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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.

The software design of this new engine control unit is based on a unique and homogenous torque structure. All input signals are converted into torque equivalents and a torque coordinator determines their influence on the final torque delivered to the powertrain. The basic torque structure is independent on the type of fuel and can be used for gasoline, diesel, or CNG injection systems. This allows better use of custom specific algorithms and facilitates reusability, which is supported by the graphical design tool that creates all modules using automatic code generation. Injection specific algorithms can be linked to the software by simply setting a software switch.

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.

Battery simulation by a DSP-controlled high current power supply is used to improve repeatability and comparability of starting tests, especially at low temperatures. The simulator's algorithm calculates the internal resistance of the battery by a timely constant resistor and a variable resistor representing the actual discharge history. The output voltage of the simulator is set as a function of internal resistor and load current with temperature and state of charge as setup parameter. The simulator was evaluated in cold start testing in comparison to real batteries. As a result, batteries are simulated with high repeatability. Deviations to real battery behavior are in the range of test to test deviations using real batteries.

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.

Recent years have witnessed a dramatic increase in global ethanol production, while cellulosic feedstock or the algae-based production approach make more sustainable ethanol production foreseeable in many countries. The ethanol produced will increasingly penetrate the markets not only as blending component, but also as main fuel component, boosting demand for flex-fuel vehicles. One of the main challenges for flex-fuel vehicles is the cold start due to the poor vapor pressure of ethanol. This is detrimental to starting capability in DISI engines in particular, with increased cylinder wall wetting causing higher oil dilution. The most efficient solution for DISI engines is a smart injection strategy, enabling fuel vaporization during injection in the compression stroke. But this requires optimum injection parameters such as injection timing, split ratio and rail pressure.

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.

The development of energy efficient air conditioning systems for electric vehicles is an ever increasing challenge, because the cooling as well as the heating of the passenger compartment reduces the cruising range dramatically. Electric cars are usually equipped with a scroll compressor and a separate electric motor with appropriate power electronics. However, this solution is critical in terms of the installation space, the weight and also the costs. Therefore, an innovative and energy efficient drivetrain structure for electric vehicles was developed, which integrates the motor of the A/C-compressor directly into the drivetrain. Thus it is possible to switch off the compressor motor and to use the main motor for the drive of the compressor at certain driving situations. As a result, the operating point of the main motor can be shifted to a better efficiency.

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 powertrain electrification is currently not only taking place in public road mobility vehicles, but is also making its way to the racetrack, where it’s driving innovation for developments that will later be used in series production vehicles. The current development focus for electric vehicles is the balance between driving power, range and weight, which is given even greater weighting in racing. To redefine the current limits, IAV developed a complete e-powertrain for a racing MX motorcycle and integrated it into a real drivable demonstrator bike. The unique selling point is the innovative direct phase-change cooling (PCC) of the three-phase e-motor and its power electronics, which enables significantly increased continuous power (Pe = 40 kW from 7,000 rpm to 9,000 rpm) without thermal power reduction. The drive unit is powered by a replaceable Lithium-Ion round cell battery (Ubat,max = 370V) with an energy storage capacity of Ebat = 5 kWh.

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.

To meet the targets of Indian future emission legislation, an electrification and automation of today’s manual transmission technology is necessary. For this reason, IAV invented an electrified automated transmission family, based on well-known manual transmission technology. This low-cost automated manual transmission (AMT) approach is equipped with a 48 V electric machine and can be used as pure electric or hybrid drivetrain. Furthermore, it is possible to realize power shifts by using just one dry friction element. A small number of standard components combined with a low voltage electric machine and an electromechanical actuation system is sufficient to create a maximum of flexibility to meet future emission fleet targets, without having the disadvantageous high costs for a high-voltage electric system. To detect the optimal powertrain configuration, IAV used a unique advance development tool called Powertrain Synthesis.

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.

Future heavy-duty (HD) concepts should fulfill very tight tail-pipe NOx emissions and simultaneously fulfill the fuel efficiency targets. In current HD Euro VII discussions, real working cycles become key to ensure emission conformity. For instance, cold start and cold ambient conditions during testing with low load profiles starting from 0% payload, require external heating measures. Knowing the trade-off between fuel consumption and tail-pipe NOx emissions a holistic engine and EAT system optimization with innovative thermal management is required. Towards a carbon neutral mobility, Hydrogen combustion engines are one of the key solutions. Advanced combustion system development enables maximal usage of lean burning as the major advantage of the Hydrogen fuel for efficiency improvement and NOx reduction.

For the NOx removal from diesel exhaust, the selective catalytic reduction (SCR) and lean NOx traps are established technologies. However, these procedures lack efficiency below 200 °C, which is of importance for city driving and cold start phases. Thus, the present paper deals with the development of a novel low-temperature deNOx strategy implying the catalytic NOx reduction by hydrogen. For the investigations, a highly active H2-deNOx catalyst, originally engineered for lean H2 combustion engines, was employed. This Pt-based catalyst reached peak NOx conversion of 95 % in synthetic diesel exhaust with N2 selectivities up to 80 %. Additionally, driving cycle tests on a diesel engine test bench were also performed to evaluate the H2-deNOx performance under practical conditions. For this purpose, a diesel oxidation catalyst, a diesel particulate filter and a H2 injection nozzle with mixing unit were placed upstream to the full size H2-deNOx catalyst.

The majority of powertrain types considered important contributors to achieving the CO2 targets in the transportation sector employ a battery as an energy storage device. The need for batteries is hence expected to grow drastically with increasing market share of CO2-optimized powertrain concepts. The resulting huge pressure on the development of future electrochemical energy storage systems necessitates the application of advanced methodologies enabling a fast and cost-efficient concept definition and optimization process. This paper presents a model-based methodology for the optimization of BEV thermal management concept layouts and operation strategies targeting minimized energy consumption. Starting at the vehicle level, the proposed methodology combines appropriate representations of all primary powertrain components with 1D cooling and refrigerant circuit models and focuses on their interaction with the battery chemistry.

The low-NOx standard for heavy-duty trucks proposed by the California Air Resources Board will require rapid warm-up of the aftertreatment system (ATS). Several different aftertreatment architectures and technologies, all based on selective catalytic reduction (SCR), are being considered to meet this need. One of these architectures, the close-coupled SCR (ccSCR), was evaluated in this study using two different physics-based, 1D models; the simulations focused on the first 300 seconds of the cold-start Federal Test Procedure (FTP). The first model, describing a real, EuroVI-compliant engine equipped with series turbochargers, was used to evaluate a ccSCR located either i) immediately downstream of the low-pressure turbine, ii) in between the two turbines, or iii) in a by-pass around the high pressure turbine.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

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.