公开(公告)号：US20200321668A1

公开(公告)日：2020-10-08

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. A method for determining a large-current lossless short-circuit time threshold is disclosed. A lossless time threshold of an external short-circuit of a battery is constructed according to a critical time and a second current peak of the short-circuit, ensuring that the service life and safety of the battery are not affected during rapid short-circuit heating; further, a temperature rise of lossless short-circuit self-heating of the battery is estimated based on a model, where if the temperature rise does not reach target temperature, the external heater is started for cooperative work, to make temperature of the battery system rise and kept in the optimal working temperature interval. 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：US20230170548A1

公开(公告)日：2023-06-01

申请号：US17915140

申请日：2021-09-01

Applicant: BEIJING INSTITUTE OF TECHNOLOGY

Inventor： Rui XIONG ,  Wanzhou SUN ,  Ruixin YANG ,  Xinggang LI

IPC: H01M10/633 ,  H01M10/615 ,  H01M10/0525 ,  H01M10/6571 ,  H01M10/42

CPC classification number: H01M10/633 ,  H01M10/425 ,  H01M10/0525 ,  H01M10/615 ,  H01M10/6571 ,  H01M2010/4271

Abstract: A lithium-ion battery system and a control method for combined internal and external heating are provided. A battery is heated in a low-temperature environment through combined internal and external heating. The energy released during self-heating of the battery is fully used, and rapid heating of the battery in the low-temperature environment is implemented. A current adjustment module in a heating module is controlled to adjust a switch on-off frequency and a current on-off time during the heating, and loops with different heating resistances in a multi-loop heating film are selected through a resistance adjustment switch. In this way, target heating requirements of the battery are met, such as a high heating rate in a low-temperature environment, low energy consumption during the heating, and a small impact on battery life without jeopardizing safety during the heating.

公开(公告)号：US20230168304A1

公开(公告)日：2023-06-01

申请号：US17916041

申请日：2021-09-01

Applicant: BEIJING INSTITUTE OF TECHNOLOGY

Inventor： Rui XIONG ,  Jinpeng TIAN ,  Yanzhou DUAN

IPC: G01R31/367 ,  G01R31/378 ,  G01R31/392

CPC classification number: G01R31/367 ,  G01R31/378 ,  G01R31/392

Abstract: An artificial intelligence (AI)-based charging curve reconstruction and state estimation method for a lithium-ion battery is provided to estimate various states of a battery. In the method, a complete charging curve is reconstructed through deep learning with charging data segments as input. Then, a plurality of states of the battery can be extracted from the complete charging curve, including a maximum capacity, maximum energy, a state of charge (SOC), a state of energy (SOE), a state of power (SOP), and a capacity increment curve. The battery charging curve reconstruction and state estimation method is adaptively updated with a change in a working state of the battery.

公开(公告)号：US20230204668A1

公开(公告)日：2023-06-29

申请号：US17916048

申请日：2021-09-01

Applicant: BEIJING INSTITUTE OF TECHNOLOGY

Inventor： Rui XIONG ,  Jinpeng TIAN ,  Yanzhou DUAN

IPC: G01R31/367 ,  G01R31/378

CPC classification number: G01R31/367 ,  G01R31/378

Abstract: A method for estimating the state of charge (SOC) of a lithium-ion battery system based on artificial intelligence (AI) is provided. In the method, the relationship between the charging data segments and the SOC of the battery system is established through deep learning, and the SOC at any stage of the charging process can be corrected. SOC in a discharging process is estimated through ampere-hour integration. The estimation method is adaptively updated with a change in the working state of the battery system.