Data-driven short circuit resistance estimation in battery safety issues

Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.

Estimation of Added Resistance for Large Blunt Ship in Waves

Under the background of the energy saving and emission reduction, more and more attention has been placed on investigating the energy efficiency of ships. The added resistance has been noted for being crucial in predicting the decrease of speed on a ship operating at sea. Furthermore, it is also significant to investigate the added resistance for a ship functioning in short waves of large modern ships. The researcher presents an estimation formula for the calculation of an added resistance study in short waves derived from the reflection law. An improved method has been proposed to calculate the added resistance due to ship motions, which applies the radiated energy theory along with the strip method. This procedure is based on an extended integral equation （EIE） method, which was used for solving the hydrodynamic coefficients without effects of the irregular frequency. Next, a combined method was recommended for the estimation of added resistance for a ship in the whole wave length range. The comparison data with other experiments indicate the method presented in the paper provides satisfactory results for large blunt ship.

Improving Detectability of Resistive Shorts in FPGA Interconnects

The behavior of resistive short defects in FPGA interconnects is investigated through simulation and theoretical analysis.The results show that these defects result in timing failures and even Boolean faults for small defect resistance values.The best detection situations for large resistance defect happen when the path under test makes a v-to-v′ transition and another path causing short faults remains at value v.Small defects can be detected easily through static analysis.Under the best test situations,the effects of supply voltage and temperature on test results are evaluated.The results verify that lower voltage helps to improve detectability.If short material has positive temperature coefficient,low temperature is better;otherwise,high temperature is better.