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Electric vehicles (EVs) are becoming increasingly recognized as an effective solution in the battle against climate change and reducing greenhouse gas emissions. Lithium-ion batteries have become the standard for energy storage in the automobile industry, widely used in EVs due to their superior characteristics compared to other batteries. The growing popularity of the Vehicle-to-grid (V2G) concept can be attributed to its surplus energy storage capacity, positive environmental impact, and the reliability and stability of the power grid. However, the increased utilization of the battery through these integrations can result in faster degradation and the need for replacement. As batteries are one of the most expensive components of EVs, the decision to deploy an EV in V2G operations may be uncertain due to the concerns of battery degradation from the owner’s perspective.

Electrification of off-road vehicle powertrains can increase mobility, improve energy efficiency, and enable new utility by providing high amounts of electrical power for auxiliary devices. These vehicles often operate in extreme temperature conditions at low ground speeds and high power levels while also having significant cooling airpath restrictions. The restrictions are a consequence of having grilles and/or louvers in the airpath to prevent damage from the operating environment. Moreover, the maximum operating temperatures for high voltage electrical components, like batteries, motors, and power-electronics, can be significantly lower than those of the internal combustion engine. Rejecting heat at a lower temperature gradient requires higher flow rates of air for effective heat exchange to the operating environment at extreme temperature conditions.

The application of power electronic converters (PEC) in electric vehicles (EVs) has increased immensely as they provide enhanced controllability and flexibility to these vehicles. Accordingly, the interest in developing innovative and sustainable technologies to ensure safe and reliable operation of PECs has also risen. One of the most difficult challenges experienced during the development of reliable PECs is the design of proper thermal management systems for controlling the junction temperature and reducing the thermal cycling of power semiconductors. The addition of Active Thermal Control (ATC) can mitigate these concerns. Moreover, the performance of the thermal management system can be enhanced further by the integration of active cooling methods. An active cooling system consumes external energy for circulating cooling air or liquid within the PECs.

The application of virtual engineering methods can streamline the product design process through improved collaboration opportunities among the technical staff and facilitate additive manufacturing processes. A product digital twin can be created using the available computer-aided design and analytical mathematical models to numerically explore the current and future system performance based on operating cycles. The strategic decision to implement a digital twin is of interest to companies, whether the required financial and workforce resources will be worthwhile. In this paper, two metrics are introduced to assist management teams in evaluating the technology potential. The usefulness and time savings metrics will be presented with accompanying definitions. A case study highlights the usefulness metric for the “Deep Orange” prototype vehicle, an innovative off-road hybrid vehicle designed and fabricated at Clemson University.

Since 1989, ISO has published procedures for developing and testing public information symbols (ISO 9186), while the SAE standard for in-vehicle icon comprehension testing (SAE J2830) was first published in 2008. Neither testing method was designed to evaluate the comprehension of symbols in modern vehicles that offer digital instrument cluster interfaces that afford new levels of flexibility to further improve drivers’ understanding of symbols. Using a driving simulator equipped with an eye tracker, this study investigated drivers’ understanding of six automotive symbols presented on in-vehicle displays. Participants included 24 teens, 24 adults, and 24 senior drivers. Symbols were presented in a symbol-only, symbol + short text descriptions, and symbol + long text description conditions. Participants’ symbol comprehension, driving performance, reaction times, and eye glance times were measured.

Testing any new safety technology of Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) requires simulation-based validation and verification. The specific scenarios used for testing, outline incidences of accidents or near-miss events. In order to simulate these scenarios, specific values for all the above parameters are required including the ego vehicle model. The ‘criticality’ of a scenario is defined in terms of the difficulty level of the safety maneuver. A scenario could be over-critical, critical, or under-critical. In over-critical scenarios, it is impossible to avoid a crash whereas, for under-critical scenarios, no action may be required to avoid a crash. The criticality of the scenario depends on various parameters e.g. speeds, distances, road/tire parameters, etc. In this paper, we propose a definition of criticality metric and identify the parameters such that a scenario becomes critical.

Electrified transportation has received significant interest recently because of sustainable and clean energy goals. However, the degradation of electrical components such as energy storage systems raises system reliability and economic concerns. In this paper, a prognostic-based control strategy is proposed for hybrid electric vehicles (HEVs) to abate the degradation of energy systems. Degradation forecasting models of electrical components are developed to predict their degradation paths. The predicted results are then used to control HEVs in order to reduce the degradation of components.

As powertrain hybridization technologies are becoming popular, their application for heavy-duty military vehicles is drawing attention. An intelligent design and operation of the energy management system (EMS) is important to ensure that hybrid military vehicles can operate efficiently, simultaneously maximize fuel economy and minimize monetary cost, while successfully completing mission tasks. Furthermore, an integrated EMS framework is vital to ensure a functional vehicle power system (VPS) to survive through critical missions in a highly stochastic environment, when needed. This calls for situational awareness and dynamic system reconfiguration capabilities on-board of the military vehicle. This paper presents a new energy management and control (EMC) framework based on holistic situational awareness (SA) and dynamic reconfiguration of the VPS.

This study proposes a multi-objective finite control set model predictive control (FCS-MPC) for traction motor drive systems in electric/hybrid-electric vehicles. The proposed method seeks to find the most optimal drive with respect to three objectives, i.e., electric power quality, inverter thermal cycling, and motor thermal cycling. Suitable lumped-parameter thermal models are used for the inverter and the motor based on validated methods in the literature to estimate temperatures. The estimated temperatures are integrated into the multi-objective control law to obtain the desired trade-off performances from the drive system. This paper shows that by adding inverter and motor thermal models into the FCS-MPC, thermal cycling can be reduced in the inverter and the motor while maintaining satisfying speed/torque requirements. The proposed methodology is tested via a standard driving schedule for an interior permanent magnet traction motor in a hybrid electric vehicle.

Artificial intelligence (AI) enhanced control system deployments are emerging as a viable substitute to more traditional control system. In particular, deep learning techniques offer an alternate approach to tune the ever increasing sets of control system parameters to extract performance. However, the systematic verification and validation (to establish the reliability and robustness) of deep learning based controllers in actual deployments remains a challenge. This is exacerbated by the need to evaluate and optimize control systems embedded within an operational environment (with its own sets of additional unknown or uncertain parameters). Existing literature comparisons of deep learning against traditional controllers, where they may exist, do not offer structured approaches to comparative performance evaluation and improvement. It is also crucial to develop a standardized controlled test environment within which various controllers are evaluated against a common metric.

In this paper, we review the literature related to the reuse of computer-based simulation models in the context of systems design. Models are used to capture aspects of existing or envisioned systems and are simulated to predict the behavior of these systems. However, developing such models from scratch requires significant time and effort. Researchers have recognized that the time and effort can be reduced if existing models or model components are reused, leading to the study of model reusability. In this paper, we review the tasks necessary to retrieve and reuse model components from repositories, and to prepare new models and model components such that they are more amenable for future reuse. Model reuse can be significantly enhanced by carefully characterizing the model, and capturing its meaning and intent so that potential users can determine whether the model meets their needs.

The increasing electrification of vehicles raises system reliability concerns as the electrical and electronic components deteriorate faster after an event. In addition, the traditional method of scheduled maintenance is not efficient for managing a fleet of vehicles; because, the degradation processes are distinct in different vehicles. Therefore, integrating an online degradation forecasting and abatement module into a vehicle that is able to assess the vehicle status and predict the degradation process to take timely appropriate actions to reach satisfactory reliability and long-term goals, is valuable. Quantifying uncertainty is one of the main challenges of degradation forecasting; because, the degradation process of a vehicular system is distinct. This paper proposes an online degradation forecasting framework to predict the degradation processes to reallocate energy sources in the system, obtaining long-term goals while adhering to the reliability requirements.

Vehicular control systems are characterized with numerous complex interactions with a steady rise of autonomous functions, which makes it more challenging for designers and safety engineers to identify unexpected failures. These systems tend to be highly integrated and exhibit features like concurrency for which traditional verification and validation techniques (i.e. testing and simulation) are insufficient to provide rigorous and complete assessment. Model Checking, a well-known formal verification technique, can be used to rigorously prove the correctness of such systems according to design Requirements. In particular, Model Checking is a method for formally verifying finite-state concurrent systems. Specifications about the system are expressed as temporal logic formulas, and efficient symbolic algorithms are used to traverse the model defined by the system and check if the specification holds or not.

In the U.S., the teenage driving population is at the highest risk of being involved in a crash. Teens often demonstrate poor vehicle control skills and poor ability to identify hazards, thus proper understanding of automotive indicators and warnings may be even more critical for this population. This research evaluates teen drivers’, between 15 to 17 years of age, understanding of symbols from vehicles featuring advanced driving assistant systems and multiple powertrain configurations. Teen drivers’ (N=72) understanding of automotive symbols was compared to three other groups with specialized driving experience and technical knowledge: automotive engineering graduate students (N=48), driver rehabilitation specialists (N=16), and performance driving instructors (N=15). Participants matched 42 symbols to their descriptions and then selected the five symbols they considered most important.

In this paper, we present a framework for the robot to learn how to place objects to a workpiece by learning from humans in smart manufacturing. In the proposed framework, the rational scene dictionary (RSD) corresponding to the keyframes of task (KFT) are used to identify the general object-action-location relationships. The Generalized Voronoi Diagrams (GVD) based contour is used to determine the relative position and orientation between the object and the corresponding workpiece at the final state. In the learning phase, we keep tracking the image segments in the human demonstration. For the moment when a spatial relation of some segments are changed in a discontinuous way, the state changes are recorded by the RSD. KFT is abstracted after traversing and searching in RSD, while the relative position and orientation of the object and the corresponding mount are presented by GVD-based contours for the keyframes.

Unmanned ground vehicles (UGVs) may encounter difficulties accommodating environmental uncertainties and system degradations during harsh conditions. However, human experience and onboard intelligence can may help mitigate such cases. Unfortunately, human operators have cognition limits when directly supervising multiple UGVs. Ideally, an automated decision aid can be designed that empowers the human operator to supervise the UGVs. In this paper, we consider a connected UGV platoon under cyber attacks that may disrupt safety and degrade performance. An observer-based resilient control strategy is designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. In addition, each UGV generates both internal and external evaluations based on the platoons performance metrics. A cloud-based trust-based information management system collects these evaluations to detect abnormal UGV platoon behaviors.

It’s important to predict human actions in the industry assembly process. Foreseeing future actions before they happened is an essential part for flexible human-robot collaboration and crucial to safety issues. Vision-based human action prediction from videos provides intuitive and adequate knowledge for many complex applications. This problem can be interpreted as deducing the next action of people from a short video clip. The history information needs to be considered to learn these relations among time steps for predicting the future steps. However, it is difficult to extract the history information and use it to infer the future situation with traditional methods. In this scenario, a model is needed to handle the spatial and temporal details stored in the past human motions and construct the future action based on limited accessible human demonstrations.

Non-linear model predictive engine control (nMPC) systems have the ability to reduce calibration effort while improving transient engine response. The main drawback of nMPC for engine control is the computational power required to realize real-time operation. Most of this computational power is spent linearizing the non-linear plant model at each time step. Additionally, the effectiveness of the nMPC system relies heavily on the accuracy of the model(s) used to predict the future system behavior, which can be difficult to model physically. This paper introduces a hybrid modeling approach for internal combustion engines that combines physics-based and machine learning techniques to generate accurate models that can be linearized with low computational power. This approach preserves the generalization and robustness of physics-based models, while maintaining high accuracy of data-driven models. Advantages of applying the proposed model with nMPC are discussed.

Energy management of hybrid vehicle has been a widely researched area. Strategies like dynamic programming (DP), equivalent consumption minimization strategy (ECMS), Pontryagin’s minimum principle (PMP) are well analyzed in literatures. However, the adaptive optimization work is still lacking, especially for reinforcement learning (RL). In this paper, Q-learning, as one of the model-free reinforcement learning method, is implemented in a mid-size 48V mild parallel hybrid electric vehicle (HEV) framework to optimize the fuel economy. Different from other RL work in HEV, this paper only considers vehicle speed and vehicle torque demand as the Q-learning states. SOC is not included for the reduction of state dimension. This paper focuses on showing that the EMS with non-SOC state vectors are capable of controlling the vehicle and outputting satisfactory results. Electric motor torque demand is chosen as action.

Knock control systems are fundamentally stochastic, regulating some aspect of the distribution from which observed knock intensities are drawn. Typically a simple threshold is applied, and the controller regulates the resultant knock event rate. Recent work suggests that the choice of threshold can have a significant impact on closed loop performance, but to date such studies have been performed only in simulation. Rigorous assessment of closed loop performance is also a challenging topic in its own right because response trajectories depend on the random arrival of knock events. The results therefore vary from one experiment to the next, even under identical operating conditions. To address this issue, stochastic simulation methods have been developed which aim to predict the expected statistics of the closed loop response, but again these have not been validated experimentally.