Data-Driven FMPC Based Dynamic Coordination Control Strategy for Power-Split Hybrid Electric Bus

The primary focus of this study is to investigate the control strategies of a hybrid system used in hydraulic excavators. First, the structure and evaluation target of hybrid hydraulic excavators are analyzed. Then the dynamic system model including batteries, motor and engine is built as the simulation environment to obtain control results. A so-called multi-work-point dynamic control strategy, which has both closed-loop speed PI (proportion integral) control and direct torque control, is proposed and studied in the simulation model. Simulation results indicate that the hybrid system with this strategy can meet the power demand and achieve better system stability and higher fuel efficiency.

In the dynamic process of mode switching of the power-split hybrid electric bus, the vehicle will have longitudinal shock due to the drastic change of the two motors’ torque and the engine’s slow response. To solved this problem, this paper will focus on the process of switching from pure electric mode to hybrid drive mode, considered the operational state changes before and after engine ignition, limited the torque fluctuation transmitted from the front planetary set to the output shaft based on the idea of motor compensation, and calculated the motor compensation torque of the rear planetary set to compensate the torque fluctuation of the output shaft, to reduce the jerk. Firstly, the dynamic models of the transmission system before and after the engine ignition are established, respectively, and then the dynamic torque of the engine is estimated by XGBoost. The estimated value is input into the model predictive controller (MPC) to track the engine speed and calculate the torque fluctuation generated by the front planetary set on the output shaft. Then, according to the estimated value of the engine torque and the predicted value of the engine’s speed, the compensation torque is calculated by the sliding mode controller (SMC). Finally, the vehicle model is built with AVL CRUISE and MATLAB/Simulink software to verify the mode switching control effect of hybrid electric bus under typical urban conditions in China. The results showed that this control strategy significantly reduces the jerk of the mode switching process.

In recent years, advanced CO2 cycles, including supercritical CO2 power cycles, transcritical CO2 power cycles and refrigeration cycles, have demonstrated significant potential for application across a broad spectrum of energy conversion processes, owing to their high efficiency and compact components that are environmentally benign and non-polluting. This study presents a comprehensive review of the dynamic performance and control strategies of these advanced CO2 cycles. It details the selection of system configurations and various control strategies, detailing the principles behind different control strategies, their applicable scopes, and their respective advantages. Furthermore, this study conducts a comparison between the joint control strategy and single control strategies for CO2 cycles, demonstrating the superiority of the joint control strategy in CO2 cycles. It then delves into the potential of novel control technologies for CO2 cycles, using model-based control technology powered by artificial intelligence as a case study. This study also offers an extensive overview of control theory, methodology, scope of application, and the pros and cons of various control strategies, with examples including extreme value-seeking control, model predictive control (MPC) based on an artificial neural network model, and MPC based on particle swarm optimization. Finally, it explores the application of AI-controlled CO2 cycles in new energy vehicles, solar power generation, aerospace, and other fields. It also provides an outlook on the development direction of CO2 cycle control strategies in light of the evolving trends in the energy sector and advancements in AI methodologies.

Multi-power sources are included in hybrid electrical vehicles, which leads to multi-driving modes co-existing when driving the vehicle. However, the frequent driving mode switching (DMS) will probably need the engine to be started frequently, which can result in extra fuel consumption. So, avoiding unnecessary DMS should be fully considered when designing the control strategy. For solving this problem, a model predictive control (MPC) strategy integrating Markov chain driving intention identification is put forward. First, the component models of the powertrain system are established. Second, according to the real driving cycle data, a driving intention model based on the Markov chain is designed according to the real driving cycle data. Then the MPC-based control strategy aiming at reducing DMS times is proposed by integrating the cost of DMS. Finally, the proposed control strategy is contrasted with three other control strategies to verify its validity in reducing the mode switching times and improving the fuel economy.

Evaluating the performance of a post-translational dynamic metabolic control system

Because of the soft dynamic performance of the fuel cell stack, the battery is usually integrated in the power system in fuel cell hybrid electric vehicles. In this article, a real time energy management strategy considering thermal constraints based on speed prediction with neuron network is proposed. The main principle of the proposed control strategy is to get the future power requirement with model predictive control based on the historic speed information, then optimize the objective, function according to the state variables. The objective function is set to minimize the equivalent fuel consumption of the vehicle and extend the life span of the fuel cell stack based on thermal constraints. Contrasting with the control strategy without thermal constraints under the World Light Vehicle Test Cycle driving cycle, the proposed energy management is 0.9% higher, but the temperature of the fuel cell stack and the battery can be limited within an appropriate range. The total equivalent fuel consumption is 3.9% lower than dynamic programming control strategy, which proves the availability of the proposed control strategy can reduce the equivalent fuel consumption while prolonging the fuel cell stack life span. Hardware in loop (HIL) experiment is implemented to testify the real time application of the proposed control strategy.

A reasonable and effective control strategy for HEV (Hybrid Electric Vehicle) with HESS (Hybrid Energy Storage System) can improve the system efficiency and battery service life. A dynamic programming-based global optimal control strategy which fully considered the efficiency of each component in HEV is presented. To compare with the nonlinear proportion factor dynamic coordination strategy, the fuel consumption is not increased. The cycle numbers of battery are further decreased which benefits its cycle life improvement. The regenerative energy callback ratio improves 3% under the shanghai drive cycle as an example. Since this optimization algorithm considers efficiency of each subsystem, the efficiency optimum under the selected driving cycle was realized. It can also provide a reference for the non-linear scaling factor allocation strategies to determine the scale factors.

In order to extend the service life of the long-chain gear transmission system of a drum shearer, an electro-mechanical coupling model of a drum shearer cutting unit is established. The model considers the dynamic characteristics of the motor, time-varying meshing stiffness, as well as the drum load characteristics. Additionally, the dynamic characteristics and control strategy for suppressing the dynamic load of the gear transmission system under impact load are investigated based on this model. Firstly, the influence of the gear transmission system of the drum shearer cutting unit under impact load is analyzed. Then, on that basis, the active control strategy based on motor torque compensation is proposed to suppress the dynamic load of the gear transmission system caused by mutational external load. Finally, the suppression effect on the dynamic load of the gear transmission system is analyzed. Research results indicate that this control strategy has good control effects to suppress the dynamic load caused by a mutational external load, which confirms the effectiveness of the proposed control strategy.

With high penetration of solar Photovoltaic (PV) resources, controlling voltage of distribution systems with conventional control strategies such as fixed set-point operation mode of Load Tap Changers (LTCs) or even in some cases with Line-Drop Compensation (LDC) operation mode is not possible anymore. In case of LDC operation mode, optimizing the R & X settings is time-consuming and is required with every change in PV penetration level. Hence, a robust and dynamic control strategy is required to handle the challenges induced by high penetration levels of solar PV resources. In this work, dynamic control of LTC and Line Voltage Regulators (LVRs) of a Hawaii substation with multiple voltage control zones is implemented in OpenDSS and controlled by Python through COM interface. Then its performance in terms of reducing voltage volatility, Operation and Maintenance (O&M) costs and ramp-rate, facilitating LDC R&X settings optimization and energy saving is evaluated. Furthermore, the impact of Dynamic Var Controllers (DVCs) in accomplishing the aforementioned objectives is illustrated through comprehensive time-series simulations. Simulation results show that by using dynamic control of voltage regulators and DVCs, PV penetration increases by 45% due to reducing voltage volatility. Moreover, generation ramp-rate reduces by 8%. Furthermore, energy savings are tripled compared to energy savings achieved by existing controls. Finally, the utilization of DVCs reduces the O&M costs by 14%.

In order to reduce the gas consumption of the engine, and maintain the battery state of charge (SOC) value in a reasonable range, the energy control strategy of HEB (Hybrid Electric Bus) is optimized by the distribution of demand torque based on driving conditions. Firstly, actual driving conditions of HEB are analyzed. Secondly, three driving conditions are divided according to characteristics of the HEB running load. Then, four types of energy control strategies are proposed according to the logic threshold algorithm, which use the optimal fuel economy as the control target and the engine operating point and battery SOC value as the control object. Finally, considering the actual driving conditions, the HEB control strategy model is established by the Matlab/Simulink and cruise software. Results show that compared with the original HEB, the proposed four types of control strategies can better reduce gas consumption under different riving conditions, and reduce the gas consumption rate of HEB by 4.4%.

In the hot strip rolling process, the performance of a monitoring system for automatic gauge control (MN-AGC) is influenced greatly by the model mismatch which is caused by the variation of model parameters values. A constrained dynamic matrix control (CDMC) strategy that includes a prediction model, rolling optimization, and feedback correction was used in the MN-AGC. First, the conventional Smith prediction-based control strategy for the MN-AGC was analyzed. Second, the performance index function and optimal control of the CDMC strategy were determined. Finally, simulations and industrial experiments were conducted. The results showed that both control strategies provided good control performance. When model mismatch occurred, the Smith predictor-based MN-AGC resulted in significant overshoot or even oscillations but the control performance of the CDMC-based MN-AGC was not influenced by changes in the model parameters.

The high-speed rotor electric drive system in control moment gyroscopes (CMGs) is essential for precise spacecraft attitude control. Rigorous testing of this system is critical for ensuring reliability and longevity throughout orbital missions. However, conventional test bench methods exhibit numerous limitations. In contrast, the electric motor emulator (EME) provides a flexible and efficient alternative for power-level testing of the CMG high-speed rotor brushless DC motor drive system. To address the challenges of trapezoidal back-electromotive force (back-EMF) emulation and insufficient square-wave current tracking accuracy in existing brushless DC motor emulator (BLDCME) implementations, this paper proposes a sliding time window-based dynamic current compensation control (STW-DCCC) strategy for the CMG high-speed rotor BLDCME. First, based on the VSC single-conversion-circuit topology, the BLDCME basic control strategy based on the motor port current and the current change rate is implemented to achieve a tracking control of the square-wave current and emulation of the trapezoidal back-EMF. Building upon this foundation, a sliding time window-based RMS current compensation optimization strategy for the BLDCME is designed to provide dynamic compensation for system disturbances and thereby enhance the tracking accuracy of the square-wave current. Furthermore, by incorporating fault information, the proposed STW-DCCC strategy can also emulate the resistance unbalance fault of the brushless DC motor. Finally, through experiments, a comparative analysis is conducted between the basic control strategy and the proposed STW-DCCC strategy under normal operating conditions, parameter mismatch operating conditions, and resistance unbalance fault conditions, thereby validating the effectiveness of the proposed method.

To improve the effectiveness of model predictive control (MPC) in dynamic traffic signal control strategies, it has been combined with graph convolutional networks (GCNs) and deep reinforcement learning (DRL) technologies. In this study, a neural-network-based traffic signal control optimization method under the MPC framework is proposed. A dynamic correlation matrix is introduced in the predictive model to adapt to the dynamic changes in correlations between nodes over time. The signal control optimization strategy is solved using DRL, where the agent explores the optimal control strategy based on pre-set constraints in the future road environment. The geometric structure and traffic flow data of a real intersection were selected as the simulation validation environment, and a joint simulation was conducted using Python and SUMO. The experimental results indicate that in low-traffic scenarios, the queue length is reduced by more than 2 vehicles compared to the selected comparison methods; in high-traffic scenarios, the queue length is reduced by an average of 17 vehicles. Under the actual traffic data of the intersection, the average speed is increased by 6.4% compared to the fixed timing method; compared to the inductive signal control method, it increases from 9.76 m/s to 11.69 m/s, an improvement of 19.7%, effectively enhancing the intersection signal control performance.

In this paper, a reinforcement learning (RL) based dynamic inverse attitude control scheme is proposed for near-space vehicle (NSV). Firstly, the conventional dynamic inverse control is employed to ensure the basic capability of NSV attitude tracking. Subsequently, RL is employed to tackle the system uncertainties. Actor-critic RL method is adopted to generate a compensation control signal in order to track attitude command better. Finally, simulation results illustrate that the proposed RL based dynamic inverse control scheme can obtain a better performance compared with the conventional dynamic inverse control scheme.

Multi-objective non-simultaneous dynamic optimal control for an ejector expansion heat pump with thermal storages