Vehicles, Volume 4, Issue 1 (March 2022) – 18 articles

Drive cycle a is primary information useful for analyzing, designing, and optimizing automotive controllers for vehicle homologation. Conventional and electric vehicles are tested and certified based on the specified standard driving cycles as per vehicle category for emission compliance and energy consumption, respectively. In countries such as India, this drive cycle fails to conceal the real-time drive cycles on urban roads with heavy traffic. This real-time drive cycle details the driving skill, congestion, road characteristics, acceleration and deceleration durations, etc. In this context, the real-time drive cycle is captured with the help of an Inertial Measurement Unit. Analysis of IMU measured data with a suitable sampling rate is carried out and energy characterizations are presented in this article. For better accuracy, the IMU data logger is set for an 8 Hz sampling rate which logs the vehicle dynamics data of a scooter. For urban traffic data collection, Pune city is selected and actual energy spent is estimated with the engine, electric, and hybrid modes. State of Charge based switching is carried out with the help of a hybrid controller and observations are tabulated. State of Charge thresholds are monitored and energy-efficient switching is decided. It is estimated from the results that hybrid conversion of a scooter is more efficient due to charge/regeneration into a Lithium-ion battery when the engine powers the wheel and while braking. The range is extended with the above configuration, and further can be increased based on higher battery capacity. Energy management is better handled with a hybrid electric controller for urban roads. Range anxiety issues of EV are lowered in HEV configuration and it is also estimated that parallel Hybrid scooters are more energy-efficient and release lower carbon emissions than conventional vehicles. Full article

Estimation of combustion phasing and power production is essential to ensuring proper combustion and load control. However, archetypal control-oriented physics-based combustion models can become computationally expensive if highly accurate predictive capabilities are achieved. Artificial neural network (ANN) models, on the other hand, may provide superior predictive and computational capabilities. However, using classical ANNs for model-based prediction and control can be challenging, since their heuristic and deterministic black-box nature may make them intractable or create instabilities. In this paper, a hybridized modeling framework that leverages the advantages of both physics-based and stochastic neural network modeling approaches is utilized to capture CA50 (the timing when 50% of the fuel energy has been released) along with indicated mean effective pressure (IMEP). The performance of the hybridized framework is compared to a classical ANN and a physics-based-only framework in a stochastic environment. To ensure high robustness and low computational burden in the hybrid framework, the CA50 input parameters along with IMEP are captured with a Bayesian regularized ANN (BRANN) and then integrated into an overall physics-based 0D Wiebe model. The outputs of the hybridized CA50 and IMEP models are then successively fine-tuned with BRANN transfer learning models (TLMs). The study shows that in the presence of a Gaussian-distributed model uncertainty, the proposed hybridized model framework can achieve an RMSE of 1.3 × 10⁻⁵ CAD and 4.37 kPa with a 45.4 and 3.6 s total model runtime for CA50 and IMEP, respectively, for over 200 steady-state engine operating conditions. As such, this model framework may be a useful tool for real-time combustion control where in-cylinder feedback is limited. Full article

Leader-follower autonomy (LFA) systems have so far only focused on vehicles following other vehicles. Though there have been several decades of research into this topic, there has not yet been any work on human-vehicle leader-follower systems in the known literature. We present a system in which an autonomous vehicle—our ACTor 1 platform—can follow a human leader who controls the vehicle through hand-and-body gestures. We successfully developed a modular pipeline that uses artificial intelligence/deep learning to recognize hand-and-body gestures from a user in view of the vehicle’s camera and translate those gestures into physical action by the vehicle. We demonstrate our work using our ACTor 1 platform, a modified Polaris Gem 2. Results show that our modular pipeline design reliably recognizes human body language and translates the body language into LFA commands in real time. This work has numerous applications such as material transport in industrial contexts. Full article

In the paper, a Finite Element (FE) Analysis for investigating the electric properties of a Wireless Power Transfer System (WPTS) devoted to charging the batteries of electric vehicles is performed. In particular, the dynamic-WPTS, which is challenging because of the position-varying properties of the system, is considered. The field analysis is computationally heavy because of thin conductive layers modelling the car chassis: an effective analytical approximation for the field calculation in thin layers is applied to both the car frame bottom and the shielding aluminum layer. This approach allows for an accurate solution and, meanwhile, for a reduction in the computational costs, making the repeated simulations feasible. Full article

The steady enhancement of driver assistance systems and the automation of driving functions are in need of advanced driver monitoring functionalities. To evaluate the driver state, several parameters must be acquired. A basic parameter is the position of the driver, which can be useful for comfort automation or medical applications. Acquiring the position through cameras can be used to provide multiple information at once. When using infrared cameras, not only the position information but also the thermal information is available. Head tracking in the infrared domain is still a challenging task. The low resolution of affordable sensors makes it especially difficult to achieve high robustness due the lack of detailed images. In this paper, we present a novel approach for robust head tracking based on template matching and optical flow. The method has been tested on various sets of subjects containing different head shapes. The evaluation does not only include the original sensor size, but also downscaled images to simulate low resolution sensors. A comparison with the ground truth is performed for X- and Y-coordinate separately for each downscaled resolution. Full article

Due to changes in mobility and the emergence of new services, it is becoming necessary to establish new vehicle classes between conventional buses and privately owned vehicles. New mobility scenarios need concrete specifications to develop the most user-centered shuttle buses. As a result, we are looking for the requirements and needs of operators and customers. Initially, we want to determine the status quo, as there is no preliminary work in this regard. During the course of extensive literature research, expert interviews, and follow-up workshops, the respective solution space was highlighted and narrowed down. Services such as ride-pooling require adapted vehicle concepts to ensure optimal implementation of their offer. Due to its optimized processes, the automotive industry depends on producing vehicles in a certain quantity and manner. Faster changes and extensive experiments are not possible with the current production approach. Purpose-built vehicle concepts can make mobility services more attractive to customers while facilitating business operations. For instance, potential improvements can be identified in the seating concept. Full article

In order to further increase the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. Therefore, a generic prediction approach is worked out in this paper, which enables a robust prediction of all traction torque-relevant variables for such strategies. It is intended to be useful for various types of electrification; however, the focus of this work is to the application in hybrid electric vehicles. In contrast to other approaches, no additional information (e.g., telemetry data) is required and thus a reliable prediction is guaranteed at all times. In particular, approaches from the fields of stochastics and artificial intelligence have proven to be effective for such purposes. Within the scope of this work, both so-called Markov Chains and Neural Networks are applied to predict real driving profiles within a required time horizon. Therefore, at first, a detailed analysis of the driver-specific ride characteristics is performed to ensure that real-world operation is represented appropriately. Next, the two models are implemented and the calibration is further discussed. The subsequent direct comparison of the two approaches is performed based on the described methodology, which includes both quantitative and qualitative analyses. Hereby, the quality of the predictions is evaluated using Root Mean Squared Error (RMSE) calculations as well as analyses in time domain. Based on the presented results, an appropriate approach is finally recommended. Full article

Many trucks are used for a class of activities involving a sequence of basic load-haul-dump operations. The repetitiveness of this operation has been an enabler for autonomous vehicle technology in efforts to increase safety and efficiency. In this paper, we present a framework for the automation of the load-haul-dump operation in a mine setting using an articulated dump truck. A simulation environment for the testing of autonomous driving algorithms is developed and a custom mining environment is generated to adapt to our simulation settings. We also present an operational decomposition of the sequence of tasks and develop a finite state machine for high-level decision making based on this decomposition. A path tracking module that considers both bodies of the articulated truck is also developed. The resulting architecture was implemented to achieve autonomy for a load-haul-dump operation in the simulated environment within a fixed path. Experiments show that the proposed FSM-path tracking system can automate the load-haul-dump operation; and that the simulation environment can support the testing and development of autonomous driving algorithms for configurations such as an articulated truck. Full article

The aim of herein work is to develop an automatized algorithm for detecting, as objectively as possible, the flame front evolution of lean/ultra-lean mixtures ignited by low temperature plasma-based ignition systems. The low luminosity characterizing the latter conditions makes both kernel formation and combustion development difficult to detect accurately. Therefore, to estimate the igniter capability to efficiently ignite the mixture, ever more performing tools are required. The present work proposes a new image analysis technique, based on a dual-exposure fusion algorithm and on Convolutional Neural Networks (CNNs), to process low brightness images captured via high-speed camera on an optical engine. The performance of the proposed algorithm (PA) is compared to the one of a base reference (BR) algorithm used by the same research group for the imaging analysis. The comparison shows the capability of PA to quantify the flame radius of consecutive combustion cycles with lower dispersion if compared to BR and to correctly detect some events considered as misfires or anomalies by BR. Moreover, the proposed method shows greater capability to detect, in advance, the kernel formation with respect to BR, thus allowing a more detailed analysis of the performance of the igniters. A metric quantitative analysis is carried out, as well, to confirm the above-mentioned results. Therefore, PA results to be more suitable for analyzing ultra-lean combustions, heavily investigated to meet the increasingly stringent legislation on the internal combustion engines. Finally, the proposed algorithm allows us to automatically estimate the flame front evolution, regardless of the user’s interpretation of the phenomenon. Full article

Vehicle drive systems are often oversized for common customer operation in order to cover the high demands of rare driving events such as towing a trailer, high acceleration or steep inclines. This high torque and power requirement affects the efficiency map and the highest efficiency is around the area of increased torque and speed. However, in everyday use, drive systems are mostly driven by customers at low speed and load, and therefore are not operating in the most efficient area. Designing a drive system that only covers the area of highest customer operation can increase efficiency by moving the sweet spot of efficiency to the relevant area, and thus reduce energy consumption. Therefore, customer data need to be analyzed in order to identify customer requirements and to localize the area of greatest operation. The method presented in this paper analyzes customer data in order to identify design-relevant parameters for a customer-specific drive system design. The available customer data results from event-based counts and are submitted as a statistical frequency distribution. These statistics are compared with discrete time series recorded during test drives in order to derive representative time series that correspond to customer behavior. By applying the time frame-based load analysis to these relevant time series, the desired design-relevant parameters are pointed out. Full article

There have been numerous attempts and investigations carried out with the objective to reduce the noise generated by railway freight wagons because noise is one of ever-present negative environmental pollution phenomena. This resulted in strong legislation requirements on noise reduction in railway transport, in the case of freight wagons, only exterior noise is a problem. However, the extremely hard metal structures of the wagons running on hard rails naturally generate high magnitudes of acoustic energy. One big initiative, especially in Germany, seeks a solution in replacement of the cast iron brake pads with the composite one which should result in so-called “silent trains”. But braking is used only during a minor part of the train run, leaving most of the acoustic phenomena of the train run unaffected. In our research, we focused on freight bogies type Y25Ls(s)e-K that are used, including in Slovakia. We simulated the structural natural frequencies to predict vibrations and consequent sound generated by these vibrations. The idea was to localize the vibrations and propose possibilities of noise attenuation. The more realistic view about sound fields was obtained by practical measurements on a moving bogie. Measurements on the test track at a maintenance workshop were done by using a digital acoustic camera Soundcam. For attenuation of noise radiated by the bogie frame, acoustic silencers made from recycled porous fiber material have been applied to the bogie frame. To determine the acoustic difference, the material was applied only on half of the bogie, and then the measurements were carried out. The results showed a promising improvement in reduced noise radiation, which gives support for further research in this area with more precise simulations and more precise coating of the bogie frame as well as the proposal and measurement of noise-attenuating coatings of other structural parts of the freight wagons. Full article

This study explores the long-term energy use implications of electrification, automation and sharing of road vehicles in British Columbia, Canada. Energy use is first analyzed for the years 1990–2016 for forward forecasting, and hypothetical scenarios ranging from conservative to disruptive, incorporating various effects of road vehicle electrification, sharing and automation, as well as influences of other technology disruptions, such as online shopping and e-learning are presented and used to project the road transportation energy use in B.C. to 2060. Transportation energy use projections are compared to those of the Canadian Energy Regulator (CER). When considering only the effect of vehicle electrification, the scenarios show higher energy savings compared to CER’s scenarios. The combined impact of vehicle electrification and automation leads to decreased energy use to 2060 for all scenarios considered. The energy savings for all scenarios, except for the conservative one, are higher than CER’s projections. When the effects of vehicle electrification, automation and sharing are merged, all scenarios yield energy savings beyond the CER projections. Inclusion of other technology disruptions and the effects of pandemics like COVID-19 reduce transportation demand and provide further energy savings. The BAU scenario given in this study shows energy use decreases compared to 2016 of 26.3%, 49%, 62.24%, 72.1% for the years 2030, 2040, 2050, and 2060 respectively. Full article

Improved mathematical and simulation modes of the active differential mechanism (DM) with controllable torque redistribution would better contribute to developing intelligent vehicle transmissions. The issue is caused by actualizing the precise steerability control using advanced automated transmissions, allowing torque vectoring for all-wheel-drive vehicles and ensuring an option for correcting the vehicle trajectory. This paper presents an alternative mathematical method for obtaining differential equations for modeling vehicle transmission components and its implementation for simulating the Audi sport DM. First, the steerability issues of sport DM technology are discussed, and the sport DM design is described in detail. Then, a mathematical approach is proposed that includes three types of equation systems: generalized dynamics equations, kinematic constraint equations, and gearing condition equations. The approach also considers the flexibility of the clutch’s frictional pack, friction torque, lockup condition, and piston dynamics. Finally, a Simulink model that reflects the DM operation and calculation procedures is developed. A series of simulations of the sport DM operation with forcible torque distribution is carried out. The results show that the proposed mathematical model is universal, efficient, and accurate. Full article

Since battery electric vehicle (BEV) sales are increasing, the calculation of necessary electric power supply, and energy consumption data, and vehicle range is important. The Worldwide harmonized Light vehicles Test Procedure (WLTP) currently in use can deliver data to collect comparable energy consumption data for different vehicles on defined chassis dynamometer test cycles. Nevertheless, the energy consumption and so the range of BEVs are also dependent on the individual trajectory of the user. Therefore, five velocity profiles are developed in this work. The maximum speeds are based on typical velocities in German city traffic and extra-urban traffic. The energy required to finish a single velocity profile is assumed to be constant despite varying maximum velocities. With this kind of driving profiles it is possible to create an individual and more precise statement on the energy consumption and the range of a BEV. In this work, the profiles are driven on a chassis dynamometer with an VW e-Up. The vehicle charging efficiency is tested with two different AC charging modes and is also taken into account. The drive efficiencies of the tested vehicle are presented in dependence of the velocity profile driven. Finally the results are compared with a real-driving velocity profile and the energy consumption data obtained by the board computer of the vehicle. Full article

Local dynamic map (LDM) is a key component in the future of autonomous and connected vehicles. An LDM serves as a local database with the necessary tools to have a common reference system for both static data (i.e., map information) and dynamic data (vehicles, pedestrians, etc.). The LDM should have a common and well-defined input system in order to be interoperable across multiple data sources such as sensor detections or V2X communications. In this work, we present an interoperable graph-based LDM (iLDM) using Neo4j as our database engine and OpenLABEL as a common data format. An analysis on data insertion and querying time to the iLDM is reported, including a vehicle discovery service function in order to test the capabilities of our work and a comparative analysis with other LDM implementations showing that our proposed iLDM outperformed in several relevant features, furthering its practical utilisation in advanced driver assistance system development. Full article

High-speed capsular vehicles are firstly suggested as an idea by Elon Musk of Tesla Company. Unlike conventional high-speed trains, capsular vehicles are individual vessels carrying passengers and freight with the expected maximum speed of near 1200 [km/h] in a near-vacuum tunnel. More individual vehicle speed, dispatch, and position control in the operational aspect are expected over connected trains. This numerical study and investigation evaluate and analyze inter-distance control and their characteristics for high-speed capsular vehicles and their operational aspects. Among many aspects of operation, the inter-distance of multiple vehicles is critical toward passenger/freight flow rate and infrastructural investment. In this paper, the system’s equation, equation of the motion, and various characteristics of the system are introduced, and in particular control design parameters for inter-distance control and actuation are numerically shown. As a conclusion, (1) Inter-distance between vehicles is a function of error rate and second car start time, the magnitude range is determined by second car start time, (2) Inter-distance fluctuation rate is a function of error rate and second car start time, however; it can be minimized by choosing the correct second car start time, and (3) If the second car start time is chosen an integer number of push-down cycle time at specific velocity error rate, the inter-distance fluctuation can be zero. Full article

With rapid transportation electrification worldwide, lithium-ion batteries have gained much attention for energy storage in electric vehicles (EVs). State of power (SOP) is one of the key states of lithium-ion batteries for EVs to optimise power flow, thereby requiring accurate online estimation. Equivalent circuit model (ECM)-based methods are considered as the mainstream technique for online SOP estimation. They primarily vary in their basic principle, technical contribution, and validation approach, which have not been systematically reviewed. This paper provides an overview of the improvements on ECM-based online SOP estimation methods in the past decade. Firstly, online SOP estimation methods are briefed, in terms of different operation modes, and their main pros and cons are also analysed accordingly. Secondly, technical contributions are reviewed from three aspects: battery modelling, online parameters identification, and SOP estimation. Thirdly, SOP testing methods are discussed, according to their accuracy and efficiency. Finally, the challenges and outlooks are presented to inspire researchers in this field for further developments in the future. Full article