Robust Modeling for Optimal Control of Parallel Hybrids With Dynamic Programming

The aim of this work is to provide insight and guidelines for engineers and researchers when developing hybrid powertrain models to be employed in a dynamic programming optimal control algorithm. In particular, we focus on the advantages and disadvantages of the various control sets that can be used to characterize the power flow (e.g. the engine torque or a torque-split coefficient). Dynamic programming is the reference optimal control technique for hybrid electric vehicles. However, its practical implementation is not exempt from numerical issues which may hamper its accuracy. Amongst these, some are directly related to the different modeling choices that can be made when defining the system dynamics of the powertrain. To treat these issues, we first define four relevant evaluation criteria: control bounds definition, numerical efficiency, model complexity and interpretability. Then, we introduce eight different control sets and we discuss and compare them in light of these criteria. This discussion is supported by an extensive set of numerical experiments on a p2 parallel hybrid. Finally, we revisit our analysis and simulation results to draw modeling recommendations

Data-Driven Model for Real-Time Estimation of NOx in a Heavy-Duty Diesel Engine

The automotive sector is greatly contributing to pollutant emissions and recent regulations introduced the need for a major control of, and reduction of, internal combustion engine emissions. Artificial intelligence (AI) algorithms have proven to hold the potential to be the thrust in the state-of-the-art for engine-out emission prediction, thus enabling tailored calibration modes and control solutions. More specifically, the scientific literature has recently witnessed strong efforts in AI applications for the development of nitrogen oxides (NOx) virtual sensors. These latter replace physical sensors and exploit AI algorithms to estimate NOx concentrations in real-time. Still, the calibration of the algorithms, together with the appropriate choice of the specific metric, strongly affects the prediction capability. In the present paper, a machine learning-based virtual sensor for NOx monitoring in diesel engines was developed, based on the Extreme Gradient Boosting (XGBoost) machine learning algorithm. The latter is commonly used in the literature to deploy virtual sensors due to its high performance, flexibility and robustness. An experimental campaign was carried out to collect data from the engine test bench, as well as from the engine electronic control unit (ECU), for the development and calibration of the virtual sensor at steady-state conditions. The virtual sensor has, since then, been tested throughout on an on-road driving mission to assess its prediction performance in dynamic conditions. In stationary conditions, its prediction accuracy was around 98%, whereas it was 85% in transient conditions. The present study shows that AI-based virtual sensors have the potential to significantly improve the accuracy and reliability of NOx monitoring in diesel engines, and can, therefore, play a key role in reducing NOx emissions and improving air quality

DynaProg: Deterministic Dynamic Programming solver for finite horizon multi-stage decision problems

DynaProg is an open-source MATLAB toolbox for solving multi-stage deterministic optimal decision problems using Dynamic Programming. This class of optimal control problems can be solved with Dynamic Programming (DP), which is a well-established optimal control technique suited for highly non-linear dynamic systems. Unfortunately, the numerical implementation of Dynamic Programming can be challenging and time consuming, which may discourage researchers from adopting it. The toolbox addresses these issues by providing a numerically fast DP optimization engine wrapped in a simple interface that allows the user to set up an optimal control problem in a straightforward yet flexible environment, with no restrictions on the controlled system’s simulation model. Therefore, it enables researchers to easily explore the usage of Dynamic Programming in their fields of expertise. Thorough documentation and a set of step-by-step examples complete the toolbox, thus allowing for easy deployment and providing insight of the optimization engine. Finally, the source code’s classoriented design allows researchers experienced in Dynamic Programming to extend the toolbox if needed

Cooperative Adaptive Cruise Control Based on Reinforcement Learning for Heavy-Duty BEVs

Advanced driver assistance systems (ADAS) are playing an increasingly important role in supporting the driver to create safer and more efficient driving conditions. Among all ADAS, adaptive cruise control (ACC) is a system that provides consistent aid, especially in highway mobility, guaranteeing safety by minimizing the possible risk of collision due to variations in the speed of the vehicle in front, automatically adjusting the vehicle velocity and maintaining the correct spacing. Theoretically, this type of system also makes it possible to optimize road throughput, increasing its capacity and reducing traffic congestion. However, it was found in practice that the current generation of ACC systems does not guarantee the so-called string stability of a vehicle platoon and can therefore lead to an actual decrease in traffic capacity. To overcome these issues, new cooperative adaptive cruise control (CACC) systems are being proposed that exploit vehicle-to-vehicle (V2V) connectivity, which can provide additional safety and robustness guarantees and introduce the possibility of concretely improving traffic flow stability

Battery temperature aware equivalent consumption minimization strategy for mild hybrid electric vehicle powertrains

An energy management strategy for mild hybrids that prevents battery overheating is introduced in this digest. Energy management strategy design for mild hybrids requires particular care to prevent overheating of the battery pack as they typically do not have an active cooling system. To tackle this issue, we extend the well-known equivalent consumption minimization strategy approach to develop a real-time capable fuel-optimal controller that is sensitive to the battery’s thermal dynamics and that can enforce constraints on its temperature. The rationale for our formulation is developed using Pontryagin’s minimum principle from optimal control theory. The same principle is also used to design an off-line numerical procedure for the energy management strategy’s calibration. The effectiveness of the procedure is corroborated by numerical experiments on two different drive cycles, whose results are also compared with the solution obtained with a dynamic programming algorithm. Several peculiar aspects of our solution procedure, such as the method used to incorporate state constraints and the approximate boundary value problem solution method using a particle swarm optimization algorithm, are also detailed and discussed. The proposed controller is computationally light-weight and can be readily extended to on-line control provided that a suitable co-state selection procedure is employed, based on the data collected by using our calibration method on a large number of driving missions

CFD modelling of natural gas combustion in IC engines under different EGR dilution and H2-doping conditions

The present paper provides a contribution to the CFD modelling of reacting flows in IC engines fueled with natural gas. Despite the fact that natural gas has been widely investigated into in the last decades, the literature still lacks reliable models and correlations to be exploited so as to efficiently support the design of internal combustion engines. The paper deals with the development of an accurate CFD model, capable of capturing the effects of the engine working conditions and mixture compositions on the combustion process. The CFD model is based on the Extended Coherent Flame Model combustion model coupled to a laminar flame speed one through a user subroutine, which replaces the commonly adopted empirical correlations. The flame speed values have been derived from the application of a reaction mechanism for natural gas-air-residual gases mixtures. In the second part of the paper, the model is validated and applied to the investigation of the dependence of the combustion quality on the fuel doping with hydrogen as well as on the mixture dilution with EGR. As a matter of fact, the attractiveness of the mixture dilution with EGR relies on the potential in containing engine-out NOx emissions as well as in reducing the pumping losses, thus further abating fuel consumption at part loads. Finally, the effects of fuel blending with H2 on the EGR tolerance is discussed in the paper

Acceleration control strategy for Battery Electric Vehicle based on Deep Reinforcement Learning in V2V driving

The transportation sector is seeing the flourishing of one of the most interesting technologies, autonomous driving (AD). In particular, Cooperative Adaptive Cruise Control (CACC) systems ensure higher levels both of safety and comfort, enhancing at the same time the reduction of energy consumption. In this framework a real-time velocity planner for a Battery Electric Vehicle, based on a Deep Reinforcement Learning algorithm called Deep Deterministic Policy Gradient (DDPG), has been developed, aiming at maximizing energy savings, and improving comfort, thanks to the exchange of information on distance, speed and acceleration through the exploitation of vehicle-to-vehicle technology (V2V). The aforementioned DDPG algorithm relies on a multi-objective reward function that is adaptive to different driving cycles. The simulation results show how the agent can obtain good results on standard cycles, such as WLTP, UDDS and AUDC, and on real-world driving cycles. Moreover, it displays great adaptability to driving cycles different from the training one

Effects of Inflow Condition on RANS and LES Predictions of the Flow around a High-Rise Building

An increasing number of engineering applications require accurate predictions of the flow around buildings to guarantee performance and safety. This paper investigates the effects of variations in the turbulent inflow, as predicted in different numerical simulations, on the flow pattern prediction around buildings, compared to wind tunnel tests. Turbulence characteristics were assessed at several locations around a model square high-rise building, namely, above the roof region, at the pedestrian level, and in the wake. Both Reynolds-averaged Navier–Stokes (RANS, where turbulence is fully modelled) equations and large-eddy simulation (LES, where turbulence is partially resolved) were used to model an experimental setup providing validation for the roof region. The performances of both techniques were compared in ability to predict the flow features. It was found that RANS provides reliable results in regions of the flow heavily influenced by the building model, and it is unreliable where the flow is influenced by ambient conditions. In contrast, LES is generally reliable, provided that a suitable turbulent inflow is included in the simulation. RANS also benefits when a turbulent inflow is provided in simulations. In general, LES should be the methodology of choice if engineering applications are involved with the highly separated and turbulent flow features around the building, and RANS provides reliable information when regions of high wind speed and low turbulence are investigated

Development of an Adaptive Model Predictive Control for Platooning Safety in Battery Electric Vehicles

The recent and continuous improvement in the transportation field provides several different opportunities for enhancing safety and comfort in passenger vehicles. In this context, Adaptive Cruise Control (ACC) might provide additional benefits, including smoothness of the traffic flow and collision avoidance. In addition, Vehicle-to-Vehicle (V2V) communication may be exploited in the car-following model to obtain further improvements in safety and comfort by guaranteeing fast response to critical events. In this paper, firstly an Adaptive Model Predictive Control was developed for managing the Cooperative ACC scenario of two vehicles; as a second step, the safety analysis during a cut-in maneuver was performed, extending the platooning vehicles’ number to four. The effectiveness of the proposed methodology was assessed for in different driving scenarios such as diverse cruising speeds, steep accelerations, and aggressive decelerations. Moreover, the controller was validated by considering various speed profiles of the leader vehicle, including a real drive cycle obtained using a random drive cycle generator software. Results demonstrated that the proposed control strategy was capable of ensuring safety in virtually all test cases and quickly responding to unexpected cut-in maneuvers. Indeed, different scenarios have been tested, including acceleration and deceleration phases at high speeds where the control strategy successfully avoided any collision and stabilized the vehicle platoon approximately 20–30 s after the sudden cut-in. Concerning the comfort, it was demonstrated that improvements were possible in the aggressive drive cycle whereas different scenarios were found in the random cycle, depending on where the cut-in maneuver occurred

Exploitation of a Particle Swarm Optimization Algorithm for Designing a Lightweight Parallel Hybrid Electric Vehicle

The dramatic global climate change has driven governments to drastically tackle pollutant emissions. In the transportation field, one of the technological responses has been powertrain electrification for passengers’ cars. Nevertheless, the large amount of possible powertrain designs does not help the development of an exhaustive sizing process. In this research, a multi-objective particle swarm optimization algorithm is proposed to find the optimal layout of a parallel P2 hybrid electric vehicle powertrain with the aim of maximizing fuel economy capability and minimizing production cost. A dynamic programming-based algorithm is used to ensure the optimal vehiclelevel energy management. The results show that diverse powertrain layouts may be suggested when different weights are assigned to the sizing targets related to fuel economy and production cost, respectively. Particularly, upsizing the power sources and increasing the number of gears might be advised to enhance HEV fuel economy capability through the efficient exploitation of the internal combustion engine (ICE) operation. On the other hand, reduction of the HEV production cost could be achieved by downsizing the power sources and limiting the number of gears with respect to conventional ICE-powered vehicles thanks to the interaction between ICE and electric motor