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This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing.

Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket.

In the face of growing concerns about environmental sustainability and urban congestion, the integration of eco-driving strategies has emerged as a pivotal solution in the field of the urban transportation sector. This study explores the potential benefits of a CAV functioning as a virtual eco-driving controller in an urban traffic scenario with a group of following human-driven vehicles. A computationally inexpensive and realistic powertrain model and energy management system of the Chrysler Pacifica PHEV are developed with the field experiment data and integrated into a forward-looking vehicle simulator to implement and validate an eco-driving speed planning and energy management strategy assuming longitudinal automation. The eco-driving algorithm determines the optimal vehicle speed profile and energy management strategy.

Concerns about climate change have significantly accelerated the process of vehicle electrification to improve the sustainability of the transportation sector. Increasing the adoption of electrified vehicles is closely tied to the advancement of reliable energy storage systems, with lithium-ion batteries currently standing as the most widely employed technology. One of the key technical challenges for reliability and durability of battery packs is the ability to accurately predict and control the temperature of the cells and temperature gradient between cells inside the pack. For this reason, accurate models are required to predict and control the cell temperature during driving and charging operations. This work presents a set of procedures tailored to characterize and measure the thermal properties in li-ion cells and modules.

Battery packs used in automotive application experience high-power demands, fast charging, and varied operating conditions, resulting in temperature imbalances that hasten degradation, reduce cycle life, and pose safety risks. The development of proper simulation tools capable of capturing both the cell electrical and thermal response including, predicting the cell’s temperature rise and distribution, is critical to design efficient and reliable battery packs. This paper presents a co-simulation model framework capable of predicting voltage, 2-D heat generation and temperature distribution throughout a cell. To capture the terminal voltage and 2-D heat generation across the cell, the simulation framework employs a high-fidelity electrical model paired with a charge balance model based on the Poisson equation. The 2-D volumetric heat generation provided by the charge balance model is used to predict the temperature distribution across the cell surface using CFD software.

Accurately predicting the future behavior of the surrounding traffic, especially the velocity of the lead vehicle is important for optimizing the energy consumption and improve the safety of Connected and Automated Vehicles (CAVs). Several studies report methods to predict short-to-mid-length lead vehicle velocity using stochastic models or other data-driven techniques, which require availability of extensive data and/or Vehicle-to-Vehicle (V2V) communication. In the absence of connectivity, or in data-restricted cases, the prediction must rely only on the measured position and relative velocity of the lead vehicle at the current time. This paper proposes two velocity predictors to predict short-to-mid-length lead vehicle velocity. The first predictor is based on a Constant Acceleration (CA) with an augmented stop mode. The second one is based on a modified Enhanced Driver Model (EDM-LOS) with line-of-sight feature.

Electrification is getting more important in the aviation industry with the increasing need for reducing emissions of carbon dioxide and fuel consumption. It is crucial to assess the behavior of Li-Ion batteries at high-altitude conditions to design safe and reliable battery packs. This paper aims at benchmarking the performance of different formats of battery cells (pouch cells and cylindrical cells) in low-pressure environments. A test setup was designed and fabricated to replicate the standard procedure defined by the RTCA DO-311 standard, such as the altitude test and rapid decompression test. During the test voltage, current, temperature, and pressure were monitored, and the evaluation criteria is based on the capacity retention, along with the structural integrity of the cell. From preliminary tests, it was observed that cylindrical cells do not show a significant change in performance at low-pressure conditions thanks to their steel casing.

Physics-based electrochemical models and empirical Equivalent Circuit Models (ECMs) are well-established and widely used modeling techniques to predict the voltage behavior of lithium-ion cells. Electrochemical models are typically very accurate and require relatively little experimental data to calibrate, but present high mathematical and computational complexity. Conversely, ECMs are more computationally efficient and mathematically simpler, making them well-suited for applications in controls, diagnosis, and state estimation of lithium-ion battery packs. However, the calibration process requires extensive testing to calibrate the parameters of the model over a wide range of operating conditions. This paper bridges the gap between these two classes of models by developing a method to analytically define the ECM parameters starting from an already-calibrated Extended Single-Particle Model (ESPM).

This paper benchmarks three different lithium-ion (Li-ion) battery voltage modelling approaches, a physics-based approach using an Extended Single Particle Model (ESPM), an equivalent circuit model, and a recurrent neural network. The ESPM is the selected physics-based approach because it offers similar complexity and computational load to the other two benchmarked models. In the ESPM, the anode and cathode are simplified to single particles, and the partial differential equations are simplified to ordinary differential equations via model order reduction. Hence, the required state variables are reduced, and the simulation speed is improved. The second approach is a third-order equivalent circuit model (ECM), and the third approach uses a model based on a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)). A Li-ion pouch cell with 47 Ah nominal capacity is used to parameterize all the models.

A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.

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 assessment of fuel economy of new vehicles is typically based on regulatory driving cycles, measured in an emissions lab. Although the regulations built around these standardized cycles have strongly contributed to improved fuel efficiency, they are unable to cover the envelope of operating and environmental conditions the vehicle will be subject to when driving in the “real-world”. This discrepancy becomes even more dramatic with the introduction of Connectivity and Automation, which allows for information on future route and traffic conditions to be available to the vehicle and powertrain control system. Furthermore, the huge variability of external conditions, such as vehicle load or driver behavior, can significantly affect the fuel economy on a given route. Such variability poses significant challenges when attempting to compare the performance and fuel economy of different powertrain technologies, vehicle dynamics and powertrain control methods.

Hybrid-electric gas turbine generators are considered a promising technology for more efficient and sustainable air transportation. The Ohio State University is leading the NASA University Leadership Initiative (ULI) Electric Propulsion: Challenges and Opportunities, focused on the design and demonstration of advanced components and systems to enable high-efficiency hybrid turboelectric powertrains in regional aircraft to be deployed in 2030. Within this large effort, the team is optimizing the design of the battery energy storage system (ESS) and, concurrently, developing a supervisory energy management strategy for the hybrid system to reduce fuel burn while mitigating the impact on the ESS life. In this paper, an energy-based model was developed to predict the performance of a battery-hybrid turboelectric distributed-propulsion (BHTeDP) regional jet.

Downsizing and boosting is currently the principal solution to reduce fuel consumption in automotive engines without penalizing the power output. A key challenge for controlling the boost pressure during highly transient operations lies in avoiding to operate the turbocharger compressor in its instability region, also known as surge. While this phenomenon is well known by control engineers, it is still difficult to accurately predict during transient operations. For this reason, the scientific community has directed considerable efforts to understand the phenomena leading to the onset of unstable behavior, principally through experimental investigations or high-fidelity CFD simulations. On the other hand, less emphasis has been placed on creating control-oriented models that adopt a physics-based (rather than data-driven) approach to predict the onset of instability phenomena.

Global regulatory targets and customer demand are driving the automotive industry to improve vehicle fuel efficiency. Methods for achieving increased efficiency include improvements in the internal combustion engine and an accelerating shift toward electrification. A key enabler to maximizing the benefit from these new powertrain technologies is proper systems integration work - including developing optimized controls for the propulsion system as a whole. The next step in the evolution of improving the propulsion management system is to make use of available information not typically associated with the powertrain. Advanced driver assistance systems, vehicle connectivity systems and cloud applications can provide information to the propulsion management system that allows a shift from instantaneous optimization of fuel consumption, to optimization over a route. In the current paper, we present initial work from a project being done as part of the DOE ARPA-E NEXTCAR program.

Downsizing and turbocharging are today considered an effective way to reduce CO2 emissions in automotive gasoline engines, especially for the European and US markets. In the broad field of research and development for engine boosting systems, the instability phenomenon of surge has gathered considerable interest in recent years, as the main limiting factor to high performance boosting and boost pressure control. To this extent, developing an in-depth knowledge of the surge dynamics and on the phenomena governing the transition from stable to unstable operation can provide very valuable information for the design of the intake system and boost pressure control algorithms, allowing optimal boost pressure without compromising the transient response.

To improve torque management algorithms for drivability, the powertrain controller must be able to compensate for the nonlinear dynamics of the driveline. In particular, the presence of backlash in the transmission and drive shafts excites sharp torque fluctuations during tip-in or tip-out transients, leading to a deterioration of the vehicle drivability and NVH. This paper proposes a model-based estimator that predicts the wheel torque in an automotive drivetrain, accounting for the effects of backlash and drive shaft flexibility. The starting point of this work is a control-oriented model of the transmission and vehicle drivetrain dynamics that predicts the wheel torque during tip-in and tip-out transients at fixed gear. The estimator is based upon a switching structure that combines a Kalman Filter and an open-loop prediction based on the developed model.

Engine downsizing, boosting, direct injection and variable valve actuation, have become industry standards for reducing CO2 emissions in current production vehicles. Because of the increasing complexity of the engine air path system and the high number of degrees of freedom for engine charge management, the design of air path control algorithms has become a difficult and time consuming process. One possibility to reduce the control development time is offered by Software-in-the-Loop (SIL) or Hardware-in-the-Loop (HIL) simulation methods. However, it is significantly challenging to identify engine air path system simulation models that offer the right balance between fidelity, mathematical complexity and computational burden for SIL or HIL implementation.

Engine downsizing and super/turbocharging is currently the most followed trend in order to reduce CO2 emissions and increase the powertrain efficiency. A key challenge for achieving the desired fuel economy benefits lies in optimizing the design and control of the engine boosting system, which requires the ability to rapidly sort different design options and technologies in simulation, evaluating their impact on engine performance and fuel consumption. This paper presents a scalable modeling approach for the characterization of flow and efficiency maps for automotive turbochargers. Starting from the dimensional analysis theory for turbomachinery and a set of well-known control-oriented models for turbocharged engines simulation, a novel scalable model is proposed to predict the flow and efficiency maps of centrifugal compressors and radial inflow turbines as function of their key design parameters.

In a conventional passenger vehicle, the AC system is the largest ancillary load. This paper proposes a novel control strategy to reduce the energy consumption of the air conditioning system of a conventional passenger car. The problem of reducing the parasitic load of the AC system is first approached as a multi-objective optimization problem. Starting from a validated control-oriented model of an automotive AC system, an optimization problem is formalized to achieve the best possible fuel economy over a regulatory driving cycle, while guaranteeing the passenger comfort in terms of cabin temperature and reduce the wear of the components. To complete the formulation of the problem, a set of constraints on the pressure in the heat exchanger are defined to guarantee the safe operation of the system. The Dynamic Programming (DP), a numerical optimization technique, is then used to obtain the optimal solution in form of a control sequence over a prescribed driving cycle.