The goal of this research is to improve the energy productivity of heavy-duty electric vehicles through the development of a predictive cruise controller (PCC), which is optimized for various payload conditions (operation-aware). Energy productivity is defined as the amount of freight transported over a given distance per unit of average energy consumed within a specified travel time. In the trucking industry, both energy consumption and travel time are critical performance metrics. While reducing energy consumption is essential for extending driving range, it must not lead to excessive increases in travel time, which can cause delayed deliveries and higher operational costs. Accordingly, the proposed PCC is designed to be practically feasible for real-world truck operations. The predictive cruise controller is formulated as an optimal control problem (OCP) that simultaneously minimizes energy consumption and reference speed tracking error, with their relative importance governed by a penalty factor. This study demonstrates that the optimal selection of this penalty factor must depend on truck operating conditions, particularly payload variation and travel time constraints, in order to achieve improved energy productivity. The formulated OCP is shown to be convex, which makes it well suited for solution using gradient-based optimization methods. Sequential Quadratic Programming (SQP) is employed to solve the OCP in both simulation and hardware-in-the-loop environments. The results demonstrate that the proposed PCC, combined with an optimal and operation-aware selection of penalty factors, achieves an energy productivity improvement of up to 3.1% while effectively balancing energy consumption and travel time increments. This balanced performance is essential for enabling efficient, reliable, and cost-effective heavy-duty truck operations.

Heavy-duty Class 8 battery electric trucks not only offer the potential to significantly reduce greenhouse gas (GHG) emissions compared to conventional diesel trucks but can also provide significant savings in fuel costs. To further enhance energy and freight efficiency, Predictive Cruise Control (PCC) algorithms can be developed that generate optimal acceleration profiles for the vehicle by minimizing a cost function which combines both energy consumption and deviation from the desired velocity. A critical component of the cost function is the penalty factor, which governs the tradeoff between energy use and travel time, which are two conflicting objectives in freight logistics. Selecting an appropriate penalty factor is essential, as freight deliveries are time sensitive, but minimizing energy consumption remains a priority. Moreover, variations in payload significantly affect vehicle dynamics and energy usage, making it critical to adapt the penalty factor to different payload conditions and maintain consistent performance. This study presents a method for optimally selecting the penalty factor for various payload scenarios. A validated powertrain simulator which is calibrated using data from an actual electric truck, was used to conduct 100 simulations across a spectrum of payloads, from no load to fully loaded. The resulting discrete search space of energy and time was used to perform a brute-force (exhaustive) search to determine the optimal penalty factor for each scenario. The proposed algorithm incorporates adjustable weightings of the penalty factor for energy and time preferences. This allows flexibility for the driver or fleet operator to prioritize either objective. The results demonstrate that using a fixed penalty factor is suboptimal for heavy-duty electric trucks. In contrast, the optimal selection of the penalty factor significantly improves consistency across different payloads. A reduction of the variation in travel time to within approximately 4% across all loading conditions was observed. This work shows the importance of adaptive penalty tuning in PCC for real-world deployment in freight applications, ensuring both energy efficiency and timely deliveries under varying payload demands.

Heavy-duty electric trucks represent a growing innovation in the transport and logistics sector, aiming to reduce emissions and reliance on fossil fuels. A major challenge with battery electric trucks is the long recharging time which takes significantly longer than refueling conventional diesel trucks. This limitation highlights the importance of optimizing powertrain operations to reduce energy losses and maximize efficiency. One effective approach is implementing optimal speed control through a predictive cruise controller. By anticipating road conditions, traffic, and elevation changes, the predictive cruise controller can adjust the truck’s speed in real time to minimize energy consumption, enhancing the range and reducing the need for frequent charging. Many problem formulations for electric trucks focus primarily on minimizing the energy required at the wheels, often overlooking the impact of powertrain efficiencies. This simplification neglects critical factors such as the efficiency of the traction electric machine (EM), gear losses, and battery dynamics, which are essential for optimizing overall energy consumption and improving vehicle performance. This research paper shows the impact of powertrain efficiencies on the optimal speed profile generation with a predictive cruise controller (PCC). The PCC optimizes electric truck operation by focusing on three primary factors in its cost function: 1) battery energy consumption, 2) total trip time, and 3) battery state of charge (SOC). To achieve the optimal speed profile, Sequential Quadratic Programming (SQP) is used. A comparison was made with a conventional cruise controller, which simplifies the vehicle model by minimizing the required energy at wheels and ignores powertrain losses in its energy calculations. The results show that the proposed PCC offers a 12.85% improvement in battery SOC and 10.83 % improvement in energy consumption as compared to baseline.

In recent years, the Engine Combustion Network (ECN) has developed as a worldwide reference for understanding and describing engine combustion processes, successfully bringing together experimental and numerical efforts. Since experiments and numerical simulations both target the same boundary conditions, an accurate characterization of the stratified environment that is inevitably present in experimental facilities is required. The difference between the core-, and pressure-derived bulk-temperature of pre-burn combustion vessels has been addressed in various previous publications. Additionally, thermocouple measurements have provided initial data on the boundary layer close to the injector nozzle, showing a transition to reduced ambient temperatures. The conditions at the start of fuel injection influence physicochemical properties of a fuel spray, including near nozzle mixing, heat release computations, and combustion parameters. To address the temperature stratification in more detail, thermocouple measurements at larger distances from the spray axis have been conducted. Both the temperature field prior to the pre-combustion event that preconditions the high-temperature, high-pressure ambient, as well as the stratification at the moment of fuel injection were studied. To reveal the cold boundary layer near the injector with a better spatial resolution, Rayleigh scattering experiments and thermocouple measurements at various distances close to the nozzle have been carried out. The impact of the boundary layers and temperature stratification are illustrated and quantified using numerical simulations at Spray A conditions. Next to a reference simulation with a uniform temperature field, six different stratified temperature distributions have been generated. These distributions were based on the mean experimental temperature superimposed by a randomized variance, again derived from the experiments. The results showed that an asymmetric flame structure arises in the computed results when the temperature stratification input is used. In these predictions, first-stage ignition is advanced by 24 µs, while second-stage ignition is delayed by 11 µs. At the same time, a lift-off length difference between the top and the bottom of up to 1.1 mm is observed. Furthermore, the lift-off length is less stable over time. Given the shown dependency, the temperature data is made available along with the vessel geometry data as a recommended basis for future numerical simulations.

Predicting the electrical loads experienced by a battery pack during the 10 hour hotel period of a long haul class 8 mild hybrid truck with a sleeper cab, and using the information to achieve an optimal energy management strategy and controlling the State of Charge (SOC) of battery pack can help in improving it's freight efficiency. In this work, Machine Learning (ML) based algorithm has been proposed to predict the driver activity during the hotel period. Hence, the power load demanded from the auxiliaries can be predicted. A special kind of Recurrent Neural Network (RNN) called Long and Short Term Memory (LSTM) is used for the prediction task because of its ability to store recurrent information of a small and a large time horizon. To train the LSTM algorithm, the synthetic load profles are synthesized using rules and observations derived from the existing baseline electrical power load profile of the hotel period. This paper entails the whole process of data synthesis to training the neural network on the synthesized data and the prediction and validation of the power load. The input to the network is a time series of 600 time steps. Dynamic Time Warping (DTW) is used to manipulate the time axis and point wise euclidean distance between the forecast and the test data is used to quantify the accuracy of the model. Then by performing hyper-parameter optimization we find the best combination for number of hidden units and the number of training days for the algorithm.

The problem of engine idling for heavy-duty trucks has been under study for decades with Auxiliary Power Units (APUs) and Truck Stop Electrification (TSE) as the most compelling solutions. With the electrification of trucks approaching feasibility in terms of cost-effective technology, hybridization offers another “degree of freedom” to tackle the problem. This work aims at exploiting a battery pack of a 48 V mild-hybrid heavy-duty truck to store sufficient onboard energy for powering the auxiliary loads during the hoteling. This problem is not trivial, as the battery packs typically cannot recover the entire energy required through regeneration alone; hence an optimal energy management strategy needs to be employed to charge the battery through the engine during drive operation. This strategy optimizes powertrain performances among the four modes: (i) Engine of Coasting (EOC), (ii) Regeneration by braking, (iii) Regeneration by engine, and (iv) engine idling. This paper presents the development of a Dynamic Programming (DP) framework that employs a multi-objective cost function to minimize the fuel consumption and maximize the regeneration using the above-mentioned four modes. A typical heavy-duty truck drive cycle is used to represent the drive phase, with mandatory hoteling stops as per regulations. A comprehensive powertrain model is developed using validated components’ model. The DP employs two state variables: battery State-of-Charge (SOC) and engine mode, and three control inputs: (i) the engine ON–OFF state, (ii) clutch engagement state, and (iii) power request at the Electric Machine (EM) for calculating optimal SOC trajectory. The framework also tackles rapid engine ON–OFF scenarios to avoid the challenges associated with DP and the compromises in fuel cost with those approaches. Finally, the effectiveness of the proposed framework is tested for potential fuel savings on two different battery packs by performing the full cycle simulations. The results show 6.47% of fuel consumption reduction as compared to traditional APU-based heavy-duty truck.

The transient and quasi-steady flame structure of reacting fuel sprays produced by single-hole injectors has been studied using chemiluminescence imaging and Planar Laser-Induced Fluorescence (PLIF) in various constant-volume facilities at different research institutes participating in the Engine Combustion Network (ECN). The evolution of the high-temperature flame has been followed based on chemiluminescence imaging of the excited-state hydroxyl radical (OH*), and PLIF of ground-state OH. Regions associated with low-temperature chemical reactions are visualized using formaldehyde (CH2O) PLIF with 355-nm excitation. We compare the results obtained by different research institutes under nominally identical experimental conditions and fuel injectors. In spite of design differences among the various experimental facilities, the results are consistent. This lends confidence to studies of transient behavior and parameter variations performed by individual research groups. We present results of the transient flame structures at Spray A reference conditions, and include parametric variations around this baseline, involving ambient temperature, oxygen concentration and injection pressure. Key results are the observed influence of an entrainment wave on the transient flame behavior, model-substantiated explanations for the high-intensity OH* lobes at the lift-off length and differences with OH PLIF, and a general analogy of the flame structures with a spray cone along which the flame tends to locate for the applied parametric variations.

Spark-ignition direct-injection engines operating in a stratified, lean-burn regime offer improved engine efficiency; however, seemingly random fluctuations in stratified combustion that result in partial-burn or misfire prevent widespread implementation. Eliminating these poor combustion events requires detailed understanding of engine flow, fuel delivery, and ignition, but knowing the dominant cause is difficult because they occur simultaneously in an engine. This study investigated the variability in fuel–air mixture linked to fuel injection hardware in a near-quiescent pressure vessel at high-temperature conditions representative of late, stratified-charge injection. An eight-hole spark-ignition direct-injection spray was interrogated using high-speed schlieren and Mie-scatter imaging from multiple, simultaneous views to acquire the vapor and liquid envelopes of the spray. The mixture fraction of vaporized sections of the spray was then quantified at a plane between plumes using Rayleigh scattering. P...

The Engine Combustion Network (ECN) is becoming a leading group concerning the experimental and computational analysis of Engine combustion. In order to establish a coherent database for model validation, all the institutions participating to the experimental effort carry out experiments at well-defined standard conditions (in particular at Spray A conditions: 22.8kg/m3, 900K, 0% and 15% O2) and with Diesel injectors having the same specifications. Due to the rising number of ECN participants and also to unavoidable damages, additional injectors are required. This raises the question of injector's characteristics reproducibility and of the appropriate method to introduce such new injectors in the ECN network. In order to investigate this issue, a set of 8 new injectors with identical nominal Spray A specification were purchased and 4 of them were characterized using ECN standard diagnostics. In particular, the measurements include the nozzle hole diameter, the rate of injection, the liquid and vapor penetrations, the auto-ignition delay and the lift-off length. Variations of ambient temperature, oxygen concentration and density have also been performed. In general the results show similar behavior to ECN standard injectors, confirming that this set of new injectors can be integrated into the pool of ECN injectors. However, discrepancies between spray characteristics were observed, although the injector specifications and the boundary conditions were sensibly the same. The sources of variations from injector to injector are analyzed in order to provide new information on the reproducibility of injectors characteristics, and improve the comparison methodology between experimental data and simulation.

Diesel spray experimentation at controlled high-temperature and high-pressure conditions is intended to provide a more fundamental understanding of diesel combustion than can be achieved in engine experiments.This level of understanding is needed to develop the high-fidelity multi-scale CFD models that will be used to optimize future engine designs.Several spray chamber facilities capable of high-temperature, high-pressure conditions typical of engine combustion have been developed, but because of the uniqueness of each facility, there are uncertainties about their operation.The Engine Combustion Network (ECN) is a worldwide group of institutions using combustion vessels, whose aim is to advance the state of spray and combustion knowledge at engine-relevant conditions.A key activity is the use of spray chamber facilities operated at specific target conditions in order to leverage research capabilities and advanced diagnostics of all ECN participants.The first target condition, called "Spray A", has been defined with detailed ambient and injector conditions.For this paper, we describe results from the constant-volume pre-burn vessel at Eindhoven University of Technology.The executed measurements include a wide range of diagnostics to characterize "spray A" in reacting and non-reacting conditions in great detail.Observations of spray penetration, ignition, liquid length and flame lift-off location by using several high-speed imaging diagnostics are discussed and compared with other ECN participating institutes.Comparison Spray A data from the other participating institutes, as it was presented during the 2 nd ECN workshop is gathered from the ECN website database [1].It can be concluded that the obtained results from the standardized ECN spray diagnostics, show satisfactory similarity, despite of the challenge to reach similar boundary conditions (ambient and injector) in each of the unique facilities.The differences in results are within the measurement deviation and uncertainty or can be explained by the usage of (slightly) different injectors.Combining the results of the different measurement techniques provides an overall (time resolved) overview where the different phases of fuel injection are directly linked and summarized.The presented overview provides a direct input for (CFD) modeling validation.

The Engine Combustion Network (ECN) is a worldwide group of institutions using combustion vessels and/or performing computational fluid dynamics (CFD) simulation, whose aim is to advance the state of spray and combustion knowledge at engine-relevant conditions. A key activity is the use of spray chamber facilities that operate at high-temperature, high-pressure conditions typical of diesel combustion, which are operated at specific target conditions in order to leverage research capabilities and advanced diagnostics of all ECN participants. The first target condition, called "Spray A," has been defined with detailed ambient (900 K, 60 bar, 22.8 kg/m(3), 15% oxygen) and injector (common rail, 1500 bar, KS1.5/86 nozzle, 0.090-mm orifice diameter, n-dodecane, 363 K) conditions. Establishing and improving these experimental boundary conditions in unique facilities throughout the world represents a major step forward in the establishment of high-quality, quantitative data sets for engine spray combustion. This paper is a review of the methodology to characterize and control the ambient and fuel-injector boundary conditions (e.g., temperature, pressure, composition) as offered by six different participating institutions of the ECN, each targeting the Spray A conditions and quantifying experimental uncertainty. Constant-pressure flow (CPF) and constant-volume preburn (CVP) chambers with various ambient gas composition are compared for the first time. Experimental diagnostics include the use of fast-response, radiation-corrected thermocouples for spatially resolved gas and fuel-injector temperature, laser-induced phosphorescence for surface temperature, and high-speed transducers for pressure. With guidance about the uncertainty and variation that exists between facilities, simplified models are then employed to understand how these boundary condition variations may affect aspects of spray combustion. Ambient gas and fuel temperature effects on liquid- and vapor-phase penetration are examined with established one-dimensional models. Chemical kinetics modeling in single- or multi-zone reactors is used to predict the influence of different preburn environments on the major and minor species present in the ambient gas at the start of injection, and their subsequent effect on spray ignition. This review article provides recognition of the challenge in creating well-controlled high-temperature, high-pressure environments, and identifies which boundary condition variations are expected to have the highest impact on spray combustion.