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

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

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

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions

Reducing the computational cost for artificial intelligence-based battery state-of-health estimation in charging events

Powertrain electrification is bound to pave the way for the decarbonization process and pollutant emission reduction of the automotive sector, and strong attention should hence be devoted to the electrical energy storage system. Within such a framework, the lithium-ion battery plays a key role in the energy scenario, and the reduction of lifetime due to the cell degradation during its usage is bound to be a topical challenge. The aim of this work is to estimate the state of health (SOH) of lithium-ion battery cells with satisfactory accuracy and low computational cost. This would allow the battery management system (BMS) to guarantee optimal operation and extended cell lifetime. Artificial intelligence (AI) algorithms proved to be a promising data-driven modelling technique for the cell SOH prediction due to their great suitability and low computational demand. An accurate on-board SOH estimation is achieved through the identification of an optimal SOC window within the cell charging process. Several Bi-LSTM networks have been trained through a random-search algorithm exploiting constant current constant voltage (CCCV) test protocol data. Different analyses have been performed and evaluated as a trade-off between prediction performance (in terms of RMSE and customized accuracy) and computational burden (in terms of memory usage and elapsing time). Results reveal that the battery state of health can be predicted by a single-layer Bi-LSTM network with an error of 0.4% while just monitoring 40% of the entire charging process related to 60–100% SOC window, corresponding to the constant-voltage (CV) phase. Finally, results show that the amount of memory used for data logging and processing time has been cut by a factor of approximately 2.3

A Multiphysics Co-Simulation Framework of a Gas Engine and Three-Way Catalyst toward a Complete Vehicle Design Model

In view of the increasingly stringent emission regulations, the automotive sector needs considerable support from the development of robust and reliable engine and aftertreatment models. Accurate reproduction of engine-out and tailpipe pollutants plays a crucial role in complying with these legislations. Given the difficulty in characterizing some critical phenomena, frequently caused by strong dynamics and related to experimental uncertainties, communication between several calibrated and reliable models is mandatory. This is certainly valid for powertrains that will be powered with alternative gas fuels such as natural gas, bio-methane and hydrogen in the future. This paper describes a methodology to co-simulate a 1D CNG HD 6-cyl engine model and a 1D quasi-steady three-way catalyst model in a global framework for high-fidelity virtual prototyping of the vehicle system. Through the implementation of a dedicated control logic in MATLAB/Simulink, the modeling architecture allows for the reproduction of the engine performance parameters together with the evaluation of the TWC pollutants’ conversion efficiency. An extensive database of experimental tests was used to assess the model response. The latter was validated in multiple steady-state operating conditions of the engine workplan. Using a semi-predictive combustion model, the validation was carried out over a wide range of different air-to-fuel ratios and during fast rich/lean transitions to evaluate the formation and conversion phenomena of the main chemical species, both engine-out and tailpipe. Subsequently, the complete model was validated in dynamic conditions throughout a WHTC, accurately reproducing the cut-off phases and their sudden accelerations. The numerical–experimental agreement on pollutant reproduction is generally good and globally below 3%. Larger deviations occur in extremely rich conditions and in CH4 emission evaluation due to the lack of information related to the combustion process and chemical mechanisms involving the Pd surface

Optimal Real-Time Velocity Planner of a Battery Electric Vehicle in V2V Driving

Autonomous driving systems are among the most interesting technologies in the transportation field nowadays, ensuring a high level of safety and comfort while also enhancing energy saving. For the following case study, a Battery Electric Vehicle (BEV) is considered able to communicate with other vehicles through vehicle-to-vehicle (V2V) technology by exchanging information on position and velocity. In this framework, an innovative real-time velocity planner has been developed aiming at maximizing the battery energy savings while improving the passenger comfort as well. This controller uses the principles of the equivalent consumption minimization strategy (ECMS) when the preceding vehicle is accelerating. Simulation results demonstrate improvements in comfort ranging from 26% to 42% ca. and in energy consumption (from 0.4% to 1.3%) when performing different drive cycles in V2V automated driving mode thanks to the proposed controller

Battery Electric Vehicles Platooning: Assessing Capability of Energy Saving and Passenger Comfort Improvement

Techniques exploiting the communication between vehicles, infrastructure or anything capable of, are being developed in the recent years due to their effectiveness in improving energy efficiency, comfort and safety. The scenario analyzed in this paper is of four vehicles platooning, in which the leader (i.e. the first in the platoon) is set to travel through different drive cycles and is followed by three other vehicles. An optimization-based algorithm based on Dynamic Programming (DP) is implemented to find the benchmark optimal control solution for the speed trajectory of the three following Battery Electric Vehicles (BEVs). Optimal control targets for planning the three automated vehicle velocity profiles involve both reducing aggressive changes in velocity, thus enhancing passenger comfort, and decreasing energy consumption. Results show a potential range of 1.8 – 7.6 % energy reduction when comparing the energy consumptions of the lead and first follower vehicle, whereas the implemented optimization-based velocity planner predicts enhanced energy economy for the second and third follower BEVs. In general, the highest advantages both in energy consumption and comfort are predicted in the urban scenarios due to the high number of acceleration/deceleration phases

Assessing lightweight layouts for a parallel Hybrid Electric Vehicle driveline

The presence of multiple power sources and the several possible architectures that can be designed when referring to hybrid electric vehicle (HEV) powertrains complicate the identification of an optimal HEV configuration. Among the diverse parameters that can be chosen in design and sizing processes of a parallel full HEV, the number of gears and the gear ratios in the transmission are considered as fulcra of this case study. For this scope, five different transmissions have been sized while assessing drivability and acceleration performance along with the fuel economy capability. A dynamic programming-based approach algorithm has been utilized for controlling the HEV, thus providing reliable outcomes and enhancing the consistency of the study. The results obtained in the sizing process suggest that the presence of an electric machine may mitigate the effect of the lower number of gears and enhance the fuel consumption efficiency even when reducing the number of gears in the transmission to 2 or 3. More precisely, even though they might be associated to slightly higher fuel consumption and, in turn, operative costs compared with the other considered configurations, these drawbacks can be overcome by the higher savings in production costs, thus suggesting parallel full HEVs with a reduced number of gears as an appealing design option