Drive Control Strategy Research Articles

Driving Control Strategy and Specification Optimization for All-Wheel-Drive Electric Vehicle System with a Two-Speed Transmission

Equipping electric vehicles with a two-speed gearbox allows for achieving high torque and maximum speed through appropriate gear ratio adjustments. Additionally, tuning motor operating points to efficient zones, considering energy efficiency, significantly enhances the vehicle’s overall performance. This paper presents an AWD system configuration method, integrating a two-speed transmission to improve energy efficiency and driving performance through front and rear motor torque distribution and powertrain specification optimization. Based on vehicle simulations conducted using MATLAB/Simulink, a strategy for torque distribution between the front/rear axles was established using fuzzy logic, considering energy efficiency and driving stability. Furthermore, a multi-objective optimization was performed using a surrogate model trained through MATLAB parallel simulations. When the optimization results were applied to various vehicle specifications, it was observed that energy efficiency was improved, and acceleration performance was increased compared to a baseline vehicle without optimization.

Research on a Chassis Stability Control Method for High-Ground-Clearance Self-Propelled Electric Sprayers

In response to the complex working conditions and poor driving stability of high-clearance self-propelled sprayers, a nonlinear model of the chassis power system was established based on the independently controllable torque of each wheel of the developed electric sprayer. A layered-architecture chassis drive control strategy was formulated, and a stability control framework comprising an instability judgment module, an upper controller, and a lower controller was constructed based on the analysis of the impact of the centroid slip angle, the yaw rate, and the wheel slip rate on driving stability. An ideal reference model was established based on the seven-degree-of-freedom model of the sprayer, and the current state of the sprayer body was determined using the instability judgment module. A drive anti-slip controller and a yaw moment controller based on fuzzy PID theory and sliding mode control theory were designed. Additionally, an optimal torque distribution algorithm was developed based on tire characteristics to rationally allocate drive torque to each wheel, ensuring the stability of the sprayer during operation. Simulation tests were conducted using MATLAB/Simulink to evaluate the sprayer under four different driving conditions during transport and field operations. The test results showed that the “SMC + optimal distribution” control method in the chassis stability control strategy reduced the maximum deviations of the yaw rate and centroid slip angle by an average of 89.5% and 13.6%, respectively, compared to no control. The wheel slip rate during straight driving was well maintained at around 15%, enhancing the driving stability of the sprayer.

Energy-saving drive control strategy for electric tractors based on terrain parameter identification

Emissions from agricultural production already have a significant impact on the global environment. New energy, zero-emission electric tractors have great potential for application. How to further reduce energy consumption under the premise of ensuring operational stability is an urgent problem to be solved to realize the broad application of electric tractors. For this purpose, this paper proposed an energy-saving drive control strategy for electric tractors based on terrain parameter identification. At first, the longitudinal dynamics model and load transfer model of the electric tractor were established by considering the terrain information of the area to be operated. Mathematical principles for adjusting wheel slip to reduce energy consumption in electric tractors are presented. Then, a wheel longitudinal force estimation algorithm was designed based on the principle of sliding-mode observer and dynamics compensation. To achieve energy-saving drives for electric tractors, a particle filter based on a wheel-soil model was designed to identify terrain parameters in real time and calculate the optimal slip to control the torque of the drive motor. Lastly, the strategy is validated by hardware-in-the-loop (HIL) simulation tests and real-vehicle tests. HIL tests show that this strategy results in a 25.35% increase in operational speed stability and a 6.79% reduction in operational energy consumption compared to the conventional drive control strategy. The real vehicle test shows a 45.09% increase in operating speed stability and a 9.51% reduction in total energy consumption of the proposed method. The proposed drive control strategy reduces operational energy consumption and operational costs on the premise of ensuring the stability of operational speed, which is conducive to the implementation of cleaner production.

Evolving Power Factor Correction in Solar Water Pumping Systems through Zeta Converter-Based BLDC Motor Drives

The aim of this paper is to present the design and implementation of a new power conditioning system for a stand-alone solar-powered water pumping system. The system integrates a Brushless DC (BLDC) motor drive with a Power Factor Correction (PFC) Zeta converter to improve energy efficiency and reliability. In order to power BLDC motors for water pumping applications—which are essential for irrigation in isolated locations without access to the grid—the suggested solution attempts to overcome the difficulties related to the direct use of variable solar power. The PFC Zeta converter, which is the system's central component, is made to maximize power extraction from photovoltaic (PV) panels under fluctuating solar irradiation, guarantee high power factor, and reduce harmonic distortion in the electrical grid. A BLDC motor that powers a water pump is efficiently operated by this converter, which also optimizes the DC link voltage. A BLDC motor is especially well-suited for solar-powered applications due to its increased efficiency, dependability, and longer lifespan when compared to traditional induction motors. In order to continuously modify the PV panels' operating point to the maximum power point and maximize the amount of energy they capture, a Maximum Power Point Tracking (MPPT) algorithm is incorporated into the PFC Zeta converter's control strategy. The BLDC motor drive control strategy is carefully designed to guarantee seamless operation and flexibility in response to the fluctuating output from the solar panels, upholding the best possible pumping action in any solar situation. The system's capacity to sustain high efficiency and power factor under a variety of operating circumstances is demonstrated by the experimental findings. When the PFC Zeta converter is used, the solar-powered water pumping system performs much better overall. It shows a notable increase in energy conversion efficiency and lowers total harmonic distortion, which prolongs the pumping system's lifespan. By offering a solid method for raising the effectiveness and dependability of solar-powered water pumping devices, this study advances the sector and holds the possibility of a long-term water supply option for agricultural irrigation in isolated locations. In addition to utilizing renewable solar energy's environmental advantages, the suggested solution tackles the real-world difficulties associated with putting dependable and effective water pumping equipment into off-grid applications.

An intelligent human-machine interaction-based longitudinal control strategy for autonomous vehicles

In the foreseeable future, the anticipation is that intelligent vehicles will transition to a mode where the intelligent driving system collaborates seamlessly with the human driver. This harmonious integration between the driver and the intelligent control system holds paramount significance for the successful execution of driving tasks, ultimately contributing to the development of more advanced and user-friendly automobiles. A pivotal element in advancing from assisted to autonomous driving lies in the establishment of a human-machine co-driving mode. This research delineates a longitudinal control strategy tailored for intelligent vehicles featuring human-machine interaction. The approach involves the creation of a personalized safe distance model for car-following by collecting driver characteristic parameters. Focused on the car-following methodology, this study formulates the kinematics state space equation, performance index function, and constraint conditions governing car-following dynamics. Subsequently, a car-following control strategy is devised based on model predictive control (MPC), which is addressed through rolling optimization techniques. Building upon this foundation, a human-machine driving control strategy is proposed to dynamically allocate driving authorities in real-time. This strategy takes into account speed and vehicle distance risk as two-dimensional inputs, employing a cooperative driving control strategy within the dual-drive dual-control system. The proposed method was validated in a simulated environment.

Ride Comfort Prediction on Urban Road Using Discrete Pavement Roughness Index

The prediction of ride comfort holds significant potential for enhancing the driving experience of both human drivers and autonomous vehicles, as it is closely correlated with pavement roughness. However, in urban road scenarios, the presence of shorter road segments and local irregularities introduces added complexity to ride comfort prediction. To better capture and characterize the irregularities and short road sections’ unevenness, we adopt the discrete roughness index (DRI) instead of the commonly used international roughness index (IRI) for assessing road profile unevenness, which is more suitable for urban roads. Ride comfort prediction is developed through numerical simulations using an eight-degree-of-freedom full-car model. The maximum transient vibration value (MTVV) is adopted to assess ride comfort. Through comparing the correlations between the MTVV and pavement roughness indices, it is indicated that the fitting degree of MTVV-DRI outperforms that of MTVV-IRI on short sections. Then, a set of speed-related DRI thresholds to estimate ride comfort distribution on a given road section is proposed, with considerations of vehicle speed, time period, and wheel paths. A hyperbolic-tangent-based speed control strategy is also proposed to avoid abrupt speed and acceleration changes during deceleration. This prediction method can assist drivers or autonomous vehicles in generating driving control strategies and maintaining a high level of ride comfort.

Research on Energy Storage Flywheel Motor Drive Control Technology

Currently there is a way to use for energy storage: batteries for chemical energy storage, super for electric field energy storage, and flywheel batteries for mechanical energy storage. Flywheel energy storage has the advantages of high energy storage density, high efficiency, short charging time, no pollution, wide applicability, no noise, long service life, easy maintenance and continuous operation. Recently the most promising and competitive new energy storage technology - flywheel energy storage technology, relative to other forms of energy storage, by domestic and foreign experts unanimously optimistic. This paper will focus on the composition and operation principle of flywheel energy storage system, the classification of drive control strategy, charging control strategy, discharge control strategy and other aspects.

Energy-Saving Impact and Optimized Control Scheme of Vertical Load on Distributed Electric Wheel Loader

During the operation of a wheel loader, the external load acting on the bucket undergoes many changes, resulting in significant changes in the load ratio on the front and rear axles. For this reason, controlling a standard wheel loader is not trivial. In addition, in the case of a distributed electric wheel loader (DEWL), the operating control algorithm is often complex and is, therefore, the subject of optimization studies. This study compared the electric power consumption across different vertical loads, speeds, and travel directions for single-front, single-rear, and dual-motor configurations, both during transporting and pre-shoveling operations. The analysis led to the development of control rules based on energy-saving objectives. Under the shoveling condition, it was observed that vertical loads can lead to an insufficient driving force and skidding, necessitating the proposal of a new optimized control scheme. The results revealed that the optimal solution for transporting is the single-motor drive control scheme without a mechanical connection between the front and rear motor. With the single-motor control scheme, comparing the preferred controlled motor with the unselected motor under different loads, the average electrical power savings for forward, backward, and circling were at least 3.51%, 3.12%, and 0.34%, respectively. Under the pre-shoveling condition, the optimal control scheme was identified as the single rear motor control scheme, effectively reducing electrical power consumption. In response to the issues encountered during the shoveling condition, an economical solution involving the modification of the front axle transmission ratio has been proposed, along with an optimized control scheme based on vertical load variations.

Design and optimization of driving mode control strategies for front-and-rear-axle-independent-drive-type electric vehicle

Design and optimization of driving mode control strategies for front-and-rear-axle-independent-drive-type electric vehicle

Multi-Intersection Ecological Cooperative Control of a Pure Electric Vehicle Platoon with Dual-Motor Dual-Axis Drive

The advent of intelligent transportation system technology has expedited the advancement of eco-driving controls. Research on energy-saving cooperative control of intelligent connected vehicles in intelligent transportation environments is still in need of ongoing refinement. This article proposes a dual-level ecological driving control strategy for a pure electric vehicle platoon with dual-motor dual-axis drive in an urban multi-intersection environment. The upper-level design incorporates optimal platoon speed decision-making based on the nonlinear model predictive control algorithm. The multi-objective optimization function considers three scenarios: energy-optimal, time-optimal, and energy-time-optimal. It also takes into account platoon following control and passing efficiency, ensuring smooth passage through multi-intersections without interruptions. Built on the upper level’s optimal speed design, an energy management strategy is proposed for achieving optimal torque distribution of pure electric vehicles with front and rear independent drives. Finally, the upper and lower levels are jointly simulated in real-time. The results indicate that, compared to the energy-optimal mode, the average passage time decreased by 14.6% and 5.97% in the time-optimal and energy-time-optimal modes, respectively. Under average torque distribution, the time-optimal and energy-time-optimal modes increased the energy consumption of the vehicle platoon by 21.05% and 5.44%, respectively, compared to the energy-optimal mode. Under the optimal torque allocation strategy, the time-optimal and energy-time-optimal modes increased energy consumption by 15.31% and 6.11%, respectively, compared to the energy-optimal mode. In contrast to the average torque allocation strategy, the optimal torque allocation strategy for the dual-motor vehicles reduced energy consumption by 10.18%, 14.44%, and 9.61% in the energy-optimal mode, time-optimal mode and energy-time-optimal mode, respectively.

A Hierarchical Driving Control Strategy Applied to Parallel SiC MOSFETs

SiC (silicon carbide) MOSFETs have been extensively used in the power electronics industry due to their exceptional characteristics. First, it was found in this study that their driving loss is larger than their conduction loss in high-frequency applications. Based on this finding, this study proposes a hierarchical driving control strategy for improving the parallel-converter efficiency of SiC MOSFETs under light loads. Efficiency under light loads is of great importance for battery-based energy storage systems. To minimize the sum of the conduction loss and driving loss in parallel devices, this study proposes a current-monitoring hierarchical driving strategy based on an active-clamped flyback converter. By monitoring the output current of the converter, the strategy minimizes the sum of the driving and conduction losses by switching the driving state under different loads. The results of simulations indicate the effectiveness of the load-current-monitoring strategy. To verify the effectiveness of this method, a principle prototype of two SiC MOSFETs connected in parallel at 12 V/5 A was fabricated and tested, and the test results showed that there was a maximum improvement of 1.4% in the converter’s efficiency when the load current was in the range of 0.5–1.5 A.

Structural design and experimental evaluation of a coarse-fine parallel dual-actuation XY flexure micropositioner with low interference behavior.

This paper proposes a novel two degree of freedom large range coarse-fine parallel dual-actuation flexure micropositioner (CFPDFM) with low interference behavior. First, the structure and working principle of the CFPDFM are introduced. Then, based on the stiffness matrix method, the analytical models of the motion range, input stiffness, and amplification ratio of the mechanism are established. Subsequently, the accuracy of the analytical model is verified by finite element analysis and experiments. Moreover, the dual-servo cooperative drive control strategy is designed to improve the closed-loop positioning ability of the CFPDFM. Finally, the CFPDFM experimental system is built to verify the plane trajectory tracking performance. The results reveal that the micropositioner can achieve a total range of 3.02 × 3.11mm2, a resolution of ≤40nm, low interference performance of coarse and fine actuators, good cross-axis decoupling, and planar complex trajectory tracking performance, while possessing a compact structure. It can be used in many plane positioning situations requiring large range and high precision.

Multi-Objective Optimization of Vehicle-Following Control for Connected Electric Vehicles Based on Deep Deterministic Policy Gradient

<div>Eco-driving plays an increasingly important role in intelligent transportation systems, where the vehicle-following economy and safety are receiving increasing attention in recent years. In this context, this article proposes a novel deep deterministic policy gradient (DDPG)-based driving control strategy for connected electric vehicles (CEVs) under vehicle-following scenarios. Three original contributions make this article distinctive from existing studies. First, a multi-objective optimization problem including driving safety, passenger comfort, and the driving economy for the following vehicle is established, in which the battery capacity degradation cost is first considered in the vehicle-following problem. Second, a DDPG-based driving control strategy is proposed where a penalty is introduced into the multi-objective optimization reward function to accelerate the convergence process. Third, the coupling relationship of the three objectives is carefully studied. Different weighting factors are tested and analyzed to balance the three objectives. Detailed discussion and comparison under different driving cycles validate the superiority of the proposed method, e.g., a 16–31% reduction of battery capacity degradation cost with better safety and comfort, compared with existing vehicle-following strategies. This work makes a potential contribution to the artificial intelligence application of intelligent transportation systems.</div>

Modeling and Simulation of Pure Electric Passenger Vehicle Based on MATLAB and AVL CRUISE

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.

Research on TD3-Based Distributed Micro-Tillage Traction Bottom Control Strategy

Due to its flexibility and versatility, the electric distributed drive micro-tillage chassis can be used more often in the future in Intelligence agriculture scenarios. However, due to the complex working conditions of the agricultural operation environment, it is a challenging task to distribute the torque demand of four wheels reasonably and effectively. In this paper, we propose a drive torque allocation strategy based on deep reinforcement learning to ensure straight-line retention and energy saving, using a distributed electric traction chassis for greenhouses as the research object. The torque assignment strategy can be represented as a Markovian decision process, and the approximate action values and policy functions are obtained through an Actor–Critic network, and the Twin Delayed Deep Deterministic Policy Gradient (TD3) is used to incorporate the vehicle straight-line retention rate into the cumulative reward to reduce energy consumption. The training results under plowing working conditions show that the proposed strategy has a better straight-line retention rate. For typical farming operation conditions, the proposed control strategy significantly improves the energy utilization and reduces the energy by 10.5% and 3.7% compared to the conventional average torque (CAT) distribution strategy and Deep Deterministic Policy Gradient (DDPG) algorithm, respectively. Finally, the real-time executability of the proposed torque distribution strategy is verified by Soil-tank experiments. The TD3 algorithm used in this study has stronger applicability than the traditional control algorithm in dealing with continuous control problems, and provides a research basis for the practical application of intelligent control algorithms in future greenhouse micro-tillage chassis drive control strategies.

Energy Savings in Elevators by Using a Particular Permanent-Magnet Motor Drive

This paper presents the energy savings achieved by using a particular three-phase permanent-magnet motor drive control strategy in an elevator application. The proposed control methodology, based on a particular variable-amplitude variable-frequency voltage control pattern technique implemented in a permanent-magnet motor, is compared to a standard induction motor elevator case. By adopting appropriate simultaneous changes in the amplitude and frequency of the motor voltage, high speeds can be attained in conjunction with smooth starting and stopping actions involving a reduced supply current during the respective movement of the elevator. In addition, this method exhibits a high power factor with a good driving quality. The control technique introduced achieves the levelling-off of the floor and the group movement of the system using in the programmable memory a speed pattern that is generated targeting proportionality to the position of the lift. In that respect, significant energy savings can be obtained, which, depending on the type of motor implemented, can be up to 30% compared to the conventional techniques. These improvements can be attained with the appropriate handling of the applied pulse width modulation techniques. Various simulated and experimental results are given, illustrating the respective energy savings achieved with the proposed methodology.

Research on automatic driving control strategy based on improved model predictive control

As a new technology direction in the automotive industry in recent years, automatic driving technology has played an important role in reducing traffic accidents, promoting energy conservation and emission reduction, and improving the efficiency of high-speed and urban roads. As one of the key technologies of autonomous vehicles, the performance of control algorithm greatly affects the smoothness of vehicle driving and the accuracy of track tracking. In order to consider the lateral control and longitudinal control accuracy at the same time, and ensure that the vehicle travels according to the given trajectory, this paper first establishes the vehicle kinematics model, and improves the traditional model predictive control algorithm, which realizes the high dynamic control of the desired trajectory while ensuring the vehicle stability and comfort. Finally, based on the built real vehicle test platform for automatic driving, the acceleration and deceleration test and the high-speed serpentine test are designed. By analyzing the test data, it can be seen that the control algorithm proposed in this paper can effectively achieve the vehicle trajectory tracking, and the control accuracy is improved.

Design and Control of a Novel Multi-Degree-of-Freedom Spherical Induction Motor

To realize the omni-directional steering function of urban micro electric vehicle and avoid the defects of traditional multi-degree-of-freedom (multi-DOF) drive, a multi-DOF spherical induction motor (SIM) as hub motor is presented, which is used to replace the conventional wheel and steering system to drive vehicles directly. SIM is mainly composed of three arc-shaped stators and one spherical rotor. And torque is directly output from the surface of the spherical rotor. The structure parameters of SIM are determined based on the design methodology of linear induction motor (LIM), and a finite element model (FEM) is established. Meanwhile, the field-oriented control (FOC) system of SIM is designed, and a multiple physics field co-simulation platform is constituted. By electromagnetic transient simulation and co-simulation, the effectiveness of structure parameters and FOC system are verified systematically. To realize the multi-dimension drive of SIM, the torque model is analyzed, and a multi-DOF drive control strategy is proposed, which is realized by mathematical modeling. The results indicate that SIM can achieve two rotational DOF drive without the angle limitation, and the control system has appropriate dynamic and tracking performances. The design scheme and control strategy proposed are reasonable.

Design of the swimming system of a bionic jellyfish robot for seabed exploration

The unmanned sampling and analysis after return of seabed rocks is an important method for deep-sea mineral resources exploration. A novel underwater robot for ultrasonic drilling and sampling tasks of seabed rocks is proposed based on the bionic design method in this paper. The jellyfish-like structure is adopted for the swimming system of the robot, which can move flexibly and stably with low noise, outstanding cruising ability and strong environmental adaptability. The mechanical system and driving control strategy of the robot are designed in this paper. Based on co-simulation method of ADAMS and MATLAB software, the structural parameters of the transmission mechanism of the swimming system are optimized to get larger swing angle under relative less driving power, which will contribute to the improvement of propulsion efficiency. Through the fluid dynamics simulation and analysis in FLUENT software, the propulsion process can be visualized and the propulsion mechanism is revealed. To verify the feasibility of the design, a robot prototype is developed and the experimental platform for the robot's swimming test is built. The underwater experiments on motion characteristics for the robot demonstrates that the maximum propulsive force generated by the swimming system can reach about 16N, and the propulsion speed can reach 0.19m/s. The design and development of the bionic robot for seabed ultrasonic drilling and sampling missions provides new ideas for seabed resource exploration technology.

Proximal Policy Optimization-Based Driving Control Strategy of Connected Cruise Vehicle Platoons to Improve Traffic Efficiency and Safety

This paper investigates connected cruise control (CCC) based on the deep reinforcement learning algorithm to mitigate oscillations and improve traffic safety in stop-and-go waves. A proximal policy optimization (PPO)-based control strategy is proposed for connected and autonomous vehicles (CAVs) in a heterogeneous longitudinal car-following platoon. The method receives the velocity and position signals of n human-driven vehicles ahead through wireless vehicle-to-vehicle communication to obtain an appropriate driving behavior and improve traffic efficiency and safety in real-time. The effectiveness and advantage of the proposed strategy are verified by using traffic simulation software (Simulation of Urban Mobility) for two simulation scenarios of trajectory curves with noticeable acceleration changes of the leading vehicle and two sections of actual traffic speed data. The results show that the proposed PPO-CCC method can successfully suppress the speed oscillations and improve the travel efficiency of CAVs in the car platoon.