CN-115959006-B - Battery thermal management control method, battery management system and electric vehicle

CN115959006BCN 115959006 BCN115959006 BCN 115959006BCN-115959006-B

Abstract

The invention discloses a battery thermal management control method, a battery management system and an electric vehicle, wherein the method comprises the steps that a vehicle BMS sends temperature data and duration data to a cloud BMS; the vehicle BMS receives the current optimal working temperature of the battery and the current target dormancy duration returned from the cloud BMS, determines the difference value of the current SOC value of the battery minus the preset minimum SOC value, controls the working temperature of the battery according to the difference value, the current optimal working temperature of the battery and the current target dormancy duration, and heats or cools the battery by self-awakening of the vehicle BMS, so that the battery reaches the optimal working temperature in advance, the waiting time of a user can be reduced, the thermal management in the whole life cycle of the battery is realized, and the service life of the battery is prolonged.

Inventors

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Assignees

章鱼博士智能技术(上海)有限公司

Dates

Publication Date: 20260512
Application Date: 20221226

Claims (9)

1. A battery thermal management control method, which is applied to a system including a vehicle BMS and a cloud BMS, the method comprising: The vehicle BMS sends temperature data and duration data to the cloud BMS, wherein the temperature data comprises a pre-calibrated optimal working temperature of a battery and a current environment temperature, and the duration data comprises a first duration and a second duration; the calibration process of the optimal working temperature of the battery delivery comprises the steps of carrying out charge-discharge cycle tests on the battery in different temperature intervals, different SOC value intervals and different charge-discharge multiplying factors, obtaining a corresponding relation table representing the corresponding relation among the temperature, the SOC value and the charge-discharge multiplying factors according to test results, and determining the optimal working temperature of the battery delivery according to the corresponding relation table; The vehicle BMS receives the current optimal working temperature of the battery and the current target dormancy time returned from the cloud BMS, wherein the current optimal working temperature of the battery is obtained after the cloud BMS inputs the temperature data into a first preset deep learning prediction model, and the current target dormancy time is obtained after the cloud BMS inputs the time data into a second preset deep learning prediction model; The vehicle BMS determines a difference value of a current SOC value of the battery minus a preset lowest SOC value; The vehicle BMS controls the working temperature of the battery according to the difference value, the current optimal working temperature of the battery and the current target dormancy duration; The first time length is determined according to each time of needed time in a preset historical time interval, the needed time length is determined according to each time of dormant time length of the vehicle BMS in the preset historical time interval, the needed time length is the needed time length for heating or cooling the battery from the lowest temperature after dormancy to the optimal working temperature of the battery under the current environment temperature, and the second time length is determined according to each time of dormant time length of the vehicle BMS in the preset historical time interval.
2. The method of claim 1, wherein the vehicle BMS controls the operating temperature of the battery according to the difference, the current battery optimal operating temperature, and the current target sleep time period, in particular: If the difference value is not smaller than the target required capacity and the current sleep time of the vehicle BMS reaches the current target sleep time, the vehicle BMS wakes up automatically, starts a heating function or a cooling function according to the current temperature of the battery, and stops the heating function or the cooling function when the working temperature reaches the optimal working temperature of the current battery; If the difference is greater than zero and less than the target required capacity and the current sleep time reaches the current target sleep time, the vehicle BMS wakes up automatically, starts the heating function or the cooling function according to the current temperature of the battery, and stops the heating function or the cooling function when the difference is zero; Wherein the target required capacity is a battery capacity required to heat or cool the battery from the current temperature to the current battery optimal operating temperature.
3. The method of claim 2, wherein the method further comprises: And if the difference value is smaller than zero and the current sleep time length reaches the current target sleep time length, the vehicle BMS is kept in a sleep state.
4. The method of claim 2, wherein the vehicle BMS initiates a countdown based on the current target sleep duration when entering a sleep state, the vehicle BMS determining that the current sleep duration reaches the current target sleep duration when the countdown is zero.
5. A battery management system, the battery management system comprising: The battery delivery optimal working temperature calibration module is used for transmitting temperature data and duration data to a cloud BMS, wherein the temperature data comprises a pre-calibrated battery delivery optimal working temperature and a pre-calibrated current environment temperature, the duration data comprises a first duration and a second duration, the battery delivery optimal working temperature calibration process comprises the steps of enabling the battery to carry out charge-discharge cycle tests in different temperature intervals, different SOC value intervals and different charge-discharge multiplying power, obtaining a corresponding relation table representing the corresponding relation among the temperature, the SOC value and the charge-discharge multiplying power according to test results, and determining the battery delivery optimal working temperature according to the corresponding relation table The receiving module is used for receiving the current optimal working temperature of the battery and the current target dormancy time returned from the cloud BMS, wherein the current optimal working temperature of the battery is obtained after the cloud BMS inputs the temperature data into a first preset deep learning prediction model, and the current target dormancy time is obtained after the cloud BMS inputs the time data into a second preset deep learning prediction model; the determining module is used for determining the difference value of subtracting the preset lowest SOC value from the current SOC value of the battery; The control module is used for controlling the working temperature of the battery according to the difference value, the current optimal working temperature of the battery and the current target dormancy duration; The first time length is determined according to each time of needed time in a preset historical time interval, the needed time length is determined according to each time of dormant time length of the battery management system in the preset historical time interval, and the second time length is determined according to the time length of needed time of heating or cooling the battery from the lowest temperature after dormancy to the optimal working temperature of the battery under the current environment temperature.
6. The battery management system of claim 5, wherein the control module is specifically configured to: If the difference value is not smaller than the target required capacity and the current dormancy time of the battery management system reaches the current target dormancy time, enabling the battery management system to wake up automatically, starting a heating function or a cooling function according to the current temperature of the battery, and stopping the heating function or the cooling function when the working temperature reaches the optimal working temperature of the current battery; If the difference value is greater than zero and less than the target required capacity and the current dormancy time reaches the current target dormancy time, enabling the battery management system to wake up automatically, starting the heating function or the cooling function according to the current temperature of the battery, and stopping the heating function or the cooling function when the difference value is zero; Wherein the target required capacity is a battery capacity required to heat or cool the battery from the current temperature to the current battery optimal operating temperature.
7. The battery management system of claim 6, wherein the control module is further specifically configured to: And if the difference value is smaller than zero and the current dormancy time reaches the current target dormancy time, keeping the battery management system in a dormancy state.
8. The battery management system of claim 6, further comprising a countdown module to: and starting countdown based on the current target dormancy time when the battery management system enters the dormancy state, wherein the control module determines that the current dormancy time reaches the current target dormancy time when the countdown is zero.
9. An electric vehicle comprising a battery management system according to any one of claims 5-8.

Description

Battery thermal management control method, battery management system and electric vehicle Technical Field The present application relates to the technical field of electric vehicles, and more particularly, to a battery thermal management control method, a battery management system, and an electric vehicle. Background The BMS (Battery MANAGEMENT SYSTEM) is mainly used for intelligently managing and maintaining each Battery unit, preventing the Battery from being overcharged and overdischarged, prolonging the service life of the Battery, and monitoring the state of the Battery. The power battery system is used as one of the core components of the new energy automobile, the working temperature of the battery directly influences the performance and the service life of the battery, and the battery can exert the energy to the maximum extent due to the proper working temperature. In the prior art, when a vehicle enters a charging or driving mode, the BMS heats or cools the battery by judging the temperature of the battery, when the temperature is lower than a threshold value, the BMS starts a heating function, when the temperature reaches a stop heating temperature, the BMS closes the heating function, when the temperature is higher than the threshold value, the BMS starts a cooling function, and when the temperature reaches the stop cooling temperature, the BMS closes the cooling function. However, the thermal management method has the disadvantages that when the battery temperature is very low, the BMS limits the request charging current to 0 or limits the power output, the vehicle cannot be charged or normally run, and the vehicle cannot be charged or normally run until the temperature rises to the optimal temperature for charging and discharging the battery, the process requires a period of time, the waiting time of a user is prolonged, the user experience is reduced, and in addition, the thermal management method only runs when the vehicle enters a charging or driving mode, not during the full life cycle of the battery, and the service life of the battery is not prolonged. Therefore, how to perform battery thermal management more reliably is a technical problem to be solved at present. Disclosure of Invention The embodiment of the application provides a battery thermal management control method, a battery management system and an electric vehicle, which are used for more reliably carrying out battery thermal management. In a first aspect, a battery thermal management control method is provided, which is applied to a system including a vehicle BMS and a cloud BMS, and the method includes: the vehicle BMS sends temperature data and duration data to the cloud BMS, wherein the temperature data comprises a pre-calibrated optimal working temperature of a battery and a current environment temperature, and the duration data comprises a first duration and a second duration; The vehicle BMS receives the current optimal working temperature of the battery and the current target dormancy time returned from the cloud BMS, wherein the current optimal working temperature of the battery is obtained after the cloud BMS inputs the temperature data into a first preset deep learning prediction model, and the current target dormancy time is obtained after the cloud BMS inputs the time data into a second preset deep learning prediction model; The vehicle BMS determines a difference value of a current SOC value of the battery minus a preset lowest SOC value; The vehicle BMS controls the working temperature of the battery according to the difference value, the current optimal working temperature of the battery and the current target dormancy duration; The first time length is determined according to each time of needed time in a preset historical time interval, the needed time length is determined according to each time of dormant time length of the vehicle BMS in the preset historical time interval, the needed time length is the needed time length for heating or cooling the battery from the lowest temperature after dormancy to the optimal working temperature of the battery under the current environment temperature, and the second time length is determined according to each time of dormant time length of the vehicle BMS in the preset historical time interval. A second aspect provides a battery management system, the battery management system comprising: The sending module is used for sending temperature data and duration data to the cloud BMS, wherein the temperature data comprises a pre-calibrated optimal working temperature of a battery and a current environment temperature, and the duration data comprises a first duration and a second duration; The receiving module is used for receiving the current optimal working temperature of the battery and the current target dormancy time returned from the cloud BMS, wherein the current optimal working temperature of the battery is obtained after the cloud BMS inputs the temperature data into