Novel stability control strategy for distributed drive electric vehicle based on driver operation intention

Advances in Mechanical Engineering

2018

The accuracy and real time are crucial in turning intention recognition. Therefore, a hybrid model of Gaussian mixture hidden Markov and generalized growing and pruning algorithm for radial basis function neural network is constructed to recognize driver turning intention real timely. The feature parameters of the driving intention identification are determined using principal component analysis. The turning initial stage is selected for identifying turning intention, thus improving the real time of hybrid model. Using support vector machine model and Gaussian mixture hidden Markov model and Gaussian mixture hidden Markov model/generalized growing and pruning algorithm for radial basis function hybrid model, driver turning intention is identified in normal turning and sharp turning. The results show that Gaussian mixture hidden Markov model/generalized growing and pruning algorithm for radial basis function hybrid model identifies turning intention accurately and more efficiently. The accuracy is as high as 96.88% in the normal turning, which is 24.53% higher than that obtained using the Gaussian mixture hidden Markov model and 25.88% higher than that obtained using the support vector machine. The recognition in the turning initial stage is 1.8 s quicker than that in the turning keeping stage. The identification accuracy and real time of hybrid model are substantially better than those of the Gaussian mixture hidden Markov model and support vector machine, especially in the normal turning. When applied in control systems, this method can improve the safety and comfort of vehicle operation and reduce the burden of drivers. Keywords Driver turning intention recognition, Gaussian mixture clustering, Gaussian mixture hidden Markov model, generalized growing and pruning algorithm for radial basis function neural network, model accuracy and real time Date

Section: Feature Parameter Selectionmentioning

confidence: 99%

Study on driver’s turning intention recognition hybrid model of GHMM and GGAP-RBF neural network

Wang

Advances in Mechanical Engineering

2018

“…erefore, accurate interpretation and prediction of the driving intention during the process of vehicle stability control and ensuring the actual vehicle state is maintained as closely as possible to the expected vehicle state would greatly improve the stability and driving safety of the vehicle. Driver intention can be obtained using fuzzy reasoning, support vector machine (SVM), artificial neural network (ANN), and hidden Markov model (HMM) [23][24][25][26][27]. Fuzzy reasoning, SVM, and ANN are used to recognize the intentions of driver at a certain moment.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Stability Control Strategy Based on Recognition of Driver Turning Intention for Dual-Motor Drive Electric Vehicle

Wang

Mathematical Problems in Engineering

2020

Vehicle stability control should accurately interpret the driving intention and ensure that the actual state of the vehicle is as consistent as possible with the desired state. This paper proposes a vehicle stability control strategy, which is based on recognition of the driver’s turning intention, for a dual-motor drive electric vehicle. A hybrid model consisting of Gaussian mixture hidden Markov (GHMM) and Generalized Growing and Pruning RBF (GGAP-RBF) neural network is constructed to recognize the driver turning intention in real time. The turning urgency coefficient, which is computed on the basis of the recognition results, is used to establish a modified reference model for vehicle stability control. Then, the upper controller of the vehicle stability control system is constructed using the linear model predictive control theory. The minimum of the quadratic sum of the working load rate of the vehicle tire is taken as the optimization objective. The tire-road adhesion condition, performance of the motor and braking system, and state of the motor are taken as constraints. In addition, a lower controller is established for the vehicle stability control system, with the task of optimizing the allocation of additional yaw moment. Finally, vehicle tests were carried out by conducting double-lane change and single-lane change experiments on a platform for dual-motor drive electric vehicles by using the virtual controller of the A&D5435 hardware. The results show that the stability control system functions appropriately using this control strategy and effectively improves the stability of the vehicle.

“…ree levels of control logic are designed in the ESC system, which contains a torque distribution algorithm based on a minimum-objectivefunction to enhance the vehicle's stability [19]. Fully utilizing the hierarchical structure, a linear quadratic regulator control method is obtained by controlling the yaw rate to design the upper-level controller, and a fast calculation method is used to achieve a fast motor torque distribution [20]. Taking two variables reflecting the advantages and disadvantages of the vehicle's lateral movement in the motion control unit, the objective yaw moment is obtained by fuzzy logic control.…”

Section: Introductionmentioning

confidence: 99%

Electronic Stability Control for Improving Stability for an Eight In-Wheel Motor-Independent Drive Electric Vehicle

Zhang

2019

Shock and Vibration

An electronic stability control (ESC) based on torque distribution is proposed for an eight in-wheel motor-independent drive electric vehicle (8WIDEV). The proposed ESC is extremely suitable for the independent driving vehicle to enhance its handling stability performance. The vehicle model is established based on a prototype 8WIDEV. A hierarchical control strategy, which includes a reference state generation controller, an upper-level vehicle controller, and a lower-level optimal control allocation controller, is utilized in the ESC. The reference state generation controller is used to obtain the ideal reference vehicle state. The upper-level vehicle controller is structured based on sliding mode control, which obtains the generalized objective force during 8WIDEV movement, therein considering the side slip angle and yaw rate. The lower-level optimal control allocation controller attempts to allocate the vehicle’s objective force in each motor optimally and reasonably. The model is validated by field measurement results under the step input condition and snake input condition. Simulation results from a hardware-in-the-loop (HIL) simulation platform indicate that the ESC based on the optimized allocation proposed for 8WIDEV achieves better stability performance compared with direct yaw moment control (DYC).