Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-Type Estimation

Estimation and control of wheel slip is a critical consideration in preventing loss of traction, minimizing power consumptions, and reducing soil disturbance. An approach to wheel slip estimation and control, which is robust to sensor noises and modeling imperfection, has been investigated in this study. The proposed method uses a simplified form of wheels longitudinal dynamic and the measurement of wheel and vehicle speeds to estimate and control the optimum slip. The longitudinal wheel forces were estimated using a robust sliding mode observer. A straightforward and simple interpolation method, which involves the use of Burckhardt tire model, instantaneous values of wheel slip, and the estimate of longitudinal force, was used to determine the optimum slip ratio that guarantees maximum friction coefficient between the wheel and the road surface. An integral sliding mode control strategy was also developed to force the wheel slip to track the desired optimum value. The algorithm was tested in Matlab/Simulink environment and later implemented on an autonomous electric vehicle test platform developed by the Nanjing agricultural university. Results from simulation and field tests on surfaces with different friction coefficients (μ) have proved that the algorithm can detect an abrupt change in terrain friction coefficient; it can also estimate and track the optimum slip. More so, the result has shown that the algorithm is robust to bounded variations on the weight on the wheels and rolling resistance. During simulation and field test, the system reduced the slip from non-optimal values of about 0.8 to optimal values of less than 0.2. The algorithm achieved a reduction in slip ratio by reducing the torque delivery to the wheel, which invariably leads to a reduction in wheel velocity.

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“…The sliding condition that guarantees the attractivity of s ωi is obtained by differentiating Equation (20)…”

Section: Design Of the Observermentioning

confidence: 99%

“…However, the most recent studies were based on slip feedback. This change in focus is connected to the fact that there is a strong relationship between slip rate and traction force, and the ease of slip estimation due to the development and availability of improved sensors [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Development of Online Adaptive Traction Control for Electric Robotic Tractors

et al. 2021

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“…This helps identify the road adhesion quality into five different conditions, namely, “very low,”“low,”“medium,”“high,” and “very high,” as summarized in Table 2. Based on the tire responses, such qualitative results are more reliable than the traditional qualitative methods, 9–11 which neglect the roles of the tire operating factors. The two stages are not mandatory and the Stage II may become unnecessary if a qualitative estimate of TRFC is acceptable in a given application.…”

Section: Vehicle Maneuvers Designmentioning

confidence: 99%

“…The qualitative methods generally provide an indication of the adhesion quality and classify the road surface by its condition in a crude manner using sensors such as optical instruments, voice recorder, and three-axis accelerometers. 9–11 For example, Yamada et al 9 proposed a method for discriminating different road conditions on the basis of luminance levels on the road surface using an imaging technique. Apart from the lack of accurate quantitative information of the TRFC, these methods invariably neglect contributions due to many important design and operating factors including the tire type, tire inflation pressure, and wear condition.…”

Section: Introductionmentioning

confidence: 99%

Tire–road friction coefficient estimation based on designed braking pressure pulse

Rakheja

Zhang

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Knowledge of tire–road friction coefficient (TRFC) is valuable for autonomous vehicle control and design of active safety systems. This paper investigates TRFC estimation on the basis of longitudinal vehicle dynamics. A two-stage TRFC estimation scheme is proposed that limits the disturbances to the vehicle motion. A sequence of braking pressure pulses is designed in the first stage to identify desired minimal pulse pressure for reliable estimation of TRFC with minimal interference with the vehicle motion. This stage also provides a qualitative estimate of TRFC. In the second stage, tire normal force and slip ratio are directly calculated from the measured signals, a modified force observer based on the wheel rotational dynamics is developed for estimating the tire braking force. A constrained unscented Kalman filter (CUKF) algorithm is subsequently proposed to identify the TRFC for achieving rapid convergence and enhanced estimation accuracy. The effectiveness of the proposed methodology is evaluated through CarSim™-MATLAB/Simulink™ co-simulations considering vehicle motions on high-, medium-, and low-friction roads at different speeds. The results suggest that the proposed two-stage methodology can yield an accurate estimation of the road friction with a relatively lower effect on the vehicle speed.

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“…The existing traction control system discusses torque control and SMC. In [109], a smart traction control This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: ) Traction Controlmentioning

confidence: 99%

A Review on Driving Control Issues for Smart Electric Vehicles

Haque

Rahman²,

Islam

et al. 2021

IEEE Access

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Smart electric vehicles (EVs) are attractive because of their clean, zero-emission, low impact on the environment whilst providing a safer and smoother riding experience. To provide the latter, driving control requires appropriate systems and algorithms to optimize smart vehicle performance, maximize vehicle stability and protection, minimize accident probability, heighten driving comfort, and optimize transportation costs. Despite advancements in these areas, the realization of optimal smart EVs still requires considerable effort. This paper reviews driving control systems and algorithms for smart EVs, including the advanced driving assistant system, implementation of sensors, vehicle dynamics, and control algorithms. The major contribution of this review is to identify promising work to assist researchers with the most advanced trends in this area for prospective regulations. INDEX TERMSSmart Electric Vehicles (EVs), Driving Control Systems (DCS), Advanced Driving Assistance System (ADAS), Algorithms.

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Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-Type Estimation

Cited by 12 publications

References 37 publications

Development of Online Adaptive Traction Control for Electric Robotic Tractors

Development of Online Adaptive Traction Control for Electric Robotic Tractors

Tire–road friction coefficient estimation based on designed braking pressure pulse

A Review on Driving Control Issues for Smart Electric Vehicles

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