Electric Power Assisted Steering System Research Articles

An Examination of Component-Based TPA Performance and Feasibility for Steering Motors

The swift progression in the automotive sector has spurred the demand for electric power-assisted steering systems, heralding a transformation towards a more effortless, fluid, and pleasurable driving experience. However, the inherent generation of substantial noise levels within the vehicle’s cabin during the operation of these systems, can compromise the comfort of occupants, underscoring the criticality of acoustics analysis in the advancement of auxiliary systems. As long-established tools for Noise, Vibration, and Harshness (NVH) diagnostics, Transfer Path Analysis (TPA) techniques are central to studying noise propagation within vehicles. Component-based TPA, with its strength in identifying interface forces in mechanical structures, has gained a foothold in the industry due to its numerous advantages. We aim to shed light on the individual acoustic properties of the electric motor, thereby gaining a more nuanced understanding of the system’s behavior. We first lay out a brief theoretical background on relevant TPA methods, followed by the presentation of our findings from experimental studies. This paper continues our commitment to advancing the precision and practical use of TPA methodologies in automotive system analysis.

Efficient NVH Analysis of Electric Motors by Means of 2D Spectral Decomposition of Airgap Forces

The growing popularity of hybrid and electric vehicles has created a need for quieter electric motors. This has led to a focus on designing electric machines with reduced audible noise. Estimating the emitted noise requires complex calculations, but analyzing electromagnetic forces alone can provide valuable insights. Electric motors’ simple cylindrical structure allows for decoupling excitation forces and mode shapes, which are closely related. By representing the internal forces in a complex-valued matrix and calculating its 2D Fast Fourier Transform (FFT), we can obtain a comprehensive 2D spectrum that reveals temporal and spatial frequencies. The method enables individual analysis of unique harmonic components. This paper discusses the mathematical representation of forces, their spectra, and the relationship between force harmonics and mode shapes. Industrial examples of electric power-assisted steering systems demonstrate the practical application of this approach.

Model Reference Control-Based Steering Feel Improvement for Electric Power-Assisted Steering System

Abstract Drivers are sensitive to steering feel of a vehicle and significant tuning efforts are placed to set the desired steering feel. This paper discusses a closed-loop torque overlay control system to improve the steering feel of an electric power-assisted steering (EPAS) system. This system has reference model, rack force estimator, and tracking controller. A target steering feel is generated from a reference model in which it uses an estimate of rack force determined from a Kalman filter-based rack force estimator to reflect the actual vehicle operating conditions. A sliding mode controller is designed for tracking the target steering feel. The performance of the proposed control system is evaluated through simulation and a hardware-in-the-loop test. The simulation and experimental results show that the EPAS system with the proposed control method can successfully improve the steering feel under various operation conditions.

Electronic Control System for Steer by Wire

Automobile industry’s focus is on efficiency, safety and performance has resulted in the rapid introduction of electronics in vehicle safety systems and engine management. Mechanical and Hydraulic systems are now gradually being replaced by electronic controllers to achieve the objectives of optimizing power consumption, improving driver convenience, and maximizing driver safety resulting in an overall improved performance and experience. Vehicle steering systems have transitioned from mechanical to hydraulic power to an electric power assisted steering system and now to the state of the art, Steer by Wire (SbW) system. Traditional mechanical systems included a steering wheel, column, gear, rack and pinion and did not support any power steering. The next generation hydraulic systems were more stable, safer and required comparatively lesser effort. Electric or DC motors drove the Electric Power System addressing the drawbacks of the hydraulic systems especially those related to environment and acoustics with the added advantage of a compact structure and power-on-demand engine performance. By-wire steering technologies was originally introduced in the Concord aircraft in 1970s. The SbW is a steering system with no steering column. The mechanical interface between the steering wheel and the wheels is replaced with by-wire electrical connection/electronic actuators. SbW system has significant advantages in terms of driving safety due to the availability of the steering command in electronic form and the removal of the steering shaft, cruising comfort with driving manoeuvring due to no space constraint and favourable to the environment with the non-usage of hydraulic oils.

Explicit parallel co-simulation approach: analysis and improved coupling method based on H-infinity synthesis

Co-simulation is widely used in the industry for the simulation of multidomain systems. Because the coupling variables cannot be communicated continuously, the co-simulation results can be unstable and inaccurate, especially when an explicit parallel approach is applied. To address this issue, new coupling methods to improve the stability and accuracy have been developed in recent years. However, the assessment of their performance is sometimes not straightforward or is even impossible owing to the case-dependent effect. The selection of the coupling method and its tuning cannot be performed before running the co-simulation, especially with a time-varying system.In this work, the co-simulation system is analyzed in the frequency domain as a sampled-data interconnection. Then a new coupling method based on the H-infinity synthesis is developed. The method intends to reconstruct the coupling variable by adding a compensator and smoother at the interface and to minimize the error from the sample-hold process. A convergence analysis in the frequency domain shows that the coupling error can be reduced in a wide frequency range, which implies good robustness. The new method is verified using two co-simulation cases. The first case is a dual mass–spring–damper system with random parameters and the second case is a co-simulation of a multibody dynamic (MBD) vehicle model and an electric power-assisted steering (EPAS) system model. Experimental results show that the method can improve the stability and accuracy, which enables a larger communication step to speed up the explicit parallel co-simulation.

Swarm-Intelligence Tuned Current Reduction for Power-Assisted Steering Control in Electric Vehicles

In electric vehicle technology, battery energy conservation is paramount due to the dependency of all system operations on the available battery. The proportional, integral and derivative (PID) controller parameters in the electric power assisted steering system for electric vehicle need to be tuned with the optimal performance setting so that less current is needed for its operation. This proposed two methods under the umbrella of swarm-intelligence technique namely particle swarm optimization (PSO) and ant colony optimization (ACO) in order to reduce current consumption and to improve controller performance. The investigation involves an analysis on the convergence behavior of both techniques in search for accurate controller parameters. A comprehensive assessment on the assist current supplied to the assist motor of the system is also presented. Investigation reveals that the proposed controllers, PID-PSO and PID-ACO are able to reduce the assist current supplied to the assist motor as compared to the conventional PID controller. This study also demonstrate the feasibility of applying both swarm-intelligence tuning method in terms of reduced time taken to tune the PID controller as compared to the conventional tuning method.

Structure Selection and LPV Model Identification of a Car Steering Dynamics

A Linear Parameter-Varying (LPV), discrete-time black box model of an electric power assisted steering system of a passenger car is identified from open-loop step response measurement data. The goal is to provide a nominal model for control design and analysis that is able to describe the principal characteristics of the system in the whole region of steering angle and speed range of 3 to 30 km/h. Examining a set of experimental data by using classical linear time-invariant black box modeling and validation techniques, the structure of the LPV model is determined. The parameters of the model are identified based on minimizing a quadratic error criterion by nonlinear optimization algorithms.

Investigation of the Driver’s Arm Viscoelastic Properties During Steering Vehicle Maneuver

Driver's arm viscoelastic properties during steering vehicle maneuver are very important information to understand how the driver regulates his torque and they could be used to design a new driver assistance system. This correspondence paper investigates the driver's arm viscoelastic properties during steering vehicle maneuver. First, a time-varying method for estimating driver's arm viscoelastic properties during driving the vehicle is proposed. Then, this method is implemented experimentally in order to examine the driver's arm viscoelastic properties during driving the vehicle. The estimation method does not require a specific perturbation torque to be applied during driving and could be applied online. The method is based on a time-varying model of the human driver's arm coupled with the electric power-assisted steering system model. Experiments were carried out with five healthy subjects who drove on a hardware-in-the-loop simulator without assistance and with assistance.

Optimisation of energy in electric power-assisted steering systems

Electric power–assisted steering (EPS) is a control system where an electric motor is used to provide assistance in vehicle steering. In this work controllers are designed for column–type EPS systems to enable energy optimisation. Two EPS systems are considered, namely brushed DC motor equipped EPS and brushless DC (BLDC) motor equipped EPS. Using mathematical models of EPS controllers are developed based on nonlinear adaptive regulation method to generate driver torque. Proportional–Integral–Derivative (PID) control is then applied to produce assistance torque in accordance to desired energy saving. Simulation results using Matlab show the trade–off between drivers comfort and energy consumption. The control paradigm introduced here fits appropriately in electric vehicles (EVs) where electrical energy is scarce.

Unknown-input estimator-based controller design of electric power-assisted steering system

In this study, the control design problem of double-pinion-type electric power assist steering (EPAS) is carefully examined. Based on a Lagrangian-based dynamical model, an optimal control approach with unknown input is formulated thereby eliminating the need for torque sensor. A new controller is developed using combination of non-linear assist curve, unknown-input estimator (UIE) and linear quadratic integral theory. It has been established that the estimation of the state, and unknown input using UIE yields a good estimate with performance compared with the well-known Kalman estimator. It is further shown that the resulting closed-loop response is able to track non-linear assist curve for different velocity. This reveals a salient feature, that is, controlling an EPAS system can be done using only single constant gain.

Modelling and Analysis for an Electric Power Assisted Steering System of Plug-In Hybrid Electric Car

Plug-in hybrid electric vehicles (PHEVs) have been seen as promising vehicles which have merits of hybrid electric vehicles and electric vehicles. Because of difficulties of plug-in hybrid electric vehicles’ development, it is necessary to study revamped method on the base of original car. Electric power steering (EPS) is an advanced steering system that uses an electric motor to provide steering assist. Because PHEVs are often on electric-electric operating mode, steering system power should be supplied with electric motor. Also, reconfigration of the power system including batteries results in added weight and the load change on front axle and rear axle. The steering characteristic of the revamped car are different from that of the original car. Based on the analysis and actual size, mass-force, etc. of the overall car and the parts of EPS, the multi-body dynamic model of the car equipped with EPS is established with multi-rigid-body dynamics analysing software (ADAMS). Based on the virtual prototype, simulation of steady state steering characteristic and transient state response characteristic of revamped car compared with original car under angulus step input of steering wheel have been finished. According to the simulation results, steering characteristic of the revamped car meets the design requirements. The research method is feasible.

A Generic Model-Based Advanced Control of Electric Power-Assisted Steering Systems

Electric power-assisted steering (EPS) systems have been used to replace traditional hydraulic power steering systems in vehicles. In an EPS system, the assisting steering torque is from an electric motor. In principle, the control of an EPS system involves two aspects: 1) motor torque control to satisfy the torque requirement and 2) steering motion control to yield a satisfied feeling of the driver during the steering process in a disturbed environment. In this paper, a column-mounted steering system is taken as a generic target system to illustrate a model-based approach for advanced steering control design. In particular, we present a two-controller structure proposal for the generic EPS system, addressing motor torque and steering motion, by applying <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> design methods, respectively. Controller model reduction is also discussed and compared to show that, actually, a reduced-order controller could be applied. This is important for industrial applications because a reduced-order control law costs less in computing resources. Finally, simulation for the EPS control system is discussed and a software-in-loop approach is presented using off-the-shelf software. It is interesting to see that, based on the simulation results, the advanced two-controller design yields superior performance to the one-controller structure for the steering control.

A Sensorless Optimal Control System for an Automotive Electric Power Assist Steering System

This paper considers the analysis and design of a double-pinion-type electric power assist steering (EPAS) control system. A simplified model of the augmented steering assembly-electric motor system is developed using Lagrangian dynamics, and an optimal controller structure for the model is proposed. Three main advances to the state of the art are presented in this paper. First, a state-space design model is used rather than an input-output model. A state-space formulation for a system model that incorporates motor electrical dynamics is obtained with the assist motor angular position as the output. Second, linear quadratic regulator (LQR) and Kalman filter techniques are employed to arrive at an optimal controller for the EPAS system. The selection of weighting coefficients for the LQR cost function is discussed. Finally, the authors present a control strategy that eliminates the steering column torque sensor, a critical component in existing EPAS controller designs. The proposed control strategy presents an opportunity to improve EPAS system performance and also reduce system cost and complexity.

Innovation drivers for electric power-assisted steering

The article is concerned with the analysis, design, and implementation of an electric power-assisted steering (EPAS) system for passenger cars. Key components of the EPAS system include a torque sensor, electric motor, electronic control unit, and control and diagnostic algorithms implemented in software. The heart of a typical EPAS system consists of a three-phase motor and its associated power electronics drive circuitry. The main functional blocks of the electrical drive system are the vehicle power supply (battery and harness), a filter circuit, a three-phase inverter bridge, and the motor windings. The single-unit integrated design of EPAS can be packaged on the steering column, steering rack, or pinion. The technical and commercial benefits of EPAS are compared with existing technologies. Detailed examples that link control and systems analysis to innovative areas of the design is also focused upon.

Development of the Non-Contact Torque Sensor for EPAS Using Maluss Polarization Law

Among the automotive steering systems, an Electric Power Assisted steering (EPAS) system utilizes an electronically controlled electric motor to provide steering assistance to the driver. The key components of the EPAS system are torque sensor, ECU (Electronic Control Unit), and DC Motor. The most important component of the EPAS is the torque sensor. The conventional torque sensor has complicated mechanical mechanism of torque detection. However, we suggest a non-contact torque sensor for EPAS using Maluss polarization law. It was found that the sensor exhibited not only excellent linearity but also superior characteristics of hysteresis, temperature and vibration.

Optical torque sensor for automotive steering systems

Abstract This paper describes the application of an optical torque sensor in a concept electric power-assisted steering (EPAS) system for small cars, which has been developed at Lucas Advanced Engineering Centre. The background to the development of the sensor is explained, followed by a description of the operation and measured performance of the device. The novel design of the sensor enables the required performance to be obtained while still having the potential of low-cost manufacture.