Control System For Electric Vehicle Research Articles

Electronic anti-theft protection for vehicles of people with special needs

The article presents an electronic anti-theft protection subsystem that was incorporated into the vehicle control system dedicated to people with special communication needs. The aim of the work was to design and test an electronic subsystem allowing for the acquisition and quick analysis of fingerprint images, checking whether they belong to the vehicle owner and generating a signal allowing the vehicle to be started. The following hypothesis was put forward: it is possible to use artificial intelligence for accurate, automatic classification of fingerprint images acquired in a system with a single-chip microcomputer in order to identify a person. The research niche includes the use of biometrics in electronic anti-theft security adapted to people with special needs. This solution reduces the risk of theft and unauthorized use of special vehicles tailored to the owners' individual needs. The use of a commercial algorithm offered by the sensor manufacturer or the use of artificial neural networks for the classification of fingerprints was considered. The results of research on the accuracy of fingerprint recognition obtained from the developed subsystem are presented. The experiments performed based on the NASNetLarge artificial neural network model confirmed the possibility of achieving recognition accuracy for the test set of 99.99%. Additionally, practical aspects of the applicability of the presented device in an electric vehicle control system supporting the movement of people with physical and/or intellectual disabilities were discussed. The presented solution, after minor modifications, can be used in access security systems for protected rooms, as well as vehicles and military equipment.

Design of lateral dynamic control objectives for multi-wheeled distributed drive electric vehicles

The lateral dynamic control system for electric vehicles aims to improve vehicle performance, making it critical to propose reasonable lateral dynamic control objectives. Therefore, in this study, a novel lateral dynamic control objectives is proposed for multi-wheeled distributed drive electric vehicles. Initially, utilizing the classical two-degrees-of-freedom (2DOF) vehicle model, two initial yaw rate references were established for handling characteristics of manoeuvrability and stability, respectively. The yaw rate references consider the vehicle velocity, steering wheel angle and road friction coefficient limitations. Considering the vital role of the sideslip angle in ensuring vehicle stability, a sophisticated weighting factor was developed based on a detailed analysis of the vehicle dynamics within the sideslip angle-sideslip angle rate phase plane. The weighting factor was determined based on the position of the vehicle state in the phase plane. This factor was adeptly applied to harmonise our dual control objectives, culminating in an integrated control aim that encapsulates the individual benefits of each. Finally, the lateral dynamic control objective was realised using a direct yaw moment control (DYC) system. The effectiveness of the proposed control objective was validated through hardware-in-the-loop (HIL) simulations.

Real-time Adaptive Control of Electric Vehicle Drives using Artificial Neural Networks

This study examines the use of artificial neural networks (ANNs) in real-time adaptive control for electric vehicle (EV) propulsion systems, with the goal of enhancing performance and efficiency. The neural network-based control system is developed and validated using experimental data that includes vehicle speed, battery temperature, battery voltage, and motor temperature. The neural network demonstrates precise control output predictions by effectively adapting to dynamic changes in input parameters, exhibiting a remarkable level of responsiveness to diverse operating settings. The analysis of the experimental findings reveals a strong correlation between the expected and actual control values, confirming the system's dependability and effectiveness in managing torque and voltage instructions for the electric vehicle (EV). The performance indicators, such as mean squared error (MSE), R-squared, mean absolute error (MAE), and root mean squared error (RMSE), demonstrate a small difference between the anticipated and actual values, indicating that the system has a high level of accuracy and predictive capacity. Furthermore, the system displays remarkable responsiveness to changes in velocity, battery temperature, and voltage, showcasing its capacity to adjust to different driving situations while still staying within acceptable levels of fluctuation. This research highlights the capacity of artificial neural networks (ANNs) to facilitate accurate and flexible control systems for electric vehicles (EVs), representing a substantial advancement in improving the performance, efficiency, and adaptability of electric vehicle propulsion in sustainable transportation systems. The neural network-based control system has been proven to be accurate, responsive, and reliable. This highlights its potential to revolutionize future electric vehicle (EV) technologies and contribute to advancements in real-time adaptive control strategies for environmentally friendly transportation systems.

A Method of Anti-Windup PID Controller for a BLDC-Drive System

This research aims to enhance control systems for Brushless DC (BLDC) motors by introducing Proportional-Integral-Derivative (PID) control as a straightforward yet reliable solution, known for its precision, quick responsiveness, and stability. Emphasizing its suitability for BLDC motor speed control, the study addresses PID controller windup challenges, highlighting anti-windup techniques crucial for maintaining system stability. The primary focus is on improving the performance of an anti-windup PID controller for BLDC motor speed control in electric vehicles. Through simulations and analyses, the research aims to minimize steady-state error and overshooting, contributing to overall operational efficiency. Practical implementation involves optimizing the PID anti-windup controller's gain using the MATLAB PID Tuner and implementing it in the Arduino IDE. Experimental tests, which cover constant and varying step function scenarios, are conducted on the designed hardware. Results show the PID anti-windup controller's superiority, exhibiting significantly lower overshoot values of 5.42% and 3.35% compared to 13.26% and 23.76%, respectively. Despite its higher control action, the traditional PID controller displays a more pronounced overshoot. This research is a significant step toward advancing control systems for electric vehicles and optimizing BLDC motor performance in practical applications.

A PMSM Control System for Electric Vehicle Using Improved Exponential Reaching Law and Proportional Resonance Theory

Permanent magnet synchronous motor (PMSM) provides a very competitive technology for electric vehicle (EV) traction drive with its higher power density and higher efficiency. When the control system of PMSM for EV is affected by the change of motor parameters and the interference from the external environment, the motor system cannot provide high-quality dynamic performance in time. In the paper, an EV motor control strategy based on improved sliding mode and proportional resonance theory is proposed, in which a speed controller is designed based on the improved exponential reaching law. The improved exponential approximation law overcomes chattering in traditional sliding mode control without increasing parameters and improves the anti-interference performance of the EV. In addition, the improved proportional resonance method is used to design the current controller of the PMSM control system, and the combination of the two has been successfully applied to EV for the first time. Finally, the experiment and urban road condition test are carried out to verify. The results show that the proposed scheme can effectively improve the dynamic performance and robustness of the PMSM system for EVs.

Development of an Intelligent Control Strategy for a Hybrid Energy System Integrated with the HEV Drive

Efficient and accurate performance of a control system plays a major role in developing an energy-efficient intelligent control system (ICS) for the Grid-connected Hybrid Electric Vehicle (HEV) applications. The ICS constitutes the control of hybrid energy split among the renewable sources (PV and Fuel Cell), HEV drive control is achieved by controlling the grid-connected inverter and bi-directional DC–DC converter. MPPT is designed in ICS to extract the maximum amount of power from solar PV. RESS control provides the maximum amount of extracted energy from PV and FC. In this proposed system, a bi-directional DCDC converter along with the storage system is designed to transfer energy in forward and reverse directions from source to storage element and storage element to load end in the process of charging and discharging of the battery which constitutes the bidirectional energy transfer. In addition to these control operations, ICS provides HEV drive control by controlling the grid-connected inverter. The operation of the developed ICS is compared with the conventional controllers and the results show that the proposed system with ICS is highly efficient than conventional controllers. The proposed system is tested with a three-phase load and HEV drive, both speed and torque controls are performed for the grid-connected HEV drive, and the system is tested with the different standard drive cycles.

Vacuum booster control system design and fault self-check strategies

In this paper, the software and hardware design of the vehicle vacuum pump control system was carried out based on single-chip microcomputer technology. Combined with the vacuum pump structure and control principle, the hardware design of the power supply, barometric pressure sensor, vacuum pressure sensor, current detection, and motor drive is carried out with the HS08 series single-chip microcomputer as the core. The software strategy design for barometric pressure signal acquisition, vacuum pressure signal acquisition, and vacuum pump driving has been completed, and the dual pressure vacuum pump control system for electric vehicles has been developed. The system can diagnose the common faults of the vacuum power system. Finally, the functional test of the control system is carried out. The results show that the system can work normally and that the design meets the expected requirements.

Cooperative adaptive cruise control system for electric vehicles through a predictive deep reinforcement learning approach

In this paper, a predictive deep reinforcement learning-based control algorithm is introduced for the cooperative adaptive cruise control (CACC) system. The proposed control algorithm achieves an optimal response in the interaction of the host vehicle with the leading vehicles. For this purpose, at first, a new algorithm based on deep neural networks is proposed for modeling and predicting the driving behavior of the leading vehicle, according to the traffic behavior of its surrounding vehicles. Then, the outputs of the predictive model are used as additional states for the upper-level controller of the CACC system, which is designed based on a deep reinforcement learning approach. Therefore, the proposed controller uses conventional states such as relative distance and speed of the host vehicle along with the leading vehicles and employs the speed profile of the leading vehicle in the future time horizons to train the reinforcement learning agent. Thus, the upper-level controller learning process is improved. In addition, for regulating the lower-level controller of CACC system against parametric uncertainties in electric vehicle dynamics, a deep reinforcement learning-based controller is proposed, which can be retuned according to the real-time estimated parameters of vehicle mass and road grade. Simulation results represent the promising performance of the proposed control algorithm for maintaining a balance between travel safety, passenger comfort, and reducing vehicle energy consumption.

ANN Optimized Hybrid Energy Management Control System for Electric Vehicles

ANN Optimized Hybrid Energy Management Control System for Electric Vehicles

Haptic Feedback Remote Control System for Electric Mechanical Assembly Vehicle Developed to Avoid Obstacles

Haptic Feedback Remote Control System for Electric Mechanical Assembly Vehicle Developed to Avoid Obstacles

Reliability Study of Electric Vehicle Drive Motor Control System

This paper focuses on the study of the reliability of the control system for electric vehicle drive motors. The methods employed include fault tree analysis, failure mode and effects analysis, typical fault mode analysis, and Bayesian uncertainty inference. Firstly, typical fault mode analysis was used to thoroughly understand the causes and characteristics of the faults and to predict and evaluate the fault modes. Secondly, a fault tree for the control system of the drive motor was constructed and FMEA analysis was carried out, identifying the potential impact and failure causes of various fault modes. Finally, Bayesian networks were used to infer the potential fault probability of the controller, providing scientific basis and support for the fault diagnosis system, and reducing maintenance and repair costs. The methods used in this paper can provide reference for the reliability research of electric vehicles.

A Dynamics Coordinated Control System for 4WD-4WS Electric Vehicles

With the aggravation of the energy crisis and environmental problems, the new energy electric vehicle industry has ushered in vigorous development. However, with the continuous increase in car ownership, traffic accidents and other issues have gradually attracted widespread attention. Some existing stability coordination control systems often have problems, such as single stability judgment method and strong coupling between different subsystems. Therefore, based on previous research, it is necessary to further optimize the method of judging the vehicle’s stability state, establish clear coordination rules, and reasonably solve the coupling problem between subsystems. This is of great significance for promoting the further development of the electric vehicle industry. Due to four-wheel-distributed driving and four-wheel-distributed steering electric vehicles having the characteristics of integrated driving, flexible steering, and easy fault-tolerant control, it has unique advantages in improving vehicle stability and is a good carrier for designing and constructing the stability coordination control system. In this paper, four-wheel-distributed driving and four-wheel-distributed steering (4WD-4WS) electric vehicles are taken as the research object, and a coordinated control strategy of four-wheel steering and four-wheel drive is proposed. Firstly, in order to realize the accurate judgment of vehicle stability, based on the vehicle two-degree-of-freedom two-track model and magic tire model, this paper uses the phase plane law to divide the phase plane stability region of the vehicle and introduces the stability quantification index PPS-region for the evaluation of vehicle stability. Secondly, a fuzzy variable parameter active rear-wheel steering controller and a compensated yaw moment controller are designed. Then, for the coupling problem between the two controllers, a coordination rule is proposed based on the stability index PPS-region of the phase plane stability region. Finally, a hardware-in-the-loop testbed is built to verify the feasibility of the coordination control strategy proposed in this paper. Experimental results show that: When the vehicle is in different stable states, according to the divided steady state, the control strategy can be correctly switched to the corresponding control strategy, and the work of each subsystem can be reasonably coordinated. Under the continuous gain sine condition, the control algorithm can reduce the maximum amplitude of the yaw rate error response curve by 73% and the side slip angle error response curve by 85%. Compared with a single stability control system, the coordinated stability control algorithm can improve the control effect of yaw rate and side slip angle by 20% and 62.5%. In the case of double lane-change, the control algorithm can reduce the maximum amplitude of the yaw rate error response curve by 68.5% and the side slip angle error response curve by 57.4%. Compared with a single stability control system, the coordinated stability control algorithm can improve the control effect of yaw rate and side slip angle by 40.6% and 44.7%.

Investigation of energy characteristics of the electromechanical control system of 4-drive electric vehicle

Purpose. To determine for the developed electromechanical control system of a 4-drive electric vehicle, the power of the drives and the coefficients of the gearboxes that ensure the minimum energy consumption during acceleration from 0 to 100 km / h in 5 s. Research methods. Mathematical analysis and modeling. Findings. The proposed solution is to determine, for a given vehicle dynamics (acceleration from 0 to 100 km / h in 5 s), by mathematical modeling, values of drive powers and gear ratios, which increase the efficiency of the electromechanical system. The condition for increasing the efficiency is to minimize the consumption of electrical energy for acceleration. The developed computer model of the electromechanical control system of the 4 - drive electric vehicle allows to carry out researches on definition of electromechanical parameters of the control system providing the minimum energy consumption at dispersal from 0 to 100 km / h for 5 s. The current consumed by the drives was determined by studying the energy characteristics of the drives obtained on a previously developed laboratory stand. Gear ratios and drive power have been found to provide minimum power consumption when accelerating a 4-wheel drive electric vehicle. From the obtained data it is seen that for each power studied there are minima in the range of gear ratios 1-3,4. Also in the course of the research it was found that among the studied powers of 30-160 kW there is a drive that uses less energy. For the studied electric vehicle, these are four drives of 75 kW with a reduction factor of 3,3. The presence of minima in the interval between the gear ratios and the interval of the studied drive powers is explained by the variability of the engine efficiency in the load ranges varying from 0.3 to 2 times and the engine shaft speed of 0-3000 rpm, due to varying degrees of loss impact, the dynamic modes of the drive. Originality. The study of energy characteristics based on the developed control system for a 4-wheel drive vehicle makes it possible to find the electromechanical parameters of the system that provide a minimum of energy consumption during acceleration from 0 to 100 km/h in 5 s. Practical value. The electromechanical parameters of the control system of the 4-drive electric vehicle obtained as a result of research allow to receive economy of electric energy at dispersal from 0 to 100 km / h for 5 s in 2-3% within one experimental nominal power of the engine.

Control System for Unmanned Transport Electric Vehicles Based on Microwave Doppler Sensors

Control System for Unmanned Transport Electric Vehicles Based on Microwave Doppler Sensors

Design of a Human-Centric Auto-Climate Control System for Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">As the global automotive industry makes a critical transition from the traditional ICEVs (Internal Combustion Engine Vehicles) to EVs (Electric Vehicles), it faces two conflicting technological challenges: 1) range degradation in cold weather conditions and 2) reducing time to thermal comfort in winter driving in absence of waste heat from the IC engine. Next to the EV drivetrain, the HVAC (Heating Ventilation and Air Conditioning) system is the highest consumer of electric power in the vehicle. To get the occupants to a thermally comfortable state as quickly and efficiently as possible, automotive OEMs (Original Equipment Manufacturers) are exploring microclimate systems that involve localized heating and cooling. Unlike the central HVAC system, localized heating and cooling devices such as climate-controlled seats, steering wheel heaters, neck warmers, etc. directly condition the occupant rather than conditioning the entire cabin environment to provide thermal comfort to the occupant. Consequently, microclimate systems improve time-to-comfort while being energy efficient. In this paper, a general methodology to design a human-centric auto-climate control system is presented. Such a system uses microclimate devices in conjunction with the electric HVAC or heat pump system and the control is based on real-time estimation of heat transfer rates to the occupant. This approach can provide additional benefits such as enhanced personalization along with energy savings and reduced time to comfort. The authors have also highlighted the challenges associated with existing measurement and data acquisition systems including calibration, verification and validation and have recommended a real-time comfort metric measurement system as a possible solution for future development.</div></div>

Learning-based supervisory control of dual mode engine-based hybrid electric vehicle with reliance on multivariate trip information

The combination of electrification and advanced dedicated hybrid engines (DHEs) has been attached with promising prospects for the abatement of fuel consumption and emissions. To explore the potential of this solution, experimental data are used from a high-efficiency DHE operated under dual-mode combustion: the lean-burn spark-induced compression ignition (SICI) and the stoichiometric spark ignition (SI). This work endeavors to develop the integrated energy management and engine control system for such DHE-based plug-in hybrid electric vehicles (PHEVs), to this end, the synergy of artificial intelligent control and trip preview can be exploited to boost the control performance, with the engine combustion process being optimally controlled in transient. This paper presents a multistate deep reinforcement learning (M−DRL)-based energy management strategy (EMS) with a discrete–continuous hybrid action space to select optimal combustion mode while output continuous engine power demand, and its state space is expanded to incorporate the multivariate traffic and terrain information processed by a spatio-temporal data processing (STDP) framework in real time. Comprehensive experiments are carried out to validate the capability of the proposed method, based on a standard driving cycle and a real-world driving cycle in China with real-time traffic data recorded. The results indicate that the proposed M−DRL strategy avoids frequent switchover of engine operation modes as well as achieves more than 93% of the dynamic programming’s performance in terms of the fuel consumption and NOx emissions abatement.

Electric Vehicle Control System by using Controller Area Network Communication

The Controller Area Network (CAN) communication protocol is designed by Bosch in the 1980s as a doubled wired, differential voltage-based serial communication system for control systems. Electric vehicles, like Gasoline vehicles, require controllers to regulate load operation, where conventional point-to-point communication system gets outcome of huge number of cables in the harness. CAN is used to minimize the amount of cables and enable various controllers to transfer data and accomplish complicated jobs simultaneously. This paper presents the design of a CAN-based control system to be used as a replacement for the existing system and shows the development of four modules as well as a proposed implementation for the complete system. Individual modules will be developed initially and later interfaced to the CAN network with CAN controller modules. All modules are connected with a twisted pair of cables for data transmission with CAN.

Control system for unmanned transport electric vehicles based on microwave Doppler sensors

Control system for unmanned transport electric vehicles based on microwave Doppler sensors

Modelling of Electric Vehicle with PID Controller Transfer Function using GA and Model-Reduced Order DRA Algorithm

In this paper, a model of an electric vehicle transfer function using GA and a model-reduced order discrete time realization (DRA) algorithm is presented. The electric vehicle (EV) control system regulates vehicle speed according to the driver’s command signal and brings the vehicle to its equilibrium point, i.e., the desired speed under any abnormal conditions. The controller transfer function is designed based on EV's dynamic differential equations. An infinite-order transcendental transfer function for the EV model is approximated to find high-reliability discrete-time state-space reduced-order models (ROMs). Reduced the robustness and increased the predictions' accuracy of ROM output in terms of accurate numerical simulations of the EV transfer function. Also, the design of the PID controller parameter using optimization techniques (OT) for the tuning of conventional controllers for the desired speed is done using a linearized model. In this study, the performance of a conventional PID controller is compared with that of a GA-based PID controller. In terms of maximum overshoot, peak time, rising time, settling time, and steady state error, the simulation results show that the suggested controller is preferable.

Regen-braking-capable ZF Brake Control System for Electric Vehicles by VW

Regen-braking-capable ZF Brake Control System for Electric Vehicles by VW