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Abstract As automated driving technology is evolving quickly and becomes more widely deployed, it is essential to validate the Safety of the Intended Functionality (SOTIF) of Automated Vehicles (AVs) prior to mass production. In general, an exhaustive real-world scenario validation of AVs is considered infeasible due to excessive time consumption. Additionally, simulation tests alone are often regarded as inadequate since it is difficult to model the system and physical properties of vehicles with full fidelity. Therefore, a SOTIF validation method for AVs is proposed in this article, which consists of structure design and scenario determination. A mature, systematic, and complete set of testing and evaluation procedures is presented in structure design, and a scenario generation method is introduced in scenario determination. The SOTIF validation method takes advantage of both simulation tests and on-road tests.

Abstract The crush energy is a key parameter to determine the delta-V in accident reconstructions. Since an accurate car crush profile can be obtained from 3D scanners, this research aims at validating the methods currently used in calculating crush energy from a crush profile. For this validation, a finite element (FE) car model was analyzed using various types of impact conditions to investigate the theory of energy-based accident reconstruction. Two methods exist to calculate the crush energy: the work based on the barrier force and the work based on force calculated by the vehicle acceleration times the vehicle mass. We show that the crush energy calculated from the barrier force was substantially larger than the internal energy calculated from the FE model. Whereas the crush energy calculated from the vehicle acceleration was comparable to the internal energy of the FE model.

Abstract The riding-comfort of high-speed trains affects the travel experience of passengers, and the lightweight design technology of the carbody increases the flexible vibration and reduces passenger comfort. To this end, a vertical dynamics model of railway vehicles is established to demonstrate the potential of using passive inerter-based suspensions to reduce the flexible vibration of the carbody and improve riding-comfort. According to the characteristics of the inerter component, an appropriate inerter-based suspension is applied to the railway vehicle to reduce low-frequency resonance. The sum of the comfort indexes of the three reference points of the carbody is optimized as the objective function to improve the passenger comfort of the whole vehicle. The results reveal that the inerter-based suspension applied to the primary or secondary suspension has different effects on vehicle vibration.

Abstract One of the key problems of battery electric vehicles is the risk of severe range reduction in winter conditions. Technologies such as heat pump systems can help to mitigate such effects, but finding adequate heat sources for the heat pump sometimes can be a problem, too. In cold ambient conditions below −10°C and for a cold-soaked vehicle this can become a limiting factor. Storing waste heat or excess cold when it is generated and releasing it to the vehicle thermal management system later can reduce peak thermal requirements to more manageable average levels. In related architectures it is not always necessary to replace existing electric heaters or conventional air-conditioning systems. Sometimes it is more efficient to keep them and support them, instead. Accordingly, we show, how latent heat storage can be used to increase the efficiency of existing, well-established heating and cooling technologies without replacing them.

Abstract As the automotive industry moves toward electrification, battery costs and vehicle range are two large issues that will delay this movement. These issues can be partially resolved through the use of series-hybrid vehicles, which can replace a portion of the batteries with a small engine that serves to recharge the battery. Given the size, weight, and operational constraints of this engine, a 2-stroke engine makes sense. Indeed, 2-stroke engines are currently being used for a number of applications including consumer products, small ground vehicles, boats, and drones. The technology has significantly improved to allow for reduced emissions and increased efficiency, especially through the use of direct injection. This article discusses the state of technology for 2-stroke engines and its application in series-hybrid vehicles. In particular, the use of a 2-stroke engine as a range extender provides significant benefit in range and cost over fully electric vehicles.

Abstract Truck crashes on Wyoming mountain passes due to brake heating has been a long-standing issue due to the steep downgrades that characterize some routes in the state. The grade severity rating system (GSRS) developed by the Federal Highway Administration (FHWA) to recommend maximum safe speeds has been identified as a viable countermeasure to reducing the incidence of downgrade truck crashes. However, several decades have passed since the GSRS was developed. In the decades since its development, truck features have undergone radical changes in terms of design. The streamlined design of tractors and trailers, use of drag reduction devices, changes in engine features, and adoption of radial tires have led to a reduction in the non-brake forces that retard motion. Truck brakes have also changed along with retarder characteristics. This has meant that maximum safe speeds recommended by the GSRS have been conservative.

Abstract The transportation sector’s growing focus on addressing environmental and sustainable energy concerns has led to a pursuit of the decarbonization path. In this context, hydrogen emerges as a promising zero-carbon fuel. The ability of hydrogen fuel to provide reliable performance while reducing environmental impact makes it crucial in the quest for net zero targets. This study compares gasoline and hydrogen combustion in a single-cylinder boosted direct injection (DI) spark ignition engine under various operating conditions. Initially, the engine was run over a wide range of lambda values to determine the optimal operating point for hydrogen and demonstrate lean hydrogen combustion’s benefits over gasoline combustion. Furthermore, a load sweep test was conducted at 2000 rpm, and the performance and emission results were compared between gasoline and optimized hydrogen combustion. An in-depth analysis was conducted by varying fuel injection time and pressure.

Abstract A numerical study using large-eddy simulations (LES) to reproduce and understand sources of cycle-to-cycle variation (CCV) in spark-initiated internal combustion engines (ICEs) is presented. Two relevantly different spark-ignition (SI) units, that is, a homogeneous-charge slow-speed single-cylinder research unit (the transparent combustion chamber (TCC)-III, Engine 1) and a stratified-charge high-revving speed gasoline direct injection (GDI) (Engine 2) one, are analyzed in fired operations. Multiple-cycle simulations are carried out for both engines and LES results well reproduce the experimentally measured combustion CCV. A correlation study is carried out, emphasizing the decisive influence of the early flame period variability (1% of mass fraction burnt (MFB1)) on the entire combustion event in both ICEs. The focus is moved onto the early flame characteristics, and the crucial task to determine the dominant causes of its variability (if any) is undertaken.

Abstract The first objective of this study, addressed in Part 1, is to use finite element (FE) human body modeling (HBM) to evaluate the tangent of the Belt-to-Pelvis angle (tanθBTP) as a submarining predictor in frontal crashes for occupants in reclined seats. The second objective, addressed in Part 2, is to use this predictor to assess two technical solutions for reducing submarining risks for two different occupant anthropometries. In Part 1, tanθBTP (the lap belt penetration from the anterior superior iliac spine [ASIS] in the abdominal direction) was evaluated in impact simulations with varying seat belt anchor positions. Sled simulations with a 56 km/h full-frontal crash pulse were performed with the SAFER HBM morphed to the anthropometry of a small female and average male. A correlation was found between the submarining predictor and submarining.

Abstract The development of a long-term sustainable hydrogen energy economy for commercial vehicle transportation will need to overcome key critical technical and logistics considerations in the near term. As compared to zero-emission powertrains, fossil-fuel-based powertrains provide mission flexibility and high uptime at a comparatively low total cost of ownership (TCO). While the incumbent carbon-intensive powertrains suffer from poor efficiency and are not sustainable to support global climate change initiatives in transportation decarbonization, techno-economic challenges continue to create complex barriers to the large-scale displacement of these with highly electrified powertrains architectures. This article specifically addresses opportunities that well-targeted subsidies would afford in achieving fuel cell electric powertrain financial parity with diesel powertrains in heavy-duty trucks (HDTs).

Abstract The variability of fuel economy (FE) is of significant importance as that of average FE to realize FE benefits of hybrid electric vehicles (HEVs) consistently by all users in the real world. Over the years, majority of the research has been focused on improving average FE overlooking the variability. Although in recent years few studies have been focused on the reduction of FE variability, no study has been concentrated to understand why certain design has lower FE variability as that of others. This article provides a detailed analysis to decipher the reasons for the FE variability in the real world. This study considered the optimum designs based on two established design optimization methodologies considering Toyota Prius non-plug-in hybrid as a base vehicle. This study analyses the impacts of the parameters of driving patterns and the operation of powertrains on FE variability.

Abstract Advances in hybrid vehicles and electric vehicles (EV) are creating a need for a new generation of lubricants and new lubricant performance tests. Copper corrosion is one prominent concern for hybrid vehicles and EVs and is routinely assessed using a coupon test. This is characterized as metal dissolution, a surface tarnish, or a corrosion layer where a corrosion product remains on the surface and is characterized by a qualitative visual rating. This deficiency does not provide insight into the nature of the corrosion deposit. In an electric drive unit, there are multiple sources of the electric potential present, which can significantly alter the formation of a corrosion deposit which is not assessed in the coupon tests. The formation of a conductive corrosion deposit can result in catastrophic failure of the electric drive unit, either through direct shorting of the motor winding or failure of the power electronics.

Abstract The automotive industry offers many applications for machine learning (ML), in general, and deep neural networks in particular. However, the real-world deployment of neural networks into safety-critical components remains a challenge as models would need to offer robustness under a wide range of operating conditions. In this work, we focus on uncertainty estimation, which can be used to deliver predictors that fail gracefully, by detecting situations where their predictions are unreliable. Following Gräber et al. [1], we use Recurrent Neural Networks (RNNs) to perform sideslip angle estimation. To perform robust uncertainty estimation, we augment the RNNs with generative models. We demonstrate the advantage of the proposed model architecture over Monte Carlo (MC) dropout [2] on the Revs data set [3].

Abstract The high-frequency noise issue is one of the most significant noise, vibration, and harshness problems, particularly in battery electric vehicles (BEVs). The sound package treatment is one of the most important approaches toward solving this problem. Owing to the limitations imposed by manufacturing error, assembly error, and the operating conditions, there is often a big difference between the actual values and the design values of the sound package components. Therefore, the sound package parameters include greater uncertainties. In this article, an uncertainty analysis method for BEV interior noise was developed based on an interval model to investigate the effect of sound package uncertainty on the interior noise of a BEV. An interval perturbation method was formulated to compute the uncertainty of the BEV’s interior noise.

Abstract This article focuses on the development of a two-way coupled methodology to predict gear temperature of oil jet lubricated transmissions using commercial software for computational fluid dynamics simulation. The proposed methodology applies an overset mesh technique to model the gear interlocking motion, multiphase of air-oil mixture, and heat transfer. Two gear pairs were used to develop and validate the methodology, an overdrive helical gear pair of a commercial vehicle transmission and a standard spur gear pair. Different oil jet lubrication methods were investigated using the proposed methodology, such as oil jet directed at the into-mesh position and at the out-of-mesh position. This investigation showed that out of mesh lubrication direction shows better cooling performance which is in well agreement with previous studies of literature.

Abstract Electric Vehicle (EV) equipped with two-speed transmission has benefit in improving dynamic performance and saving battery consumption. However, during gear shift process, torque interruption and shift impact may lead to a bad shift quality. This work investigates gear shift process in an Automated Manual Transmission (AMT) configuration-based two-speed transmission. First of all, a typical gear shift process is analyzed. Parameters like motor speed, shift force, motor torque change rate, and speed difference between synchronizer and target engage gear are all included to find the relationships with shift duration. Then vehicle jerk is introduced as a criterion to evaluate shift impact. Besides, a comprehensive shift control strategy is developed. While keeping the output torque at wheels unchanged, the shift strategy also improved motor working efficiency after gear shift.

Abstract China is driving the transition away from internal combustion engine vehicles (ICEVs) to plug-in electric vehicles (PEVs, including plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs)) to address its pressing energy security and environmental pollution problems. The recent enactment of the dual-credit scheme mandate will compensate for the phase out of the subsidy program, while ostensibly shifting the burden of filling in the cost gap between PEVs and ICEVs from the government to the automakers (though in practice to car buyers).

Abstract The external driving environment of an autonomous driving vehicle is complex and changeable. In this article, the trajectory tracking control with obstacle avoidance based on model predictive control was presented. Specifically, double-level control scheme by controlling the front steering angle was used in our research, and the double level is composed of the high level of model predictive controller for local trajectory planning and low level of model predictive controller for trajectory tracking. At high level, the local trajectory planner based on the point-mass model was designed. Then, at low level, the linear time-varying vehicle dynamics model was presented, and the trajectory tracking controller was proposed considering control variable, control increment, and output constraint. Finally, the trajectory tracking performance was tested in co-simulation environment with CarSim and Simulink, and the tracking errors were analyzed.

Abstract Autonomy and connectivity are considered among the most promising technologies to improve safety and mobility and reduce fuel consumption and travel delay in transportation systems. In this paper, we devise an optimal control-based trajectory planning model that can provide safe and efficient trajectories for the subject vehicle while incorporating platoon formation and lane-changing decisions. We embed this trajectory planning model in a simulation framework to quantify its fuel efficiency and travel time reduction benefits for the subject vehicle in a dynamic traffic environment. Specifically, we compare and analyze the statistical performance of different controller designs in which lane changing or platooning may be enabled, under different values of time (VoTs) for travelers.

Abstract Recently, high-resolution sensors capable of multiple detections (MDs) per object are available for perception applications in autonomous or semi-autonomous vehicles. Conventional multi-target tracking (MTT) approaches start with the point-target assumption and thus cannot be applied directly to the MDs of high-resolution sensors. A popular solution widely used in literature starts with a measurement partitioning approach, followed by repurposing conventional tracking algorithms to accommodate the resulting partitions. However, the computational requirement increases combinatorially, especially under multi-sensor applications that also independently return multiple radar reflections as in the automotive radar sensors used in this work. Thus, a hybrid approach that combines a clustering technique (such as DBSCAN) to alleviate the computational complexity and an MD tracking scheme that admits multiplicity of the target detections is employed.