Battery state of health estimation with improved generalization using parallel layer extreme learning machine

The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed with a dataset of other batteries of the same type that were aged under a constant load condition. An optimum performance with low error variance was obtained from the model result. The root mean square error (RMSE) of the validated model varies from 0.064% to 0.473%, and the mean absolute error (MAE) error from 0.034% to 0.355% for the battery sets tested. On the basis of performance, the model was compared with a deterministic extreme learning machine (ELM) and an incremental capacity analysis (ICA)-based scheme from the literature. The algorithm was tested on a Texas F28379D microcontroller unit (MCU) board with an average execution speed of 93 µs in real time, and 0.9305% CPU occupation. These results suggest that the model is suitable for online applications

State of Health Estimation of Lithium‐Ion Batteries in Electric Vehicles under Dynamic Load Conditions

Among numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estimation minimizes failure risk and improves reliability by predicting battery health conditions. The challenge of accurate estimation of SOH is based on the uncertain dynamic operating condition of the EVs and the complex nonlinear electrochemical characteristics exhibited by the lithium‐ion battery. This paper presents an artificial neural network (ANN) classifier experimentally validated for the SOH estimation of lithium‐ion batteries. The ANN‐based classifier model is trained experimentally at room temperature under dynamic variable load conditions. Based on SOH characterization, the training is done using features such as the relative values of voltage, state of charge (SOC), state of energy (SOE) across a buffer, and the instantaneous states of SOC and SOE. At implementation, due to the slow dynamics of SOH, the algorithm is triggered on a large‐scale periodicity to extract these features into buffers. The features are then applied as input to the trained model for SOH estimation. The classifier is validated experimentally under dynamic varying load, constant load, and step load conditions. The model accuracies for validation data are 96.2%, 96.6%, and 93.8% for the respective load conditions. It is further demonstrated that the model can be applied on multiple cell types of similar specifications with an accuracy of about 96.7%. The performance of the model analyzed with the confusion matrices is consistent with the requirements of the automotive industry. The classifier was tested on a Texas F28379D microcontroller unit (MCU) board. The result shows that an average real‐time execution speed of 8.34 μs is possible with a negligible memory occupation

Innovative torque-based control strategy for hydrogen internal combustion engine

Over the past years, several efforts have been made to reduce greenhouse gas emissions coming from the transport sector. Due to the highly efficient CO2-free combustion and low manufacturing costs, Hydrogen Internal Combustion engines (H2ICEs) are considered one of the most promising solutions for the future of medium and heavy duty vehicles. However, the combustion of an air-hydrogen mixture presents challenges related to the production of nitrogen oxides (NOx) and high knock tendency, mainly due to the chemical characteristics of the fuel. Although these problems can be mitigated by the use of a lean mixture, which is also useful to increase the combustion efficiency, the presence of excess air reduces exhaust temperatures and, consequently, the enthalpy content in the exhaust would be limited, leading to a reduced boosting capability. Therefore, a proper control of mixture preparation and combustion phasing is mandatory to limit NOx emissions, avoid abnormal combustions, and maximize efficiency without performance limitations. This paper focuses on the design of a dedicated control strategy for H2ICEs. Starting from a previously validated 1-D engine model operated with hydrogen, a 0-D Artificial Neural Network (ANN) - based engine model has been designed and calibrated. By using the obtained fast running ANN-based model, an innovative torquebased engine controller has been developed and both engine and controller models have been tested covering different torque profiles. The results show good accuracy within a range of +/- 5% on producing the requested torque by controlling the centre of combustion

Accelerometer-based SOC estimation methodology for combustion control applied to Gasoline Compression Ignition

The European Community's recent decision to suspend the marketing of cars with conventional fossil-fueled internal combustion engines from 2035 requires new solutions, based on carbon-neutral technologies, that ensure equivalent performances in terms of reliability, trip autonomy, refueling times and end-of-life disposal of components compared to those of current gasoline or diesel cars. The use of bio-fuels and hydrogen, which can be obtained by renewable energy sources, coupled with high-efficiency combustion methodologies might allow to reach the carbon neutrality of transports (net-zero carbon dioxide emissions) even using the well-known internal combustion engine technology. Bearing this in mind, experiments were carried out on compression ignited engines running on gasoline (GCI) with a high thermal efficiency which, in the future, could be easily adapted to run on a bio-fuel. Despite the well-reported benefits of GCI engines in terms of efficiency and pollutant emissions, combustion instability hinders the diffusion of these engines for industrial applications. A possible solution to stabilize GCI combustion is the use of multiple injections strategies, typically composed by 2 early injected fuel jests followed by the main injection. The heat released by the combustion of the earlier fuel jets allows to reduce the ignition delay of the main injection, directly affecting both delivered torque and center of combustion. As a result, to properly manage GCI engines, a stable and reliable combustion of the pre-injections is mandatory. In this paper, an estimation methodology of the start of combustion (SOC) position, based on the analysis of the signal coming from an accelerometer sensor mounted on the engine block, is presented (the optimal sensor positioning is also discussed). A strong correlation between the SOC calculated from the accelerometer and that obtained from the analysis of the rate of heat release (RoHR) was identified. As a result, the estimated SOC could be used to feedback an adaptive closed-loop combustion control algorithm, suitable to improve the stability of the whole combustion process

Electron temperature fluctuation measurements in the pedestal of improved confinement regimes at ASDEX Upgrade

US DOE (DE-SC0006419, DE-SC0014264, and DE- SC0017381)EUROfusion Consortium (No. 633053

Optical and electronic properties of silver nanoparticles embedded in cerium oxide

Wide bandgap oxides can be sensitized to visible light by coupling them with plasmonic nanoparticles (NPs). We investigate the optical and electronic properties of composite materials made of Ag NPs embedded within cerium oxide layers of different thickness. The electronic properties of the materials are investigated by X-ray and ultraviolet photoemission spectroscopy, which demonstrates the occurrence of static charge transfers between the metal and the oxide and its dependence on the NP size. Ultraviolet-visible spectrophotometry measurements show that the materials have a strong absorption in the visible range induced by the excitation of localized surface plasmon resonances. The plasmonic absorption band can be modified in shape and intensity by changing the NP aspect ratio and density and the thickness of the cerium oxide film

FRI0255 Patterns of disease activity impact on organ damage in systemic lupus erythematosus

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Fuel Economy Assessment of MPC-ACC on Powertrain Testbed

The development and testing of Advanced Driver Assistance Systems (ADAS) is one of the most active fields in the automotive industry towards Automated Driving (AD). This work presents the deployment and testing of an Adaptive Cruise Control (ACC) based on Model Predictive Control (MPC). The goal is to design and validate through the experimental campaign a computationally efficient longitudinal dynamics controller and assess its fuel economy potential. The development of the control structure as well as the definition of the testing method for energy efficiency assessment are central aspects of this work. The performance of the approach is tested on a light-duty commercial vehicle on a state-of-the-art 4-axis powertrain testbed. The findings demonstrate that the speed profile can be optimized to achieve a fuel reduction of up to while maintaining mission timing and comfort

Analysis of the Vibrational Behavior of dual-fuel RCCI combustion in a Heavy-Duty Compression Ignited Engine fueled with Diesel-NG at Low Load

In the field of internal combustion engines, the Low Temperature Combustions (LTC) appear to have the potential to reduce the formation of both soot and nitrogen oxides. One of the most promising LTC is Reactivity .Controlled Compression Ignition (RCCI) which is based on the combustion of a lean low reactivity fuel-air mixture generated in the intake manifold and autoignited by small injections of high reactivity fuel introduced at high pressure in the combustion chamber. By the combination of net-zero natural gas and biodiesel, such LTC methodology might represent a suitable solution moving toward zero-emissions in transportation sector. Despite the potential to reduce pollutant emissions, Low Temperature Combustion strategies face a challenge in controlling the angular position where the combustion takes place which can be overcome by a proper management of the high-pressure injections. One potentially interesting application is related to trucks, mainly because they have long periods of idling, since emissions can be drastically reduced by means LTC. A single cylinder research engine for heavy duty application is operated under steady state conditions at low load and speed to analyze the possibility of controlling the engine behavior in dual fuel RCCI mode. The results indicate that the combustion mode switches from the dual-stage to gaussian within a narrow angular range. A further advance of the start of injection can generate misfires and significant variations in typical combustion indexes, while a delayed start of injection can cause impulsive combustion that rises the cylinder temperature and results in high-frequency pressure oscillations inside the combustion chamber. These oscillations are related to the combustion chamber typical resonance frequency, and if relevant in amplitude and persist over a prolonged period, they might be represent a potential source of failures