Delay Compensation Control Strategy for Electric Vehicle Participating in Frequency Regulation Based on MPC Algorithm

The introduction of a large amount of renewable energy to the power system is causing system frequency stability issues due to the randomness of its generation. Electric vehicles (EVs) are regarded as one of the most prospective solutions for system frequency regulation. EVs, when aggregated, can join the ancillary market for frequency regulation as the demand-side response. However, the communication delay of the control signal could degrade the performance of EVs and reduce the payback. This paper proposed a novel control method to compensate for the delay of frequency control signals for EVs in a power system which applies Flat Frequency Control (FFC). The implementation cost of the proposed method is small, and the competitiveness of EVs in the frequency regulation market could be boosted with the proposed method.

Aiming at the problems of poor anti disturbance and insufficient regulation potential of traditional load frequency control (LFC) methods, model predictive control (MPC) is used to take large-scale electric vehicles (EVs) as controllable loads to participate in power system frequency regulation, so as to eliminate the frequency deviation caused by system delay. Firstly, the system control model of EV participating in primary frequency regulation is established, aggregated and added to the traditional load frequency control model. On this basis, the state-space dynamic frequency model of power system with EV is established. The MPC controller and a delay compensator based on predictive compensation method (PCM) are designed, which makes full use of the prediction and compensation characteristics, and makes dynamic adjustment for different time delays to compensate communication delay and strengthen frequency stability. Finally, simulations are conducted in MATLAB and the results show that compared with the traditional proportional integral control methods, the proposed method has less frequency fluctuation, faster response speed and better robustness.

High penetration of renewable energy in the power grid causes serious frequency deviations. Large-scale integrations of electric vehicles (EVs) in the power grid provide considerable vehicle-to-grid potential for frequency regulation. Existing frequency regulation strategies with aggregated EVs realize accurate power control that relies on complete information interaction between the EV aggregator and individual EVs. However, the data collection for all EV parameters is not applicable due to privacy protection and the limited communication environment. Considering the limited data collection from grid-connected EVs, this paper provides a novel frequency regulation strategy and tends to address the uncertain influence from EV users’ charging requirements, the EV aggregator’s power regulation, and the frequency regulation performance. Firstly, considering the influence of the limited data collection by EVs on the users’ requirement of traveling and regulation preference, a probabilistic evaluation model for the available regulation capacity of the EV aggregator and the probabilistic control method for EVs are developed. Then, a frequency regulation strategy with error correction control and progressive regulation recovery is developed to simultaneously guarantee the system frequency regulation performance and the regulation requirements of EV users. Finally, case studies are carried out to validate the effectiveness of frequency regulation strategy for decreasing the uncertain influence from the limited data collection, ensuring the EV users’ requirements, and improving the system frequency stability.

To enhance the balance between lateral stability and energy efficiency, we propose an adaptive compound controller based on phase plane analysis for four-wheel independent drive electric vehicles (4WID-EVs). The adaptive stability and energy-saving controller (SEC) is designed with a three-layer structure. The upper-layer controller employs model predictive control (MPC) to compute the external yaw moment based on the desired yaw rate and side slip angle derived from a reference model. The adaptive-layer controller utilizes a phase plane diagram to evaluate vehicle stability and reduces unnecessary external yaw moment consumption by accounting for the vehicle’s steering state and battery’s state-of-charge (SOC) level. The lower-layer controller implements an optimal torque distribution algorithm to minimize an objective function that considers tire workload, energy consumption, and smooth motor control. Numerical simulations are performed in MATLAB/Simulink using three distinct steering angles to evaluate the performance of the proposed control strategy. At each steering angle, the SEC’s stability and energy efficiency are compared to those of the energy-saving controller (EC) and stability controller (SC) under varying battery charge levels. The results indicate that, at small steering angles, the vehicle operates in a highly stable state, enabling a reduction in the external yaw moment to achieve substantial energy savings. As the steering angle increases, the vehicle approaches a critical stability state, where the external yaw moment is applied to maintain lateral stability. Furthermore, as the SOC decreases, the SEC strategy will increasingly prioritize energy savings. Simulation results verify that the SEC strategy effectively balances lateral stability and energy savings while maintaining consistent performance across a range of operating conditions.

Regenerative braking plays an important role in improving the efficiency of electric vehicles. But excessive regenerative torque may affect the anti-lock brake control. The paper presents a novel brake control strategy for electric vehicles which integrates the regenerative brake control and anti-lock brake control. The strategy is designed based on a novel idea called trial method, where the regenerative brake torque and the friction brake torque are coordinated according to the slip of driving wheels. The strategy depends on neither road condition identification nor longitudinal braking force estimation. Simulation studies show that the strategy can obtain almost the optimal regenerative brake efficiency, and the tire slips are regulated in the stable region.

This study proposes an optimal control strategy for an electric vehicle (EV) powered by PV arrays and a battery employing a current-fed converter topology regulated via sliding mode control and linear quadratic regulator (LQR). To enhance the efficiency of the photovoltaic system, an ANFIS-based MPPT approach is implemented. Current-fed converters are particularly attractive for EV applications due to their inherent advantages in continuous input current, magnetic isolation, and high reliability under regenerative braking conditions. An LQR controller is then systematically designed to minimize a quadratic cost function, balancing tracking performance and control effort. The proposed scheme ensures robust voltage regulation and smooth tracking of the reference currents. Simulation results of SMC demonstrate better performance in terms of dynamic response and current ripple minimization compared to a linear quadratic regulator. The proposed system has been validated through Delfino F28379D in a hardware-in-the-loop setup.

Electric explosion-proof trackless rubber-wheeled vehicles in coal mines usually use braking energy recovery technology to improve cruising range. In order to improve the efficiency of vehicle braking energy recovery and improve vehicle braking safety performance, this paper proposes a braking energy recovery control strategy for electric vehicles in coal mines based on road condition recognition. This strategy is based on the coal mine underground positioning technology, and adopts different braking energy recovery control strategies for different road conditions, while improving the braking energy recovery ratio of the vehicle, it ensures a good feeling of the brake pedal and ensures the safety performance of the vehicle braking. Finally, the feasibility of the control strategy is verified by simulation.

With the development of complex renewable energy systems, the frequency control and regulation of the power grid powered by such renewable energies (e.g., wind turbine) are more critical, since the adopted different power generators can lead to frequency variations. To address the frequency regulation of such power grids, we will present a variable coefficient coordinated primary frequency regulation scheme for synchronous generator (SG) and doubly fed induction generator (DFIG). The variable adjustment coefficient of DFIG is defined according to the current reserve capacity, which can be applied to adjust different operation conditions to regulate the frequency variation within a predefined allowable range. Since the DFIG can make full use of the reserve wind power in the system frequency regulation, the proposed method can address both the frequency regulation response and the economic performance. Simulation results indicate that the proposed coordinated control scheme can achieve satisfactory frequency regulation response and lead to reduced demand for frequency regulation of SG.

Electric vehicles (EVs) as distributed storage devices have the potential to provide frequency regulation services due to the fast adjustment of charging/discharging power. In our previous research, decentralized vehicle-to-grid (V2G) control methods for EVs were proposed to participate in primary frequency control. In this paper, our attention is on bringing a large number of EVs into the centralized supplementary frequency regulation (SFR) of interconnected power systems. An aggregator is the coordinator between EVs and the power system control center. The aggregator calculates the total frequency regulation capacity (FRC) and expected V2G (EV2G) power of EVs based on the data communicated between the aggregator and individual EVs or EV charging stations. With FRC and EV2G power, a V2G control strategy is proposed for the aggregator to dispatch regulation requirements to EVs and EV charging stations. In individual EV charging stations, the FRC is calculated on the basis of the V2G power at present time, and EV2G power is presented considering both frequency regulation and charging demands. Besides, V2G control strategies are developed to distribute regulation requirements to each EV. Simulations on an interconnected power grid based on a practical power grid in China have demonstrated the effectiveness of the proposed strategies.

The improvement of smart grid interactive technology provides the basis for the development and utilization of demand-side resources, and the auxiliary frequency regulation by demand-side resources becomes development direction. The electric vehicles (EVs) not only meet the requirements of the society for energy and environmental protection, but also have the characteristics of fast response speed, which is suitable for frequency regulation. This paper establishes the power system frequency response model with the coordination of EVs and GFAs. Based on the model, the paper analyzes the frequency regulation performance with different capacity of EVs and GFAs in single area, and calculates the optimized capacity that can satisfy the requirements. Because centralized control has better response effects than decentralized, this paper focuses on centralized controlled EVs in multi area grid frequency regulation. The control logic contains frequency deviation and tie line power deviation, and sets the dead band to ensure centralized controlled EVs to quit in time. The optimal coordinated capacity, proportion and control strategy are analyzed. The simulation results show that the collaboration of EVs and GFAs has a better effect than only insufficient EVs involved in. Also, centralized controlled EVs can participate in restraining power deviation of tie line in multi area interconnected system, which contributes to the frequency recovery after disturbance.

In this paper, the vehicle control strategy of pure electric passenger vehicle is designed in MATLAB/simulink, and the driving control strategy and the braking recovery mode control strategy modeling and Simulation are analyzed in detail. The model of the pure electric passenger car is built in AVL CRUISE software, and the parameters of the pure electric passenger car, driving motor, power battery, car tire and other modules are set, and then the established modules are connected. The results show that the drive control strategy of pure electric passenger vehicle does have its unique practicability and superiority.

At the same time, the burning of a large amount of fossil fuels has aggravated environmental pollution and the greenhouse effect. In order to solve the problems of energy shortage and environmental pollution, electric vehicle technology has been highly valued by many countries. Electric vehicles are the most promising new energy vehicles. Therefore, on the basis of fully considering the charging load characteristics of electric vehicles, how to better establish charging facilities and adjust the impact of a large number of random loads on the grid. Therefore, it is of great significance and application value to study the charging strategy and control method of electric vehicles to improve the stable operation level of the power grid and the ability to accept new energy. Here, the frequency modulation control strategy of electric vehicles considering the restriction of electricity price is studied to ensure the regulation ability and charging demand of electric vehicles.

This paper proposes a distributed energy storage control strategy for electric vehicles to improve the security and stability of distribution network when electric vehicles are connected. Firstly, the load characteristics of electric vehicles are investigated, and the optimal power flow model including energy storage power station, electric vehicle charging station considering V2G and distribution network is established. The objective function is established to minimize network loss, voltage deviation and load peak to peak. The problem is transformed into a mixed integer second-order cone optimization problem for solution, and based on the analysis of distributed energy storage model and constraints, the distributed energy storage control strategy of electric vehicle is established. Through the solution analysis in IEEE33 node system, it can be concluded that using this strategy can improve the power quality of distribution network and effectively improve the security and stability of distribution network.

When electric vehicles (EVs) are integrated into the grid for frequency regulation, the energy stored the EVs would be released excessively without a proper schedule. The system cannot appropriately utilize the energy of the generator and EVs. Many studies only focus on frequency stability but ignore the output of EV cluster. Based on this, this paper proposes a control strategy (Fuzzy-PI) that uses fuzzy control to generate a variable coefficient, which is multiplied with the PI control signal as the control signal for the EV cluster. The coefficient can increase or decrease the EV output power depending on the variation of the frequency deviation. In order to contribute an appropriate number of EVs, this paper optimizes the ratio parameter of EVs invested in V2G with the goal of reducing EV cluster output and improving frequency stability. The private cars, buses and taxis are considered for integrating into an EV cluster to participate in the grid auxiliary frequency regulation. A single-area system model for the participation of EV cluster in the micro-grid frequency regulation is established. Finally, by the comparative study of simulation algorithms, the results demonstrate the superiority of EV cluster participation in frequency regulation, as well as the effectiveness of the proposed Fuzzy-PI control and optimization strategy.

In this work, we formulate a new, nonlinear, and time-variant transmitter equalization method based on the Model Predictive Control (MPC) algorithm. MPC is a class of control algorithms in which the current control action is obtained by solving, perhaps approximately, an online open-loop optimal control problem. One important advantage of the MPC in peak-power constrained link environment is its ability to cope with hard constraints on controls and states. Knowing the state of the channel enables a very fine nonlinear equalization. We utilize this flexibility to create various MPC formulations that control the entire eye-mask, receive signal dynamic range as well as the required quantization. Our MPC equalization significantly outperforms traditional transmitter techniques such as linear feed-forward and Tomlinson-Harashima equalizers, and gets very close to the optimized decision-feedback equalization at lower transmitter resolutions. We also describe the possible complexity reduction techniques that enable efficient implementation of our MPC algorithm in hardware.