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The study emphasizes transitioning school buses from diesel to electric to mitigate their environmental impact, addressing challenges like limited driving range through predictive models. This research introduces a comprehensive control-oriented model for estimating auxiliary loads in electric school buses. It begins by developing a transient thermal model capturing cabin behavior, divided into passenger and driver zones. Integrated with a control-oriented HVAC model, it estimates heating and cooling loads for desired cabin temperatures under various conditions. Real-world operational data from school bus specifications enhance the model’s practicality. The models are calibrated using experimental cabin-HVAC data, resulting in a remarkable overall Root Mean Square Error (RMSE) of 2.35°C and 1.88°C between experimental and simulated cabin temperatures.

Delivery trucks and vans represent a growing transportation segment which reflects the shift of consumers towards on-line shopping and on-demand delivery. Therefore, electrification of this class of vehicles is going to play a major role in the decarbonization of the transportation sector and in the transition to a sustainable mobility system. Hybrid electric vehicles can represent a medium-term solution and have gained an increasing share of the market in recent years. These vehicles include two power sources, typically an internal combustion engine and a battery, which gives more degrees of freedom when controlling the powertrain to satisfy the power request at the wheels. Components sizing and powertrain energy management are strongly coupled and can make a substantial impact on the final energy consumption of a hybrid vehicle.

Super Truck II is a 48V mild hybrid class 8 truck with an all auxiliary loads powered purely by the battery pack. Electric Heating Ventilation and Air Conditioning (HVAC) load is the most prominent battery load during the hotel period, when the truck driver is resting inside the sleeper. For the PACCAR Super Truck II (ST-II) project a 48 V battery system provides the required power during the hotel period. A cabin-HVAC model estimates the electric load on the 48V battery system, allowing the control system to implement an efficient energy management strategy that avoids engine idling during the hotel period. The thermal model accounts for the sun load due to the time of day and the geographic location of the truck during the hotel period. The cabin-HVAC model has two parts. First, a grey box model with two heat exchangers (Condenser and Evaporator) working in unison with refrigerant mass flow rate as an input and HVAC load as an output.

With the advent of increasing autonomous vehicles on public roads, the safety of vulnerable road users such as pedestrians, cyclists, etc. has never been more important. These especially include Blind or Visually Impaired (BVI) pedestrians who face difficulty in making confident decisions in road crossings without the help of accessible pedestrian signals (APS). This paper addresses some of the safety measures that can be taken to improve and assess the safety of BVI pedestrians in a controlled environment like a BVI school campus where autonomous vehicles are operated. The majority of research on autonomous vehicle safety does not consider the edge cases of encounters with BVI pedestrians. Based on this motivation, requirements and characteristics of Non-BVI and BVI pedestrians have been stated along with the motion models used to predict their future movements. Existing tools based on Bayesian multi-model filters were used for pedestrian tracking and motion predictions.

Roundabout is a unique approach of managing traffic at intersections because it relies on driver’s instincts of safety. Roundabouts are considered safer than other ways of intersection traffic management due to low speed limits, smoother merging, and reduced fatal accidents. Despite their benefits and increasing usage, there is lack of clear understanding of the roundabouts, particularly due to scarcity of data and simulation models and the complexity of the structure. Real-time and offline traffic data recorded at a roundabout provides a basis for 1) identification of the safety issues, 2) understanding unexpected and risky driver behavior, 3) proposing potential mobility solutions, and 4) developing simulation models. The processed data may be used in controlling metered roundabouts, communicating with connected and automated vehicles (CAVs) etc. In this paper an approach to obtain useful traffic information from video feed data at a roundabout is presented.

Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long-term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting.

Electrochemical models of lithium ion batteries are today a standard tool in the automotive industry for activities related to the computer-aided engineering design, analysis, and optimization of energy storage systems for electrified vehicles. One of the challenges in the development or use of such models is the need of detailed information on the cell and electrode geometry or properties of the electrode and electrolyte materials, which are typically unavailable or difficult to retrieve by end-users. This forces engineers to resort to “hand-tuning” of many physical and geometrical parameters, using standard cell-level characterization tests. This paper proposes a method to provide information and data on individual electrode performance that can be used to simplify the calibration process for electrochemical models.

The platooning of connected automated vehicles (CAV) possesses the significant potential of reducing energy consumption in the Intelligent Transportation System (ITS). Moreover, with the rapid development of eco-driving technology, vehicle platooning can further enhance the fuel efficiency by optimizing the efficiency of the powertrain. Since road grade is a main factor that affects the energy consumption of a vehicle, the estimation of the road grade with high accuracy is the key factor for a connected vehicle platoon to optimize energy consumption using vehicle-to-vehicle (V2V) communication. Commonly, the road grade is quantified by single consumer grade global positioning system (GPS) with the geodetic height data which is rough and in the meter-level, increasing the difficulty of precisely estimating the road grade.

Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. This paper concentrates on low speed autonomous shuttles that are transitioning from being tested in limited traffic, dedicated routes to being deployed as SAE Level 4 automated driving vehicles in urban environments like college campuses and outdoor shopping centers within smart cities. The Ohio State University has designated a small segment in an underserved area of campus as an initial autonomous vehicle (AV) pilot test route for the deployment of low speed autonomous shuttles. This paper presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment.

An important part of automotive driving assistance systems and autonomous vehicles is speed optimization and traffic flow adaptation. Vehicle sensors and wireless communication with surrounding vehicles and road infrastructure allow for predictive control strategies taking near-future road and traffic information into consideration to improve fuel economy. For the development of autonomous vehicle speed control algorithms, it is imperative that the controller can be evaluated under different realistic driving and traffic conditions. Evaluation in real-life traffic situations is difficult and experimental methods are necessary where similar driving conditions can be reproduced to compare different control strategies. A traditional approach for evaluating vehicle performance, for example fuel consumption, is to use predefined driving cycles including a speed profile the vehicle should follow.

As the development of autonomous vehicles rapidly advances, the use of convoying/platooning becomes a more widely explored technology option for saving fuel and increasing the efficiency of traffic. In cooperative adaptive cruise control (CACC), the vehicles in a convoy follow each other under adaptive cruise control (ACC) that is augmented by the sharing of preceding vehicle acceleration through the vehicle to vehicle communication in a feedforward control path. In general, the desired velocity optimization for vehicles in the convoy is based on fuel economy optimization, rather than driveability. This paper is a preliminary study on the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles and the controller gain impact on the driveability. A simple low-level longitudinal model of the vehicle has been used along with a PD type cruise controller and a generic spacing policy for ACC/CACC.

The Ohio State University EcoCAR 3 team is building a plug-in hybrid electric vehicle (PHEV) post-transmission parallel 2016 Chevrolet Camaro. With the end-goal of improving fuel economy and reducing tail pipe emissions, the Ohio State Camaro has been fitted with a 32 kW alternator-starter belt coupled to a 119 kW 2.0L GDI I4 engine that runs on 85% ethanol (E85). The belted alternator starter (BAS) which aids engine start-stop operation, series mode and torque assist, is powered by an 18.9 kWh Lithium Iron Phosphate energy storage system, and controlled by a DC-AC inverter/controller. This report details the modeling, calibration, testing and validation work done by the Ohio State team to fast track development of the BAS system in Year 2 of the competition.

The EcoCAR 2 team at the Ohio State University has designed an extended-range electric vehicle capable of 44 miles all-electric range, which features a 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes made possible by a 1.8-L ethanol (E85) engine and a 6-speed automated manual transmission. This vehicle is designed to reduce fuel consumption, with a utility factor weighted fuel economy of 50 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report documents the team's refinement work on the vehicle during Year 3 of the competition, including vehicle improvements, control strategy calibration and dynamic vehicle testing, culminating in a 99% buy off vehicle that meets the goals set forth by the team. This effort was made possible through support from the U.S. Department of Energy, General Motors, The Ohio State University, and numerous competition and local sponsors.

The EcoCAR 2: Plugging into the Future team at the Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 50 miles of all-electric range. The vehicle features a 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes. This is made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 51 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report details the fabrication and control implementation process followed by the Ohio State team during Year 2 of the competition. The fabrication process includes finalizing designs based on identified requirements, building and assembling components, and performing extensive validation testing on the mechanical, electrical and control systems.

Plug-In Hybrid Vehicles (PHEVs) represent the middle point between Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs), thus combining benefits of the two architectures. PHEVs can achieve very high fuel economy while preserving full functionality of hybrids - long driving range, easy refueling, lower emissions etc. These advantages come at an expense of added complexity in terms of available fuel. The PHEV battery is recharged both though regenerative braking and directly by the grid thus adding extra dimension to the control problem. Along with the minimization of the fuel consumption, the amount of electricity taken from the power grid should be also considered, therefore the electricity generation mix and price become additional parameters that should be included in the cost function.

Automating road vehicle control can increase the range and reliability of dynamic testing. Some tests, for instance, specify precise steering inputs which human test drivers are only able to approximate, adding uncertainty to the test results. An automated steering system has been developed which is capable of removing these limitations. This system enables any production car or light truck to follow a user-defined path, using global position feedback, or to perform specific steering sequences with excellent repeatability. The system adapts itself to a given vehicle s handling characteristics, and it can be installed and uninstalled quickly without damage or permanent modification to the vehicle.

As vehicle development timelines continue to shorten, it is necessary for the full vehicle NVH engineer to be able to predict performance without actual prototypes. There has been significant advancement in the accuracy of finite element modeling techniques of trimmed bodies; however accuracy is still low in the road noise mid frequency range from 150-400Hz. Also, calculation times for these frequencies are long, with very large results files in some cases. To alleviate these limitations, a Hybrid approach has been used, where a finite element suspension and drive train model is coupled with a test based Frequency Response Function (FRF) model of the trimmed body. The predicted road noise level was compared to actual vehicle tests and exhibited excellent correlation.

During Phase VI of the National Highway Traffic Safety Administration's (NHTSA) Light Vehicle Rollover Research Program, three of the twenty-six light vehicles tested exhibited significant response asymmetries with respect to left versus right steer maneuvers. This paper investigates possible vehicle asymmetric characteristics and unintended inputs that may cause vehicle asymmetric response. An analysis of the field test data, results from suspension and steering parameter measurements, and a summary of a computer simulation study are also given.

The Ohio State University Center for Intelligent Transportation Research (CITR) has developed three automated vehicles demonstrating advanced cruise control, automated steering control for lane keeping, and autonomous behavior including automated stopping and lane changes in reaction to other vehicles. Various sensors were used, including a radar reflective stripe system and a vision based system for lane position sensing, a radar system and a scanning laser rangefinding system for the detection of objects ahead of the vehicle, and various supporting sensors including side looking radars and an angular rate gyroscope. These vehicles were demonstrated at the National Automated Highway System Consortium (NAHSC) 1997 Technical Feasibility Demonstration in a scenario involving mixed autonomous and manually driven vehicles. This paper describes the demonstration, the vehicle sensing, control, and computational hardware, and the vehicle control software.