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With the advent of this new era of electric-driven automobiles, the simulation and virtual digital twin modeling world is now embarking on new sets of challenges. Getting key insights into electric motor behavior has a significant impact on the net output and range of electric vehicles. In this paper, a complete 3D CFD model of an Electric Motor is developed to understand its churning losses at different operating speeds. The simulation study details how the flow field develops inside this electric motor at different operating speeds and oil temperatures. The contributions of the crown and weld endrings, crown and weld end-windings, and airgap to the net churning loss are also analyzed. The oil distribution patterns on the end-windings show the effect of the centrifugal effect in scrapping oil from the inner structures at higher speeds. Also, the effect of the sump height with higher operating speeds are also analyzed.

The study focuses on understanding the air and oil flow characteristics within a ball bearing during high-speed rotation, with a particular emphasis on optimizing frictional heat dissipation and oil lubrication methods. Computational fluid dynamics (CFD) techniques are employed to analyze the intricate three-dimensional airflow and oil flow patterns induced by the motion of rotating and orbiting balls within the bearing. A significant challenge in conducting three-dimensional CFD studies lies in effectively resolving the extremely thin gaps existing between the balls, races, and cages within the bearing assembly. In this research, we adopt the ball-bearing structured meshing strategy offered by Simerics-MP+ to meticulously address these micron-level clearances, while also accommodating the rolling and rotation of individual balls. Furthermore, we investigate the impact of different designs of the lubrication ports to channel oil to other locations compared to the ball bearings.

Effective design of the lubrication path greatly influences the durability of any transmission system. However, it is experimentally impossible to estimate the internal distribution of the automotive transmission fluid (ATF) to different parts of the transmission system due to its structural complexities. Hybrid vehicle transmission systems usually consist of different types of bearings (ball bearings, thrust bearings, roller bearings, etc.) in conjunction with gear systems. It is a perennial challenge to computationally simulate such complicated rotating systems. Hence, one-dimensional models have been the state of the art for designing these intricate transmission systems. Though quantifiable, the 1D models still rely heavily on some testing data. Furthermore, HEVs (hybrid electric vehicles) desire a more efficient lubrication system compared to their counterparts (Internal combustion engine vehicles) to extend the range of operation on a single charge.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.

Selecting the right transportation platform is challenging, whether it is at a personal level or at an organizational level. In settings where predominantly the functional aspects rule the decision making process, defining the mobility of a vehicle is critical for comparing different offerings and making acquisition decisions. With the advent of intelligent vehicles, exhibiting partial to full autonomy, this challenge is exacerbated. The same vehicle may traverse independently and with greater tolerance for acceleration than human occupied vehicles, while, at the same time struggle with obstacle avoidance. The problem presents itself at the individual vehicle sensing level and also at the vehicle/fleet level. At the sensing and information level, one can be looking at issues of latency, bandwidth and optimal information fusion from multiple sources including privileged sensing. At the overall vehicle level, one focuses more on the ability to complete missions.

With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs.

Knowledge of the tire slip ratio can greatly improve vehicle longitudinal stability and its dynamic performance. Most conventional slip ratio observers were mainly designed based on input of non-driven wheel speed and estimated vehicle speed. However, they are not applicable for electric vehicles (EVs) with four in-wheel motors. Also conventional methods on speed estimation via integration of accelerometer signals can often lead to large offset by long-time integral calculation. Further, model uncertainties, including steady state error and unmodeled dynamics, are considered as additive disturbances, and may affect the stability of the system with estimated state error. This paper proposes a novel slip ratio observer based on input-to-state stability (ISS) method for electric vehicles with four-wheel independent driving motors.

The design and development of electric vehicles involves many unique challenges. One such challenge involves accurately predicting driveline abuse torque loads early in the design cycle to aid with sizing drive-unit and driveline components. Since electrified drivelines typically lack a torque-limiting “fuse” element such as a torque converter or slipping clutch, they can be vulnerable to sudden transient events involving high wheel acceleration or deceleration. Component sizing must account for the loads caused by such events, and these loads must be accurately quantified early on when vehicle parameters haven’t been finalized yet. Early load predictions can be made by completing abuse maneuver simulations where key parameters are varied to gauge their influence on simulated loads. Understanding how these parameters impact loads allows for better risk assessment during the design process, as these parameters will inevitably change until a final design is iterated upon.

Speaker performance in Acoustic Vehicle Alerting System (AVAS) plays a crucial role for pedestrian safety. Sound radiation from AVAS speaker has obvious directivity pattern. Considering this feature is critical for accurately simulating the exterior sound field of electrical vehicles. This paper proposes a new process to characterize the sound directivity pattern of AVAS speaker. The first step of the process is to perform an acoustic testing to measure the sound pressure radiated from the speaker at a certain number of microphone locations in a free field environment. Based on the geometry of a virtual speaker, the locations of each microphone and measured sound pressure data, an inverse method, namely the inverse pellicular analysis, is adopted to recover a set of vibration pattern of the virtual speaker surface. The recovered surface vibration pattern can then be incorporated in the full vehicle numerical model as an excitation for simulating the exterior sound field.

De-centralized brake actuation – that is, brake systems that incorporate individual actuators at each wheel brake location to both provide the apply energy and the modulation of braking force – is not a new area of study. Typically realized in the form of electro-mechanical brake calipers or drum brakes, or as “single corner” hydraulic actuators, de-centralized actuation in braking systems has already been deployed in production on General Motor EV1 Electric Vehicle (1997) in the form of electric drum brakes and has been studied continually by the automotive industry since then. It is frequently confused with “brake by wire,” and indeed practical implementations of de-centralized actuation are a form of brake by wire technology. However, with millions of vehicles on the road already with “brake by wire” systems - the vast majority of which have centralized brake actuation – the future of “brake by wire” is arguable settled.

The past five years have seen significant research into autonomous vehicles that employ a by-wire steering rack actuator and no steering wheel. There is a clear synergy between these advancements and the parallel development of complete Steer-by-Wire systems for human-operated passenger vehicle applications. Steer-by-Wire architectures presented thus far in the literature require multiple layers of electrical and/or mechanical redundancy to achieve the safety goals. Unfortunately, this level of redundancy makes it difficult to simultaneously achieve three key manufacturer imperatives: safety, reliability, and cost. Hindered by these challenges, as of 2020 only one production car platform employs a Steer-by-Wire system. This paper presents a Steer-by-Wire architectural solution featuring fail-operational steering control architected with the objective of achieving all system safety, reliability, and cost goals.

The automotive industry is facing many significant challenges that go far beyond the design and manufacturing of automobile products. Connected, autonomous and electric vehicles, smart cities, urbanization and the car sharing economy all present challenges in a fast-changing environment which the automotive industry must adapt to. Cars no longer are just standalone systems, but have become constituent systems (CS) in larger System of Systems (SoS) context. This is reflected in the emergence of several acronyms such as vehicle-to-everything (V2X), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-grid (V2G) expressions. System of Systems are defined systems of interest whose elements (constituent systems) are managerially and operationally independent systems. This interoperating and/or integrated collection of constituent systems usually produce results unachievable by the individual systems alone, for example the use of car batteries as virtual power plants.

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced Long short-term memory (LSTM) network integrated with a state refinement module.

Electric motor whine is one of the main noise sources of electric vehicles (EVs). Without engine masking noise, high pitch tonal noise from electric motor can be highly annoying and raise sound quality issues for electrified propulsion systems. This paper describes a patented new technology that controls electric motor to actively mask annoying high-pitch tonal noise by (i) controlling electric motor to create complementary low order tones to enrich sound complexity and distract high pitch tones; (ii) controlling motor to generate random dithering noise to raise masking noise floor and reduce tone-to-noise ratio around tonal targets; (iii) combining complementary injection at low frequency and dithering at high frequency for enhanced masking. This new technology enables controlling masking noise level, frequency, order and bandwidth as a function of motor torque and speed for most effective masking.

The Continuous Fluid Level and Quality Indicator (CFLQI) technology is focused on increasing the sampling frequency of brake fluid reservoir volume and detecting specific brake fluid contaminants. CFLQI targets to improve diagnostics detection range and resulting degraded vehicle operation strategies by increasing sensitivity to brake fluid loss and the addition of a fluid quality feature. The theory of CFLQI is to improve future autonomous and highly automated vehicle performance, both of which will have reduced driver input and service schedules, by providing earlier fluid level and fluid health warnings. The two technologies selected to prove theory of operation were ultra-sonic sensor and capacitive sense element technology. Both technologies show initial capability to meet fluid sensing targets with system level ASIL D ASIC design. The CFLQI compliments and improves upon current technology of brake pad wear sensors, leak detection diagnostics and brake fluid level monitoring.

With the increased market share of electric vehicles, the demand for energy-efficient routing algorithms specifically optimized for electric vehicles has increased. Traditional routing algorithms are focused on optimizing the shortest distance or the shortest time in finding a path from point A to point B. These traditional methods have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power limits, battery capacity limits, and vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present an ant colony based, energy-efficient routing algorithm that is optimized and designed for electric vehicles. Simulation results show improvements in the energy consumption of electric vehicles when applied to a start-to-destination routing problem.

Pulse Width Modulation or PWM has been widely used in traction motor control for electric propulsion systems. The associated switching noise has become one of the major NVH concerns of electric vehicles (EVs). This paper presents a multi-disciplinary study to analyze and validate current ripple induced switching noise for EV applications. First, the root cause of the switching noise is identified as high frequency ripple components superimposed on the sinusoidal three-phase current waveforms, due to PWM switching. Measured phase currents correlate well with predictions based on an analytical method. Next, the realistic ripple currents are utilized to predict the electro-magnetic dynamic forces at both the motor pole pass orders and the switching frequency plus its harmonics. Special care is taken to ensure sufficient time step resolution to capture the ripple forces at varying motor speeds.

Battery Electric Vehicles (BEVs) are becoming more competitive day by day to achieve maximum peak power and energy requirement. This poses challenges to the design of Thermal Interface Material (TIM) which maintains the cell temperature and ensure retention of cell and prevent electrolyte leak under different crash loads. TIM can be in the form of adhesives, gels, gap fillers. In this paper, TIM is considered as structural, and requires design balance with respect to thermal and mechanical requirements. Improving structural strength of TIM will have negative impact on its thermal conductivity; hence due care needs to be taken to determine optimal strength that meets both structural and thermal performance. During various crash conditions, due to large inertial force of cell and module assembly, TIM is undertaking significant loads on tensile and shear directions. LS-DYNA® is used as simulation solver for performing crash loading conditions and evaluate structural integrity of TIM.

Electric motor whine is one of the main noise sources of hybrid and electric vehicles. This paper describes a comprehensive analytical and experimental investigation of permanent magnetic electric motor NVH designs focusing on the contribution from torque ripple (TR) and radial forces (RF). A design-of-experiment method is adopted to design and build candidate motors with (i) high TR and high RF; (ii) high TR and low RF; (iii) low TR and high RF and (iv) low TR and low RF. Four prototype motors are built and tested on motor fixtures to measure dynamic stator forces in radial, tangential and axial directions, track dominant motor orders, and estimate motor Operational Deflection Shapes (ODS). Finite-element based electromagnetic and NVH analyses are performed and correlated to test data. Both tests and analyses confirm reducing TR and RF improves motor NVH performance at dominant pole pass orders.

As the number of electric vehicles (EVs) within society rapidly increase, the concept of maximizing its efficiency within the electric smart grid becomes crucial. This research presents the impacts of integrating EV charging infrastructures within a smart grid through a vehicle to grid (V2G) program. It also observes the circulation of electric charge within the system so that the electric grid does not become exhausted during peak hours. This paper will cover several different case studies and will analyze the best and worst scenarios for the power losses and voltage profiles in the power distribution system. Specifically, we seek to find the optimal location as well as the ideal number of EVs in the distribution system while minimizing its power losses and optimizing its voltage profile. Verification of the results are primarily conducted using GUIs created on MATLAB.