Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems

The economy of electrified vehicles can be improved by using the motor to recover the energy released during braking. However, the vehicle's regenerative braking system (RBS) and anti-lock braking system (ABS) are not compatible, so the energy dissipated during braking cannot be recovered under emergency braking conditions. This paper employs the method of logic threshold control combined with phase plane theory to analyze the relationship between the slip rate and the braking torque during the ABS braking process and to obtain the composition rule of the braking torque required for ABS braking. Based on this rule, a control strategy to coordinate RBS and ABS when triggering ABS is proposed to improve the efficiency of braking energy recovery. Furthermore, a comparative simulation is conducted to analyze the braking performance of electrified vehicle on roads with different adhesion coefficients by adopting the proposed control strategy and the traditional control strategy. The results show that, compared with the traditional coordinated control strategy, the braking energy recovery efficiency of the proposed coordinated control strategy is improved by 23.08%-38.54%, and can effectively shorten the braking distance and braking time, with better braking performance. Therefore, this paper offers a useful theoretical reference to the design of RBS and ABS coordinated control strategies for electrified vehicles. INDEX TERMS Anti-lock braking system, Coordinated control strategy, Energy recovery, Regenerative braking.

Section: Braking Performance Comparisonmentioning

confidence: 99%

Dynamic Coordinated Control for Regenerative Braking System and Anti-Lock Braking System for Electrified Vehicles Under Emergency Braking Conditions

Yang

Tang

et al. 2020

“…Precise information on vehicle state is crucial for these systems [1]. For example, as the slip ratio is a controlled variable in ABS and TCS [2], an accurate longitudinal speed is required to determine the slip ratio. Maintaining a slip ratio in the desired region is essential for vehicle safety and performance because it allows the wheel to sustain a friction coefficient with the road surface above a certain level.…”

Section: Introductionmentioning

confidence: 99%

Dual Deep Neural Network Based Adaptive Filter for Estimating Absolute Longitudinal Speed of Vehicles

Kim

Yoon

2020

This study employs a dual deep neural network (D-DNN) to accurately estimate the absolute longitudinal speed of a vehicle. Accuracy in speed estimation is crucial for vehicle safety, because longitudinal speed is a common parameter employed as a state variable in active safety systems such as antilock braking system and traction control system. In this study, DNNs are applied to determine the gain of an adaptive filter to estimate vehicle speed. The used data consists of longitudinal acceleration, wheel speed, filter gain, and estimated vehicle speed. The data generated from Carsim software are collected and preprocessed using a Simulink model. To acquire data with numerous wheel slip patterns, various acceleration and deceleration conditions are applied to four different road conditions. Though, it is challenging to achieve a single DNN model that is optimally cope with the various driving situations. Thus, we adopt two DNN models that were individually trained in low and high acceleration regions. The dual DNN model results in error reductions of 74% and 65%, compared with a single DNN and classical adaptive Kalman filter approaches, respectively. INDEX TERMS Adaptive filter, deep neural network, slip ratio, vehicle speed estimation This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

“…With the development of the automobile industry, automobile safety requirements have risen, especially the braking performance at high speeds [1][2][3]. The antilock braking system (ABS) is an active safety device that is used to control and adjust the braking torque to prevent the wheels from locking during braking, so that the vehicle makes maximum use of the ground adhesion to slow down and stop [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Second, changes in the road conditions cause uncertainty in control objectives. For the first problem, numerous nonlinear controllers were designed, such as sliding mode control (SMC) [9] ， PID control [10], model predictive control (MPC) [11] ， nonlinear optimal control [12,13], fuzzy logic control [14], neural network control [15,16], iterative learning control [17], and other intelligent control methods [18]. The SMC method was widely used in control engineering because of its potential for handling the nonlinearity and to achieve the inherent robustness.…”

Section: Introductionmentioning

confidence: 99%

An Improved Nonlinear Predictive Control Strategy Enhanced by Fractional Order Extremum Seeking Control of the Antilock Braking System of a Vehicle

Ren

Yuan

2020

Extremum seeking control can search the optimal slip rate of the antilock braking system of a vehicle through a high-frequency sinusoidal excitation signal. However, because of the bandwidth limitation of the braking actuator, the search speed of the optimal slip rate decreases and the stability of the extremum seeking control system becomes worse. To search and control the optimal slip rate, an improved nonlinear predictive control strategy enhanced by fractional order extremum seeking control is proposed for the vehicle antilock braking system. First, the nonlinear dynamic model of the braking system is established. Then, nonlinear prediction control is designed with the prediction of the slip rate response based on the nonlinear model to achieve slip rate control. Using fractional order calculus, a fractional extremum seeking controller is proposed to search for the optimal slip rate. Nonlinear predictive control integrated with fractional extremum seeking control is proposed to achieve the function of vehicle antilock braking. Finally, the effectiveness of the proposed method is verified by simulating the vehicle antilock braking system under different road conditions. The result shows that by considering the actuator available bandwidth, the proposed fractional order extremum seeking control can improve the search speed of the optimal slip rate compared with traditional integer order extremum seeking control. The proposed integrated controller achieves wheel slip rate optimal control regardless of the road conditions. INDEX TERMS Extremum seeking control, fractional order, ABS, nonlinear predictive control, slip rate.