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The possibility to operate current diesel engines in dual-fuel mode with the addition of an alternative fuel is fundamental to accelerate the energy transition to achieve carbon neutrality. The simulation of the dual- fuel combustion process with 0D/1D combustion models is fundamental for the performance prediction, but still particularly challenging, due to chemical interactions of the mixture. The authors defined a novel data-driven workflow for the development of combustion reaction mechanisms and used it to generate a dual-fuel mechanism for Ammonia and Diesel Primary Reference Fuels (DPRF) suitable for efficient combustion simulations in heavy duty engines, with variable cetane number Diesel fuels. A baseline reaction mechanism was created by merging the detailed ammonia mechanism by Glarborg et al. with reaction pathways for n- hexadecane and 2,2,4,4,6,8,8-heptamethylnonane from a well-established multi-component fuel mechanism.

We propose a novel dual-fuel combustion model for simulating heavy-duty engines with the G-Equation. Dual-Fuel combustion strategies in such engines features direct injection of a high-reactivity fuel into a lean, premixed chamber which has a high resistance to autoignition. Distinct combustion modes are present: the DI fuel auto-ignites following chemical ignition delay after spray vaporization and mixing; a reactive front is formed on its surroundings; it develops into a well-structured turbulent flame, which propagates within the premixed charge. Either direct chemistry or the flame-propagation approach (G- Equation), taken alone, do not produce accurate results. The proposed Dual-Fuel model decides what regions of the combustion chamber should be simulated with either approach, according to the local flame state; and acts as a “kernel” model for the G- Equation model. Direct chemistry is run in the regions where a premixed front is not present.

A path tracking controller is essential for an autonomous vehicle to navigate a complex environment while avoiding obstacles. Many research studies have proposed new controller designs and strategies. However, it is often unclear which control strategy is the most suitable for a specific Autonomous / ADAS user application. This study proposes a benchmark workflow by comparing different control observer models and their control strategies integration for an autonomous parking lot sweeper in a complex and dense environment at low-speed utilizing model-in-the-loop simulation. The systematic procedure consists of the following steps: (1) vehicle observer model validation (2) control strategy development (3) model-in-the-loop simulation benchmark for specific user scenarios. The kinematic and dynamic vehicle models were used to validate the truck’s behavior using physical data.

As computer-aided engineering software tools advance, more simulation-based processes are utilized to reduce development time and cost. Traditionally, during the development of a new control algorithm dyno or on-road testing is necessary to validate a new function, however, physical testing is both costly and time consuming. This study introduces a co-simulation platform and discusses its use as an improved method of powertrain control logic development. The simulation platform consists of a dynamic vehicle model, virtual road network and simulated traffic objects. Engineers can utilize Matlab/Simulink along with other programs such as PTV Vissim, Tass Prescan, and AVL Cruise to create an integrated platform capable of testing and validating new control strategies. The structure and configuration of this virtual platform is explained in this paper, and an example use case is demonstrated. A driver model was developed to simulate realistic vehicle inputs.

In this paper, a novel online coverage path planning (CPP) method for autonomous sweeper trucks in closed areas is proposed. This method can efficiently generate executable paths for sweeper trucks that cover all feasible uncleaned areas without getting tracked in dead-end, i.e., no backward behaviors required and avoid dynamic obstacles. To reach that end, a modified biological inspired neuron network method considering vehicle constrains is developed, where the dynamic of each neuron is determined by the shunting function. The path will be iteratively generated based on local neuron dynamics. In order to avoid dead-end, a detour algorithm combing with back iteration is introduced to search the nearest uncleaned area that can be reached within vehicle constrains. The proposed method is empirically approved to be computationally efficient and adaptive to maps with arbitrary shapes.

For commercial-vehicle Original Equipment Manufacturers (OEMs), predictive maintenance has drawn attention for the benefits of money saving and increased road safety. Data-driven models have been widely explored and implemented as predictive maintenance solutions. However, the working scenarios for different commercial-vehicles vary a lot, which makes it difficult to build a universal model suitable for all the cases. In this paper, we propose a Recurrent Neural Adaptive Processes (RNAPs) network to adapt to different scenarios by modeling the uncertain at the same time. The ensemble network combines the traits of neural processes, recurrent neural network and meta learning together. Neural processes consider the context information to calculate the uncertainty and improve the prediction results. Meta-learning works well when dealing with few-shot multi-tasks learning, and recurrent networks are utilized as the encoder of the proposed model to process time-series data.

In the current study, an automatic and interpretable predictive maintenance system is proposed. The system provides a fully automatic training process for predictive maintenance models without human intervention. On the other hand, as failure reasons are critical for product development. The proposed pipeline also demonstrates the interpretation on automatic trained model to present insights for engineers to acquire mechanism of interested events. To study the system, four automatic machine learning methods and two interpretation modules are evaluated for the pipeline with Isuzu’ real vehicle data correspondingly. The overall performance of the automatic and interpretable system is demonstrated as well. Key words: predictive maintenance, AutoML, interpretation

Idle Stop-and-go (ISG), also known as Auto Stop/Start, is a fuel saving technology common to many modern vehicles that enables the engine to shut down when the vehicle comes to a stop. Although it may help with fuel efficiency, many drivers in the North American market find the feature to be an annoyance due to hesitation in vehicle re-launch and engine shudder during stop or restart. This paper introduces the usage of traffic signal phase and timing (SPaT) information for controlling the activation of ISG with the goal of reducing driver complaints and increasing acceptance of the function. Previous studies proposed the utilization of Advanced Driver Assistance System (ADAS) to introduce adaptability in powertrain controls to traffic situation changes.

With the advancement of simulation software development, the efficiency of vehicle and powertrain controls research and development can be significantly improved. Traditionally, during the development of a new control algorithm, dyno or on-road testing is necessary to validate the algorithm. Physical testing is not only costly, but also time consuming. In this study, a virtual platform is developed to reduce the effort of testing. To improve the simulation accuracy, co-simulation of multiple software is suggested as each software specializes in certain area. The Platform includes Matlab Simulink, PTV Vissim, Tass Prescan and AVL Cruise. PTV Vissim is used to provide traffic environment to PreScan. PreScan is used for ego vehicle simulation and visualization. Traffic, signal and road network are synchronized in Vissim and PreScan. Powertrain system is simulated in Cruise. MATALB/Simulink serves as master of this co-simulation, and integrates the different software together.

Recently developed gasoline engines utilize more aggressive EGR rate to meet the emissions and fuel economy regulations. The EGR temperature is often estimated by the ECU and its accuracy affects the estimations of EGR flow rate and intake air flow rate and temperature. Therefore, the accuracy of EGR temperature estimation becomes more important than ever for precise EGR rate control. Typical lookup map based EGR cooler model without the sensitivity to the coolant flow rate is acceptable and widely used if the heat capacity of the coolant side is high enough. However, the coolant flow rate under real vehicle driving conditions often visit low-speed high-load part of the engine map where the lookup map based model suffers from the accuracy issues. This paper presents an investigation of the accuracy of the lookup map based model under different heat capacity conditions. In this study, a simple EGR cooler model based on effectiveness-NTU method was also developed.

As the automotive industry faces tighter fuel economy and emission regulations, it is becoming increasingly important to improve powertrain system efficiency. One of the areas to improve powertrain efficiency is the thermal management system. By controlling how to distribute the heat rejected by the engine, especially during the warm-up stage under cold temperatures, an engine thermal management system can improve the overall energy efficiency of the powertrain system. Conventionally, engine thermal management systems have been operated by a mechanical water pump and a thermostat. However, the recent introduction of electric water pumps and electrically-controlled flow valves allow for more sophisticated control of the thermal management system. In this study, these two different thermal management system architectures are investigated by conducting simulations.

In vehicle accident, the bumper beam generally requires high stiffness for sufficient survival space for occupants while it may cause serious pedestrian lower extremity injuries. The aim of this study is to promote an aluminum-steel hybrid material double-hat bumper to meet the comprehensive requirements. The hybrid bumper is designed to improve the frontal crash and pedestrian protection performances in collision accidents. Finite element (FE) models of the hybrid bumper was built, validated, and integrated into an automotive model. The Fixed Deformable Barrier (FDB) and Transport Research Laboratory (TRL) legform model were used to obtain the vehicle crashworthiness and pedestrian lower leg injury indicators. Numerical results showed that the hybrid bumper had a great potential for crashworthiness performance and pedestrian protection characteristics. Based on this, a multi-objective optimization design (MOD) was performed to search the optimal geometric parameters.

Exploring an unknown place autonomously is a challenge for robots, especially when the environment is changing. Moreover, in real world application, efficient path planning is crucial for autonomous vehicles to have timely response to execute a collision-free motion. In this paper we focus on environment exploration which enables an automated system to establish a map of an unknown environment with unforeseen objects moving within it. We introduce an exploration package that enables robots self-exploration with an online collision avoidance planner. The package consists of exploration module, global planner module and local planner module. We modularize the package so that developers can easily make modifications or even substitutions to some modules for their specific application. In order to validate the algorithm, we designed and built a robot car as a low cost validation platform to test the autonomous vehicle algorithms in the real world.

Global deployment of autonomous capability for commercial vehicles is a big challenge. In order to improve the robustness of autonomous approach under different traffic scenarios, environments, road conditions, and driver behaviors, a combined approach of virtual simulation, vehicle-in-the-loop (VIL) testing, proving ground testing, and final field testing have been established for algorithms validation. During the validation platform setup, different platforms for different functionalities have been studied, including open source virtual testing environment (CARLA, AirSim), and commercial one (IPG). We also cooperate with MCity to do proving ground validation. In virtual testing, the functionality of sensors (camera, radar, Lidar, GPS, IMU) and vehicle dynamic models can be applied in the virtual environment. In VIL testing, real world and virtual test will be connected for different validation purposes.

Field Issue (Malfunction) incidents are costly for the manufacturer’s service department. Especially for commercial truck providers, downtime can be the biggest concern for our customers. To reduce warranty cost and improve customer confidence in our products, preventive maintenance provides the benefit of fixing the problem when it is small and reducing downtime of scheduled targeted service time. However, a normal telematics system has difficulty in capturing useful information even with pre-set triggers. Some malfunction issue takes weeks to find the root cause due to the difficulty of repeating the error in a different vehicle and engineers must analyze large amounts of data. In order to solve these challenges, a machine-learning-based predictive software/hardware system has been implemented.

Urea/SCR systems have been proven effective at reducing NOx over a wide range of operating conditions on mid/heavy duty diesel vehicles. However, design changes due to reduction in the size of modern compact Urea/SCR systems and lower exhaust temperature have increased the possibility of urea deposit formation. Urea deposits are formed when urea in films and droplets undergoes undesirable secondary reactions and generate by-products such as ammelide, biuret and cyanuric Acid (CYA). Ammelide and CYA are difficult to decompose which lead to the formation of solid deposits on the surface. This phenomenon degrades the performance of the after treatment system by decreasing overall mixing efficiency, lowering de-NOx efficiency and increasing pressure drop. Therefore, mitigating urea deposits is a primary design goal of modern diesel after-treatment systems.

Water injection (WI) can improve gasoline engine performance and efficiency, and on-board water recovery technology could eliminate the need for customers to refill an on-board water reservoir. In this regard, the technical feasibility of exhaust water recovery (EWR) is described in this paper. Water injection testing was conducted at a full load condition (5000 rpm/18.1 bar BMEP) and a high load condition (3000 rpm/14.0 bar BMEP) on a turbocharged gasoline direction injection (GTDI) engine. Water recovery testing was conducted both after the exhaust gas recirculation (EGR) cooler and after the charge air cooler (CAC) at a high load (3000 rpm/14.0 bar BMEP), as well as a part load (2080 rpm/6.8 bar BMEP) condition, at temperatures ca. 10-15 °C below the dew point of the flow stream. Three types of water separation designs were tested: a passive cyclone separator (CS), a passive membrane separator (MEM), and an active separator (AS).

External Exhaust Gas Recirculation (EGR) has been used on diesel engines for decades and has also been used on gasoline engines in the past. It is recently reintroduced on gasoline engines to improve fuel economy at mid and high engine load conditions, where EGR can reduce throttling losses and fuel enrichment. Fuel enrichment causes fuel penalty and high soot particulates, as well as hydrocarbon (HC) emissions, all of which are limited by emissions regulations. Under stoichiometric conditions, gasoline engines can be operated at high EGR rates (> 20%), but more than diesel engines, its intake gas including external EGR needs extreme cooling (down to ~50°C) to gain the maximum fuel economy improvement. However, external EGR and its problems at low temperatures (fouling, corrosion & condensation) are well known.

The 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

The increasing demand for lightweight design of the whole vehicle has raised critical weight reduction targets for crash components such as front rails without deteriorating their crash performances. To this end the last few years have witnessed a huge growth in vehicle body structures featuring hybrid materials including steel and aluminum alloys. In this work, a type of tapered tailor-welded tube (TTWT) made of steel and aluminum alloy hybrid materials was proposed to maximize the specific energy absorption (SEA) and to minimize the peak crushing force (PCF) in an oblique crash scenario. The hybrid tube was found to be more robust than the single material tubes under oblique impacts using validated finite element (FE) models. Compared with the aluminum alloy tube and the steel tube, the hybrid tube can increase the SEA by 46.3% and 86.7%, respectively, under an impact angle of 30°.