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Fault diagnosis for automotive systems is driven by government regulations, vehicle repairability, and customer satisfaction. Several methods have been developed to detect and isolate faults in automotive systems, subsystems and components with special emphasis on those faults that affect the exhaust gas emission levels. Limit checks, model-based, and knowledge-based methods are applied for diagnosing malfunctions in emission control systems. Incipient and partial faults may be hard to detect when using a detection scheme that implements any of the previously mentioned methods individually; the integration of model-based and knowledge-based diagnostic methods may provide a more robust approach. In the present paper, use is made of fuzzy residual evaluation and of a fuzzy expert system to improve the performance of a fault detection method based on a mathematical model of the engine.

Kinetic energy recovery systems (KERS) placed on one axle coupled to a traditional thermal engine on the other axle is possibly the best solution presently available to dramatically improve the fuel economy while providing better performances within strict budget constraints. Different KERS may be built purely electric, purely mechanic, or hybrid mechanic/electric differing for round trip efficiency, packaging, weights, costs and requirement of further research and development. The paper presents an experimental analysis of the energy flow to and from the battery of a latest Nissan Leaf covering the Urban Dynamometer Driving Schedule (UDDS). This analysis provides a state-of-the-art benchmark of the propulsion and regenerative braking efficiencies of electric vehicles with off-the-shelve technologies.

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.

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.

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.

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.

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.

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.

The innovative highly flexible wings made of extremely light structures, yet still capable of carrying a considerable amount of non- structural weights, requires significant effort in structural simulations. The complexity involved in such design demands for simplified mathematical tools based on appropriate nonlinear structural schemes combined with reduced order models capable of predicting accurately their aero-structural behaviour. The model presented in this paper is based on a consistent nonlinear beam-wise scheme, capable of simulating the unconventional aeroelastic behaviour of flexible composite wings. The partial differential equations describing the wing dynamics are expanded up to the third order and can be used to explore the effect of static deflection imposed by external trim, the effect of gust loads and the one of nonlinear aerodynamic stall.

Driver’s license examinations require the driver to perform either a parallel parking or a similar maneuver as part of the on-road evaluation of the driver’s skills. Self-driving vehicles that are allowed to operate on public roads without a driver should also be able to perform such tasks successfully. With this motivation, the S-shaped maneuverability test of the Ohio driver’s license examination is chosen here for automatic execution by a self-driving vehicle with drive-by-wire capability and longitudinal and lateral controls. The Ohio maneuverability test requires the driver to start within an area enclosed by four pylons and the driver is asked to go to the left of the fifth pylon directly in front of the vehicle in a smooth and continuous manner while ending in a parallel direction to the initial one. The driver is then asked to go backwards to the starting location of the vehicle without stopping the vehicle or hitting the pylons.

This paper shows the influence of different battery charge management strategies on the fuel economy of a hybrid parallel axle-split vehicle in a real driving scenario, for a vehicle control system that has the additional possibility to split the torque between front and rear axles. The first section regards the validation of a self-developed Model in the Loop (MiL) environment of a P1-P4 plug-in hybrid electric car, using experimental data of a New European Driving Cycle test. In its original version, which is implemented on-board the vehicle, the energy management supervisor implements a heuristic, or rule-based, Energy Management Strategy (EMS). During this project, a different EMS has been developed, consisting of a sub-optimal control scheme called Equivalent Consumption Minimization Strategy (ECMS), explained in detail in the second section.

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 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.