Standalone solar PV systems have emerged as potential alternatives to electricity problems in areas where a grid is unavailable. Obtaining full power from a photoelectric system, DC-DC inverter, DC-AC converter, and control system presents great difficulties when building these devices. A standalone two-stage approach is introduced in this work with a boost converter followed by an inverter and a battery with a bidirectional converter. In this paper, a novel virtual synchronous generator (VSG) controller is designed and implemented to adjust the inverter output. The VSG element and the maximum power point tracking (MPPT) used in this study serve the following purposes: to adjust the inverter output and to realize the maximum power of the PV scheme. The new control strategy design was evaluated and validated using extensive MATLAB simulations under different scenarios, including load variations. The system output was evaluated using extensive MATLAB simulations. A hands-on experiment was conducted for the VSG using console testing. Due to the lack of laboratory equipment, we could not experiment with the entire system.

A real-time control is proposed for plug-in-hybrid electric vehicles (PHEVs) based on dynamic programming (DP) and equivalent fuel consumption minimization strategy (ECMS) in this study. Firstly, the resulting controls of mode selection and series mode are stored in tables through offline simulation of DP, and the parallel HEV mode uses ECMS-based real-time algorithm to reduce the application of maps and avoid manual adjustment of parameters. Secondly, the feedback energy management system (FMES) is built based on feedback from SoC, which takes into account the charge and discharge reaction (CDR) of the battery, and in order to make full use of the energy stored in the battery, the reference SoC is introduced. Finally, a comparative simulation on the proposed real-time controller is conducted against DP, the results show that the controller has a good performance, and the fuel consumption value of the real-time controller is close to the value using DP. The engine operating conditions are concentrated in the low fuel consumption area of the engine, and when the driving distance is known, the SoC can follow the reference SoC well to make full use of the energy stored in the battery.

Water and fertilizer control equipment generally controls different matching ratio between water and fertilizer to achieve the integration of water and fertilizer irrigation. Most of the current machine for matching water and fertilizer cannot make fertilizer flow quickly track changes of water flow. To solve this problem, first of all, STM32F103ZET6 acts as the core of controller, the controller will output different PWM signal to change output voltage of DC motor driver, then the fertilizer flow can vary according to the water flow changes, and keep water and fertilizer holding at a set proportion; Secondly, in order to ensure the control precision, incremental PID algorithm is used to control the ratio of water and fertilizer, and RBF neural network is used to build the online parameter setting model to achieve precise and quick ratio control. Finally, using incremental PID and RBF- PID model, the simulation model is designed and different flow ratio and water flow are set up to validate the test. The result shows that the 5 kinds of different flow ratios and changes in water flow conditions, the highest average relative error of RBF-PID model is 7.55%, the highest coefficient of variation is 11.9%, the slowest flow ratio arrival time is 13s; through the test, RBF - PID model is more accurate and stable controlling than the incremental PID model.

In order to overcome the disadvantages of the commonly used object detection algorithm, this paper proposed a multiframes integration object detection algorithm based on time-domain and space-domain (MFITS). At first, the consecutive multiframes were observed in time-domain. Then the horizontal and vertical four-direction extension neighborhood of each target pixel were selected in space-domain. Transverse and longitudinal sections were formed by fusing of the time-domain and space-domain. The mean and standard deviation of the pixels in transverse and longitudinal section were calculated. We also added an improved median filter to generate a new pixel in each target pixel position, eventually to generate a new image. This method is not only to overcome the RPAC method affected by lights, shadows, and noise, but also to reserve the object information to the maximum compared with the interframe difference method and overcome the difficulty in dealing with the high frequency noise compared with the adaptive background modeling algorithm. The experiment results showed that the proposed algorithm reserved the motion object information well and removed the background to the maximum.

This paper proposes a real-time energy management strategy for Dedicated Hybrid Transmission (DHT) Hybrid Electric Vehicles based on Equivalent Consumption Minimization Strategy-Dynamic Programming (ECMS-DP), which supports four operating modes: pure electric, boost electric, series and parallel modes. The strategy uses Dynamic Programming (DP) algorithm to solve the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) cycle off-line to obtain the optimal shift line with the objective of energy loss minimization. And the real-time control strategy is based on Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) and the optimal shift line, with minimizing energy loss, engine start/stop times, engine power change and engine speed change as mixed target functions. Then a real-time energy management control system based on ECMS-DP is established. The system utilizes series and parallel Hamiltonian functions to judge the series and parallel modes, and significantly reduces the calibration workload. Through verification in real-vehicle revolving drum test, it has been verified that this strategy can effectively utilize the power battery capacity while maintaining electric balance compared to rule-based strategies such as Rule-Based Threshold Control, and it achieves better fuel economy, with a fuel-saving rate of approximately 5.18%.