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Low temperature combustion (LTC) modes are among the advanced combustion technologies which offer thermal efficiencies comparable to conventional diesel combustion and produce ultra-low NOx and particulate matter (PM) emissions. However, combustion timing control, excessive pressure rise rate and high cyclic variations are the common challenges encountered by the LTC modes. These challenges can be addressed by developing model-based control framework for the LTC engine. In the current study, in-cylinder pressure data for dual-fuel LTC engine operation is analyzed for 636 different operating conditions and the heat release rate (HRR) traces are classified into three distinct classes based on their distinct shapes. These classes are named as Type-1, Type-2 and Type-3, respectively.

Eco Approach and Departure (Eco-AnD) is a Connected Automated Vehicle (CAV) technology aiming to reduce energy consumption for crossing a signalized intersection or set of intersections in a corridor that features vehicle-to-infrastructure (V2I) communication capability. This research focuses on developing a Dynamic Programming (DP) based algorithm for a PHEV operating in Charge Depleting mode. The algorithm used the Reduced Order Energy Model (ROM) to capture the vehicle powertrain characteristics and road grade to capture the road dynamics. The simulation results are presented for a real-world intersection and 20-25% energy benefits are shown by comparing against a simulated human driver speed profile. The vehicle-level validation of the developed algorithm is carried out by performing closed-course track testing of the optimized speed solutions on a real CAV vehicle.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV). The objective of the optimization is to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile. The optimization reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The optimization method splits drive cycles into constant distance segments and then uses a reduced-order model to sort the segments by the best use of battery energy vs. fuel energy. The PHEV used in this investigation is the Stellantis Pacifica. Results support energy savings up to 20% which depend on the route and initial battery State of Charge (SOC). Initial optimization takes 1 second for 38 km and 3 seconds for 154 km.

Vehicle navigation in off-road environments is challenging due to terrain uncertainty. Various approaches that account for factors such as terrain trafficability, vehicle dynamics, and energy utilization have been investigated. However, these are not sufficient to ensure safe navigation of optionally manned ground vehicles that are prone to detection using thermal infrared (IR) seekers in combat missions. This work is directed towards the development of a vehicle IR signature aware navigation stack comprised of global and local planner modules to realize safe navigation for optionally manned ground vehicles. The global planner used A* search heuristics designed to find the optimal path that minimizes the vehicle thermal signature metric on the map of terrain’s apparent temperature. The local planner used a model-predictive control (MPC) algorithm to achieve integrated motion planning and control of the vehicle to follow the path waypoints provided by the global planner.

The global push towards reducing green-house gas and criteria pollutant emissions is leading to tighter emission standards for heavy-duty engines. Among the most stringent of these standards are the California Air Resource Board (CARB) 2024+ HD Omnibus regulations adopted by the agency in August 2020. The CARB 2024+ HD Omnibus regulations require up to 90% reduction in NOx emissions along with updated compliance testing methods for on-road heavy-duty engines. Subsequently, the agency announced development of new Tier 5 standards for off-road engines in November 2021. The Tier 5 standards aim to reduce NOx/PM emissions by 90%/75% respectively from Tier 4 final levels, along with introduction of greenhouse gas emission standards for CO2/CH4/N2O/NH3. Furthermore, CARB is also considering similar updates on compliance testing as those implemented in 2024+ HD Omnibus regulations including, low-load cycle, idle emissions and 3-bin moving average in-use testing.

Connected vehicles have the potential to transform the way we commute and travel in a multitude of ways. Vehicles will cooperate and coordinate with each other to solve problems appropriate for the environment in which they are operating. In this paper, we focus on the development of test equipment that includes the infrastructure and vehicles to measure and record all of the information necessary to quantify the performance of cooperative driving algorithms in realistic scenarios. The system allows tests to include real vehicles on the track and virtual vehicles in a digital twin. Real and virtual vehicles interact through the road-side units and test facility network, allowing each test vehicle to receive messages from virtual vehicles as well as the infrastructure. Messages transmitted from the test vehicles are received in the digital twin, allowing the real vehicle to interact with virtual vehicles.

Water injection has been used to reduce the charge temperature and mitigate knocking due to its higher latent heat of vaporization compared to gasoline fuel. When water is injected into the intake manifold or into the cylinder, it evaporates by absorbing heat energy from the surrounding and results in charge cooling. However, the effect of detailed evaporation process on the combustion characteristics under gasoline direct injection relevant conditions still needs to be investigated. Therefore, spray study was firstly conducted using a multi-hole injector by injecting pure water and water-methanol mixture into constant volume combustion chamber (CVCC) at naturally aspirated and boosted engine conditions. The target water-fuel ratio was fixed at 0.5. Mie-scattering and schlieren images of sprays were analyzed to study spray characteristics, and evaluate the amount of water vaporization.

Vehicle OEM’s for MHD applications are facing significant challenges in meeting the stringent 2027 low-NOx and GHG emissions regulations. To meet such challenges, advanced engine and aftertreatment technologies along with powertrain electrification are being applied to achieve robust solutions. FEV has previously conducted model-based assessments to show the potential of 48V engine and aftertreatment technologies to simultaneously meet GHG and low NOx emission standards. This study focuses on evaluating the full potential of 48V electrification technology through addition of 48V P3 hybrid system to the previously developed 48V advanced engine and aftertreatment technology package. Previously, a model-based approach was utilized for selection and sizing of a 48V system-enabled engine and aftertreatment package for class 6-7 MHD application.

The medium and heavy-duty powertrain industry trend is to reduce reliance on diesel fuel and is aligned with continued efforts of achieving ultra-low emissions and high brake efficiencies. Compression Ignition (CI) of late cycle Directly Injected (DI) Natural Gas (NG) shows the potential to match diesel performance in terms of brake efficiency and power density, with the benefit of utilizing a lower carbon content fuel. A primary challenge is to achieve stable ignition of directly injected NG over a wide engine speed and load range without the need for a separate ignition source. This project aims to demonstrate the CI of DI NG through experimental studies with a Single Cylinder Research Engine (SCRE), leading to the development of a mono-fueled NG engine with equivalent performance to that of current diesel technology, 25% lower CO2 emissions, and low engine out methane emissions.

This paper presents the energy savings of an automated driving control applied to an electric vehicle based on the on-track testing results. The control is a universal speed planner that analytically solves the eco-driving optimal control problem, within a receding horizon framework and coupled with trajectory tracking lower-level controls. The automated eco-driving control can take advantage of signal phase and timing (SPaT) provided by approaching traffic lights via vehicle-to-infrastructure (V2I) communications. At each time step, the controller calculates the accelerator and brake pedal position (APP/BPP) based on the current state of the vehicle and the current and future information about the surrounding environment (e.g., speed limits, traffic light phase).

The increase in regulatory demand to reduce CO2 emissions resulted in a focus on the development of novel combustion modes such as gasoline compression ignition (GCI). It has been shown by others that GCI can improve the overall engine efficiency while achieving soot and NOx emissions targets. In comparison with diesel fuel, gasoline has a higher volatility and has more resistance to autoignition, therefore, it has a longer ignition delay time which facilitates better mixing of the air-fuel charge before ignition. In this study, a GCI combustion system has been tested using a 2.2L compression ignition engine as part of a US Department of Energy funded project. For this purpose, a multiple injection strategy was developed to improve the pressure rise rates and soot emission levels for the same engine out NOx emissions.

The Environmental Protection Agency (EPA) and California Air Resources Board (CARB) have recently announced rulemakings focused on tighter emission limits for oxides of nitrogen (NOx) from heavy-duty trucks. As part of the new rulemaking CARB has proposed a Low Load Cycle (LLC) to specifically evaluate NOx emission performance over real-world urban and vocational operation typically characterized by low engine loads, thereby demanding the implementation of continuous active thermal management of the engine and aftertreatment system. This significant drop in NOx levels along with continued reduction in the Green House Gas (GHG) limits poses a more significant challenge for the engine developer as the conventional emission reduction approaches for one species will likely result in an undesirable increase in the other species.

This paper introduces a new systematic workflow for the rapid evaluation of energy-efficient automated driving controls in real vehicles in controlled laboratory conditions. This vehicle-in-the-loop (VIL) workflow, largely standardized and automated, is reusable and customizable, saves time and minimizes costly dynamometer time. In the first case study run with the VIL workflow, an automated car driven by an energy-efficient driving control previously developed at Argonne used up to 22 % less energy than a conventional control. In a VIL experiment, the real vehicle, positioned on a chassis dynamometer, has a digital twin that drives in a virtual world that replicates real-life situations, such as approaching a traffic signal or following other vehicles.

A diesel piloted natural gas engine's performance varies depending on operating conditions and has performed best under medium to high loads. It can often equal or better the fuel conversion efficiency of a diesel-only engine in this operating range. This paper presents a study performed on a multi-cylinder Cummins ISB 6.7L diesel engine converted to run stoichiometric natural gas/diesel micro-pilot combustion with a maximum diesel contribution of 10%. This study systematically quantifies and ranks the sensitivity of control factors on combustion and performance while operating at medium loads. The effects of combustion control parameters, including the pilot start of injection, pilot injection pressure, pilot injection quantity, exhaust gas recirculation, and global equivalence ratio, were tested using a design of experiments orthogonal matrix approach.

As the world searches for ways to reduce humanity’s impact on the environment, the automotive industry looks to extend the viable use of the gasoline engine by improving efficiency. One way to improve engine efficiency is through more effective control. Torque-based control is critical in modern cars and trucks for traction control, stability control, advanced driver assistance systems, and autonomous vehicle systems. Closed loop torque-based engine control systems require feedback signal(s); indicated mean effective pressure (IMEP) is a useful signal but is costly to measure directly with in-cylinder pressure sensors. Previous work has been done in torque and IMEP estimation using crankshaft acceleration and ion sensors, but these systems lack accuracy in some operating ranges and the ability to estimate cycle-cycle variation.

The advancement in embedded systems and positional accuracy with base station GPS modules created opportunity to develop high performance autonomous ground vehicles. However, the development of vehicle model and making accurate state estimations play vital role in reducing the cross track error. The present research focus on developing Linear Quadratic Gaussian (LQG) with Kalman estimator for autonomous ground vehicle to track various routes, that are made with the series of waypoints. The model developed in the LQG controller is a kinematic bicycle model, which mimics 1/5th scale truck. Further, the cubic spline fit has been used to connect the waypoints and generate the continuous desired/target path. The testing and implementation has been done at APS labs, MTU on the mentioned vehicle to study the performance of controller. Python has been used for simulations, controller coding and interfacing the sensors with controller.

Electrification of heavy-duty trucks has received significant attention in the past year as a result of future regulations in some states. For example, California will require a certain percentage of tractor trailers, delivery trucks and vans sold to be zero emission by 2035. However, the relatively low energy density of batteries in comparison to diesel fuel, as well as the operating profiles of heavy-duty trucks, make the application of electrified powertrain in these applications more challenging. Heavy-duty vehicles can be broadly classified into two main categories; long-haul tractors and vocational vehicles. Long-haul tractors offer limited benefit from electrification due to the majority of operation occurring at constant cruise speeds, long range requirements and the high efficiency provided by the diesel engine.

Electrification of heavy-duty trucks has received significant attention in the past year as a result of future regulations in some states. For example, California will require a certain percentage of tractor trailers, delivery trucks and vans sold to be zero emission by 2035. However, the relatively low energy density of batteries in comparison to diesel fuel, as well as the operating profiles of heavy-duty trucks, make the application of electrified powertrain in these applications more challenging. Heavy-duty vehicles can be broadly classified into two main categories; long-haul tractors and vocational vehicles. Long-haul tractors offer limited benefit from electrification due to the majority of operation occurring at constant cruise speeds, long range requirements and the high efficiency provided by the diesel engine.

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.