Distributed Drive Electric Vehicle Research Articles

Distributed-Drive Vehicle Lateral-Stability Coordinated Control Based on Phase-Plane Stability Region

The lateral stability control of vehicles is one of the most crucial aspects of vehicle safety. This article introduces a coordinated-control strategy designed to enhance the handling stability of distributed-drive electric vehicles. The upper controller uses active front steering and direct yaw moment-control controllers designed based on sliding-mode control theory. The lower controller optimally allocates control inputs to the upper controller, considering factors such as load transfer and tire load rate. It divides the stability region by relying on the phase plane and develops a coordinated-control strategy based on the degree of deviation of the vehicle state from the stability region. The results of the simulation experiments demonstrate that the proposed control strategy effectively improves handling stability under extreme working conditions.

Adaptive Nonsingular Fast Terminal Sliding Mode-Based Direct Yaw Moment Control for DDEV under Emergency Conditions

Adaptive Nonsingular Fast Terminal Sliding Mode-Based Direct Yaw Moment Control for DDEV under Emergency Conditions

Sideslip Angle Estimation for Distributed Drive Electric Vehicles Based on Robust Unscented Particle Filter

An accurate and reliable sideslip angle is crucial for active safety control systems and advanced driver-assistance systems (ADAS). The direct measurement method of the sideslip angle suffers from challenges of high costs and environmental sensitivity, so sideslip angle estimation has always been a significant research issue. To improve the precision and robustness of sideslip angle estimation for distributed drive electric vehicles (DDEV) in extreme maneuvering scenarios, this paper presents a novel robust unscented particle filter (RUPF) algorithm based on low-cost onboard sensors. Firstly, a nonlinear dynamics model of DDEV is constructed, providing a theoretical foundation for the design of the RUPF algorithm. Then, the RUPF algorithm, which incorporates the unscented Kalman filter (UKF) to update importance density and utilizes systematic random resampling to mitigate particle degradation, is designed for estimation. Eventually, the availability of the proposed RUPF algorithm is validated on the co-simulation platform with non-Gaussian noises. Simulation results demonstrate that RUPF algorithm attains a higher precision and stronger robustness compared with the traditional PF and UKF algorithms.

Coordinated Longitudinal and Lateral Motion Control for Distributed Drive Electric Vehicles With Mechanical Elastic Wheels

The distributed drive electric vehicle (DDEV) with active front wheel steering is an over-actuated system. For such a system, more optimization targets should be considered to make the system better in addition to meeting the driving command of drivers and keeping the vehicle stable. The slip energy generated by excessive tire slipping reduces the utilization of the output energy of the motor, resulting in a reduction of electric vehicle mileage. In order to improve the vehicle performance, a coordinated longitudinal and lateral controller is designed for DDEV with mechanical elastic wheels (MEWs). The motion control layer of the hierarchical controller adopts the integral sliding mode control (ISMC) to determine the desired longitudinal force, lateral force and yaw moment as virtual inputs of the vehicle. Then, a novel tire force distribution strategy is proposed to dynamically assign the virtual inputs among front wheels and four motors with the goal of optimizing the vehicle stability and longitudinal tire slip energy dissipation. To characterize tire slip energy more accurately, the brush model matching MEW is deduced in the combined slip conditions. The proposed controller is evaluated on the hardware-in-the-loop (HIL) test platform under different driving conditions. Simulation results demonstrate that this controller not only ensures the vehicle handling stability performance, but also improves the utilization of motor output energy and reduces tire wear.

Distributed Intelligent Vehicle Path Tracking and Stability Cooperative Control

To enhance the path tracking capability and driving stability of intelligent vehicles, a controller is designed that synergizes active front wheel steering (AFS) and direct yaw moment (DYC), specifically tailored for distributed-drive electric vehicles. To address the challenge of determining the weight matrix in the linear quadratic regulator (LQR) algorithm during the path tracking design for intelligent vehicles on conventional roads, a genetic algorithm (GA)-optimized LQR path tracking controller is introduced. The 2-degree-of-freedom vehicle dynamics error model and the desired path information are established. The genetic algorithm optimization strategy, utilizing the vehicle’s lateral error, heading error, and output front wheel steering angle as the objective functions, is employed to optimally determine the weight matrices Q and R. Subsequently, the optimal front wheel steering angle control (AFS) output of the vehicle is calculated. Under extreme operating conditions, to enhance vehicle dynamics stability, while ensuring effective path tracking, the active yaw moment is crafted using the sliding mode control with a hyperbolic tangent convergence law function. The control weights of the sliding mode surface related to the center-of-mass lateral declination are adjusted based on the theory of the center-of-mass lateral declination phase diagram, and the vehicle’s target yaw moment is calculated. Validation is conducted through Matlab/Simulink and Carsim co-simulation. The results demonstrate that the genetic algorithm-optimized LQR path tracking controller enhances vehicle tracking accuracy and exhibits improved robustness under conventional road conditions. In extreme working conditions, the designed path tracking and stability cooperative controller (AFS+DYC) is implemented to enhance the vehicle’s path tracking effect, while ensuring its driving stability.

Coordinated control of path tracking and yaw stability for distributed drive electric vehicle based on AMPC and DYC

Maintaining both path-tracking accuracy and yaw stability of distributed drive electric vehicles (DDEVs) under various driving conditions presents a significant challenge in the field of vehicle control. To address this limitation, a coordinated control strategy that integrates adaptive model predictive control (AMPC) path-tracking control and direct yaw moment control (DYC) is proposed for DDEVs. The proposed strategy, inspired by a hierarchical framework, is coordinated by the upper layer of path-tracking control and the lower layer of direct yaw moment control. Based on the linear time-varying model predictive control (LTV MPC) algorithm, the effects of prediction horizon and weight coefficients on the path-tracking accuracy and yaw stability of the vehicle are compared and analyzed first. According to the aforementioned analysis, an AMPC path-tracking controller with variable prediction horizon and weight coefficients is designed considering the change of vehicle speed in the upper layer. The lower layer involves DYC based on the linear quadratic regulator (LQR) technique. Specifically, the intervention rule of DYC is determined by the threshold of the yaw rate error and the phase diagram of the sideslip angle. Extensive simulation experiments are conducted to evaluate the proposed coordinated control strategy under different driving conditions. The results show that, under variable speed and low adhesion conditions, the vehicle yaw stability and path-tracking accuracy have been improved by 21.58% and 14.43%, respectively, compared to AMPC. Similarly, under high speed and low adhesion conditions, the vehicle yaw stability and path-tracking accuracy have been improved by 44.30% and 14.25%, respectively, compared to the coordination of LTV MPC and DYC. The results indicate that the proposed adaptive path-tracking controller is effective across different speeds. Furthermore, the proposed coordinated control strategy successfully enhances the vehicle stability while maintaining good path-tracking accuracy even under extreme conditions.

Research on Collaborative Control of Differential Drive Assisted Steering and Active Front Steering for Distributed Drive Electric Vehicles

A collaborative control strategy for distributed drive electric vehicles (DDEVs) focusing on differential drive assisted steering (DDAS) and active front steering (AFS) is proposed to address the issues of sudden torque changes, reduced steering characteristics, and weak collaborative control capabilities caused by the coupling of the AFS and DDAS systems in DDEVs. This paper establishes a coupled dynamic model of the AFS and DDAS systems and, on this basis, designs AFS controllers for yaw velocity feedback control and DDAS controllers for steering wheel torque control, respectively. Additionally, it analyzes the interference factors of the two control systems and develops a collaborative control strategy for DDAS and AFS; this control strategy establishes a corner motor correction module, steering wheel torque correction module, and assistance correction module. Co-simulation is carried out on Matlab/Simulink and the Carsim platform to verify the correctness of the model under typical working conditions; to reduce the sudden change in the steering wheel torque caused by AFS additional angle interventions; to improve the poor steering characteristics caused by DDAS, introducing additional yaw torque; to greatly enhance the collaborative control effect; and to meet the requirements for vehicle handling stability, portability, and safety.

DYC Design for Autonomous Distributed Drive Electric Vehicle Considering Tire Nonlinear Mechanical Characteristics in the PWA Form

This paper presents a novel direct yaw moment control (DYC) strategy for autonomous distributed drive electric vehicle (DDEV) to improve path-following driving stability under critical maneuvers. Due to the fact that the tire mechanical characteristics show highly nonlinear under those critical conditions, the piecewise affine (PWA) identification method is chosen in this work to model the tire nonlinear mechanical characteristics since it shows best model accuracy/simplicity compromise for system control design. On this basis, to properly reflect the coupling behaviors between longitudinal motion and lateral motion, the vehicle dynamics model which considers the tire nonlinear mechanical characteristics in the PWA form is established. By analyzing the phase plane of vehicle nonlinear model, a novel external yaw moment calculation method based on global fast terminal sliding mode (GFTSM) control method is applied to the upper layer of DYC architecture for eliminating the errors of yaw rate and sideslip angle. Then, different weight coefficients are allocated to obtain the final external yaw moment. In the lower controller, the optimal four-wheel torque is distributed according to a concise and accurate algorithm in which adaptive weight coefficients are considered. The simulation results verify the effectiveness of the proposed control strategy in improving the driving stability performance of autonomous DDEV against two other control strategies under three extreme conditions.

An Energy-Oriented Torque-Vector Control Framework for Distributed Drive Electric Vehicles

The over-actuated characteristics of distributed drive electric vehicles (DDEVs) provide a flexible platform to pursue higher holistic performance. This paper proposes a dual-model predictive control (MPC)-based hierarchical framework to realize the energy saving while improving the handling stability for DDEVs. The upper layer allocates the torque vector through the front/rear axles, which can provide a high-efficiency zone for the in-wheel motors and reduce the energy consumption. The lower layer generates a direct-yaw-moment control input by differential longitudinal forces of the left/right wheels to ensure the vehicle handling stability. Considering the time-varying state variables, a linear-time-varying model-predictive-control (LTV-MPC) method is adopted to guarantee the accuracy of the model. The combined magic formula tire model is used to modify the tire parameters, including tire longitudinal stiffness and cornering stiffness. The soft constraint constructed by β-γ phase plane is introduced in the LTV-MPC to ensure the vehicle stability, based on which, a relaxation factor is designed to reduce the energy consumption due to the excessive direct-yaw-moment control inputs. The simulation and hardware-in-the-loop (HIL) test results show that the proposed control framework can effectively reduce the energy consumption for DDEVs while ensuring the vehicle handling stability.

Combined Estimation of Vehicle Dynamic State and Inertial Parameter for Electric Vehicles Based on Dual Central Difference Kalman Filter Method

Distributed drive electric vehicles (DDEVs) possess great advantages in the viewpoint of fuel consumption, environment protection and traffic mobility. Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size, and inertial parameter has seldom been tackled and systematically estimated. This paper presents a dual central difference Kalman filter (DCDKF) where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters, such as vehicle sideslip angle, vehicle mass, vehicle yaw moment of inertia, the distance from the front axle to centre of gravity. The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs. The four-wheel nonlinear vehicle dynamics estimation model considering payload variations, Pacejka tire model, wheel and motor dynamics model is developed, the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory. To address system nonlinearities in vehicle dynamics estimation, the DCDKF and dual extended Kalman filter (DEKF) are also investigated and compared. Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-Carsim®. The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions. This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.

Research on the High-Speed Collision Avoidance Method of Distributed Drive Electric Vehicles

High-speed autonomous emergency collision avoidance for intelligent vehicles is a challenging task. Many existing studies have not considered the effect of the road adhesion coefficient in the vehicle high-speed collision avoidance planning layer. Additionally, in lateral control, the effect of strong coupling of vehicle motion on the system model is not considered. This effect increases the uncertainty in the collision avoidance process and correlates with driving safety. In this regard, this paper conducted relevant studies. First, a distributed drive electric vehicle (DDEV) with controlled longitudinal speed was selected as the research platform and derived the transformed square-root cubature Kalman filter (T-SCKF) algorithm with cubature point sampling rule transformation to estimate the road adhesion coefficient. Second, the road adhesion coefficient information was used to calibrate the following vehicle safety distance model and the lane change collision avoidance lateral acceleration information was used to determine the vehicle autonomous emergency collision avoidance trajectory and to optimize the planning decision of the vehicle collision avoidance process. Last, the DDEV was laterally controlled to complete the tracking of the high-speed emergency collision avoidance trajectory by the adaptive model predictive control (Adaptive MPC) method with real-time updating of the system model. The relevant validation shows that the T-SCKF algorithm proposed in this paper can effectively estimate the road adhesion coefficient. The tracking control of the high-speed collision avoidance trajectory by the adaptive MPC method has better tracking accuracy and stability than traditional MPC and linear quadratic regulator (LQR) methods.

Parameter Optimization of Model Predictive Direct Motion Control for Distributed Drive Electric Vehicles Considering Efficiency and the Driving Feeling.

This paper presents a novel motion control strategy based on model predictive control (MPC) for distributed drive electric vehicles (DDEVs), aiming to simultaneously control the longitudinal and lateral motion while considering efficiency and the driving feeling. Initially, we analyze the vehicle's dynamic model, considering the vehicle body and in-wheel motors, to establish the foundation for model predictive control. Subsequently, we propose a model predictive direct motion control (MPDMC) approach that utilizes a single CPU to directly follow the driver's commands by generating voltage references with a minimum cost function. The cost function of MPDMC is constructed, incorporating factors such as the longitudinal velocity, yaw rate, lateral displacement, and efficiency. We extensively analyze the weighting parameters of the cost function and introduce an optimization algorithm based on particle swarm optimization (PSO). This algorithm takes into account the aforementioned factors as well as the driving feeling, which is evaluated using a trained long short-term memory (LSTM) neural network. The LSTM network labels the response under different weighting parameters in various working conditions, i.e., "Nor", "Eco", and "Spt". Finally, we evaluate the performance of the optimized MPDMC through simulations conducted using MATLAB and CarSim software. Four typical scenarios are considered, and the results demonstrate that the optimized MPDMC outperforms the baseline methods, achieving the best performance.

Coordinated control of longitudinal and lateral movements considering dynamics for distributed drive electric vehicle platoon on curved roads

The control goal of the vehicle platoon is to maintain the same speed and desired distance. Most current studies are based on simplified vehicle models, and the leader’s state is also rarely considered. However, under complex working conditions, such as low adhesion or curves, the lateral stability of the platoon will be difficult to guarantee, and tracking errors of desired speed and spacing may further increase. To solve the above problems, a new hierarchical coordinated control strategy is proposed. Taking distributed drive electric vehicles (DDEVs) as research objects, the upper control level establishes a stability situation assessment model according to the vehicle’s dynamic characteristics. At the medium control level, variable weight model predictive control (MPC) coordinates conflicts between longitudinal tracking and lateral stability. A correction term is also introduced to revise the prediction model. At the same time, the weight of different control objectives of the leader and following vehicle was adjusted, respectively. Torque distribution is carried out at the lower level controller. Finally, the control strategy is tested on a hardware-in-the-loop (HIL) platform. The results show that the proposed control strategy can ensure lateral stability while improving the tracking performance of the vehicle platoon.

A Robust Dynamic Game-Based Control Framework for Integrated Torque Vectoring and Active Front-Wheel Steering System

Distributed drive electric vehicles (DDEVs) eliminate the complex drivetrain. The independently driven in-wheel motors also endow the vehicle with more ability for improving maneuverability. To this end, this paper proposes an integrated control framework of torque vectoring (TV) and active front-wheel steering system (AFS) to ensure the vehicle lateral motion stability performance. First, the polytope method with finite vertices is employed to deal with the system uncertainties and simplify the modeling structure, based on which a distributed model predictive control is adopted to construct a dynamic interactive model between agents. Then, through introducing the game theory, a distributed parallel control scheme is developed to obtain the cooperative strategy of agents. Such a design can also satisfy the modular and scalable requirement for integrated chassis control. To ensure the system asymptotic stability, the terminal input combined with the terminal cost function are treated as the constraints in the game paradigm and then transformed as the linear matrix inequalities. Furthermore, a robust H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\infty $</tex-math> </inline-formula> compensation method is used to suppress the system disturbance. Finally, the hardware-in-the-loop (HIL) tests are conducted to assess the control performance. The results verify the proposed integrated control scheme is effective to enhance the vehicle handling stability.

Research on Fault-Tolerant Control of Distributed-Drive Electric Vehicles Based on Fuzzy Fault Diagnosis

This paper addresses the fault problem in distributed-four-wheel-drive electric vehicle drive systems. First, a fault-factor-based active fault diagnosis strategy is proposed. Second, a fault-tolerant controller is designed to reconstruct motor drive torque based on vehicle stability. This controller ensures that the vehicle maintains stability by providing fault-free motor output torque based on fault diagnosis results. To validate the effectiveness of the fault diagnosis and fault-tolerant control, SIL simulation is conducted using MATLAB/Simulink and CarSim. A hardware-in-the-loop (HIL) simulation platform with the highest confidence level is established based on NI PXI and CarSim RT. Through the HIL simulation experiments, it is shown that the proposed control strategy can accurately diagnose the operating state of the motor, rebuild the motor torque based on stability, and demonstrate robust stability when the drive system fails. Under various fault conditions, the maximum error in the vehicle lateral angular velocity is less than 0.017 rad/s and the maximum deviation in the lateral direction is less than 0.7 m. These findings substantiate the highly robust stability of the proposed method.

Stability control of distributed drive electric vehicle based on adaptive fuzzy sliding mode

To improve the driving stability of a distributed drive electric vehicle (DDEV), we propose a hierarchical direct yaw moment control strategy. In the upper-level controller, an adaptive fuzzy sliding mode controller (AFSMC) is designed with joint control objectives of sideslip angle and yaw rate. In the conventional controller, the weight coefficients of the joint control objectives are fixed values, which reduces the adaptability of the system. To improve this problem and achieve adaptive adjustment of the weight coefficients of the control objectives with the vehicle motion state, an adaptive fuzzy control-based target weight coefficient controller is designed. In the lower controller, a quadratic programing method is used to assign torque with the optimization objective of minimizing tire attachment utilization. A joint simulation with Carsim and Simulink software was performed to compare and analyze the AFSMC vehicle and the traditional SMC vehicle under the steering wheel turning angle sinusoidal input and double shift line conditions. The results show that AFSMC effectively reduces the magnitude of vehicle sideslip angle, yaw rate, and lateral acceleration, enhances the driving stability of the vehicle, and enables adaptive adjustment of the weight coefficients of the joint control objectives, improving the adaptability of the controller. Finally, the effectiveness of the control strategy is verified by hardware-in-the-loop (HIL) test.

Estimation of state parameters and road adhesion coefficients for distributed drive electric vehicles based on a strong tracking SCKF

To address the difficulties in accurately measuring driving state parameters and the road adhesion coefficient, in this paper, the permanent magnet synchronous motor (PMSM) was selected as the automotive in-wheel motor, and the distributed drive electric vehicle (DDEV) simulation model was built based on CarSim and MATLAB/Simulink software. PMSM speed estimation by Adaptive Sliding Mode Observer (ASMO) was used as input information for subsequent studies of state parameters and road adhesion coefficient estimation. Next, this paper established a nonlinear three-degrees-of-freedom whole-vehicle dynamics reference model based on the Dugoff tire model. We derived a suboptimal fading factor using strong tracking theory (STT) and introduced it into the square root of the prediction error covariance of the square-root cubature Kalman filter (SCKF) algorithm. A strong tracking square-root cubature Kalman filter (ST-SCKF) observer was constructed. Improved algorithm estimation accuracy and the ability to cope with sudden changes in operating conditions improve the estimation accuracy of the DDEV driving state parameters and road adhesion coefficient. Finally, a joint CarSim and MATLAB/Simulink simulation and experiments on real vehicles were carried out. The results show that, compared to the traditional CKF (cubature Kalman filter, CKF) and SCKF algorithms, the ST-SCKF algorithm is able to estimate vehicle state parameters and road adhesion coefficients more accurately while maintaining good estimation accuracy for docked road conditions with sudden changes in the road adhesion coefficient.

Multi-Level Coordinated Yaw Stability Control Based on Sliding Mode Predictive Control for Distributed Drive Electric Vehicles Under Extreme Conditions

It is an undeniable fact that the instant sideslip of the vehicle in case of high-speed obstacle avoidance will cause vehicles to lose stability. Current yaw moment control methods for distributed drive electric vehicles (DDEVs) does not fully consider the relationship between the vehicle instability degree and the optimal yaw moment control center, which greatly restricts the attitude correction ability of the four-wheel drive torque. To solve this problem, this article proposes a novel multi-level coordinated yaw stability control method based on robust sliding mode predictive control (SMPC) for DDEVs to improve the maneuverability and lateral stability. Firstly, a novel multi-scale vehicle dynamic model of yaw motion is constructed with the consideration that the vehicle instability center is time-varying under various sideslip conditions. Then, an online sliding mode prediction model of dynamics nonlinear system of DDEVs is established according to the local modeling method based on just-in-time learning. In addition, the linear time-varying model predictive control algorithm (LTV-MPC) with less predictive horizon is used to obtain the optimal sliding mode control law. Besides, an adaptive weighted soft switching function based on data driven hierarchical predictive proportional-integral-derivative (PID) control is put forward to determine the optimal weight value of the three yaw moment control modes in real time. Finally, the simulation and experimental results have verified that the proposed control method has strong vehicle attitude correction ability which can improve the trajectory tracking accuracy and the handling stability for DDEVs under extreme conditions.

Optimal Control Design for Decoupling the Direct Drive Stability of Distributed Drive Electric Vehicles

As a new type of drive for electric vehicles, distributed drive has many advantages such as simple structure and easy control, and is one of the important development directions of new energy vehicles in the future. In the past decade, the research on the active safety control of distributed drive electric vehicles has become more and more mature. In this paper, we propose a decoupled optimal control strategy based on the drive torque for the vehicle run-off and motion coupling problems that exist during the straight-line driving of distributed-drive electric vehicles. The upper control logic layer is responsible for cross-swing motion control and drive anti-skid control. The middle control logic layer is responsible for secondary planning of the additional torque from the upper control layer, based on which the decoupled cross-swing motion control with wheel anti-skid is realised. The lower control logic layer is responsible for the optimal distribution of the drive torque for speed following control. The decoupled optimal control of drive torque proposed in this paper can achieve the optimal distribution of drive torque, avoiding vehicle deflection on the premise of ensuring vehicle speed, while preventing excessive wheel slip, realising the decoupled control of vehicle linear motion and enhancing the linear driving stability of the vehicle.

Integrated Direct Yaw Control and Antislip Regulation Mixed Control of Distributed Drive Electric Vehicle Using Cosimulation Methodology

The improvement of handling and stability performance of Distributed Drive Electric Vehicle (DDEV) is analyzed, visualized, and designed by proposing and deploying the mixed control strategies in this paper including Direct Yaw Control (DYC), Antislip Regulation (ASR) and a novel Dual-mode Switching Control (DMSC). First, by drawing the phase trajectory stability domain, the vehicle stability limit boundary can be determined, which provides the basis for the lateral stability constraint of the vehicle. Then, the practicability and real time visualization of driving efficiency and timeliness of DDEV is achieved to reduce the margin of error for the desired torque value by employing the DYC strategy which uses a fuzzy PID algorithm. Furthermore, the ASR strategy which adopts the optimal slip rate algorithm to determine the requirement of desired torque value based on the different road conditions is used to reduce slip phenomenon effectively and to maintain handling control of DDEV. In response to different scenes especially conflict and coexistence between DYC and ASR, the DMSC strategy is applied to find a more suitable slip rate range by using the root mean square error method (REME). Finally, the cosimulation platform of ADAMS/Car and MATLAB/Simulink is built to simulate the mixed control strategies by integrating DYC, ASR, and DMSC. The simulation results show that the DMSC can effectively prevent DDEV from entering the dangerous limit driving state when turning and driving. The strategy has a more significant control effect needed to meet the requirements of the driving safety of the vehicle and handling stability. The DMSC is adopted and downloaded into the electronic control unit of our student type formula vehicle called Flash V6 which was designed and developed by a team of students, the ZUST ATTACKER Team.