Electric Power Steering System Research Articles

Diagnosis-based design of electric power steering system considering multiple degradations: role of designable generative adversarial network anomaly detection

Abstract Recently, interest in functional safety has surged because vehicle technology increasingly relies on electronics and automation. Failure of certain system components can endanger driver safety and is costly to address. The detection of abnormal data is crucial for enhancing the reliability, safety, and efficiency. This study introduces a novel anomaly-detection method of designable generative adversarial network anomaly detection (DGANomaly). DGANomaly combines the data augmentation method of a designable generative adversarial network (DGAN) with a generative adversarial network anomaly-detection data classification technique. DGANomaly not only generates virtual data that are challenging to obtain or simulate but also produces a range of statistical design variables for normal and abnormal data. This approach enables the specific identification of normal and abnormal design variables. To demonstrate its effectiveness, the DGANomaly method was applied to an electric power steering (EPS) model when multiple degradations of gear stiffness, gear friction, and rack displacement were considered. An EPS model was constructed and validated using simulation programs such as Prescan, Amesim, and Simulink. Consequently, DGANomaly exhibited a higher classification accuracy than the other methods, allowing for more accurate detection of abnormal data. Additionally, a clearer range of statistical designs can be obtained for normal data. These results indicate that the statistical design variables that are less likely to fail can be obtained using minimal data.

A novel approach to the FPIBSC strategy for an electric power steering system

The electric power steering system is used to improve comfort and steering feel for the user. In this article, we propose to use an integrated nonlinear control strategy for the electric steering system, which is described based on a complicated dynamic model. This work provides two new contributions. First, this control algorithm combines backstepping and proportional–integral (PI) techniques with parameters adjusted by a fuzzy algorithm, so it is called Fuzzy proportional–integral backstepping control (FPIBSC). The output of the PI algorithm is the input of the backstepping technique, while system stability is evaluated based on the Lyapunov function with virtual control variables. Second, road reaction torque is calculated based on a spatial dynamic model, which considers the influence of many other factors. Numerical simulation methods are used to evaluate the performance of the system. According to research findings, the value of the steering motor angle (controlled object) continuously tracks the desired value with negligible error. Under some specific conditions, the error between signals can be reduced to zero. In addition, other outputs that are obtained from the FPIBSC algorithm also tend to follow the reference signal with high accuracy. The phase difference phenomenon only occurs when using the conventional backstepping algorithm instead of FPIBSC. The assisted torque increases as speed decreases or the driver torque increases. In general, the system’s stability is always guaranteed under many different simulation conditions.

ANALYSIS OF A NEW TYPE OF ELECTRIC POWER STEERINGGEAR WITH TWO PINIONS ENGAGED ON THE SAME SETOF TEETH ON THE RACK

Electric power steering is the most commonly used solution in passenger cars. It is a more economical andreliable solution than the hydraulic equivalent. Its main parts are usually the rack, pinion, and worm wheel,which are subject to wear during driving. A steering gear that works incorrectly poses a direct threat to thesafety of the driver and other road users. The paper presents various types of electric power steering gears,with a particular emphasis on the problems that arise during the design of the pinion and rack. In this paper,a new electric power steering system consisting of a steering gear with two pinions working on a commontoothing of the rack (TPEPS) is proposed. The benefits of placing two pinions on one tooth set of a steeringrack are described, which eliminates many problems during production, and thus allows the reduction inproduction costs. As part of the analysis, design challenges for a new type of transmission are presented,taking into account key customer requirements, with a particular emphasis on durability and ensuring thedesired noise level, which is important due to the replacement of cars with an internal combustion drive withan electric drive. This paper presents selected friction and noise test results for a new power steering system.A comparison is made with a system that has two pinions and different teeth on the rack (DPEPS). The resultsindicate that the new system could become an alternative solution due to its lower friction value and lowernoise.

Zero-Position Offset Calibration of PMSM Based on I/f Control Strategy and Slide Mode Observer for Electric Power Steering System

Zero-Position Offset Calibration of PMSM Based on I/f Control Strategy and Slide Mode Observer for Electric Power Steering System

Multilevel Evaluation Model of Electric Power Steering System Based on Improved Combination Weighting and Cloud Theory

In order to rapidly evaluate the working performance of an Electric Power Steering System (EPS) and clarify the key indicators that affect its working condition, targeted maintenance measures can be taken to improve the vehicle’s handling, stability, and safety. This paper took an EPS system as the research object and decomposed it into five working indicators: steering lightness, steering returnability, steering wheel mid-zone performance, steering wheel vibration, and power assist characteristics. On this basis, the subjective exponential extension Analytic Hierarchy Process (AHP) was combined with the objective improved entropy weight method, and then the combination weighting method of game theory was employed to ensure that the results were reliable. At the same time, a cloud model was introduced to understand the actual situation of each indicator by using cloud parameters and multi-level feedback results. According to the principle of maximum membership and the comprehensive evaluation method, the EPS performance evaluation result was obtained. The results showed that the comprehensive cloud parameter of EPS was U (74.31, 6.08, 0.50), which indicated that its working performance was in good condition, with some minor damage that did not affect its normal use; however, attention should be paid to the maintenance and repair of vulnerable components to ensure the handling, stability, and safety of the vehicle.

Electric power steering system controller design using induction machine

Electric power steering system controller design using induction machine

A Federated Learning Approach for Efficient Anomaly Detection in Electric Power Steering Systems

A Federated Learning Approach for Efficient Anomaly Detection in Electric Power Steering Systems

Design of Electric Power Steering System Identification and Control for Autonomous Vehicles Based on Artificial Neural Network

Design of Electric Power Steering System Identification and Control for Autonomous Vehicles Based on Artificial Neural Network

Development of a Novel Integrated Control Strategy for Automotive Electric Power Steering Systems

Development of a Novel Integrated Control Strategy for Automotive Electric Power Steering Systems

Optimal Design of a Surface Permanent Magnet Machine for Electric Power Steering Systems in Electric Vehicle Applications Using a Gaussian Process-Based Approach

The efficient design optimization of electric machines for electric power steering (EPS) applications poses challenges in meeting demanding performance criteria, including high power density, efficiency, and low vibration. Traditional optimization approaches often fail to find a global solution or suffer from excessive computation time. In response to the limitations of traditional approaches, this paper introduces a novel methodology by incorporating a Gaussian process-based adaptive sampling technique into a surrogate-assisted optimization process using a metaheuristic algorithm. Validation on a 72-slot/8-pole interior permanent magnet (IPM) machine demonstrates the superiority of the proposed approach, showcasing improved exploitation–exploration balance, faster convergence, and enhanced repeatability compared to conventional optimization methods. The proposed design process is then applied to two surface PM (SPM) machine configurations with 9-slot/6-pole and 12-slot/10-pole combinations for EPS applications. The results indicate that the 12-slot/10-pole SPM design surpasses the alternative design in torque density, efficiency, cogging torque, torque ripple, and manufacturability.

Deep Learning-based Motor Prognostics for Electric Power Steering Systems

The electric power steering (EPS) system is a fundamental unit for modern automotive vehicles, providing motor assistance and various control functions to aid the driver's manual maneuvering. Preventing loss of assist (LoA) in advance is critical for EPS systems to mitigate fatal accidents and maintenance costs. As part of the recent interest in intelligent software-defined vehicles (SDV), data-driven approaches have gained much attention to overcome the limitations of conventional fail-safe and maintenance strategies. However, while related works in the field have shown promising results, they are limited to proof-of-concept studies for detecting and isolating artificially injected faults using simulation and test-bed data, which lacks relevance and poses limitations for practical in-vehicle implementation. Herein, we present a deep learning (DL)-based method to predict the motor degradation level in an EPS system using in-vehicle data acquired from the controller area network (CAN) bus. Our approach proposes a novel multivariate transformer (MVT)-based neural twin model of the EPS system, which is trained using an adversarial learning strategy by utilizing only the normal data. Our method can detect degradation levels down to ten percent from normal working conditions based on an anomaly detection mechanism that outperforms baseline methods in quantitative and qualitative measures.

A novel dual power-driven electric power steering system for electric commercial vehicles

To solve the power limitation problem faced by the development of EPS system for commercial vehicles and achieve the high-performance steering, this article presents a novel dual power-driven electric power steering system (DPEPS) for commercial vehicles by using the open winding permanent magnet synchronous motor (OPMSM) fed by dual inverter with high and low DC voltage supplies. First, the two-level control strategy is introduced for the developed DPEPS system, where the upper control is designed to make the actual steering follow the expected steering performance as determined by the assistant torque curve and to generate the appropriate target current according to the vehicle speed and steering torque. And then the lower control consists of the synthetical space vector control scheme and dual PI control loop is used to execute commands from the upper control, which can make the motor execute the appropriate power supply method according to the steering torque and assist current. Finally, a co-simulation model is carried out for an electric bus, and based on which the proposed DPEPS system is validated.

Research on active disturbance rejection control strategy of electric power steering system under extreme working conditions

In response to the influence of motor interference, damping, friction, and other uncertain factors on the operation of electric power steering systems under extreme working conditions, this study proposes a control strategy for electric power steering systems based on an active disturbance rejection algorithm. In ADRC, the fastest tracking differentiator is used to arrange the transition process for the target signal, and the extended state observer compensates for the total disturbance in the system. Phase compensation has been performed on the monitoring torque by using the torque differentiation method. The Simulink/Carsim simulation results show that ADRC has significantly improved anti-disturbance performance compared to PID and fuzzy PID. When using ADRC, the tracking accuracy of the assisted current is enhanced by 45.8%–75.8%, and the current adjustment time is reduced by 35.6%–61.7%. After phase compensation, the monitoring torque overshoot is reduced by 83.3%. Therefore, the proposed control strategy improves EPS’s robustness and steering feel.

Neural Approximation-Based Adaptive Control Using Reinforced Gain for Steering Wheel Torque Tracking of Electric Power Steering System

In practice, steering wheel torque (SWT) control performance for an electric power steering (EPS) system may degrade owing to various reasons, such as model/parameter uncertainties and unpredictable large external disturbances (e.g., reaction force). Furthermore, the desired SWT suddenly varies and its sign also changes because the direction of the steering wheel angle (SWA) often changes according to the driver. In this article, a neural network (NN) approximation-based adaptive nonlinear control (ANC) using reinforced gain (RG) for an EPS system is proposed to resolve the above-mentioned problems using an SWT model. The proposed control method employs an NN approximator, ANC, and RG. The NN approximator is designed to estimate the unknown complex nonlinear function in EPS modeling. The ANC is designed via backstepping to compensate for parametric uncertainties and external disturbances. Further, the RG is designed as a positive nonlinear function of the error to sufficiently suppress the error according to variations in the desired SWT and external disturbance. The RG increases to suppress the error at a rapid transient response, owing to the sudden change in the sign of the desired SWT and the external disturbance. Thus, a high gain (increased RG) is used only when necessary so that an unnecessarily high gain can be avoided.

Robust Nonlinear Torque Control Using Steering Wheel Torque Model for Electric Power Steering System

In this paper, a robust nonlinear torque control method using the steering wheel torque (SWT) model is proposed for electric power steering (EPS) systems. We develop an EPS model using SWT as the state variable to directly control the SWT. A robust nonlinear torque control method is designed to improve the SWT control performance of EPS. The proposed method consists of an extended state observer and a nonlinear sliding mode control (SMC) method. The extended state observer is designed to estimate the lumped disturbance, which includes modeling errors and external disturbances for robustness. The SMC is designed to track the desired SWT and suppress the estimation error. The nonlinear gain is proposed for reducing the chattering in the SMC. The proposed method is designed based on the SWT model; thus, it can improve the torque control performance while remaining robust to parameter uncertainty and external disturbances. The performance of the proposed method is validated via experiments using a pinion type EPS hardware-in-the-loop simulation.

Linear assistance filter design for electric power steering systems with improved delay margin

This paper studies the achievable delay margin using lead-lag filter structures for Electric Power Steering systems. We propose an approximation of the delay margin as an explicit function of filter parameters. Our results provide useful guidelines for adjusting the different filter parameters to ensure system stability in the presence of a time delay. Simulation results show that the proposed filter structures can attenuate vibrations and improve steering feel and road feel. As a consequence, the influence of the delay on the system can be minimised with little performance degradation.

Power Electronics Converters for Electric Vehicle Auxiliaries: State of the Art and Future Trends

Electric vehicles (EVs) are expected to take over the transportation and mobility market over traditional internal combustion engine (ICE) vehicles soon. The internal power demands of EVs are expected to increase. The reason for this is to achieve a longer driving range for the EV and to provide the required power for the low-voltage (LV) network auxiliary loads. To illustrate, there are extra added sensors, cameras, and small actuating motors, especially for future autonomous vehicles. Therefore, a new electrical/electronic (E/E) architecture is required to convert the high-voltage (HV) traction battery voltage (e.g., 320–800 V DC) to the standard LV levels with high current ratings of 5 kW and more. This HV-LV DC-DC converter is known in the literature as an auxiliary power module (APM). The standard LV rails in an EV are the 12 V/24 V rail to supply for an instant the EV’s lighting and electronic control units (ECUs), while the 48 V rail is required for propulsive loads, such as air compressors and electric power steering systems. Furthermore, in a few applications, this converter is responsible for voltage upwards to support the start of a hybrid vehicle or emergency backup power handling, which requires bidirectional capability. Therefore, in this paper, possible APM topologies for EV applications are presented. In line with this, the main standards and safety requirements of the APMs are presented. Detailed quantitative and qualitative comparisons between topologies and their associated control schemes are discussed. In addition, the placement of the APM in the EV cooling cycle has been investigated and demonstrated. Finally, the industrial trends and future research targets for the APM in automotive applications are outlined.

Study on Control Strategy of Electric Power Steering for Commercial Vehicle Based on Multi-Map

In order to solve the problem where the traditional electric power steering system (EPS) provides too much assist torque under low load and low adhesion coefficient, which damages the driver’s road feeling and affects driving safety, this paper designs a multi-map EPS control strategy. First, based on the change of steering resistance torque under different front axle loads and adhesion coefficients, EPS power characteristics considering the front axle load and adhesion coefficient were designed. In addition, the BP (Back Propagation, BP) neural network is used to determine steering resistance torque under different front axle loads and adhesion coefficients. Furthermore, the EPS control strategy based on multi-map is proposed. The proposed control strategy is evaluated through the co-simulation of Trucksim and Simulink. Simulation results show that the proposed EPS control strategy gives the vehicle good steering portability, with the handling torque meeting the ideal handling torque for a commercial vehicle. Under light load and low adhesion coefficient conditions, the lateral acceleration and yaw rate with traditional EPS are 0.1674 g and 5.641 deg/s, and with multi-map EPS are 0.1399 g and 4.715 deg/s. Therefore, the vehicle’s handliitung stability is improved. The steering wheel torque gradient is also increased, and the driver’s road feeling is improved.

Speed Response Improvement Design of Electric Motor for Vehicle Electrification Based on Electro-Mechanical Analytic Model

According to vehicle electrification trends, the application of electric motors more often occurs. Therefore, in the aspect of the supply instability preparation for rare-earth materials, research on the rare-earth-free motor is getting more attention. Moreover, the electric motor for the vehicles should have not only high power density but also fast response. Especially, electric motors for advanced driver assistant systems (ADAS) or autonomous driving vehicles should have a fast response speed to secure the driver’s safety. This paper introduces the electro-mechanical response improvement design of concentrated flux synchronous motors for vehicle electrification. Improvement design is carried out targeting motors for electric power steering (EPS) system. The development of the electro-mechanical analytic (MEA) model of the electric motor is firstly explained. Then, based on the developed MEA model, electro-mechanical response modeling is performed. Finally, the speed response improvement design is conducted using the developed MEA model. In addition, the results are validated not only in finite element analysis but also experimentally.

Fault Estimation of Rack-Driving Motor in Electrical Power Steering System Using an Artificial Neural Network Observer

In this paper, we present the fault estimation of a motor in rack-type electrical power steering (R-EPS) system using an artificial neural network (ANN) observer and the comparison study of estimation performance between ANN observer and model-based ones. For various amplitudes and frequencies of fault, it is not easy to obtain the accurate fault estimation using the model-based observers inherently possessing the accumulated errors and disturbance effect. Such model-based methods often result in undesired consequences; hence, this study employed the “model-free” ANN observer, without using any dynamics and parameters of a motor, to accomplish the better outcomes. Furthermore, the advantages of ANN observer for the fault estimation of the motor have been clearly investigated under several control/fault scenarios, and the effectiveness of proposed work has been validated through an actual experimental study. It is found that the performance of model-based approaches is degraded when the frequencies and amplitudes of fault and control scenarios are changed, but the ANN observer guaranteed performance that was almost the same regardless of fault and control scenarios. Notably, ANN observers showed 84% to 95% of estimation accuracy with almost no delay between estimates and actual faults while model-based approaches did 68% to 86% accuracy along with noticeable delay.