Control strategies design for the hybrid engineering vehicle drive system

This article presents a control strategy for the single-stage dual active bridge (DAB) ac-dc converters. To reduce the conduction loss and achieve zero voltage switching (ZVS), the global optimal working modes are reviewed. Based on the global optimal working modes, the input current of the DAB ac-dc converter is analyzed for the sinusoidal input voltage. The control algorithm of the DAB ac-dc converter is proposed to achieve the unit power factor with the minimum current stress and ZVS. The range of the working modes is analyzed to meet the wide load power. The working mode is varied with the line-frequency angle and load power, which guarantees that the DAB ac-dc converter works in the optimal working modes. The conduction loss in the converter with the variation of the series inductance and the turns ratio of the transformer is analyzed. The series inductance and the turns ratio of the transformer are designed to further improve the conduction loss. The proposed control strategy is applied to a 1 kW DAB ac-dc converter with 220 V/50 Hz input voltage and 200 V output voltage. The experimental results demonstrate the working mode, the soft switching performance, fast dynamic response, high efficiency, and high power quality.

Compared to traditional, static-based flywheel systems, vehicle-mounted magnetic suspension flywheels face more complex operating conditions, and existing control strategies usually regard disturbances in vehicles under different operating conditions to be the same problem. Therefore, it is necessary to determine the interference from complex operating conditions and reasonably distinguish among them under different operating conditions to provide flywheel systems with strong stability (the rotor offset was less than 0.025 mm). Thus, this paper proposes a high-stability control strategy for flywheels based on the classification of vehicle-driving conditions and designs its control strategy by taking the vehicle-mounted magnetic suspension flywheel with a virtual inertia spindle as an example. First, according to the different vehicle working conditions and the varying interference intensities affecting the flywheel system, the working mode is divided into four modes. Considering the obvious differences in each working mode, it is proposed to use BP neural network optimization based on the simulated annealing algorithm (SA-BPNN) to determine the flywheel’s working condition. A relatively simple neural network can improve the response speed of the whole system. It also has a good effect. Secondly, it is proposed to use deep learning models based on convolutional neural networks, long short-term memory networks and attention mechanisms (CNN+LSTM+ATTENTION) to train the corresponding control parameters under each working condition to judge and predict the control parameters under different working conditions. Three evaluation parameters are used to evaluate the training results, and all achieved good results. Finally, the classification of working conditions and performance tests are carried out. The experimental results show the effectiveness and superiority of the proposed control strategy.

Input-parallel output-series (IPOS) power system is widely used in high output voltage and high-power applications. To improve the reliability and redundancy of the IPOS system, it is necessary to adopt a distributed and autonomous control strategy without a centralized controller. This paper investigates an autonomous voltage control strategy for IPOS DC power system. The principle and characteristics of the control strategy are studied in detail. In particular, the limitations of the control strategy in power balance performance and output voltage accuracy are analyzed. To overcome the limitations, an autonomous voltage control strategy with power-ripple-based low bandwidth communication is proposed. The power ripple on the output power line is used to share voltage compensation information among modules, which realizes voltage regulation accuracy control of the IPOS DC power system. The power-ripple-based communication is analyzed in detail. The stability of the proposed control strategy is also explored. The system has high redundancy and reliability in both control strategy and communication. The experimental prototype based on a three-module IPOS system is built to validate the proposed control and communication strategies.

In low-temperature conditions, a reasonable control strategy for thermal management systems can effectively alleviate range anxiety in pure electric vehicles and improve their adaptability to various working conditions. To further enhance the adaptability of thermal management system control strategies in different working conditions, this paper proposes a multi-objective control strategy based on Q-learning algorithm. Firstly, a pure electric vehicle model based on power-thermal coupling is established. The accuracy of the model is validated by comparing the simulation results from combined Amesim and Matlab/Simulink simulations with experimental data. Secondly, taking into consideration the factors such as vehicle economy, powertrain performance, and cabin comfort, a novel control strategy utilizing the Q-learning algorithm for the thermal management system of pure electric vehicle is developed. Finally, the efficacy of Q-learning control strategy is analyzed by simulations conducted under NEDC and WLTC conditions, with an initial temperature of −20°C. The results showed that, compared to the rule-based control strategy in WLTC and NEDC working conditions, the comprehensive improvement effect of Q-learning control strategy is 9.35% and 10.76% respectively. Moreover, the Q-learning control strategy achieves 94.25% and 90.19% of the global optimal control effect obtained through DP. The results indicate that the proposed control strategy has good adaptability to different working conditions.

This paper proposes an adaptive overall control strategy of the permanent magnet synchronous generator-based wind energy conversion system (WECS) in the whole wind speed range. For the machine side, the maximum power point tracking (MPPT) operation is realized by stator current and mechanical rotation speed control under below-rated wind speeds. Under above-rated wind speeds, the extracted wind power is limited via pitch control. For the grid side, the reactive and active power injected into grid is regulated by DC-Link voltage and grid current control loop. In addition, under grid voltage dips, the pitch control is employed for limiting grid current and maintaining the DC-Link voltage around its rated value. The fault ride-through capability (FRTC) can be enhanced. The overall control strategy is based on perturbation estimation technique. A designed observer is used for estimating the perturbation term including all system nonlinearities, uncertainties and disturbances, so as to compensate the real perturbation. Then, an adaptive control for the original nonlinear system can be realized. The effectiveness of the proposed overall control strategy is verified by applying the strategy to a 2-MW WECS in MATLAB/Simulink. The results show that, compared with the feedback linearizing control (FLC) strategy and conventional vector control (VC) strategy, the proposed perturbation observer based adaptive control (PO-AC) strategy realizes the control objectives without knowing full state information and accurate system model, and improves the robustness of the WECS parameter uncertainties and FRTC.

Study on the dynamic characteristics and control strategies of the coupled system of the FHR and SCO2 cycle

Effects of photovoltaic/thermal (PV/T) control strategies on the performance of liquid-based PV/T assisted heat pump for space heating

From adaptive control to variable structure systems – seeking harmony

Background: Pure electric vehicles still have the problem with range anxiety, but hybrid vehicles can solve this problem well. Parameter optimization and adaptation of control strategies are the keys to improving the economy of hybrid vehicles. Objective: This study aimed to improve the economy of hybrid vehicles to get more mileage in the same working conditions. On the basis of a large number of invention patents, this study establishes and optimizes the parameters and control strategy of hybrid vehicles in order to obtain better driving parameters and a more appropriate control strategy for hybrid vehicle drive system. Methods: The key parameters of each component of the drive system are defined under dynamic objectives of hybrid vehicles. The control strategy adopts a logic gate-based approach to determine driving mode and braking of hybrid vehicles by limiting the speed, SOC, and power demand. Finally, the particle swarm optimization algorithm is used to optimize the key parameters to obtain the economic optimal solution without losing the vehicle power. Results: In the China light-duty vehicle test cycle-passenger (CLTC-P) cycle condition, the optimized parameters can improve the fuel economy of fuel economy by 16.06%, and in the worldwide harmonized light vehicles test cycle (WLTC) cycle condition, the optimized parameters can improve the fuel economy of hybrid vehicles by 12.17%. Conclusion: By establishing and optimizing driving system parameters and control strategy of the hybrid vehicles, this study improves the economy and achieves the expected effect without losing the vehicle power. However, in further research, driving conditions and mileage under different working conditions should be further studied, and on this basis, the optimization of control strategies should be continued.

With the continuous development of hybrid vehicle control technology, great progress has been made in the research of multi-power flow collaborative control. Due to the internal delay link of each power component, the role of energy storage element, and the limitation of electric power in the whole system, the inevitable delay characteristic of state transfer is caused. Therefore, the speed of multi-power flow control torque coordinated response of hybrid vehicles needs to be improved. The dual-mode power-split hybrid electric vehicle (DMPS-HEV) overall structure and working modes are analyzed, by adopting the combination of theory and experiment method. In order to solve the problem that the power components of dual-mode power-split hybrid electric vehicle cannot follow the optimal control command of the upper energy management strategy quickly due to the engine response delay, thus affecting the control effect of the upper energy management strategy. The research on torque coordination control strategy is carried out, the reference model of electromechanical composite drive is established, and the model reference adaptive coordination control strategy based on Lyapunov stability theory is proposed. The results show that the proposed model reference adaptive torque coordinated control strategy significantly improves the effect of engine response delay on the optimization effect of energy management strategy, and can achieve the control effect of the optimal control strategy of 93.58%. The test platform of the dual-mode power-split hybrid electric vehicle was built. The control system was built based on the rapid control prototype, and the data acquisition system was built based on the NI data acquisition module. The coordinated control strategy of the dual-mode power-split hybrid electric vehicle power system proposed in this paper was verified through the bench test to significantly improve the vehicle fuel economy and the real-time performance of the control strategy, which has a good practical value

by developing a start-stop system of a BSG motor, this paper systematically presents the vehicle control principle, strategy and verification and assessment indicators of the start-stop system, and applies it successfully in a motor. Exhaust emissions and fuel-consumption tests of a vehicle were performed in the rotating hub, and the results show a 5% reduction in fuel consumption for the motor, and a 12% decline in fuel consumption when the experiments were carried out in urban areas. The control technology and vehicle examination and evaluation indicators have important guiding significance to the subsequent research of the start-stop system. Introduction A BSG start-stop system belongs to a micro-hybrid technology. In other words, at the engine’s idle speed, if the external environment and the vehicle status satisfy the start-stop system requirement, the engine automatically turns off when the driver releases the clutch pedal, and the engine automatically starts when the driver depresses the clutch pedal or the brake pedal[1,2]. The technology makes small changes to conventional vehicles and can significantly reduce fuel consumption, so it has become an important technology for vehicle energy conservation and emission reduction. The vehicle control unit receives the sensor signal and the signal coming from the CAN bus, and judges whether the BSG system is working and determines whether to start or stop the engine according to the internal control strategy[3], and controls engine starting and flameout. The vehicle control unit can control the starting and shutdown relay, thereby controlling the original vehicle ECU as well as controlling engine starting and flameout. When the vehicle control unit’s shutdown relay controls the high level output, the shutdown relay switches off, the ECU power cord is disconnected, and the engine stalls. The starting gear of the original vehicle ECU ignition switch is connected to the signal line[4,5,6]. When the vehicle control unit starts, the relay control terminal has low level output, and the starting relay is closed. The starting gear of the ECU ignition switch is joined, and ECU controls the engine starting. Therefore, the vehicle control unit controls engine starting and flameout by using the relay simulation key. Vehicle Control Strategy The vehicle control strategy consists of three parts: the vehicle and driver safety, driving operation habits, and the environment and road adaptability. According to the abovementioned strategiese, the vehicle formulates a start-stop system control strategy, the automatic engine stopping control strategy and the automatic engine starting control strategy. Factors taken into account for developing the vehicle control strategy Factors regarding the vehicle and driver safety: whether the seat belt is fastened, whether the engine hood is closed, whether the BSG system is faulty, engine coolant temperature, brake system pressure after the engine is switched off, automatic vehicle sliding speed after the engine is switched off, the state of charge of a battery, and catalyst temperature, etc. 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2015) © 2015. The authors Published by Atlantis Press 286 Factors regarding driving operation habits: whether the seat belt is fastened, whether the transmission is in neutral, whether the clutch is disconnected, and whether the transmission is in reverse gear and so on. Factors regarding the environment and road adaptability: the ambient temperature, the air conditioner application signal and automatic engine flameout when the vehicle speed is smaller than a specific value, etc. Fig. 1 The principle diagram of the vehicle control strategy The Main Content of the Vehicle Control Strategy The Start-stop System Closing Control Strategy The start-stop system closing control strategy can be divided into subjective and objective levels: when the driver wants to close the start-stop system subjectively, he/she can close the start-stop system only by pressing the system closing button. If one of these conditions is met: the driver is not wearing a seatbelt, or engine hood is not closed, or the BSG system is faulty, the start-stop system automatically stops working, and the vehicle condition is the same as that without a start-stop system. The start-stop system closing control strategy is shown in Figure 2. Fig. 2 The block diagram of start-stop system shutdown control The Automatic Engine Stopping Control Strategy When the driver encounters traffic lights or needs long-stay parking, the start-stop system can play a role and achieve automatic engine shutdown only when all of the following conditions are met. The automatic engine stoping control strategy is shown in Figure 3. engine cutout/idling engine start engine flamout start-top system working

A study was conducted to assess the energy performance of an optimal predictive control strategy for radiant floor district heating systems. A four-zone radiant floor heating system model was developed. The simulated performance of the optimal predictive control strategy was studied. The results showed 10% energy savings compared to a Proportional-Integral (PI) control strategy. Experiments were conducted in a laboratory radiant floor district heating system test facility. The description of the test facility, its operating conditions, and the results obtained are described. Experimental results further confirm the findings from the simulation study. Being simple and energy efficient, the optimal predictive control strategy is a good candidate control strategy for radiant floor district heating systems. Practical application: Energy efficiency is a major issue of interest in the design and operation of sustainable heating systems. The radiant floor district heating systems have been successfully installed and operated in many countries resulting in significant energy savings. The optimal predictive control strategy proposed in this study further enhances the potential for higher energy savings from the district heating systems. The optimal control strategy is simple to implement as it relies on the predicted outdoor air temperature and computes future temperature set-points for the boiler water temperature. A suitably tuned Proportional-Integral (PI) controller can be used to track the optimal set-point thus realizing potential energy savings. A programmable logic controller or a supervisory control system would be appropriate to implement the designed optimal control strategy. The local control can be realized by using industrial PI feedback controllers.

A novel control strategy and power management of hybrid PV/FC/SC/battery renewable power system-based grid-connected microgrid

In this paper, we present a multiple neural control and stabilization strategy for nonlinear and unstable systems. This control strategy method is efficient especially when the system presents different behaviors or different equilibrium points and when we hope to drive the whole process to a desired state ensuring stabilization. The considered control strategy has been applied on a nonlinear unstable system possessing two equilibrium points. It has been shown that the use of the multiple neural control and stabilization strategy increases further the stability domain of the system further than when we use a single neural control strategy.

This study investigates a new hybrid energy storage system (HESS), which consists of a battery bank and an ultra-capacitor (UC) bank, and a control strategy for this system. The proposed topology uses a bi-directional DC-DC converter with a lower power rating than those used in the traditional HESS topology. The proposed HESS has four operating modes, and the proposed control strategy chooses the appropriate operating mode and regulates the distribution of power between the battery bank and the UC bank. Additionally, the control system prevents surges during mode switching and ensures that both the battery bank and the bi-directional DC-DC converter operate within their power limits. The proposed HESS is used to improve the performance of an existing power-split hybrid electric vehicle (HEV). A method for calculating the parameters of the proposed HESS is presented. A simulation model of the proposed HESS and control strategy was developed, and a scaled-down experimental platform was constructed. The results of the simulations and the experiments provide strong evidence for the feasibility of the proposed topology and the control strategy. The performance of the HESS is not influenced by the power limits of the bi-directional DC-DC converter.