An electronically controlled fuel injection system for new diesel engines

The new system makes it possible to improve accuracy and provide more flexibility in the control of fuel injection volume and injection timing compared with conventionally-used mechanical control systems.     

Development of engine control using the in-cylinder pressure signal in a high speed direct injection diesel engine

Emission regulations are becoming more stringent and remain a principal issue for vehicle manufacturers. Many engine subsystems and control technologies have been introduced to meet the demands of these regulations. For diesel engines, combustion control is one of the most effective approaches for reducing not only engine exhaust emissions but also cylinder-by-cylinder variation. However, the high cost of pressure sensors and the complex engine head design for additional equipment present difficulties for manufacturers. In this paper, cylinder pressure-based engine control logic is introduced for a multi-cylinder high speed direct injection (HSDI) diesel engine. The time for 50% of the mass fraction to be burned (MFB50) and the IMEP are valuable for determining the combustion status. These two in-cylinder quantities are measured and applied to the engine control logic. Fuel injection timing is controlled to adjust the operating MFB50 to the target MFB50 using PID control logic, and the fuel injection quantity is controlled to adjust the measured IMEP to the desired IMEP. The control logic is demonstrated at steady state and during transient conditions and is applied to an NEDC mode test.     

Multimode trip information detection using personal trajectory data

Handheld global positioning system (GPS) devices can serve as a new tool to collect an individual's trip information with advantages of low cost, accurate data, and intensive spatial coverage. Various machine learning algorithms have been explored to detected trip train information in previous studies; however, few of them focused on the evaluation and comparison of the performance and applicability of different models. Meanwhile, according to previous studies, car and bus mode detection is a thorny issue due to their similar travel characteristics, and algorithms still need to be well explored and improved to solve this problem. In this article, an innovative method is proposed to detect trip information, including trip modes, mode-changing time and location, and other attributes, from personal trajectory data. The method is a two-step process. A machine learning algorith-based module (including artificial neural network, support vector machine, random forests, and Bayesian network) is firstly used to identify walk, bicycle, and motorized trip modes (bus or car); we thoroughly compared the performance of these four algorithms. Then a second module, using critical points on the GPS trajectories, is further developed to distinguish car and bus mode, incorporated with GIS map information. Field test results show that the proposed machine learning models can all be applied for walk, bicycle, and motorized mode detection with high detection rates exceeding 90%; however, the algorithms work relatively poorly for bus and car mode detection, with results mostly below 75%. The proposed two-step method can greatly improve bus and car mode detection accuracy by 14–30%. As a result, the average mode detection rates for all the four modes are above 90%. Compared with mode detection results by using only the machine learning algorithm, the proposed two-step method has much better performance in both accuracy and consistency.     

A study of a torque control algorithm for direct-injection gasoline engines

A new torque control system was developed for direct injection gasoline engines. The control algorithm was obtained through an analysis of torque behavior under lean combustion with the application of EGR. Tests were conducted in which the openings of both the throttle valve and EGR valve were used as the control parameters, and the fuel injection quantity was synchronized with the phase of the air flow into the cylinders. It was found that good correspondence was obtained between the actual torque and the target torque even when the air–fuel ratio and the EGR rate were changed under transient operating conditions.     

Gain-scheduled EGR control algorithm for light-duty diesel engines with static-gain parameter modeling

This paper presents a model-based gain scheduling algorithm of a PI-based EGR controller for light-duty diesel engines. In order to capture nonlinear characteristic of the EGR system, we have proposed a new scheduling variable to illustrate the static-gain of the plant model as a linear function. The proposed scheduling variable is composed of the air-tofuel ratio of the exhaust gas and the pressure ratio between the exhaust and intake manifolds. Using the scheduling variable, a static-gain model achieved 0.94 of the R-squared value with 810 of steady-state measurements which include key engine operating conditions. Based on the model of the static-gain parameter, the gains of the PI controller are decided by Skogestad internal model control (SIMC) tuning rule in real-time. Through various scenarios of engine experiments, the proposed gain scheduling algorithm represented that the PI gains were successfully adapted according to the changes of the engine operating conditions.     

Development of an idle speed engine model using in-cylinder pressure data and an idle speed controller for a small capacity port fuel injected SI engine

An idle speed engine model has been proposed and applied for the development of an idle speed controller for a 125 cc two wheeler spark ignition engine. The procedure uses the measured Indicated Mean Effective Pressure (IMEP) at different speeds at a constant fuel rate and throttle position obtained by varying the spark timing. At idling conditions, IMEP corresponds to the friction mean effective pressure. A retardation test was conducted to determine the moment of inertia of the engine. Using these data, a model for simulating the idle speed fluctuations, when there are unknown torque disturbances, was developed. This model was successfully applied to the development of a closed loop idle speed controller based on spark timing. The controller was then implemented on a dSPACE Micro Autobox on the actual engine. The Proportional Derivative Integral (PID) controller parameters obtained from the model were found to match fairly well with the experimental values, indicating the usefulness of the developed idle speed model. Finally, the optimized idle speed control algorithm was embedded in and successfully demonstrated with an in-house built, low cost engine management system (EMS) specifically designed for two-wheeler applications.     

Improving pollutant emissions in diesel engines for heavy-duty transportation using retarded intake valve closing strategies

A combined theoretical and experimental study has been carried out to determine the real potential of reducing pollutant emissions in a HD diesel engine by means of retarding the intake valve closing. The effects produced by this alteration of the basic operation cycle have been examined by a preliminary modelling study, and from the obtained results, a modified camshaft was manufactured with a delayed intake valve closing of 60 crank angle degrees. Single-cylinder engine tests were carried out with this modified camshaft, and the emissions and fuel consumption were recorded. The results showed that the retarded intake valve closing can enhance the premixed combustion phase, and thus simultaneously reduce soot and NOx emissions. Moreover, the combustion process attained is extremely tolerant to exhaust gas recirculation, and by adoption of this measure, Euro-5 emissions limits can be achieved at the tested conditions without after-treatment.     

Real-time empirical NO<Subscript>x</Subscript> model based on In-cylinder pressure measurements for light-duty diesel engines

This paper proposes a real-time empirical model of NO_x emissions for diesel engines. The proposed model predicts the level of NO_x emissions using an empirical model developed based on the thermal NO formation mechanism, the extended Zeldovich mechanism. Since it is difficult to consider the exact physical NO formation phenomena in real-time applications, the proposed algorithm adapts the key factors of the NO formation mechanism from the extended Zeldovich mechanism: temperature of the burned gas, concentration of the gas species, and combustion duration where NO is generated. These factors are considered in a prediction model as four parameters: exhaust gas recirculation rate (EGR rate), crank angle location of 50 % of mass fraction burned (MFB50), exhaust lambda value, and combustion acceleration. The proposed prediction model is validated with various steady engine experiments that showed a high linear correlation with the NO_x emission measured by a NO_x sensor. Furthermore, it is also validated for transient experiments.     

Parallel model based fault detection algorithm for electronic parking brake system

This paper describes a parallel model-based fault detection algorithm for an electronic parking brake (EPB) system, which consists of an electronic control unit with built-in current sensor and braking force sensor. For the EPB system to supply sufficient parking force to a vehicle, the parking force sensor is of utmost importance. If a fault occurs in this sensor, sufficient parking force may not be supplied, thereby seriously threatening the safety of the vehicle. Thus, a fault detection method is required for the parking force sensor of the EPB system to improve the safety of vehicles. For this purpose, a highly reliable fault detection method is needed to detect abnormal fault signals, which cannot be detected by the existing on-line sensor monitoring fault detection methods. This paper proposes a novel parallel model-based fault detection algorithm for the EPB system, which compares the physical sensor data with the mathematical model, the fuzzy model, and the neural network model at the same time. In order to reduce false alarms, the magnitude of thresholds and the operation counts are changed adaptively. When the proposed parallel model-based fault detection algorithm detects severe failures of the force sensor, it warns the driver in advance to prevent accidents due to the failures. The proposed algorithm is verified by hardware-in-theloop simulations in various situations.     

Development of route information based driving control algorithm for a range-extended electric vehicle

A route information based driving control algorithm was developed for an RE-EV which consists of two motorgenerators, MG1 and MG2. A threshold power which controls the engine on/off to charge the battery was obtained by an optimization process using route information, such as the vehicle velocity and altitude. The threshold power allows the vehicle to travel to the final destination while making the final battery SOC close to SOC _low. Using the threshold power, route based control (RBC) was proposed by considering the driver’s characteristics and traffic conditions using the driving data base. In addition, a relationship between the threshold power and various initial battery SOC was obtained by off-line optimization. The performance of the RBC was evaluated by simulation and human-in-the-loop simulation (HILS) for city driving. It was found from the simulation and HILS results that the RBC achieved approximately 4 % to 12 % reduction in fuel consumption compared to the existing charge depleting/charge sustaining (CD/CS) driving control.     

Development of a new time domain-based algorithm for train detection and axle counting

This paper presents an innovative train detection algorithm, able to perform the train localisation and, at the same time, to estimate its speed, the crossing times on a fixed point of the track and the axle number. The proposed solution uses the same approach to evaluate all these quantities, starting from the knowledge of generic track inputs directly measured on the track (for example, the vertical forces on the sleepers, the rail deformation and the rail stress). More particularly, all the inputs are processed through cross-correlation operations to extract the required information in terms of speed, crossing time instants and axle counter. This approach has the advantage to be simple and less invasive than the standard ones (it requires less equipment) and represents a more reliable and robust solution against numerical noise because it exploits the whole shape of the input signal and not only the peak values. A suitable and accurate multibody model of railway vehicle and flexible track has also been developed by the authors to test the algorithm when experimental data are not available and in general, under any operating conditions (fundamental to verify the algorithm accuracy and robustness). The railway vehicle chosen as benchmark is the Manchester Wagon, modelled in the Adams VI-Rail environment. The physical model of the flexible track has been implemented in the Matlab and Comsol Multiphysics environments. A simulation campaign has been performed to verify the performance and the robustness of the proposed algorithm, and the results are quite promising. The research has been carried out in cooperation with Ansaldo STS and ECM Spa.     

用弯沉对路基压力注浆效果结构性评价

结合绵广高速公路路基病害压力注浆处治工程,通过对施工路段注浆前后的落锤式弯沉仪FWD检测,采用多种模量反算方法,对压力注浆效果进行了结构性评价。     

Fuel consumption parameters for realizing and verifying fuel consumption prospect algorithm of vehicle driving route information system

Emissions of CO₂, as the main component of greenhouse gases, and high fuel consumption rates are worldwide problems. To solve them, most car manufacturers have concentrated on developing various techniques to improve the efficiencies of engines and transmissions and ECO-ROUTEs to meet environmental regulations. In this study, an algorithm for determining routes that cause the least fuel consumption was developed. The core of this algorithm is a specific EEC (energy efficiency constant) map containing logic that is able to predict fuel consumption. The accuracy of the algorithm was confirmed by vehicle tests for various driving patterns. Parameters affecting vehicle fuel economy were studied and verified. Improvement in the accuracy of this algorithm was confirmed by applying these parameters to ECO-ROUTE logic.     

Development of a path planning system using mean shift algorithm for driver assistance

The longitudinal and lateral vehicle control techniques have been widely used in several active driver assistance systems. The adaptive cruise control, lane keeping assistant control, vehicle platooning and stop-and-go control are typical examples of the most important applications. In this study, a novel path planning method is proposed considering the driving environment such as road shape, ego vehicle and surrounding vehicles’ movement. The relative distance and velocity between the ego vehicle and surrounding vehicles are identified with respect to the predicted lane shape in front of the ego vehicle. Based on the identified information, the road shape and surrounding vehicles are mapped into the intensity image and the desired vector for the ego vehicle’s movement is determined by the maximum intensity density tracing method. The desired vehicle path is followed by the acceleration/deceleration control and the steering assist control, respectively. In order to evaluate the performance of the proposed system, simulations are conducted and compared with ACC systems.     

Lightweight mixture faults detection method for gasoline engine using on-line trend analysis

Mixture faults detection is meaningful for gasoline engines because proper mixture is the basic prerequisite for healthy running of a combustion engine. Among existing methods for faults detection, the data-driven trend analysis technique is widely used due to the simplicity and efficiency in time-domain. The CUSUM (Cumulative Sum Of Errors) algorithm is good at real-time trend extraction, but it’s easy to be costly on the fuel trim signal during the engine in normal working conditions, which will increase battery energy consumption because engine failure is rarely occurs. Hence, the conventional treatment methods of artifacts in the CUSUM algorithm are modified by means of decay function and detection time analysis. The thresholds are tuned according to the characteristics of artifacts instead of residual variability, which leads to better results of trend extraction and less computation. Then, the revised CUSUM algorithm is used for monitoring the mixture abnormal behaviors, and the mixture faults can be detected in real time through analyzing the variation features of fuel trim signal. The lightweight faults detector using the advanced CUSUM algorithm (FD-A-CUSUM) is evaluated on the experimental data collected from a Ford engine. The validation results show that while engine works under normal conditions, the computation of FD-A-CUSUM has decreased by 72.79 % in comparison with the detection method using the original CUSUM algorithm (FD-O-CUSUM), and the false alarm ratio of FD-A-CUSUM is 3.37 %. Futhermore, the detection results of FD-A-CUSUM for two leakage faults have achieved 91.18 % test accuracy.     

Optimal control of brakes and steering for autonomous collision avoidance using modified Hamiltonian algorithm

ABSTRACT

This paper considers the problem of collision avoidance for road vehicles, operating at the limits of friction. A two-level modelling and control methodology is proposed, with the upper level using a friction-limited particle model for motion planning, and the lower level using a nonlinear 3DOF model for optimal control allocation. Motion planning adopts a two-phase approach: the first phase is to avoid the obstacle, the second is to recover lane keeping with minimal additional lateral deviation. This methodology differs from the more standard approach of path-planning/path-following, as there is no explicit path reference used; the control reference is a target acceleration vector which simultaneously induces changes in direction and speed. The lower level control distributes vehicle targets to the brake and steer actuators via a new and efficient method, the Modified Hamiltonian Algorithm (MHA). MHA balances CG acceleration targets with yaw moment tracking to preserve lateral stability. A nonlinear 7DOF two-track vehicle model confirms the overall validity of this novel methodology for collision avoidance.     

Shockwave-based queue estimation approach for undersaturated and oversaturated signalized intersections using multi-source detection data

With the progress of information and sensing technologies, estimating vehicular queue length at signalized intersections becomes feasible and has attracted considerable attention. The existing studies provided a solid theoretical foundation for the estimation; however, the studies have some restrictions or limitations more or less. This paper presents a new methodology for estimating vehicular queue length at signalized intersections using multi-source detection data under both undersaturated and oversaturated conditions. The methodology applies the shockwave theory to model queue dynamics. Using data from probe vehicles and point detectors, analytical formulations for calculating the maximum and minimum (residual) queue lengths of each cycle are developed. Ground truth data were collected from numerical experiments conducted at two intersections in Shanghai, China, to verify the proposed methodology. It is found that the methodology has mean absolute percentage errors of 17.09% and 12.28%, respectively, for maximum queue length estimation in two tests, which are reasonably effective. However, the methodology is unsatisfactory in estimating the residual queue length. Other limitations of the proposed models and algorithms are also discussed in the paper.     

Risk management algorithm for rear-side collision avoidance using a combined steering torque overlay and differential braking

This paper describes a risk management algorithm for rear-side collision avoidance. The proposed risk management algorithm consists of a supervisor and a coordinator. The supervisor is designed to monitor collision risks between the subject vehicle and approaching vehicle in the adjacent lane. An appropriate criterion of intervention, which satisfies high acceptance to drivers through the consideration of a realistic traffic, has been determined based on the analysis of the kinematics of the vehicles in longitudinal and lateral directions. In order to assist the driver actively and increase driver's safety, a coordinator is designed to combine lateral control using a steering torque overlay by motor-driven power steering and differential braking by vehicle stability control. In order to prevent the collision while limiting actuator's control inputs and vehicle dynamics to safe values for the assurance of the driver's comfort, the Lyapunov theory and linear matrix inequalities based optimisation methods have been used. The proposed risk management algorithm has been evaluated via simulation using CarSim and MATLAB/Simulink.