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Abstract In recent years, several buses have ignited in some cities in China, causing numerous deaths and significant property damage. However, few research studies have been conducted to deal with such accidents. Therefore, in this work, a linear-shaped charge jet with rectangular cross sections was used to hew out evacuation routes for burning buses, and the parameter design for the shaped charge jet was improved according to asymmetry limitations and human tolerance. A numerical finite element simulation model of the behavior of a jet penetrating the jambs was established using ANSYS/LS-DYNA software. The asymmetrical characteristics of an arc segment in the structure of a rectangular-shaped charge were analyzed, in addition to the influence on the deviations of the jet penetration capacity and blast injuries to occupants caused by the side effects of detonation.

Abstract Exhaust manifolds are one of the most important components on the engine assembly, which is mounted on engine cylinder head. Exhaust manifolds connect exhaust ports of cylinders to the turbine for turbocharged diesel engine therefore they play a significant role in the performance of engine system. Exhaust manifolds are subjected to very harsh thermal loads; extreme heating under very high temperatures and cooling under low temperatures. Therefore designing a durable exhaust manifold is a challenging task. Computer aided engineering (CAE) is an effective tool to drive an exhaust manifold design at the early stage of engine development. Thus advanced CAE methodologies are required for the accurate prediction of temperature distribution. However, at the end of the development process, for the design verification purposes, various tests have to be carried out in engine dynamometer cells under severe operating conditions.

Abstract A kinematic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towing vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point.

Abstract Commercial vehicles contribute to the majority of freight transportation in the United States. They are also significant fuel consumers, with over 23% of fuel used in transportation in the United States. The gas price volatility and increasingly stringent regulation on greenhouse-gas emissions have driven manufacturers to adopt new fuel-efficient technologies. Among others, an advanced transmission control strategy, which can provide tangible improvement with low incremental cost. In the commercial sector, individual drivers have little or no interest in vehicle fuel economy, contrary to fleet owners. Aggressive driving behavior can greatly increase the real-world vehicle fuel consumption. However, the effectiveness of transmission calibration to match the shift strategy to the driving characteristics is still a challenge.

Abstract Although ground grading is one of the most common tasks that hydraulic excavators perform in typical work sites, proper grading is not easy for less-skilled operators as it requires coordinated manipulation of multiple hydraulic cylinders. In order to help alleviate this difficulty, automated grading systems are considered as an effective alternative to manual operations of hydraulic excavators. In this article, a sliding mode controller design is presented for automated grading control of a hydraulic excavator. First, an excavator manipulator model is developed in Simulink by using SimMechanics and SimHydraulics toolboxes. Then, a sliding mode controller is designed to control the manipulator to trace a predefined trajectory for a grading task. For a comparison study, a PI controller is used to control the manipulator to perform a grading task following the same desired trajectory and the performance is compared with those obtained by the sliding mode controller.

Abstract In this article, a unique design for Hydro-Pneumatic Energy Harvesting Suspension HPEHS system is introduced. The design includes a hydraulic rectifier to maintain one-way flow direction in order to obtain maximum power generation from the vertical oscillation of the suspension system and achieve handling and comfort car drive. A mathematical model is presented to study the system dynamics and non-linear effects for HPEHS system. A simulation model is created by using Advanced Modeling Environment Simulations software (AMEsim) to analyze system performance. Furthermore, a co-simulation platform model is developed using Matlab-Simulink and AMEsim to optimize the PID controller parameters of the external variable load resistor applied on the generator by using Particle Swarm Optimization (PSO).

Abstract Recently, electric heavy-duty tractor-trailers (EHDTTs) have assumed significance as they present an immediate solution to decarbonize the transportation sector. Hence, to illustrate the economic viability of electrifying the freight industry, a detailed numerical model to estimate the battery capacity for an EHDTT is proposed for a route between Washington, DC, to Knoxville, TN. This model incorporates the effects of the terrain, climate, vehicular forces, auxiliary loads, and payload in order to select the appropriate motor and optimize the battery capacity. Additionally, current and near-future battery chemistries are simulated in the model. Along with equations describing vehicular forces based on Newton’s second law of motion, the model utilizes the Hausmann and Depcik correlation to estimate the losses caused by the capacity offset of the batteries. Here, a Newton-Raphson iterative scheme determines the minimum battery capacity for the required state of charge.

Abstract This research developed methods for a virtual operator model (VOM) to learn the optimal control inputs for operation of a virtual excavator. Virtual design, used to model, simulate, and test new features, has often been limited by the fidelity of the virtual model of human operators. Human operator learns, over time, the capability, limits, and control characteristics of new vehicles to develop the best strategy to maximize the efficiency of operation. However, VOMs are developed with fixed strategies and for specific vehicle models (VMs) and require time-consuming re-tuning of the VOM for each new vehicle design. Thus, there typically is no capability to optimize strategies, taking account of variation in vehicle capabilities and limitations. A VOM learning capability was developed to optimize control inputs for the swing-to-pile task of a trenching operation. Different control strategies consisted of varied combinations of speed control, position control, and coast.

Abstract This study investigates methods to precisely control a coolant pump in an internal combustion engine. The goal of this research is to minimize power consumption while still meeting optimal performance, reliability and durability requirements for an engine at all engine-operating conditions. This investigation achieves reduced fuel consumption, reduced emissions, and improved powertrain performance. Secondary impacts include cleaner air for the earth, reduced operating costs for the owner, and compliance with US regulatory requirements. The study utilizes mathematical modeling of the cooling system using heat transfer, pump laws, and boiling analysis to set limits to the cooling system and predict performance changes.

Abstract An accurate and rapid thermal model of an axle-brake system is crucial to the design process of reliable braking systems. Proper thermal management is necessary to avoid damaging effects, such as brake fade, thermal cracking, and lubricating oil degradation. In order to understand the thermal effects inside of a lubricated braking system, it is common to use Computational Fluid Dynamics (CFD) to calculate the heat generation and rejection. However, this is a difficult and time-consuming process, especially when trying to optimize a braking system. This article uses the results from several CFD runs to train a Stacked Ensemble Model (SEM), which allows the use of machine learning (ML) to predict the systems’ temperature based on several input design parameters. The robustness of the SEM was evaluated using uncertainty quantification.

Abstract This paper presents a machine learning application of the force/torque sensor in a human-robot collaborative manufacturing scenario. The purpose is to simplify the programming for physical interactions between the human operators and industrial robots in a hybrid manufacturing cell which combines several robotic applications, such as parts manipulation, assembly, sealing and painting, etc. A multiclass classifier using Light Gradient Boosting Machine (LightGBM) is first introduced in a robotic application for discriminating five different contact states w.r.t. the force/torque data. A systematic approach to train machine-learning based classifiers is presented, thus opens a door for enabling LightGBM with robotic data process. The total task time is reduced largely because force transitions can be detected on-the-fly. Experiments on an ABB force sensor and an industrial robot demonstrate the feasibility of the proposed method.

Abstract This article investigates the fuel savings potential of a series hybrid military truck using a simultaneous battery pack design and powertrain supervisory control optimization algorithm. The design optimization refers to the sizing of the lithium-ion battery pack in the hybrid configuration. The powertrain supervisory control optimization determines the most efficient way to split the power demand between the battery pack and the engine. Despite the available design and control optimization techniques, a generalized mathematical formulation and solution approach for combined design and control optimization is still missing in the literature. This article intends to fill that void by proposing a unified framework to simultaneously optimize both the battery pack size and power split control sequence. This is achieved through a combination of genetic algorithm (GA) and Pontryagin’s minimum principle (PMP) where the design parameters are integrated into the Hamiltonian function.

Abstract Due to the increasing requirements for efficiency, the wide range of characteristics and the improved possibilities of modern development and production processes, compressors in turbochargers have become more individualized in order to adapt to the requirements of internal combustion engines. An understanding of the working mechanisms as well as an understanding of the way that losses occur in the flow allows a reduced development effort during the optimization process. This article presents three-dimensional (3D) Computational Fluid Dynamics (CFD) investigations of the loss mechanisms and quantitative calculations of individual losses. The 3D-CFD method used in this article will reduce the drawbacks of one-dimensional calculation as far as possible. For example, the twist of the blades is taken into account and the “discrete” method is used for loss calculation instead of the “average” method.

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Abstract In this article, an adaptive state estimation algorithm for precise air-fuel ratio (AFR) control is presented. AFR control is a critical part of internal combustion engine (ICE) control, and tight AFR control delivers lower engine emissions, better engine fuel economy, and better engine transient performance. The proposed control algorithm significantly improves transient AFR control to eliminate and reduce the amplitude of the lean and rich spikes during transients. The new algorithm is first demonstrated in simulation (using Matlab/SimulinkTM and GT-PowerTM) and then verified on a test engine. The engine tests are conducted using the European Transient Cycle (ETC) with HoribaTM double-ended dynamometer. The developed algorithm utilizes a nonlinear physics-based engine model in the observer and advanced control principles with modifications to solve real industrial control issues.

Abstract The linear engine as compared with the traditional internal combustion engine has high efficiency and low emissions, so as a new type of hybrid power unit, it is very suitable for a hybrid electric vehicle to improve energy efficiency and environmental protection performances. In this article, a novel linear engine-based hybrid power system that is primarily selected for hybrid electric urban light commercial vehicles is introduced. Furthermore, the working efficiency of the proposed hybrid power system is briefly analyzed through a validation study example, and various inherent factors affecting the working efficiency of the hybrid power system are analyzed and discussed in detail. This work can provide a reference implementation for the research on the power unit for the hybrid electric urban light commercial vehicles.

Abstract To solve the problem that it is difficult for drivers to control the vehicle at low speed, a new setting scheme of pedal map is proposed to ensure that the vehicle has the speed controllability in the full speed range. In this scheme, based on obtaining the maximum and minimum driving characteristics of the vehicle and the driving resistance characteristics of the vehicle, the pedal map is divided into a sensitive area and insensitive area. In the insensitive area, acceleration hysteresis is formed, which ensures that the throttle is slightly fluctuated and has good speed stability. At the same time, the sensitive area of the accelerator pedal is formed far away from the driving resistance curve to ensure that the vehicle has a great acceleration ability. To verify the effectiveness of the proposed scheme, the data of a commercial vehicle is selected for the design of the pedal map, and the driver-vehicle closed-loop test based on the driving simulator is conducted.

Abstract Diesel trucks play a crucial role in transportation activity and a major contributor to fuel consumption and air pollution. To improve the energy efficiency of diesel trucks, we develop a truck eco-approach and departure (EAD) system based on Signal Phase and Timing (SPaT) message from signal controllers and road grade information along the path. The proposed model consists of two levels—the lower level for powertrain-based fuel consumption estimation and the upper level for optimal trajectory planning. The powertrain model is designed for a diesel engine with a six-gear transmission and well calibrated using on-board Electronic Control Unit (ECU) data. The trajectory planning model is formulated as a shortest path problem with the combination of time, distance, and speed as the state on each node and fuel consumption rate as the cost on state transition.

Abstract An energy management strategy based on model predictive control (MPC) was proposed for the hybrid bus. For the series configuration, MPC was used for power distribution among transmission components. Real-time optimization of the control strategy was achieved, which improved the fuel economy. First, a rule-based energy management strategy was proposed, and the logical thresholds of the stage of charge (SOC) and the demand power were formulated to underlie the subsequent study of the control strategy. Second, an energy management strategy based on global optimization was established where the dynamic programming algorithm was used to determine the SOC optimal reference curve and the limitation of fuel economy. In this way, the target and reference can be provided for the subsequent control strategy. Third, a radial basis neural network speed prediction model based on wavelet transform was formulated.

Abstract As electric vehicles (EVs) begin to increase their market share in the transport sector, the efficiency of battery packs becomes critical to their performance. Within large battery packs, cell variations occur due to manufacturing processes but can also become prominent during operation due to ineffective thermal management and accelerated degradation of some cells. A battery management system (BMS) will generally account for variations in state of charge (SOC) for cells in series through balancing, but conventional BMSs do not tend to consider the imbalances of cells in parallel as their SOCs should eventually converge themselves. This can, however, lead to cells experiencing higher currents and therefore increased degradation compared to other cells within the pack.