and safety of battery management systems (BMS) in applications such as electric vehicles and energy storage systems. However, there is a lack of comprehensive comparative studies evaluating different SOC estimation methods under standardized conditions. In this paper, we address this gap by providing a comprehensive, objective comparison of various Kalman and H-Infinity filter variants for battery SOC estimation, utilizing a detailed validation method based on well-defined criteria. The main contributions of this work are: (1) Implementation of multiple filter variants using a consistent equivalent circuit battery model; (2) Development of a standardized validation method for objective performance evaluation; (3) Detailed mathematical formulations enhancing reproducibility; (4) Evaluation of computational efficiency on a digital signal processor (DSP) to provide practical insights for real-time applications. Our findings reveal that while neither filter type is universally superior, the Extended Kalman Filter (EKF) and H-Infinity Filter (HIF) offer a solid balance between estimation accuracy and computational load, making them reliable choices for general applications. This work advances the understanding of SOC estimation methods and aids practitioners in balancing accuracy and computational efficiency for real-world applications. Full article

Fault diagnosis in power converters is essential for keeping electrical systems stable, efficient and long-lasting. Park’s Vector Transform, discrete wavelet transform, Artificial Neural Network, Fuzzy Logic and other methods are used to diagnose faults in the power converter in both single and multiple open switch situations. These methods are implemented on the digital signal processor or controller, which needs additional hardware and consumes more processing time. This paper presents a hardware-based open switch fault diagnostic method in a 3ϕ voltage source inverter to minimize fault diagnosis time and cost. An innovative hardware-based approach that utilizes a single neuron for open switch fault diagnosis in 3ϕ voltage source inverters was successfully implemented without using a digital signal processor or controller. A gradient descent algorithm calculates the weight and bias values of a single processing neuron. Furthermore, a high-speed multiplier and adder circuit seamlessly integrate with the single processing neuron, enabling rapid real-time fault diagnosis. This method is capable of diagnosing single and multiple switch open circuit faults in switching devices under variable load conditions at different frequencies. The proposed system ensures good effectiveness and resistivity, detecting faults in less than one cycle with low implementation effort and no tuning or threshold dependence. It achieves 98% accuracy, 96% precision and 95% recall, with a 2% false positive rate. Unlike traditional methods, it eliminates DSP/controller dependency by using a single neuron-based processing circuit, reducing cost and improving real-time fault diagnosis in three-phase voltage source inverters. Full article

This paper addresses the issue of wheelset slip in trains caused by low-adhesion track surfaces and proposes an active anti-slip tracking control strategy. Considering the wide operational range of trains and the complex adhesion conditions between wheels and rails, a comprehensive model of the train, incorporating adhesion effects, is developed and then transformed into a mathematical model with perturbations. To tackle the slip phenomenon on low-adhesion track surfaces, a robust adhesion observer with high dynamic accuracy is designed. Building on this, an active anti-slip strategy is proposed to ensure that the control command does not exceed the maximum traction force available from the track surface. To further enhance controller performance, higher-order sliding mode control is integrated with a saturation compensation law. Finally, a Hardware-in-the-Loop (HIL) platform is constructed using a Digital Signal Processor (DSP) controller and a Modular Test (MT) PXI real-time simulator. The simulator loads the adhesion model, while the DSP controller executes the designed anti-slip control algorithm. Experimental results demonstrate that the proposed controller effectively prevents wheelset slip under low-adhesion conditions and significantly reduces tracking errors along the target speed-displacement curve. Full article

Hydrophone is a key component of marine seismic exploration systems, divided into a scalar hydrophone and vector hydrophone. The electrochemical vector hydrophone has attracted much attention due to its high sensitivity and low-frequency detection capability. With the development of noise reduction technology, high-frequency noise has been effectively suppressed, while low-frequency noise is still difficult to control, which has become a key issue in the monitoring of underwater target radiation noise. The traditional electrochemical vector hydrophone based on the molecular electron transfer (MET) principle is limited in the working bandwidth in the low-frequency band, which affects the detection capability of low-frequency radiation signals from underwater targets. In order to solve this problem, a frequency response extension method of a MET electrochemical vector hydrophone based on dynamic feedback network is proposed. By introducing a dynamic force balance negative feedback system based on a digital signal processor (DSP), the working bandwidth of the hydrophone is extended, and the detection capability of low-frequency signals is enhanced. At the same time, the system has field adjustability and can resist the long-term system frequency characteristic drift. Experimental results show that the proposed method effectively improves the frequency response performance of the electrochemical vector hydrophone, providing a new technical solution for its application in the monitoring of low-frequency radiation noise from underwater targets. Full article

This article provides a detailed analysis of non-invasive techniques for the prediction and diagnosis of faults in internal combustion engines, focusing on the application of the Proknow-C and Methodi Ordinatio systematic review methods. Initially, the relevance of these techniques in promoting energy sustainability and mitigating greenhouse gas emissions is discussed, aligning with the Sustainable Development Goals (SDGs) of Agenda 2030 and the Paris Agreement. The systematic review conducted in the subsequent sections offers a comprehensive mapping of the state of the art, highlighting the effectiveness of combining these methods in categorizing and systematizing relevant scientific literature. The results reveal significant advancements in the use of artificial intelligence (AI) and digital signal processors (DSP) to improve fault diagnosis, in addition to highlighting the crucial role of non-invasive techniques such as the digital twin in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop the integration of AI algorithms with digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques. Full article

The application-specific instruction set processor (ASIP) has been gradually accepted in AI, communication, media, game and industry control. The digital signal processor (DSP) is a typical ASIP, whose benefits include high performance in specific domains, low power consumption, high flexibility and low silicon consumption. One of the challenges for DSP design is to handle problems induced by datapath acceleration. The datapath acceleration (instruction fusion, black box instructions) induces control complexities. To most efficiently utilize hardware, control challenges can be summarized as RAW (Read-After-Write) handling, hardware hazard handling, and WAW (Write-After-Write) handling. Both an advanced compiler and hardware hazard handler can be used as solutions. In this paper, we introduced both solutions and exposed the benefits from the hardware solution. The benefits include utilizing low silicon to achieve higher performance and program memory reduction on chip. In summary, our solution only uses 0.91% extra silicon area yet achieves 32.75% performance improvement. So, the overall performance-to-cost ratio could be evaluated as 32%. Full article

Solar energy is considered the major source of clean and ubiquitous renewable energy available on various scales in electric grids. In addition, solar energy is harnessed in various electronic devices to charge the batteries and power electronic equipment. Due to its ubiquitous nature, the corresponding market for solar-charged small-scale batteries is growing fast. The most important part to make the technology feasible is a portable battery charger and the associated controllers to automate battery charging. The charger should consider the case of charging to be convenient for the user and minimize battery degradation. However, the issue of slow charging and premature battery life loss plagues current industry standards or innovative battery technologies. In this paper, a new pulse charging technique is proposed that obviates battery deterioration and minimizes the overall charging loss. The solar-powered battery charger is prototyped and executed as a practical, versatile, and compact photovoltaic charge controller at cut rates. With the aid of sensor fusion, the charge controller is disconnected and reconnects the battery during battery overcharging and deep discharging conditions using sensors with relays. The laboratory model is tested using a less expensive PV panel, battery, and digital signal processor (DSP) controller. The charging behavior of the solar-powered PWM charge controller is studied compared with that of the constant voltage–constant current (CV–CC) method. The proposed method is pertinent for minimizing energy issues in impoverished places at a reasonable price. Full article

The You Only Look Once (YOLO) object detection network has garnered widespread adoption in various industries, owing to its superior inference speed and robust detection capabilities. This model has proven invaluable in automating production processes such as material processing, machining, and quality inspection. However, as market competition intensifies, there is a constant demand for higher detection speed and accuracy. Current FPGA accelerators based on 8-bit quantization have struggled to meet these increasingly stringent performance requirements. In response, we present a novel 4-bit quantization-based neural network accelerator for the YOLOv5 model, designed to enhance real-time processing capabilities while maintaining high detection accuracy. To achieve effective model compression, we introduce an optimized quantization scheme that reduces the bit-width of the entire YOLO network—including the first layer—to 4 bits, with only a 1.5% degradation in mean Average Precision (mAP). For the hardware implementation, we propose a unified Digital Signal Processor (DSP) packing scheme, coupled with a novel parity adder tree architecture that accommodates the proposed quantization strategies. This approach efficiently reduces on-chip DSP utilization by 50%, offering a significant improvement in performance and resource efficiency. Experimental results show that the industrial object detection system based on the proposed FPGA accelerator achieves a throughput of 808.6 GOPS and an efficiency of 0.49 GOPS/DSP for YOLOv5s on the ZCU102 board, which is 29% higher than a commercial FPGA accelerator design (Xilinx’s Vitis AI). Full article

An induction motor is an electric machine widely used in various industrial and commercial applications due to its efficiency and simple design. In this regard, a methodology based on the electric phasor analysis of line currents and the variations in the phase angles among these line currents is proposed. The values in degrees of the angles between every pair of line currents were introduced to a fuzzy logic algorithm based on the Mamdani model, developed using the Matlab toolbox for detection and isolation of the inter-turn short-circuit faults on the windings of an induction motor. To carry out the analysis, the induction motor was modified in its stator windings to artificially induce short-circuit faults of different magnitudes. The current signals are acquired in real time using a digital platform developed in the Delphi 7 high-level language communicating with a float point unit Digital Signal Processor (DSP) TMS320F28335 by Texas Instruments. The proposed method not only detects the short circuit faults but also isolates the faulty winding. Full article

Multi-motor servo systems are widely used in industrial control. However, the single-core microprocessor architecture based on the microcontroller unit (MCU) and digital signal processor (DSP) is not well suited for high-performance multi-motor servo systems due to the inherent limitations in computing performance and serial execution of code. The bus-based distributed architecture formed by interconnecting multiple unit controllers increases system communication complexity, reduces system integration, and incurs additional hardware and software costs. Field programmable gate array (FPGA) possesses the characteristics of high real-time performance, parallel processing, and modularity. A single FPGA can integrate multiple motor servo controllers. This research uses MCU + FPGA as the core to realize high-precision multi-axis real-time control, combining the powerful performance of the MCU processor and the high-speed parallelism of FPGA. The MCU serves as the central processor and facilitates data interaction with the host computer through the controller area network (CAN). After data parsing and efficient computation, MCU communicates with the FPGA through flexible static memory controller (FSMC). A motor servo controller intellectual property (IP) core is designed and packaged for easy reuse within the FPGA. A 38-axis micro direct current (DC) motor control system is constructed to test the performance of the IP core and the heterogeneous embedded platforms. The experimental results show that the designed IP core exhibits robust functionality and scalability. The system exhibits high real-time performance and reliability. Full article