Electric Power Steering Research Articles

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.

CarTwin—Development of a Digital Twin for a Real-World In-Vehicle CAN Network

Digital twins are used to replicate the behavior of physical systems, and in-vehicle networks can greatly benefit from this technology. This is mainly because in-vehicle networks circulate large amounts of data coming from various sources such as wired, or in some cases even wireless, sensors that are fused by actuators responsible for safety-critical tasks that require careful testing. In this work, we build a laboratory in-vehicle network that mimics a real vehicle network in regards to wire length, number of stubs and devices that are connected to it. The Controller Area Network (CAN), which is still the most popular communication bus inside cars, is used as a network layer. Using models defined in MATLAB for various subsystems, e.g., Anti-lock Braking System (ABS), Powertrain and Electric Power-Steering, deployed on automotive-grade microcontrollers, we evaluate the in-vehicle bus digital twin by providing realistic inputs and recording and reproducing in-vehicle network traffic. The experimental results showed good correlation between the output of the implemented digital twin and the data collected from an actual car.

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.

Design and analysis of electric power-assisted steering in vehicles for mountain forests

This study investigates the problem of vehicle exploitation in the mountainous and wooded regions of the Russian Federation. Considering how critical rollover accidents are in the logging industry, it is vital to ensure safe and stable driving. This study aims to develop a model of electric power steering (EPS) control using a quasi-continuous sliding-mode controller in MATLAB/Simulink. Experiments were carried out under the following conditions: maximum slope angle, 30°; maximum steering angle, 30°; maximum driving speed, 7 m/s. The dynamic characteristics of the suspension and EPS systems were optimized using the receding horizon optimization technique. The theoretical and experimental results suggest that the model reaches its critical state of stability (−0.2) at a speed range of 0–7 m/s with a steering angle of 25°. After optimization, critical stability (0.3) occurs at the driving speed of 7 m/s when the steering angle is 30°. The paper discussed the possibility of installing the EPS control system into vehicles that operate in mountainous wooded areas.

Simulation and application of the electric power steering system simulation platform based on Simulink

The principle of the electric power steering system is introduced in this paper, and the simulation platform based on Simulink/Simscape is established. The mechanical structure of the steering gear, the timing scheduling strategy, the hardware control circuit, and the integration of the software control algorithm are modeled and verified in detail. It is proved that the simulation model is reliable and highly precise. The simulation platform is used to analyze the actual problems, and propose an improvement plan. The results show that the platform can improve the intelligence of development and quickly locate, effectively reproduce and analyze actual problems, and reduce the trial-produce cost of the early production line, which has a certain guiding significance for software and hardware development.

A Deep Learning Approach to Detect Anomalies in an Electric Power Steering System.

As anomaly detection for electrical power steering (EPS) systems has been centralized using model- and knowledge-based approaches, EPS system have become complex and more sophisticated, thereby requiring enhanced reliability and safety. Since most current detection methods rely on prior knowledge, it is difficult to identify new or previously unknown anomalies. In this paper, we propose a deep learning approach that consists of a two-stage process using an autoencoder and long short-term memory (LSTM) to detect anomalies in EPS sensor data. First, we train our model on EPS data by employing an autoencoder to extract features and compress them into a latent representation. The compressed features are fed into the LSTM network to capture any correlated dependencies between features, which are then reconstructed as output. An anomaly score is used to detect anomalies based on the reconstruction loss of the output. The effectiveness of our proposed approach is demonstrated by collecting sample data from an experiment using an EPS test jig. The comparison results indicate that our proposed model performs better in detecting anomalies, with an accuracy of 0.99 and a higher area under the receiver operating characteristic curve than other methods providing a valuable tool for anomaly detection in EPS.

The control strategy of the electric power steering system for steering feel control

The steering feel is mostly indirectly adjusted by directly modifying the assist’s magnitude of the electric power steering system. The adjustment is complex, and workload is large, and the steering feel is highly dependent on the performance of the hardware. It has poor robustness to vehicle performance degradation and external disturbance, so providing a consistent and controllable steering feel is not easy. This paper proposes an EPS control strategy for steering feel control based on closed-loop control of steering motion to solve the above problems. Firstly, take the driver’s desired steering motion intensity and pinion angle position as intermediate variables to decompose the EPS control strategy into three parts: steering style, the desired pinion angle calculation, and the desired steering resistance moment calculation. This decomposition method makes the steering feel control independent of the vehicle dynamics characteristics and reduces the difficulty of vehicle characteristic calibration and correction. In addition, a steering style model based on Stevens’ law and considering hysteresis characteristics is proposed. The steering feel can be directly controlled based on calibrating the vehicle's dynamic characteristics and the designed steering style. The test results driven by ACSL driving simulators of Jilin University show that adopting the control strategy proposed in this paper can improve the vehicle’s return-to-center performance, realize the designed driving style, and provide consistent and controllable steering feel with the design hysteresis characteristic. In addition, it has good robustness against the deterioration of vehicle hardware performance and external disturbance.

자동차 전자제어장치의 열 피로를 고려한 가속수명시험법 개발에 관한 연구

The automotive electronic control unit outputs control signals using electrical signals of various input sensors installed in the vehicle to control the state of the engine, automatic transmission, and electric power steering (EPS). These units are installed inside the vehicle or engine room, and the temperature rises and falls by several tens of degrees due to the heat of the engine and the self-heating of the electronic control unit. Therefore, it was exposed to a thermal fatigue environment due to the difference in the coefficient of thermal expansion between the components, which caused frequent component damage. Solder cracks due to thermal fatigue in electronic control units are a key failure mode. However, because of its great heat capacity, the electronic control unit for automobiles took a long time to attain the desired temperature of high or low, and as a result, the 1,000-cycle test for thermal fatigue life verification required 3,167 hours (or 4.4 months). Therefore, in this study, the thermal shock cycle test time for the verification of the thermal fatigue life of electronic control units for automobiles was reduced by dividing it into two types.

Data-Driven Electrical Machines Structural Model Using the Vibration Synthesis Method

This article proposes a data-driven structural modeling approach for electrical machine airborne vibration that can be used both in the design stage for fast optimization routines and in system-level simulations. This method is able to capture both in-plane and out-of-plane modal behavior along with local effects, such as tooth bending. The model relies on fitting unitary force-shape responses to transfer function models using a zero-pole representation. These frequency response functions are obtained from applying the vibration synthesis process to a series of finite-element (FE) stator models that have material properties updated using the experimental modal analysis. The obtained zeros and poles are used to construct three lookup table models with their accuracy varying between the three of them with the number of samples used in the data generation process. The accuracy of these models is validated at the unitary force-shape response level using four stator FE model samples. Also, at the run-up vibration spectrogram level, the models are validated, with good results, using radial and tangential force excitation obtained from an updated electromagnetic FE model of an interior permanent magnet (PM) synchronous machine designed for electric power steering. The computational cost compared to the standard FE vibration synthesis method is shown.

Path Planning and Path Tracking for Collision Avoidance of Autonomous Ground Vehicles

How to plan the collision-free path effectively is crucial for autonomous ground vehicles to avoid collisions. The vehicle may lose its lateral dynamic stability due to high speed, time-varying speed and multifriction road during tracking the collision-free path. In this article, an advanced collision avoidance framework is proposed to avoid collision efficiently with road friction estimation and dynamic stability control. First, an improved A* algorithm is constructed to generate a desired trajectory for collision avoidance, which is capable of observing road regulations and overcoming the vehicle mechanical drawbacks. Next, a model predictive control-based path-tracking controller is established to solve the tracking task as a multiconstraint and multitarget optimization problem, where optimized steering angle and additional yaw moment can be calculated. Meanwhile, a novel long short-term memory based road friction coefficient estimator is built to observe road friction. Moreover, electric power steering system controls the steering motor to realize the desired steering angle. The additional yaw moment can be obtained by differential braking. Finally, hardware-in-the-loop platform tests are conducted to validate that the proposed controller can not only avoid collisions effectively, but also have a good performance on keeping the vehicle dynamic stability with accurate road friction estimation.

Design of hybrid SiC/Si based T‐type three‐level LLC$ LLC$ resonant converter with wide‐input range and low conduction loss for automotive auxiliary power module

The auxiliary power module (APM) in the electrical vehicle converts the high voltage of battery pack to the low voltage to supply the electric power for the internal loads, including electric power steering, electric turbo, headed windshield and cooling pumps. The increasing demands on charging time and mileage have made the battery packs with even higher voltage (e.g. 800 V) a promising option. However, it brings a new challenge for the APM, and either the 1.2-kV silicon carbide (SiC) device or the three-level (TL) topology are used conventionally. This work proposes a cost-effective hybrid SiC/Si-based T-type TL L L C $ LLC$ resonant converter for the vehicular APM application with a high step-down ratio. The 1.2-kV SiC and 650-V Si devices are used as the main and auxiliary switches, respectively. It fully utilizes the voltage rating of device, and also improves the efficiency compared with conventional TL topology. To improve the gain range and electromagnetic interference performance, a variable frequency and adjustable phase-shift modulation scheme is adopted. At the same time, the steady-state time-domain model is established to elaborate the operation principles, topology features, and boundary conditions for soft-switching. Furthermore, the precise solutions for switching frequency and phase-shift angle are figured out. Finally, the effectiveness of model is verified by the simulation as well as the experiment. A 1-kW prototype for the application in the APM is demonstrated with the input voltage of 640–840 V and a constant output of 48 V/ 21 A.

전자식 조향 장치에서 발생하는 실차 소음 예측을 위한 다중 센서 스펙트로그램 트랜스포머

Noise level management has become an important task in the automotive industry because of the increased demand for low-noise automobiles. In particular, it is essential to reduce the automobile noises incurred by electric power steering(EPS) and an automotive steering system. Although existing methods measure the noise levels by mounting EPS on actual automobiles and detect the importance of frequency bands based on experts’ judgment, they were subjective, time consuming and expensive. Therefore, an efficient method is required to predict the automobile noise levels and detect influential frequency bands based on EPS acceleration data. We propose a multi-sensor spectrogram transformer(MuST) for predicting the EPS automobile noise levels. The proposed method allows us to predict automobile noise levels by reflecting the individual characteristics of multiple sensors in EPS and detecting influential frequency bands related to noise levels. The experimental results showed that the proposed MuST performed well and detected influential frequency bands similar to the experts. We believe that the framework presented in this study can efficiently identify the automobile noise levels and help experts design quiet automobiles in the future by providing information on influential frequency bands. In addition, our method can help to handle various tasks which use data collected through multiple sensors in the automotive industry.

다중 합성곱 모델을 이용한 R-EPS 차량 내부 소음 예측

Because dealing with noises in automobile becomes more important, it is valuable to predict in-vehicle noise levels and use them for the product noise design. With the recent development of artificial intelligence, many studies have attempted to use deep learning models for various types of data generated in automobile industry. However, to the best of our knowledge, no studies have been conducted to predict in-vehicle noise levels based on deep learning models. In this study, we propose a deep learning framework that can predict in-vehicle noise levels and identify the causes of noises. Our framework is developed to recognize in-vehicle noise levels with automobile acceleration data from various locations of electric power steering devices. Our deep learning framework consists of several convolutional neural backbone networks to extract representation vectors for each acceleration axis. In addition, acceleration data are converted into a spectrogram through the short-term Fourier transformation technique, and high frequency bands in the spectrogram are removed to better represent the input data. We demonstrated that our proposed framework is suitable for predicting in-vehicle noises and identifying the major causes of noises. We expect that the explanation for prediction results will be helpful in the design low-noise vehicles.

Training and laboratory installation for testing vehicle steering

The article presents the stages of designing an educational and laboratory installation for testing the steering of a car with a rack-and-pinion mechanism and an electric amplifier. The created installation has small mass-dimensional dimensions (desktop), low cost and allows students of transport directions to study the device and the processes occurring in them with the use of full-scale units and steering drives. The methodology of laboratory work using the manufactured stand is given. Keywords car steering, electric power steering, educational and laboratory installation

Road Torque Modeling for Electric Power Steering Systems

Road Torque Modeling for Electric Power Steering Systems

A Robust Electric Power-Steering-Angle Controller for Autonomous Vehicles with Disturbance Rejection

This paper addresses the challenges associated with steering-angle control of electric power steering for autonomous vehicles, including steering model parameter uncertainty, dependency of self-aligning moment disturbance estimation on tire parameters, and compensation for the asymmetrical hysteresis behavior in the steering system caused by backlash in gears and static friction. A variable gain-sliding mode steering-angle controller is developed to deal with these challenges. Replacing the fixed gain with variable gain in the sliding mode controller solves two problems: it eliminates chattering, and it allows for automatic gain adjustment based on the maneuver and the size of the error, which eliminates the need for gain scheduling. Both fixed and variable gain-sliding mode controllers are derived and compared in simulations to prove the superiority of the variable gain controller. A sliding mode observer is developed to estimate the self-aligning moment disturbance without required information about the tire parameters, which makes it vehicle independent. The observer also treats the static friction as disturbance and estimates it along with any other disturbance, such as driver torque disturbance. The stability of both the controller and the observer is proven using Lyapunov stability theory. Simulation and experimental results proved the robustness of the presented methods to the above challenges.

Application of The DMAIC Method in Improving The Quality of Electric Power Steering Housing Products

PT. ABC is a manufacturing company that produces Electric Power Steering (EPS) Housing components. For the past three months, there have been problems with the EPS production process. The problem is related to the finding of defective products which on average reached 3.9% of the total production, and has exceeded the maximum limit allowed by the company, which is 2%. This study aims to overcome these problems by making improvements using the DMAIC method in the hope of finding the root cause of the emergence of defects and making improvements to reduce the number of defects that occur. The improvement results have shown that the number of defects reduced from 3086 pcs to 1948 pcs per 22 working days or decreased by 0.54% with an increase in sigma level from 3.95 to 4.01 sigma. In this case, DMAIC implementation is effectively reduced defects by 1.5% based on the sigma level. To achieve a 6-sigma level equivalent to a world-class industry, companies must make continuous improvements by fixing other root causes.

On the Analysis and Torque Enhancement of Flux-Switching Permanent Magnet Machines in Electric Power Steering Systems

Modern road vehicles are more and more often being equipped with electric actuators. These are intended to play critical roles in passengers comfort and safety. Among the electrified components onboard road vehicles, one can distinguish electric power steering (EPS) systems, which have been the subject of intensive investigations covering both design and control aspects. The abilities of several AC motor topologies to fulfil the EPS systems’ requirements have been assessed by a large scientific community in both academia and industry. The present work was aimed at the prediction of the electromagnetic features of the flux-switching permanent magnet machines (FSPMMs), with an emphasis on the air gap flux density. The latter was firstly formulated while neglecting the slotting effect at both sides of the air gap. Then, stator and rotor permeance functions, taking into account the slotting effect and the PM flux concentrating arrangement, were incorporated into the derived flux density spatial repartition. Moreover, the accuracy of the latter was improved through two dedicated correction functions that take into account the rotor position and the magnetic saturation. The last part of the paper presents a simple approach to enhance the developed torque of FSPMMs in an attempt to meet the EPS requirements.

Harmonic Order Analysis of Cogging Torque for Interior Permanent Magnet Synchronous Motor Considering Manufacturing Disturbances

This paper is a study of unintended cogging torque for the IPMSM (Internal Permanent Magnet Synchronous Motor) of an EPS (Electric Power Steering) system considering manufacturing disturbances. The IPMSM has been used recently in EPS systems with high power density. However, due to the complex rotor shape of the IPMSM, considering manufacturing disturbances, it is expected to reduce the quality of IPMSM performance. Therefore, the unintended cogging torque for motor quality is also expected to increase. This paper analyzes the causes of unintended cogging torque in the IPMSM of an EPS system considering manufacturing disturbances. Based on the harmonic order analysis of measured cogging torque for the IPMSM prototypes, the causes of unintended cogging torque in the IPMSM are verified due to the manufacturing disturbances.

Dynamic NVH Numerical Analysis of Power Steering in the Presence of Lubricant in the System.

The ongoing shift towards hybrid and electric vehicles has a strong impact on noise and vibration engineering. New, complex dynamic phenomena are brought to vehicle user attention due to the absence of internal combustion engines and the significant role in vehicle and drive feel perception. This paper presents an FEM (Finite Element Method) dynamic simulation model of an automotive Electric Power Steering assembly. Preliminary modal simulations and experiments as well as field data replication techniques were implemented to identify the phenomena and prepare and validate model components. A full dynamic model of an Electric Power Steering was presented, and fine-tuned including the presence of lubrication at the gear mesh interface. Experimental investigations were conducted alongside FEM simulations for various model setups. Linear and nonlinear contact stiffness models were implemented, as well as contact damping, and simulated at chosen assembly interfaces. The results indicated that in the case of NVH (Noise Vibration and Harshness) analysis of shock/impact originating problems, contact parameters used for static, quasi-static, and low velocity analyses were not applicable. Nonlinear and damped contact stiffness provided better results in such a case.