公开(公告)号：US20210364574A1

公开(公告)日：2021-11-25

申请号：US17398004

申请日：2021-08-10

Applicant: Beijing Institute of Technology

Inventor： Zhongbao Wei ,  Hongwen He ,  Jian Hu

IPC: G01R31/367 ,  G01R31/3842 ,  G01R31/374 ,  H01M10/48 ,  H01M10/42

Abstract: A state-of-charge (SOC) online estimation method of an intelligent battery includes steps of performing online estimation on the SOC based on a real-time estimation model, wherein the real-time estimation model is based on an equivalent circuit model, and then obtaining online estimates of the SOC through the SOC-OCV (open circuit voltage) relationship, the current-voltage relationship under different working conditions, and the current terminal voltage and temperature of the intelligent battery. The intelligent battery that is able to apply the SOC online estimation method includes a battery cell and a cell management unit, wherein the cell management unit includes a sensor module, a switching device, a controller module, a communication module and a printed circuit board (PCB), the sensor module includes a voltage sensor and a temperature sensor; the voltage sensor, the temperature sensor, the switching device, the controller module and the communication module are integrated on the PCB.

公开(公告)号：US11897344B2

公开(公告)日：2024-02-13

申请号：US18212143

申请日：2023-06-20

Applicant: BEIJING INSTITUTE OF TECHNOLOGY

Inventor： Jianwei Li ,  Zhonghao Tian ,  Xinming Wan ,  Hongwen He ,  Fengchun Sun ,  Wenjun Guo ,  Zhanxin Mao

IPC: F02C9/46 ,  B60L3/04

CPC classification number: B60L3/04

Abstract: The present disclosure relates to a risk early warning method and system for hydrogen leakage, and relates to the field of hydrogen leakage. The method includes: obtaining ventilation information of a hydrogen-related area; carrying out grid division on a pipeline system of the hydrogen-related area to obtain a gridded pipeline system; determining a risk coefficient corresponding to each grid of the gridded pipeline system according to leakage sources of the pipeline system; determining a high-risk region by means of a jet cone model according to the risk coefficients; determining a medium-risk region, a low-risk region and a safe region according to the risk coefficients and the ventilation information; and carrying out risk early warning according to the high-risk region, the medium-risk region, the low-risk region and the safe region.

公开(公告)号：US11114711B2

公开(公告)日：2021-09-07

申请号：US16732660

申请日：2020-01-02

Applicant: Beijing Institute of Technology

Inventor： Rui Xiong ,  Zeyu Chen ,  Hongwen He ,  Fengchun Sun

IPC: H01M10/637 ,  H01M10/615 ,  H01M10/633 ,  H01M10/48 ,  H05B3/00

Abstract: The present invention relates to a rapid low-temperature self-heating method and device for a battery. Active controllable large-current lossless short-circuit self-heating cooperates with an external heater to implement rapid composite heating, so that a battery is rapidly heated in a low-temperature environment and is controlled to fall within an optimal working temperature interval, so as to improve energy utilization of the battery and durability of a battery system. Before the battery system is started, battery temperature is first determined; when the temperature is less than a threshold, an external short-circuit is first proactively triggered to generate a large current to implement self-heating inside the battery. The method is simple, easy to implement, and safe and reliable, and can effectively resolve a problem that an electric vehicle has large capacity degradation and poor working performance in a low-temperature severe cold working condition.

公开(公告)号：US11789083B2

公开(公告)日：2023-10-17

申请号：US17398004

申请日：2021-08-10

Applicant: Beijing Institute of Technology

Inventor： Zhongbao Wei ,  Hongwen He ,  Jian Hu

IPC: G01R31/367 ,  G01R31/3842 ,  G01R31/374 ,  H01M10/42 ,  H01M10/48

CPC classification number: G01R31/367 ,  G01R31/374 ,  G01R31/3842 ,  H01M10/4257 ,  H01M10/486 ,  H01M2010/4271 ,  H01M2220/20

Abstract: A state-of-charge (SOC) online estimation method of an intelligent battery includes steps of performing online estimation on the SOC based on a real-time estimation model, wherein the real-time estimation model is based on an equivalent circuit model, and then obtaining online estimates of the SOC through the SOC-OCV (open circuit voltage) relationship, the current-voltage relationship under different working conditions, and the current terminal voltage and temperature of the intelligent battery. The intelligent battery that is able to apply the SOC online estimation method includes a battery cell and a cell management unit, wherein the cell management unit includes a sensor module, a switching device, a controller module, a communication module and a printed circuit board (PCB), the sensor module includes a voltage sensor and a temperature sensor; the voltage sensor, the temperature sensor, the switching device, the controller module and the communication module are integrated on the PCB.