Electric vehicle side-slip control via electronic differential

Fu, Chunyun; Hoseinnezhad, Reza; Bab–Hadiashar, Alireza; Jazar, Reza N.

doi:10.1504/ijvas.2015.070724

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…In research related to electronic differential device control, an electronic differential device controller is designed to improve the performance of high-performance electric vehicles [16,17]. Slip angle proportional-integral-derivative feedback control is performed to improve cornering stability through side-slip angle reduction and yaw-rate optimization.…”

Section: Study Objectivementioning

confidence: 99%

Enhanced Vehicle Dynamics and Safety through Tire–Road Friction Estimation for Predictive ELSD Control under Various Conditions of General Racing Tracks

Woo,

Jeon,

Joa

et al. 2024

Applied Sciences

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This study focuses on the tire–road friction estimation for the predictive control strategy of electronically limited slip differential (ELSD) to improve the handling and acceleration performance of front-wheel drive cars, which typically suffer from excessive understeer and inner drive wheel spin during acceleration while turning due to reduced vertical load on the wheel. To mitigate this, we propose a control logic for ELSD that enhances course followability and acceleration by pre-transferring the driving torque from the inside to the outside wheel, considering the estimated traction potential for rapid response. It is essential to improve the control accuracy of wheel spin prediction by predicting the friction coefficient of the road surface. Furthermore, this study extends to the analysis of vehicle dynamics during lane-change maneuvers on low-friction surfaces, emphasizing the role of accurate tire–road friction estimation in vehicle safety. A CarSim 2023-based simulation study was conducted to investigate the vehicle response on snowy roads with low friction coefficients (μ = 0.2) and low temperatures (−5 °C). The results demonstrated that even minimal steering input could result in significant side-slip angles, highlighting the nonlinear vehicle behavior and the critical need for robust traction estimation in such challenging conditions of general racing tracks. The proposed friction-estimation method was evaluated through vehicle testing and has been substantiated by patents for its originality in control and friction-estimation approaches. The outcomes of these combined methodologies underline the critical importance of tire–road friction coefficient estimation in both the effectiveness of the ELSD system and the broader context of active safety systems.

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Section: Study Objectivementioning

confidence: 99%

Enhanced Vehicle Dynamics and Safety through Tire–Road Friction Estimation for Predictive ELSD Control under Various Conditions of General Racing Tracks

Woo,

Jeon,

Joa

et al. 2024

Applied Sciences

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“…In particular, the estimation of the sideslip angle can be combined with sideslip control, particularly in high-performance and sports driving scenarios. This approach is designed to maintain the vehicle's sideslip angle within a specific range by employing various controls [22][23][24]. Given the significant relationship between the sideslip angle and the tire's operating conditions, it plays a critical role as an input for estimating the maximum friction coefficient between the tire's contact patch and the road surface [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Four-Wheeled Vehicle Sideslip Angle Estimation: A Machine Learning-Based Technique for Real-Time Virtual Sensor Development

Napolitano Dell’Annunziata,

Ruffini,

Stefanelli

et al. 2024

Applied Sciences

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In the last few decades, the role of vehicle dynamics control systems has become crucial. In this complex scenario, the correct real-time estimation of the vehicle’s sideslip angle is decisive. Indeed, this quantity is deeply linked to several aspects, such as traction and stability optimization, and its correct understanding leads to the possibility of reaching greater road safety, increased efficiency, and a better driving experience for both autonomous and human-controlled vehicles. This paper aims to estimate accurately the sideslip angle of the vehicle using different neural network configurations. Then, the proposed approach involves using two separate neural networks in a dual-network architecture. The first network is dedicated to estimating the longitudinal velocity, while the second network predicts the sideslip angle and takes the longitudinal velocity estimate from the first network as input. This enables the creation of a virtual sensor to replace the real one. To obtain a reliable training dataset, several test sessions were conducted on different tracks with various layouts and characteristics, using the same reference instrumented vehicle. Starting from the acquired channels, such as lateral and longitudinal acceleration, steering angle, yaw rate, and angular wheel speeds, it has been possible to estimate the sideslip angle through different neural network architectures and training strategies. The goodness of the approach was assessed by comparing the estimations with the measurements obtained from an optical sensor able to provide accurate values of the target variable. The obtained results show a robust alignment with the reference values in a great number of tested conditions. This confirms that the adoption of artificial neural networks represents a reliable strategy to develop real-time virtual sensors for onboard solutions, expanding the information available for controls.

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Recurrent Neural Network Model for On-Board Estimation of the Side-Slip Angle in a Four-Wheel Drive and Steering Vehicle

Giuliacci,

Ballesio,

Fainello

et al. 2023

SAE Int. J. Passeng. Veh. Syst.

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<div>A valuable quantity for analyzing the lateral dynamics of road vehicles is the side-slip angle, that is, the angle between the vehicle’s longitudinal axis and its speed direction. A reliable real-time side-slip angle value enables several features, such as stability controls, identification of understeer and oversteer conditions, estimation of lateral forces during cornering, or tire grip and wear estimation. Since the direct measurement of this variable can only be done with complex and expensive devices, it is worth trying to estimate it through virtual sensors based on mathematical models. This article illustrates a methodology for real-time on-board estimation of the side-slip angle through a machine learning model (SSE—side-slip estimator). It exploits a recurrent neural network trained and tested via on-road experimental data acquisition. In particular, the machine learning model only uses input signals from a standard road car sensor configuration. The model adaptability to different road conditions and tire wear levels has been verified through a sensitivity analysis and model testing on real-world data proves the robustness and accuracy of the proposed solution achieving a root mean square error (RMSE) of 0.18 deg and a maximum absolute error of 1.52 deg on the test dataset. The proposed model can be considered as a reliable and cheap potential solution for the real-time on-board side-slip angle estimation in serial cars.</div>

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Electric vehicle side-slip control via electronic differential

Cited by 4 publications

References 38 publications

Enhanced Vehicle Dynamics and Safety through Tire–Road Friction Estimation for Predictive ELSD Control under Various Conditions of General Racing Tracks

Enhanced Vehicle Dynamics and Safety through Tire–Road Friction Estimation for Predictive ELSD Control under Various Conditions of General Racing Tracks

Four-Wheeled Vehicle Sideslip Angle Estimation: A Machine Learning-Based Technique for Real-Time Virtual Sensor Development

Recurrent Neural Network Model for On-Board Estimation of the Side-Slip Angle in a Four-Wheel Drive and Steering Vehicle

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