Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation

Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that could comprehensively address issues of tracking capability, driving safety, energy saving, and driving comfort during vehicle following. A hierarchical control architecture is utilized in this paper. The lower controller is based on the nonlinear vehicle dynamics model and adjusts vehicle acceleration with consideration to the changes of bus mass and road slope by extended Kalman filter (EKF). The upper controller adapts Model Predictive Control (MPC) theory to solve the multi-objective optimal problem in ACC process. Cost functions are developed to balance the tracking distance, driving safety, energy consumption, and driving comfort. The simulations and Hardware-in-the-Loop (HIL) test are implemented; results show that the proposed control strategy ensured the driving safety and tracking ability of the bus, and reduced the vehicle’s maximum impact to 5 m/s3 and the State of Charge (SoC) consumption by 10%. Vehicle comfort and energy economy are improved obviously.

Clustering Algorithm for Vehicle’s Driving Data Feature based on Integrated Navigation

The clustering result of K-means clustering algorithm is affected by the initial clustering center and the clustering result is not always global optimal. Therefore, the clustering analysis of vehicle’s driving data feature based on integrated navigation is carried out based on global K-means clustering algorithm. The vehicle mathematical model based on GPS/DR integrated navigation is constructed and the vehicle’s driving data based on GPS/DR integrated navigation, such as vehicle acceleration, are collected. After extracting the vehicle’s driving data features, the feature parameters of vehicle’s driving data are dimensionally reduced based on kernel principal component analysis to reduce the redundancy of feature parameters. The global K-means clustering algorithm converts clustering problem into a series of sub-cluster clustering problems. At the end of each iteration, an incremental method is used to select the next cluster of optimal initial centers. After determining the optimal clustering number, the feature clustering of vehicle’s driving data is completed. The experimental results show that the global K-means clustering algorithm has a clustering error of only 1.37% for vehicle’s driving data features and achieves high precision clustering for vehicle’s driving data features.

Evaluation of energy consumption in the acceleration process of a passenger car

The analysis of the vehicle acceleration process is a current topic based on the aspects related to the general characteristics of the car, its parameters, design, drive unit performance, and the influence of external factors. Therfore in the article, the authors assessed the dynamic and energy parameters of the car motion, in which the intensity of acceleration of the car with different intensities was examined. Acceleration was carried out in two variants, the first for a normal internal combustion engine and the second for the same engine but additionally equipped with a short-term boost system. In this way, it influences the increase in power and energy in the car drive system, changing its acceleration intensity. Variable car acceleration intensity was obtained in the range from 0.12 to 1.37 m/s2 , and energy consumption at the level of 0.4 to 1.2 MJ in the distance of 1/4 mile. The article proposes a combination of energy parameters and engine power in order to assess the acceleration dynamics, for this purpose, the specific energy consumption of the car was determined, ranging from 0.35 to 2.0 J/(kg∙m), which was related to the engine power, denoting it with the dynamics index. The study focuses on the assessment of the relationship between the specific energy consumption and acceleration of passenger cars in the available powertrain system using a new dynamics index. The proposed dynamics index combines the energy and dynamic parameters of the car to be able to objectively quantify the acceleration process.

Unmanned Surface Vehicle (USV) performance test in Bintan Island Waters

An unmanned surface vehicle (USV) is an unmanned vehicle that is operated on the surface of the water for certain purposes, for example, bathymetry measurement, underwater imaging, etc. These unmanned surface vehicles can be used in impassable waters for crewed vessels in dangerous waters. This research measures the movement of the vehicle acceleration and then calculates it as the USV roll and pitch values. The direction of movement and wind speed and the height of the water surface at low tide are also aspects measured in this research. An accelerometer is a sensor that can measure the acceleration of an object, both dynamic and static. Based on the observations, the highest roll value is 6.0° deep while the highest pitch value is 6.5°. The standard deviation value at roll conditions of 2.92 and the standard deviation value at pitch conditions of 1.25. The average frequency of roll conditions is 2.18 and pitch conditions of 1.13. The dominant wind moves from the south to the southwest with a dominant speed ranging from 3.0 to 4.0 m/s. The results of this research indicate that the USV has a good performance so that it is possible to collect data in the water.

Simulation of а car parameters impact on the process of its acceleration

Goal. The purpose of the work is mathematical modeling of Daewoo Lanos passenger car acceleration dynamics. Methodology. The mathematical model is based on the methodology of E.A. Chudakov and N.A.Yakovlev. According to this method, the main factor that determines the current value of vehicle acceleration at an elementary speed section is the dynamic factor. This factor depends on the traction force, the air resistance force and the weight of the vehicle. The paper proposes formulas for determining the dynamic factor and parameters of vehicle acceleration at an elementary speed section, where gear shift takes place. The model is implemented in the MATLAB software environment. The software product allows to determine the parameters of the car during acceleration to the maximum speed when the engine is running at the external speed characteristic modes. Results Based on the results of mathematical modeling for the Daewoo Lanos car, the loads arising in the drive of the car were analyzed. It is shown that the tractive effort is mainly spent on overcoming the inertial forces, which at the beginning of the movement exceed the resistance forces of the road and air by more than 50 times. With an increase in the vehicle speed, the inertia force decreases and at a speed of 100 km / h it is only twice the other load components. It is shown that with the accepted initial data, the Daewoo Lanos car accelerates to 100 km/h in 17.7 s, which corresponds to the experimental data. The influence of the mass of the car, the rated power of the engine, the mode and time of gear shifting, the radius of the wheels, the height of the car, the coefficient of aerodynamic drag on the dynamics of acceleration of the car is analyzed. It was revealed that the vehicle weight and the nominal power of the engine affect the dynamics of acceleration from 0 to 100 km/h to the greatest extent. The influence of other parameters in the indicated speed range is not somewhat significant. The explanation of the obtained results is given. Practical value. The mathematical model presented in the work allows to determine the parameters of the engine and the car during acceleration, take into account the influence of the design and adjusting parameters of the engine and the car on these indicators, and carry out optimization studies.

MATHEMATICAL MODELING OF THE ENGINE PARAMETERS INFLUENCE ON VEHICLE ACCELERATION DYNAMICS

The throttle response of a vehicle determines its dynamic properties and is characterized by an acceleration time from 0 to 100 km/h. An experimental study of the influence of vehicle parameters on its throttle response is associated with significant material and labor costs. At the stage of sketching the design of the vehicle, preliminary determination of design parameters and settings, it is rational to use mathematical models. In the existing models of the vehicles movement dynamics, the engine power, as a rule, is set by empirical dependencies and does not take into account the possibility of changing its parameters and characteristics. The paper proposes a mathematical model that combines models of the engine workflow and the dynamics of vehicle acceleration. The mathematical model of the engine workflow is a quasi-stationary thermodynamic model, in which combustion is described by the Vibe equation, and heat transfer with the walls is described by the Woschni equation. To check its adequacy, an experimental study of the VAZ-2108 engine was carried out to obtain external speed, load and control characteristics. Good agreement between the calculated and experimental data is shown. Vehicle acceleration simulation was carried out according to the method of E.A. Chudakov. The parameters of the VAZ-2108 car and the resistance forces during acceleration from 0 to 100 km / h have been determined. It is shown that the car accelerates from 0 to 100 km / h in 18.3 s, which corresponds to the experimental data and indicates the adequacy of the chosen techniques. The influence of changing the parameters and settings of the engine on the dynamics of vehicle acceleration has been investigated. It is shown that in order to achieve better dynamics of motion, the cylinder diameter and compression ratio must be maximized. The ignition timing, intake valve closing angle and excess air ratio have extremes. The efficiency of using a 16-valve cylinder head instead of an 8-valve one is shown. Based on the results of the studies, it was proposed to apply a set of engine parameters, which made it possible to reduce the acceleration time of the VAZ-2108 from 18.3 s to 13.2 s. Thus, the developed mathematical model makes it possible to quantitatively evaluate the influence of engine parameters on the dynamics of vehicle acceleration, to optimize the parameters and settings of the power plant and the vehicle as a whole.

Creating the Illusion of Sportiness: Evaluating Modified Throttle Mapping and Artificial Engine Sound for Electric Vehicles

Modern computerized vehicles offer the possibility of changing vehicle parameters with the aim of creating a novel driving experience, such as an increased feeling of sportiness. For example, electric vehicles can be designed to provide an artificial sound, and the throttle mapping can be adjusted to give drivers the illusion that they are driving a sports vehicle (i.e., without altering the vehicle’s performance envelope). However, a fundamental safety-related question is how drivers perceive and respond to vehicle parameter adjustments. As of today, human-subject research on throttle mapping is unavailable, whereas research on sound enhancement is mostly conducted in listening rooms, which provides no insight into how drivers respond to the auditory cues. This study investigated how perceived sportiness and driving behavior are affected by adjustments in vehicle sound and throttle mapping. Through a within-subject simulator-based experiment, we investigated (1) Modified Throttle Mapping (MTM), (2) Artificial Engine Sound (AES) via a virtually elevated rpm, and (3) MTM and AES combined, relative to (4) a Baseline condition and (5) a Sports car that offered increased engine power. Results showed that, compared to Baseline, AES and MTM-AES increased perceived sportiness and yielded a lower speed variability in curves. Furthermore, MTM and MTM-AES caused higher vehicle acceleration than Baseline during the first second of driving away from a standstill. Mean speed and comfort ratings were unaffected by MTM and AES. The highest sportiness ratings and fastest driving speeds were obtained for the Sports car. In conclusion, the sound enhancement not only increased the perception of sportiness but also improved drivers’ speed control performance, suggesting that sound is used by drivers as functional feedback. The fact that MTM did not affect the mean driving speed indicates that drivers adapted their “gain” to the new throttle mapping and were not susceptible to risk compensation.

Research on Exhaust Emissions in Dynamic Operating States of a Combustion Engine in a Real Driving Emissions Test

The paper describes the method of investigations of exhaust emissions from a combustion engine under operation classified in terms of its dynamic states. In this paper, the engine operating states are determined through the vehicle driving under actual traffic conditions in the RDE (Real Driving Emissions) test. Based on the recorded tracings of the vehicle velocity, the engine states were classified as static for the acceleration of the absolute value lower than the adopted classification limit. Besides, the authors analyzed the engine operating states for the positive as well as negative acceleration. For the adopted engine operating states, zero-dimensional characteristics of the emission intensity for individual exhaust components were determined (average value, coefficient of variation). The influence of the analyzed operating states on the emission of individual exhaust components was assessed. The greatest increase in the intensity of the emission of nitrogen oxides was observed for the positive vehicle acceleration model and the lowest (also for the nitrogen oxides) for the negative vehicle acceleration. On average, the greatest increase in the emission intensity of pollutants and the intensity of particle number occurred for the dynamic states of the engine corresponding to positive acceleration. The conclusions from the performed investigations entitle the authors to propose a greater allowance for the exhaust emission-related criteria in the engine control algorithms.

Acceleration based black-box accident detection and warning system

This research paper has been consecrated to design a black-box accident warning system that combines both Global System for Mobile Communication (GSM) and Global Positioning System (GPS) technologies to locate and send a Minimum Amount of Data containing important information about the accident gathered using different sensors such as the accident type, geographic coordinates, time and velocity of the vehicle to the intervention services (Hospitals, civil protection and police). In addition, the system exploits the data obtained via OBD-II standard to provide a reliable accident detection method based on the vehicle acceleration. Obviously, this system seemed to be the best solution for countries where the average age of vehicles is quite higher than others. The system has been placed in a real vehicle in order to test the accident detection algorithm by applying sudden medium braking.

Fuel Economy Improvement of Urban Buses with Development of an Eco-Drive Scoring Algorithm Using Machine Learning

Eco-drive is a widely used concept. It can improve fuel economy for different driving behaviors such as vehicle acceleration or accelerator pedal operation, deceleration or coasting while slowing down, and gear shift timing difference. The feasibility of improving the fuel economy of urban buses by applying eco-drive was verified by analyzing data from drivers who achieved high fuel efficiencies in urban buses with a high frequency of acceleration/deceleration and frequent operation. The items that were monitored for eco-drive were: rapid take-off/acceleration/deceleration, accelerator pedal gradient, coasting rate, shift indicator violation, average engine speed, over speed, and gear shifting under low-end engine speed. The monitoring method for each monitored item was set up, and an index was produced using driving data. A fuel economy prediction model was created using machine learning to determine the contribution of each index to the fuel economy. Furthermore, the contribution of each monitoring item was analyzed using the prediction model explainer. Accordingly, points (defined as the eco-drive score) were allocated for each monitoring item. It was verified that this score can represent the eco-drive characteristics based on the relationship between the score and fuel economy. In addition, it resulted in an average annual fuel economy improvement of 12.1%.