In order to investigated the influence on the liquid cooling system cooling
effect by changing the structural parameters, single Li-ion battery heat
generation model is conducted, and used in following simulation.
Subsequently, sixteen models are designed by orthogonal array, and the
results are obtained by extremum difference analysis, which can quantify the
influence degree, identify major and minor factors, and find the relatively
optimum combination. Finally, different channel entrance layout is adopted
to investigated. With a series of work, the effective of single battery heat
generation model is proved by the discharge experiment. The coolant velocity
has most evident influence on the Li-ion battery temperature rise,
rectangular channel aspect ratio is second one, and the heat conducting
plate thickness has the smallest influence. Similarly, for Li-ion battery
temperature difference, the effect of heat conducting plate thickness and
rectangular channel aspect ratio as the same, both are secondary factor, and
coolant velocity is main factor. With different channel entrance layout,
both the maximum temperatures denote a same upward trend, and better balance
temperature distribution is obtained by adopt Case C system which with
alternating arrange channel entrance layout.
Piston engines fueled by kerosene have a strong application prospect in special vehicles and small aircrafts, but the low amount of octane in kerosene fuel causes its knock combustion phenomenon to be particularly serious. A knock will deteriorate the power and economy of the engine and will damage the engine in serious cases. Therefore, knocking is the key problem with kerosene engines. We propose a knock-prediction system for kerosene engines based on in-cylinder pressure signals. Firstly, the intrinsic mode function (IMF) caused by knock resonance is extracted from the in-cylinder pressure signal via empirical mode decomposition (EMD) and a time–frequency domain analysis. A time-domain statistical analysis (TDSA) combined with a principal component analysis (PCA) is used to extract features from the IMF. Finally, the data collected from the test bench are trained by a support vector machine to obtain the knock-prediction model. Compared with other technical combinations for training, the proposed scheme achieved more accurate results in knock prediction. Considering the working characteristics of kerosene engines, a slight knock can increase the power of a kerosene engine. Therefore, some incorrectly predicted cycles (slight-knock cycles) do not affect the normal operation of the engine.
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