Control Method Of Electric Vehicle Research Articles

A Multi-Source Braking Force Control Method for Electric Vehicles Considering Energy Economy

Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance.

Layered energy-saving speed planning and control method for electric vehicle on continuous signal lights road

Emergency start-stop in front of signal lights is one of the main reasons for additional energy consumption and ride discomfort of Electric Vehicle (EV). Existing research on this issue rarely takes into account both energy consumption and ride comfort. Therefore, the layered energy-saving speed planning and control method is proposed. The upper is the layer of energy-saving speed planning. This layer reduces energy consumption of EV by reducing the number of stops on continuous signal lights road and minimizing the range of speed change. On this basis, the sinusoidal variable speed curve is used to smooth the acceleration process to improve ride comfort. Finally, the energy-saving speed considering ride comfort is obtained. This layer makes up for the issue that existing research rarely takes into account both energy consumption and ride comfort of EV, and is an extension and innovation of existing research. The lower is the layer of Model Predictive Controller (MPC)-based speed control. Based on the longitudinal dynamics model of EV, the MPC-based speed controller is established to control EV to track the energy-saving speed. The controller is easy to understand and implement, and it is also suitable for other research on EV, which has certain application value. The simulation results show that under various working conditions, the maximum energy consumption of EV passing through continuous signal lights road without stopping is 604.29 kJ/km, and the minimum is 244.76 kJ/km. The energy consumption is lower than that of actual road test, and it can be saved by 23.18 % compared with the method in the same field. The maximum Root Mean Square of accelerations (RMSa) is 0.25 m/s2, and the minimum is 0.10 m/s2. The values of RMSa above are lower than 0.315 m/s2, which indicates that the ride comfort is good. The utilized method can reduce energy consumption of EV, improve its range and ride comfort, which has important reference significance for promoting the development of EV.

A Distributed Self-Consistent Control Method for Electric Vehicles to Coordinate Low-Carbon Transportation and Energy

Dear Editor, In this letter, a distributed self-consistent control method to coordinate low-carbon transportation and energy is proposed to address the efficient utilization of regional transportation energy and renewable energy. Specifically, taking into account the coordinated development of transportation, power grids, and renewable energy, transportation energy self-consistent, including instant self-consistent rate and power self-consistent rate, is introduced to the distributed control, which considers electric vehicle (EV) charging and discharging willingness, and integrates the multiple demands of transportation, power grids, and renewable energy. The simulation results show that the proposed method can effectively increase the instant self-consistent rate and improve the renewable energy accommodation.

Research on a PMSM control strategy for electric vehicles

This paper proposes a novel state estimation based permanent magnet synchronous motor (PMSM) control method for electric vehicle (EV) driving. Firstly, a state feedback decoupling control with disturbance feed-forward (SFDCDF) is described. As motor angular speed and rotary angle are key information for the proposed control algorithm and park’s transformation, a novel observer based angular speed estimator (OBASE) is proposed for angular speed estimation. Moreover, an extended Kalman filter (EKF) based rotary angle estimator (EBRAE) is used for rotary angle estimation with information of the estimated angular speed. The convergence of angular speed estimation is proven through Lyapunov stability theory. Simulation results also indicate that the proposed algorithms can control PMSM torque, current, and angular speed to accurately follow reference values without severe fluctuation. In addition, in order to provide SFDCDF with load torque information, the OBASE is slightly modified to work as a vehicle load estimator (VLE) so PMSM responds more rapidly and speed fluctuates more slightly when the load suddenly changes. Then a series of hardware in the loop (HIL) simulations are carried out. Results indicate that the proposed control strategy can precisely estimate PMSM’s angular speed and rotor angle. Also, it can improve the driving performance of PMSM used on EVs.

Voltage-Based Droop Control of Electric Vehicles in Distribution Grids under Different Charging Power Levels

If left uncontrolled, electric vehicle charging poses severe challenges to distribution grid operation. Resulting issues are expected to be mitigated by charging control. In particular, voltage-based charging control, by relying only on the local measurements of voltage at the point of connection, provides an autonomous communication-free solution. The controller, attached to the charging equipment, compares the measured voltage to a reference voltage and adapts the charging power using a droop control characteristic. We present a systematic study of the voltage-based droop control method for electric vehicles to establish the usability of the method for all the currently available residential electric vehicle charging possibilities considering a wide range of electric vehicle penetrations. Voltage limits are evaluated according to the international standard EN50160, using long-term load flow simulations based on a real distribution grid topology and real load profiles. The results achieved show that the voltage-based droop controller is able to mitigate the under voltage problems completely in distribution grids in cases either deploying low charging power levels or exhibiting low penetration rates. For high charging rates and high penetrations, the control mechanism improves the overall voltage profile, but it does not remedy the under voltage problems completely. The evaluation also shows the controller’s ability to reduce the peak power at the transformer and indicates the impact it has on users due to the reduction in the average charging rates. The outcomes of the paper provide the distribution grid operators an insight on the voltage-based droop control mechanism for the future grid planning and investments.

Nonlinear lateral motion stability control method for electric vehicle based on the combination of dual extended state observer and domination approach via sampled-data output feedback

This paper studies lateral motion stability control method for an electric vehicle (EV) with uncertain disturbances. Aiming to suppress the influence of uncertain disturbances, a novel method based on the combination of dual extended state observer (dESO) and domination approach is proposed. The proposed method enables the designed controller just to adopt a smaller scaling gain than the classic domination method, which is more practical for the controller in engineering. Firstly, a dESO is proposed to estimate the uncertain disturbances of the system. Also, a sampled-data output feedback domination approach is designed to dominate the disturbances by using a scaling gain. Then, the sampled-data output feedback control law is constructed based on the dESO and domination approach called dESOD. Furthermore, the stability analysis is presented to show that the proposed sampled-data controller can guarantee the closed-loop system globally asymptotically stable. Finally, the simulations and experiment results show the effectiveness of the proposed method.

Optimised controlled charging of electric vehicles under peak power‐based electricity pricing

This study presents a practical control method for electric vehicle (EV) charging optimisation for detached and attached houses. The developed EV charging control method utilises real-time measurements to minimise charging costs of up to two EVs in a single household. Since some Finnish distribution system operators have already launched peak power-based distribution tariffs for small-scale customers and because there is a lot of discussion on this kind of tariff development, the control method considers peak power-based charges. Additionally, the proposed smart charging control method utilises charging current measurements as feedback to reallocate unused charging capacity if an EV does not utilise the whole capacity allocated for it. The control method is implemented and tested with commercial EVs. The conducted hardware-in-the-loop simulations and measurements confirm that the control method works as intended. The proposed smart charging control reduces EV charging electricity distribution costs around 60% when compared to the uncontrolled EV charging.

Research on Household Energy Control Method Based on Electric Vehicle

With the development of the automobile industry, the extensive use of automobiles is a key factor in the global energy crisis and environmental pollution, and the clean and environmentally friendly characteristics of electric vehicles have become an ideal means to deal with these two major problems. Connecting household electric vehicles to the home will not only affect the distribution network, but also adversely affect the home electricity network. Therefore, it is necessary to connect electric vehicles to household charging load for quantitative analysis, so as to design energy control methods for electric vehicles to connect to households, and minimize the adverse effects of electric vehicles connected to household power grids. First of all, this article analyzes the uncertainty of electric vehicle access to the grid. After considering the random characteristics of electric vehicle charging load, an electric vehicle charging load model is built based on Monte Carlo simulation. Secondly, according to the electric vehicle charging load power curve obtained by the above method, combined with the daily load probability model of the grid, a household electricity model is established. Finally, based on the Particle Swarm Optimization(PSO), a family energy control method is proposed, and the optimization goal is to minimize the daily load fluctuation of the family, so as to minimize the impact of electric vehicles connected to the grid.

Cluster Control for EVs Participating in Grid Frequency Regulation by Using Virtual Synchronous Machine with Optimized Parameters

In this paper, a control method for electric vehicles (EVs) participating in grid Frequency regulation is proposed. Firstly, considering dispatching large-scale electric vehicles, the K-means clustering algorithm is applied to cluster EVs with different battery state of charge and with different average vehicle daily travel miles. Then, for each class of electric vehicle group, a multi-objective optimization model considering reducing power imbalance and feeding the driving power demand for electric vehicles is proposed. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to solve the optimization model and obtain the best control parameters for “virtual synchronous machine”, which is functioned as the power controller between EVs and the power grid. At last, based on a Monte Carlo sampling, the simulation analysis of 50 EVs with the normal distribution of battery state of charge and average vehicle daily travel miles is carried out by using the proposed method. The results show that the proposed method can effectively classify the electric vehicles with different battery state of charge and different average vehicle daily travel miles. The parameters of the power converter controller for different classes of electric vehicles are optimized considering power grid frequency, their battery state of charge and their average daily travel miles, so as to maintain the balance of power grid frequency, and to meet the power needs of EV daily drive.

Lateral motion stability control of electric vehicle via sampled-data state feedback by almost disturbance decoupling

ABSTRACTThe paper presents a lateral motion stability control method for electric vehicle (EV) driven by four in-wheel motors, which considers time-variable vehicle speed and uncertain disturbance caused by external factors. First, an EV lateral motion dynamics tracking control model is presented. Then in order to deal with the uncertain disturbance in the lateral motion model, an almost disturbance decoupling method using sampled-data state feedback is proposed. Third, a sampled-data state feedback controller is constructed based on the state feedback domination approach. The proposed controller can attenuate the disturbances’ effect on the output to an arbitrary degree of accuracy. Simulation and test results under different vehicle speeds show the effectiveness of the control method.

Research on Electronic Differential Control Method of Electric Vehicle Based on Relatively Slip Rate

Research on Electronic Differential Control Method of Electric Vehicle Based on Relatively Slip Rate

Speed Control Method of Electric Vehicle for Improving Passenger Ride Quality

This paper proposes the new speed control method of electric vehicles by generating the longitudinal speed pattern for improving the ride comfort against the longitudinal acceleration/deceleration. Since the longitudinal acceleration/deceleration of EVs causes the discomfort of passengers, reducing such bad effect of acceleration/deceleration acting on passengers is very important for not only the ride comfort of passengers but also vehicle running safety. The proposed method is applying the general optimal control theory and it generates the speed pattern for improving the passenger ride quality based on evaluating the variations of acceleration and the jerk which is time derivative of the acceleration. The effectiveness of the present method is demonstrated through numerical experiments compared with some conventional methods.

A new anti-skid control method for electric vehicles using the motor torque and the wheel acceleration with experimental verification

Driving electric vehicles by electric motors can result in many unique advantages for dynamic control of electric vehicles. With the superior fast and accurate torque control performance of electric motors, electric vehicles, in particular, can achieve higher levels of safety and handling performance. A simple, effective and efficient anti-skid control method specified for electric vehicles is proposed in this paper by considering the real-world resistance factors. This method is developed on the basis of sensing and regulating a newly defined parameter, namely the ratio of the drive motor torque to the angular acceleration of the wheels, both of which can be easily obtained for electric motors. The monotonic relationship between the slip ratio and the ratio of the drive motor torque to the angular acceleration of the wheels is proved under both acceleration conditions and deceleration conditions, by considering the real-world resistance factors. The simulations and the experimental results show that the ratio of the drive motor torque to the angular acceleration of the wheels can be efficiently used, instead of the slip ratio, in anti-skid control. The results indicate that electric vehicles can achieve high-performance vehicle motion control with more flexible and simplified configurations by using in-wheel electric motors.

Vehicle-to-Grid Control for Supplementary Frequency Regulation Considering Charging Demands

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.

A sliding mode algorithm for antilock braking/traction control of EVs

This paper presents a slip suppression controller using sliding mode control method for electric vehicles which aims to improve the control performance of Evs in both driving and braking mode. In this method, a sliding mode controller is designed to obtain the maximum driving force by suppressing the slip ratio. The numerical simulations for one wheel model under variations in mass of vehicle and road condition are performed and demonstrated to show the effectiveness of the proposed method.

Modeling Joint Charging and Parking Choices of Electric Vehicle Drivers

Electric vehicles (EVs) offer significant opportunities to improve sustainability of the road transport sector. But simultaneously, widespread adoption of EVs would create new challenges. For example, spatiotemporal concentration of charging events in high-density residential or commercial areas would place extreme demands on the power network, causing bottlenecks and grid instability. A novel approach to the typical decentralized control methods for EV charging service providers (CSPs) is presented. First, static price signals based on anticipated demand define a set of charging offers, targeted to segments of EV users. Prices are differentiated either only by time or both by time and place and allow comparison and evaluation of both scenarios. A choice-based revenue management method is employed to optimize allocation of generated charging offers, with respect to revenue outcome for the CSP. The charging coordination techniques are demonstrated through simulation. Data come from the London Travel Demand Survey and particularly trips around Westfield, one of Europe's largest urban shopping malls, representing out-of-home charging behavior for short intervals in a high-demand area. Findings suggest that in a first-come, first-served system, locational pricing might create opportunities both for increased revenue and for relocation of charging events to less-congested facilities. In the revenue management system, locational pricing significantly favors total revenue outcome but without discharging vulnerable areas. However, because agents with conflicting interests participate in the process (infrastructure owners, power system operators, EV drivers), opportunity exists for the CSP to adapt constraints according to the priority of its objectives.

Study on Current Sensorless Vector Control Method for Electric Vehicle Drive Motor

With the aggravation of environment pollution and the reduction of petroleum resources, the development of electric vehicle (EV) draws more and more people’s attention. In the EV research field, that seeking for a high efficient and reliable motor control method that suits the operating conditions and characteristics of the vehicle drive motor has become one of the key techniques that need to be broken through urgently. Owing to the problems that the efficient work area is narrow and it leads to over-current phenomenon when traditional motor vector control method is applied to vehicle drive motor, this paper presents a current sensorless vector control technique for electric vehicle drive motor. According to motor speed and command torque which is gained from the speed loop control, this method directly controls the magnitude and phase angle of voltage vector to realize the orientation control of the magnetic field and then achieve the purpose of controlling the motor torque and speed. The feasibility and effectiveness of this method are verified by simulation results and bench test. Moreover, this method can not only improve the efficient work area, but also increase the reliability of motor control system. At the same time, it overcomes the dependence on the current sensor, circumvents the over-current defect caused by traditional motor vector control approach and reduces its cost. So it is a suitable and efficient control method for electric vehicle drive motor. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3964

Research on Electronic Differential Control Method of Electric Vehicle with Motorized Wheels Based on Adhesion Coefficient

This paper presents an electronic differential control strategy based on equal adhesion coefficient, it makes electric vehicle (EV) with two dual-motorized-wheels achieve differential by distributing and controlling the driving torque. First, six degree-of-freedom (6-DOF) vehicle dynamic model was set up and electronic differential system based on permanent magnet synchronous motor (PMSM) vector control was designed, then the simulation of differential control method with Matlab/Simulink was performed. The results show that when the high-speed vehicle steering in the low road adhesion coefficient, the electronic differential control method based on the principle of equal adhesion coefficient can keep two driving wheels’ adhesion coefficient utilization ratio the same, avoiding single wheel skid and improving the steering stability.

Slip Suppression of Electric Vehicles Using Sliding Mode Control Method

This paper presents a slip suppression controller using sliding mode control method for electric vehicles which aims to improve the control performance and the energy conservation by controlling the slip ratio of wheel. In this method, a robust sliding mode controller against the model uncertainties is designed to obtain the maximum driving force by suppressing the slip ratio. The numerical simulations for one wheel model under variations in mass of vehicle and road condition are performed and demonstrated to show the effectiveness of the proposed method.

Driving torque control method for electric vehicle with in‐wheel motors

AbstractAntislip control or slip ratio control can help improve the stability of EVs on a low‐μ road. However, these control methods cannot control the driving torque. In this paper we propose a driving torque control method for EVs with in‐wheel motors. By using this method we can control the driving torque directly. Simulations and experiments are carried out to demonstrate the effectiveness of the proposed method. © 2012 Wiley Periodicals, Inc. Electr Eng Jpn, 181(3): 49‐58, 2012; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21286