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Accurate estimation of battery state of health (SOH) has become indispensable in ensuring the predictive maintenance and safety of electric vehicles (EVs). While supervised machine learning excels in laboratory settings with adequate SOH labels, field-based SOH data collection for supervised learning is hindered by EVs' complex conditions and prohibitive data collection costs. To overcome this challenge, a battery SOH estimation method based on semi-supervised regression is proposed and validated using field data in this paper. Initially, the Ampere integral formula is employed to calculate SOH labels from charging data, and the error of labeled SOH is reduced by the open-circuit voltage correction strategy. The calculation error of the SOH label is confirmed to be less than 1.2%, as validated by the full-charge test of the battery packs.

Power supply systems play a very important role in everyday life applications. There are mainly two ways of producing energy for low power generation: electrochemical batteries and small engines. In the last few years, many improvements have been carried out in order to obtain lighter batteries with longer durations but unfortunately the energy density of 1 MJ/kg seems to be an asymptotic value. An energy source constituted of an organic fuel with an energy density around 29 MJ/kg and a minimum overall efficiency of only 3.5% could surpass batteries. Nowadays, the most efficient combustion process is HCCI combustion which has the ability to combine a high energy conversion efficiency with low emission levels and a very low fuel consumption. The present paper describes an investigation carried out on a modified model airplane engine, on how a pure HCCI combustion behaves in a small volume, Vd = 4.11 cm3, at very high engine speeds (up to 17,500 [rpm]).

Nowadays for small-scale power generation there are electrochemical batteries and mini engines. Many efforts have been done for improving the power density of the batteries but unfortunately the value of 1 MJ/kg seems to be asymptotic. If the energy source is an organic fuel which has an energy density of around 29 MJ/kg with a minimum overall efficiency of only 3.5%, this device would surpass the batteries. This paper is the fifth of a series of publications aimed to study the HCCI combustion process in the milli domain at high engine speed in order to design and develop VIMPA, Vibrating Microengine for Low Power Generation and Microsystems Actuation. Previous studies ranged from general characterization of the HCCI combustion process by using metal and optical engines, to more specific topics for instance the influence of the boundary layer and quenching distance on the quality of the combustion.

Power supply systems play a very important role in applications of everyday life. Mainly, for low power generation, there are two ways of producing energy: electrochemical batteries and small engines. In the last few years many improvements have been carried out in order to obtain lighter batteries with longer duration but unfortunately the energy density of 1 MJ/kg seems to be an asymptotic value. If the energy source is an organic fuel with an energy density of around 29 MJ/kg and a minimum overall efficiency of only 3.5%, this device can surpass the batteries. Nowadays the most efficient combustion process is HCCI combustion which is able to combine high energy conversion efficiency and low emission levels with a very low fuel consumption. In this paper, an investigation has been carried out concerning the effects of the compression ratio on the performance and emissions of a mini, Vd = 4.11 [cm3], HCCI engine fueled with diethyl ether.

Nowadays the power supplying systems have a fundamental importance for all small and portable devices. For low power applications, there are two main ways for producing power: electrochemical batteries and mini engines. Even though in recent years many developments have been carried out in improving the design of batteries, the energy density of 1MJ/kg seems to be an asymptotic value. If the energy source is a hydrocarbon fuel, whose energy density is 46 MJ/kg, with an overall efficiency of only 2.5 % it is possible to surpass the electrochemical batteries. On the other hand, having a mini engine, as energy source, implies three main problems: vibrations, noise and emissions. A light (230 g) model airplane engine with a displacement volume of 4.11 cm3 and a geometrical compression ratio of 13.91 has been studied. The work carried out in this paper can be divided basically in three parts.

Accurate and robust knowledge of battery internal states and parameters is a prerequisite for the safe, efficient, and reliable operation of electric flying cars. Battery states such as state of charge (SOC), state of temperature (SOT), and state of power (SOP) are of particular interest for urban air mobility (UAM) applications. This article proposes a new safety-oriented multi-state estimation framework for collaboratively updating the SOC, SOT, and SOP of lithium-ion batteries under typical UAM mission profiles that explicitly incorporates the underlying interplay among these three states. Specifically, the SOC estimation is performed by combining an adaptive extended Kalman filter with a timely calibrated battery electrical model, and the key temperature information, including the volume-averaged temperature, highest temperature, and maximum temperature difference, is estimated using an adaptive Kalman filter based on a simplified 2-D spatially-resolved thermal model.

Today's engine and combustion process development is closely related to the intake port layout. Combustion, performance and emissions are coupled to the intensity of turbulence, the quality of mixture formation and the distribution of residual gas, all of which depend on the in-cylinder charge motion, which is mainly determined by the intake port and cylinder head design. Additionally, an increasing level of volumetric efficiency is demanded for a high power output. Most optimization efforts on typical homogeneous charge spark ignition (HCSI) engines have been at low loads because that is all that is required for a vehicle to make it through the FTP cycle. However, due to pumping losses, this is where such engines are least efficient, so it would be good to find strategies to allow the engine to operate at higher loads.