Shandong University

San Diego State University

Hefei Gotion High-tech Power Energy Co., Ltd.

Yunlong Shang

Chenghui Zhang

Bin Duan

Naxin Cui

Qi Zhang

Zhongkai Zhou

Ke Li

Yan Li

Dingguo Wu

Bing Han

Panpan Hu

Yuhong Fu

Xiaohong Hu

Chunting Chris Mi

Hankun Liu

The present invention provides a dual-level thermal runaway warning method of a lithium battery based on a sound signal, comprising: obtaining a battery sound signal sequence; performing outlier identification on the battery sound signal sequence, and providing a level-1 thermal runaway warning when an abnormal data point exists; extracting a time-frequency domain feature of the abnormal data point, and identifying a presence of a thermal runaway expansion sound through a sparrow search algorithm-optimized eXtreme Gradient Boosting (SSA-XGBoost) algorithm, to providing a level-2 thermal runaway warning. In the SSA-XGBoost algorithm, optimal parameter adjustment is performed on a number of iterations, a learning rate, and a decision tree depth of the XGBoost algorithm through the SSA. A dual-level thermal runaway warning strategy is adopted to perform grading identification on general anomalies or deep anomalies, thereby effectively improving identification accuracy of a weak abnormal sound signal in an early stage.

Dual-level thermal runaway warning method and system of lithium battery based on sound signal

Provided is a system and method for the distributed networked test of electric vehicles based on a cloud platform, comprising a cloud computing platform and a plurality of remote test benches, wherein the remote test benches, being provided at least two, to form a remote distributed networked structure and transmit test data to the cloud computing platform; the cloud computing platform, being in real-time bidirectional data communication with the remote test bench and configured to receive each test data in real-time, perform a data cleaning, a data classing washing, a data fusion and a data mining, extract useful data information from the test data, build corresponding a data model and a mechanism model on the cloud computing platform according to historical data and a mechanism of control object, control for different test benches, and complete a fault diagnosis and an early warning.

System and method for distributed networked test of electric vehicles, storage medium and terminal device

Embodiments of the present disclosure provide an active equalizer circuit, a battery management system, and a power supply system. The active equalizer circuit comprises a plurality of switching transistors, a driving transformer, a multi-port converter, a buck converter, and a microcontroller. Each of the switching transistors is coupled to a battery cell in the series battery pack on a one-to-one basis. The multi-port converter comprises an equalizing transformer and a bridge converter. The microcontroller is configured to output a first control signal to the buck converter, to facilitate the buck converter to transform an output voltage of the series battery pack and output the transformed output voltage to the bridge converter, and output a second control signal to the bridge converter, to control an operation state of the bridge converter.

Active equalizer circuit, battery management system, and power supply system

A multi-fault diagnosis method has the following steps: measuring cell voltages of a battery pack to be diagnosed; constructing a cell voltage sequence according to measured cell voltages of the battery pack to be diagnosed, and calculating a sample entropy value of the cell voltage sequence; setting a correction coefficient for representing voltage fluctuation information, and correcting the sample entropy value through the correction coefficient to obtain a corrected sample entropy value; and judging and outputting a fault type of the battery pack to be diagnosed according to a numerical value change of the corrected sample entropy value. Faults of cells can be accurately diagnosed without a model, sample entropy values under different faults can be distinguished by setting the correction coefficient, the intuitiveness and efficiency of fault detection are improved, and the fault type and time of the lithium-ion cells can be quickly, accurately and stably diagnosed and predicted.

Multi-fault diagnosis method and system for battery packs based on corrected sample entropy

A fractional-order KiBaM battery model considering nonlinear capacity characteristics and a parameter identification method includes a temporary capacity portion and an available capacity portion for describing nonlinear capacity characteristics of a battery, wherein the temporary capacity portion represents the power that can be directly obtained during the discharge, indicating the state of charge (SOC) of the battery; the available capacity portion represents the power that cannot be directly obtained, and such two portions are connected; when the battery is discharged, the load current i flows out from the temporary capacity portion, and a power passing rate coefficient of such capacity portions is obtained; and the nonlinear capacity effect and recovery effect of the battery are denoted by the height ratio of the temporary capacity and available capacity portions in view of the magnitude of the fractional order of battery capacity characteristics.

Fractional-order KiBaM battery model considering nonlinear capacity characteristics and parameter identification method

A new method for iteratively identifying parameters of an equivalent circuit model of battery, including the steps of: Firstly, dividing the model parameters into two parts, and the parameters in the first part are set to initial values, and the ones in the second part are identified with a least square method. Secondly, determining whether the obtained values of the second part meet the requirements of the equivalent circuit model of battery. If the requirements are not met, the parameters that do not meet the requirements in the second part are set to zeros. Then, the parameters in the first part are identified with the least square method. Otherwise, the ones in the first part are directly identified with the same method. Terminating the iteration process until all the parameters meet the requirements.

Method for iteratively identifying parameters of equivalent circuit model of battery

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Jinan, China

Yunlong Shang