Automotive Control Research Articles

Automotive controls: Past, present, & future

Automotive controls: Past, present, & future

The design of automotive electronic control suspension system based on digital simulation

The design of automotive electronic control suspension system based on digital simulation

Safety analysis of integrated adaptive cruise and lane keeping control using multi-modal port-Hamiltonian systems

A modern vehicle can be viewed as a complex cyber–physical system (CPS) where the vehicle dynamics interact with the software control systems. Adaptive cruise control (ACC) and lane keeping control (LKC), in particular, are foundational features for semi-autonomous and autonomous driving. Safety analysis of such systems is extremely important for realizing vehicle autonomy. Ensuring safety in such complex CPS is very challenging, especially in the presence of interactions between multiple subsystems, nonlinearities, hybrid dynamics, and disturbances. This paper presents an approach for safety analysis of automotive control systems using multi-modal port-Hamiltonian systems. The approach uses the Hamiltonian function as a barrier between the energy levels of the safe and unsafe states and employs passivity to prove that trajectories cannot cross this barrier. The approach is applied to the safety analysis of a vehicle dynamics composed with ACC and LKC. The goal is to ensure that the host vehicle will not collide with a lead vehicle and will not skid off of the road. The control design is implemented and evaluated using a hardware-in-the-loop simulation platform. The experimental results demonstrate the safety analysis approach including the impact of implementation effects such as discretization and quantization.

Vibration Control of Automotive Drive System With Nonlinear Gear Backlash

Abstract In automotive drive systems, differential gear backlash degrades the control performance. Specifically, a shock torque, which is generated when the gear runs freely and collides with the backlash, increases the vibration amplitude. Consequently, it is important to develop a vibration control method to suppress the adverse effect of nonlinearity due to backlash. Furthermore, considering implementations on actual vehicles, design at the development site, and mass production, a simple and practical control method is necessary. This paper describes the configuration of a basic experimental device, which abstracts an actual vehicle to focus on the influence due to backlash while reflecting the basic structure of an automotive drive system. Next, a basic controller is designed using a mixed H2/H∞ control theory, and a servo system is constructed to track the target value. A simple control mode switching algorithm is proposed for backlash compensation. This algorithm is suited to practical applications because it uses only an output without a state estimation and it compensates for performance deteriorations due to the nonlinearity by operating a single linear controller. Finally, simulations and experiments verify the effectiveness of the proposed control system.

Adaptive neural tracking control for automotive engine idle speed regulation using extreme learning machine

The automotive engine idle speed control problem is a compromise among low engine speed for fuel saving, minimum emissions and disturbance rejection ability to prevent engine stall. However, idle speed regulation is very challenging due to the presence of high nonlinearity and aging-caused uncertainties in the engine dynamics. Therefore, the engine idle speed system is a typical uncertain nonlinear system. To address the problems of inherent nonlinearity and uncertainties in idle speed regulation, an extreme learning machine (ELM)-based adaptive neural control algorithm is proposed for tracking the target idle speed adaptively. The purpose of ELM is to rapidly deal with the uncertain nonlinear engine system. Since the original ELM is not designed for adaptive control, a new adaptation law is designed to update the weights of ELM in the sense of Lyapunov stability. Experiment is conducted to validate the performance of the proposed control method. Experimental result indicates that the ELM-based adaptive neural control outperforms the classical proportional–integral–derivative (PID), fuzzy-PID and back-propagation-neural-network-based controllers in terms of tracking performance under the variation of engine load.

Evaluation of a high-throughput communication link for future automotive ADAS controllers

The trend toward further integration of automotive electronic control units functionality into domain control units as well as the rise of computing-intensive driver assistance systems has led to a demand for high-performance automotive computation platforms. These platforms have to fulfill stringent safety requirements. One promising approach is the use of performance computation units in combination with safety controllers in a single control unit. Such systems require adequate communication links between the computation units. While Ethernet is widely used, a high-speed serial link communication protocol supported by an Infineon AURIX safety controller appears to be a promising alternative. In this paper, a high-speed serial link IP core is presented, which enables this type of high-speed serial link communication interface for field-programmable gate array–based computing units. In our test setup, the IP core was implemented in a high-performance Xilinx Zynq UltraScale+, which communicated with an Infineon AURIX via high-speed serial link and Ethernet. The first bandwidth measurements demonstrated that high-speed serial link is an interesting candidate for inter-chip communication, resulting in bandwidths reaching up to 127 Mbit/s using stream transmissions.

Off-design performance of a louvered fin-tube heater core in an automotive climate control system

Louvered fin-and-tube heat exchanger cores, operating inside of an automotive climate control system (ACCS), are used to control temperature and humidity in a passenger cabin. The thermal performance of these cores is typically sensitive to flow features inside the core, which have been well studied for uniform flow fields as well as some non-uniform and inclined flow-fields upstream of the core. The compact geometry of an ACCS unit results in the flow-fields upstream of the core being different from those used in previous studies. When measuring the performance of a louvered fin-and-tube heater core inside a commercially manufactured ACCS unit, up to a 60% reduction in the core’s thermal performance compared to the performance when exposed to a uniform upstream flow is detected. Such a drastic performance reduction has not been reported in the literature. As such a systematic breakdown is made to elaborate how some key and specific ACCS unit features designed for the sake of compactness could play a detrimental role in reducing the overall thermal performance of a given louvered fin-and-tube heater core. By mimicking the actual ACCS features the performance deterioration in the commercial unit could be largely replicated. The individual contributing effects of the various fluidic features such as flow separation and recirculation are determined. Only 50% of the measured 60% deterioration, however, could be accounted for, suggesting that other ACCS unit properties not assessed in detail in this study may also adversely affect the core’s performance.

LM Algorithm Neural Network Predictive Control of FlexRay Bus System

In view of the uncertainty of data transmission due to the irregular data flow in the network and the limitation of network bandwidth resources, which makes the control performance and stability of FlexRay network decrease when data is transmitted at high speed and the reliability and safety of the control system can not be guaranteed, this paper proposes a method based on the Levenberg-Marquardt (LM) algorithm for neural network predictive control of the FlexRay bus. The method can predict the next moment operating state of the car network according to the current moment working state of the FlexRay car network to adaptively adjust task workload to adapt to changes in vehicle network system load and improve the reliability and stability of FlexRay network control system. The simulation results show that the neural network predictive control has good adaptability and robustness, which improves the control performance of the FlexRay automotive networked control system effectively.

Thermal Management for the Cabin of a Battery Electric Vehicle Considering Passengers’ Comfort

Due to the absence of an internal combustion engine and the corresponding waste heat, battery electric vehicles have a significantly reduced range in cold environments. Moreover, also at high ambient temperatures, the energy consumption of the air-conditioning system has a negative effect on the range. In this paper, a thermal management strategy for the passenger compartment of a battery electric vehicle is developed with the aim to reduce the power consumption of the heating, ventilation, and air-conditioning (HVAC) system. Simultaneously, the thermal comfort of the passengers has to be ensured. To address both objectives, a predictive, optimization-based approach for the thermal management system is developed. Models for the vehicle cabin and the HVAC system are derived and identified using measurement data. These models are used to formulate an optimal control problem, where also system limitations are considered explicitly. To avoid the computationally expensive solution of a nonlinear optimal control problem, it is approximated by a linear-quadratic model predictive control problem. This can be solved very efficiently and is suitable for real-time implementation on an automotive control unit. The proposed linear-quadratic strategy is compared with a baseline strategy and the nonlinear strategy to show the effectiveness of the proposed approach.

Technical Committee on Automotive Control [Technical Activities

Technical Committee on Automotive Control [Technical Activities

An Explicit Nonlinear Model Predictive ABS Controller for Electro-Hydraulic Braking Systems

This paper addresses the development and Hardware-in-the-Loop (HiL) testing of an explicit nonlinear model predictive controller (eNMPC) for an antilock braking system (ABS) for passenger cars, actuated using an electro-hydraulic braking unit. The control structure includes a compensation strategy to guard against the performance degradation due to actuation dead times, identified by experimental tests. The eNMPC is run on an automotive rapid control prototyping unit, which shows its real-time capability with comfortable margin. A validated high-fidelity vehicle simulation model is used for the assessment of the ABS on a HiL rig equipped with the braking system hardware. The eNMPC is tested in seven emergency braking scenarios, and its performance is benchmarked against a proportional-integral-derivative (PID) controller. The eNMPC results show: 1) the control system robustness with respect to the variations in tire-road friction condition and initial vehicle speed; and 2) consistent and significant improvement of the stopping distance and wheel slip reference tracking, with respect to the vehicle with the PID ABS.

Extended Range Electric Vehicle With Driving Behavior Estimation in Energy Management

Battery and energy management methodologies have been proposed to address the design challenges of driving range and battery lifetime in electric vehicles (EVs). However, the driving behavior is a major factor which has been neglected in these methodologies. In this paper, we propose a novel context-aware methodology to estimate the driving behavior in terms of future vehicle speeds and integrate this capability into EV energy management. We implement a driving behavior model using a variation of artificial neural networks called nonlinear autoregressive model with eXogenous inputs (NARX). We train our novel context-aware NARX model based on historical behavior of real drivers, their recent driving reactions, and route average speed retrieved from Google Maps in order to enable driver-specific and self-adaptive driving behavior modeling and long-term estimation. We analyze the estimation error of our methodology and its impact on a battery lifetime-aware automotive climate control, comparing to the state-of-the-art methodologies for various estimation window sizes. Our methodology shows only 12% error for up to 30-s speed prediction which is an improvement of 27% compared to the state-of-the-art. Therefore, the higher accuracy helps the controller to achieve up to 82% of the maximum energy saving and battery lifetime improvement achievable in ideal methodology where the future vehicle speeds are known.

An overview of various control benchmarks with a focus on automotive control

There exists a gap between control theory and control practice, i.e., all control methods suggested by researchers are not implemented in real systems and, on the other hand, many important industr ...

Special issue on benchmark problems in automotive system control

We hope this special issue will provide new inspiring to the community of control theory, especially for the youngerresearchers and students. We wish to thank Yiguang Hong, Editor of the journal of ...

READ: Reverse Engineering of Automotive Data Frames

Security analytics and forensics applied to in-vehicle networks are growing research areas that gained relevance after recent reports of cyber-attacks against unmodified licensed vehicles. However, the application of security analytics algorithms and tools to the automotive domain is hindered by the lack of public specifications about proprietary data exchanged over in-vehicle networks. Since the controller area network (CAN) bus is the de-facto standard for the interconnection of automotive electronic control units, the lack of public specifications for CAN messages is a key issue. This paper strives to solve this problem by proposing READ: a novel algorithm for the automatic Reverse Engineering of Automotive Data frames. READ has been designed to analyze traffic traces containing unknown CAN bus messages in order to automatically identify and label different types of signals encoded in the payload of their data frames. Experimental results based on CAN traffic gathered from a licensed unmodified vehicle and validated against its complete formal specifications demonstrate that the proposed algorithm can extract and classify more than twice the signals with respect to the previous related work. Moreover, the execution time of signal extraction and classification is reduced by two orders of magnitude. Applications of READ to CAN messages generated by real vehicles demonstrate its usefulness in the analysis of CAN traffic.

Efficient Intrusion Detection With Bloom Filtering in Controller Area Networks

Due to its cost efficiency, the controller area network (CAN) is still the most wide-spread in-vehicle bus, and the numerous reported attacks demonstrate the urgency in designing new security solutions for CAN. In this paper, we propose an intrusion detection mechanism that takes advantage of Bloom filtering to test frame periodicity based on message identifiers and parts of the data-field which facilitates detection of potential replay or modification attacks. This proves to be an effective approach since most of the traffic from in-vehicle buses is cyclic in nature and the format of the data-field is fixed due to rigid signal allocation. Bloom filters provide an efficient time-memory tradeoff which is beneficial for the constrained resources of automotive grade controllers. We test the correctness of our approach and obtain good results on an industry-standard CANoe-based simulation for a J1939 commercial-vehicle bus and also on CAN with flexible data-rate traces obtained from a real-world high-end vehicle. The proposed filtering mechanism is straightforward to adapt for any other time-triggered in-vehicle bus, e.g., FlexRay, since it is built on time-driven characteristics.

An Analysis of the Impact of Transient Faults on the Performance of the CAN-FD Protocol

The increasing complexity of distributed real-time control systems in the vehicular area has led to the development of new protocols, such as controller area network with a flexible data rate (CAN-FD), that provides high-bandwidth communication with FD rate. Different topologies are used to interconnect electronic control units in safety-critical applications in the automotive area and the applied communication protocols, such as CAN-FD, must comply with reliability requirements. Moreover, the functional operations must be tested to their limits, since they require appropriate assessment techniques for different application scenarios. Recent research has highlighted that power switching systems cause transient faults that affect the communication network. In light of this concern, this paper explores the IEC/TS 62228:2007 and ISO 26262-3/4/9:2018 standards that can act as guidelines for the development of a test method and testing board for evaluating the impact of electrical fast transients on the performance of a distributed automotive control system. Metrics such as difference jitter, average jitter, and packet loss, are used to determine the fault impact on the control law of a critical vehicular control system. The experiments that were conducted show that during the four test scenarios in which the testing board was used, the average jitter increased from 10.41 to 29.05% in the worst case scenario. These results highlight the importance of carrying out consistent tests to prevent critical situations and that these data can be used in software requirements specification phases to improve reliability in vehicular control systems.

Rapid Road Friction Estimation using Independent Left/Right Steering Torque Measurements

Sensing the friction coefficient between the tyres of an automobile and the road surface is a key challenge in automotive control. Accurate estimation of this parameter enables higher performance and safer operation of vehicles, whether human-operated, assisted or autonomously controlled. Previous approaches have utilised braking or lateral excitation to generate estimates but are limited by the conflict between generating sufficient excitation and preserving ride comfort and efficient operation. Further complicating the problem is the need to provide the friction estimate without adding costly sensors to the vehicle. This paper presents an approach to friction estimation that leverages separate measurements of the left and right steering torques to generate an estimate of the surface friction through direct model inversion. Uncertainty of the method is explored using experimental sensor properties and analysis of the inverted model. The method is shown to be sensitive to reduced friction coefficients and can identify sudden changes before travelling a full contact patch length. Experimental validation of the approach is demonstrated with calibrated wheel force transducers as well as low-cost sensors suitable for incorporation into production vehicles.

Comprehensive high speed automotive SM-PMSM torque control stability analysis including novel control approach

Permanent magnet synchronous machines (PMSM) are widely used in the automotive industry for electric vehicle (EV) and hybrid electric vehicle (HEV) propulsion systems, where the trend is to achieve high mechanical speeds. High speeds inevitably imply high current electrical frequencies, which can lead to a lack of controllability when using field oriented control (FOC) due to sampling period constraints. In this work, a comprehensive discrete-time model is fully developed to assess the stability issues in the widely used FOC. A speed-adaptive control structure that overcomes these stability problems and extends the speed operation range of the PMSM is presented. Also, a numerical methodology from which the maximum operating stable frequency can be computed in advance of any experimentation, is developed. All contributions are accompanied and supported by numerical results obtained from an accurate MATLAB/Simulink model.

Unravelling origins of Pd ensembles’ activity in CO oxidation via state-to-state microkinetic analysis

The size and configuration of small Pdn ensembles (monomers, dimers, trimers, and tetramers) is of great importance not only for the fundamental understanding of single-site catalysis design, but also for practical applications such as automotive emission control and fuel cells. In this study, by coupling the competitive gaseous adsorptions and multiple reaction pathways via a state-to-state microkinetic formalism, we reveal CO oxidation mechanisms on small Pd ensembles under realistic experimental conditions. It is found that reaction of O2 with preadsorbed CO on adjacent Pd sites is the dominant pathway at low temperatures (denoted as the preadsorbed CO oxidation pathway, or PCOP). As the temperature increases, different PCOPs dominate CO oxidation. At even higher temperatures, the CO oxidation reaction proceeds primarily via the recombination of dissociated O atoms and CO (denoted as the dissociated O2 pathway, or DO2P). Among different ensembles investigated, the Pd dimer possesses the best low-temperature CO oxidation activity, while the tetramer outperforms other forms at higher temperatures. It is concluded that oxygen adsorption ability is the main factor influencing the reactivity. This work unravels the intrinsic activity of the catalyst on the microscopic scale and shed light on ensemble engineering for maximizing its reactivity.