CN111091632B - Method and device for predicting service life of automobile storage battery - Google Patents
Method and device for predicting service life of automobile storage battery Download PDFInfo
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- CN111091632B CN111091632B CN201811245503.XA CN201811245503A CN111091632B CN 111091632 B CN111091632 B CN 111091632B CN 201811245503 A CN201811245503 A CN 201811245503A CN 111091632 B CN111091632 B CN 111091632B
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/006—Indicating maintenance
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0808—Diagnosing performance data
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0816—Indicating performance data, e.g. occurrence of a malfunction
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Abstract
本申请公开了一种汽车蓄电池寿命预测方法应用于蓄电池寿命预测系统中，该蓄电池寿命预测系统具体包括蓄电池、蓄电池传感器、车辆信息采集模块、网关、远程信息处理器和服务器；蓄电池传感器采集蓄电池的电池特性数据，将蓄电池的电池特性数据发送至网关；车辆信息采集模块采集车辆状态数据，将车辆状态数据发送至网关；网关根据电池特性数据确定SOH初始值，通过远程信息处理器将SOH初始值和车辆状态数据发送至服务器；服务器根据SOH初始值和车辆状态数据计算SOH校正值，当判断该SOH校正值是否小于标定校正值时，向终端设备发送蓄电池更换提醒消息，以保证及时准确地提醒用户需要更换蓄电池。
The present application discloses a method for predicting the life of an automobile battery, which is applied to a battery life prediction system. The battery life prediction system specifically includes a battery, a battery sensor, a vehicle information acquisition module, a gateway, a remote information processor and a server; the battery sensor collects the information of the battery. The battery characteristic data sends the battery characteristic data of the battery to the gateway; the vehicle information collection module collects the vehicle status data and sends the vehicle status data to the gateway; the gateway determines the initial value of SOH according to the battery characteristic data, and sends the initial value of SOH through the telematics processor and vehicle status data are sent to the server; the server calculates the SOH correction value according to the SOH initial value and the vehicle status data, and when judging whether the SOH correction value is less than the calibration correction value, it sends a battery replacement reminder message to the terminal device to ensure timely and accurate reminders The user needs to replace the battery.
Description
Technical Field
The application relates to the technical field of automotive electronics, in particular to a method and a device for predicting the service life of an automotive storage battery.
Background
The storage battery plays a main role in supplying electric energy to the starter when the vehicle is started so as to drive the engine or attract the High-Voltage relay, and when the load demand of the whole vehicle exceeds the output of the generator or a High-Voltage DC-to-DC Converter (HVDCDC) and the whole vehicle is not started, the storage battery participates in power supply. In a word, the storage battery is indispensable to the operation of the whole vehicle, and can ensure the safe and stable operation of a whole vehicle system.
When the service life of the storage battery is as long as possible, the normal operation of the whole vehicle is affected, and the situation that the vehicle cannot be started suddenly possibly occurs in severe cases. In the prior art, a user can only indirectly know the current service life information of the storage battery according to certain phenomena shown in the running process of the whole vehicle, and then judge whether the storage battery needs to be replaced, for example, in the running process of the whole vehicle, if the phenomena of powerless starting of the whole vehicle, insufficient light brightness, weakened loudspeaker sound and the like occur, the user can correspondingly judge that the current service life of the storage battery is as long as possible, and the storage battery needs to be replaced.
However, the service life information of the storage battery determined by observing the phenomenon occurring in the running process of the whole vehicle is generally inaccurate and reliable, a user cannot accurately determine the time for replacing the storage battery only according to the service life information of the storage battery determined in such a way, and if the storage battery is not replaced timely, the vehicle cannot guarantee a better working state in the running process of the whole vehicle, so that the vehicle using experience of the user is influenced.
Disclosure of Invention
In order to solve the technical problem, the application provides a method for predicting the service life of an automobile storage battery, which can accurately calculate the capacity of the storage battery and timely remind a user of the need of replacing the storage battery according to the calculated capacity of the storage battery.
The embodiment of the application discloses the following technical scheme:
in a first aspect, an embodiment of the present application provides an automobile storage battery life prediction method, which is applied to a storage battery life prediction system, where the storage battery life prediction system includes a storage battery, a storage battery sensor, a vehicle information acquisition module, a gateway, a remote information processor, and a server, and the method includes:
the storage battery sensor collects battery characteristic data of the storage battery and sends the battery characteristic data to the gateway;
the vehicle information acquisition module acquires vehicle state data and sends the vehicle state data to the gateway, and the vehicle state data can reflect the user's vehicle using habits and vehicle running conditions;
the gateway determines the initial value of the capacity SOH of the storage battery according to the battery characteristic data;
the gateway sending the SOH initial value and the vehicle state data to the server through the telematics processor;
the server calculates an SOH correction value according to the SOH initial value and the vehicle state data; and judging whether the SOH correction value is smaller than the calibration correction value, and if so, sending a storage battery replacement reminding message to the terminal equipment.
Optionally, the vehicle information acquisition module includes an engine management system, a generator and a load;
the vehicle state data comprises vehicle running state information, vehicle charging voltage, load rate and load state signals; the vehicle state information is collected by the engine management system, the charging voltage and the load rate of the whole vehicle are output by the generator, and the load state signal is fed back by the load.
Optionally, the vehicle information acquisition module includes a vehicle controller, a high voltage direct current dc converter HVDCDC and a load;
the vehicle state data comprises vehicle running state information, vehicle charging voltage, load rate and load state signals; the vehicle state information is collected by the vehicle controller, the charging voltage and the load rate of the whole vehicle are output by the high-voltage direct-current converter, and the load state signal is fed back by the load.
Optionally, the determining, by the gateway, an initial value of the capacity SOH of the storage battery according to the battery characteristic data includes:
the gateway determines an initial SOH value by inquiring a storage battery parameter table according to the battery characteristic data; the storage battery parameter table comprises battery characteristic data recorded when the storage battery with the same model as the storage battery is subjected to aging test.
Optionally, the server calculates an SOH correction value according to the SOH initial value and the vehicle state data, and includes:
the server calculates an SOH correction coefficient according to the vehicle state data by adopting a weighting algorithm;
and correcting the initial value of the SOH by using the SOH correction coefficient to obtain the SOH correction value.
Optionally, the terminal device includes: intelligent terminal, internet large-size screen and vehicle instrument.
Optionally, the method further includes:
and the server sends a vehicle using habit guidance prompt message to the terminal equipment according to the vehicle state data.
Optionally, the server sends a battery replacement reminding message to the terminal device, including:
and after judging that the vehicle is started, the server sends the storage battery replacement reminding message to the terminal equipment.
Optionally, the server sets the prompting times of the storage battery replacement prompting message;
and the terminal equipment displays the storage battery replacement reminding message according to the prompting times.
In a second aspect, an embodiment of the present application provides a battery life prediction system, where the system includes a battery, a battery sensor, a vehicle information acquisition module, a gateway, a telematics processor, and a server;
the storage battery sensor is used for acquiring battery characteristic data of the storage battery and sending the battery characteristic data to the gateway;
the vehicle information acquisition module is used for acquiring vehicle state data and sending the vehicle state data to the gateway, and the vehicle state data can reflect the vehicle using habits of users and the vehicle running conditions;
the gateway is used for determining an initial value of the capacity SOH of the storage battery according to the battery characteristic data, and sending the initial value of the SOH and the vehicle state data to the server through the remote information processor;
the server is used for calculating an SOH correction value according to the SOH initial value and the vehicle state data; and judging whether the SOH correction value is smaller than the calibration correction value, and if so, sending a storage battery replacement reminding message to the terminal equipment.
According to the technical scheme, the method for predicting the service life of the automobile storage battery is applied to a storage battery service life prediction system, and the storage battery service life prediction system specifically comprises a storage battery, a storage battery sensor, a vehicle information acquisition module, a gateway, a remote information processor and a server; specifically, when the service life of the storage battery is predicted, the storage battery sensor collects battery characteristic data of the storage battery and sends the collected battery characteristic data of the storage battery to the gateway; the vehicle information acquisition module acquires vehicle state data and sends the acquired vehicle state data to the gateway; the gateway determines an initial value Of the capacity (SOH) Of the storage battery according to the battery characteristic data, and then sends the initial value Of the SOH and the vehicle state data to the server through the remote information processor; and correspondingly, the server calculates the SOH correction value according to the SOH initial value and the vehicle state data, judges whether the SOH correction value is smaller than the calibration correction value, and sends a storage battery replacement reminding message to the terminal equipment to remind a user to replace the storage battery if the SOH correction value is smaller than the calibration correction value. According to the storage battery service life prediction method, the server corrects the SOH initial value by further combining the vehicle state data capable of reflecting the user's vehicle using habits and the vehicle running conditions on the basis of the SOH initial value calculated by the gateway based on the battery characteristic parameters of the storage battery to obtain the SOH correction value, compared with the SOH value directly calculated based on the battery characteristic data of the storage battery, the SOH correction value is more accurate, the SOH correction value is used as a basis for judging whether the storage battery needs to be replaced, the fact that the user needs to be replaced by the storage battery can be further guaranteed to be timely and accurately reminded, and the vehicle using experience of the user is prevented from being influenced due to the fact that the storage battery is not replaced timely.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic structural diagram of a battery life prediction system according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a method for predicting the life of a storage battery according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the application provides a method for predicting the service life of an automobile storage battery, which can accurately calculate the SOH of the storage battery by combining vehicle state data, and further determine whether a user needs to be reminded to replace the storage battery according to the SOH of the storage battery.
The core technical idea of the method for predicting the service life of the automobile storage battery provided by the embodiment of the application is introduced as follows:
the method for predicting the service life of the automobile storage battery is applied to a storage battery service life prediction system, and the storage battery service life prediction system specifically comprises a storage battery, a storage battery sensor, a vehicle information acquisition module, a gateway, a remote information processor and a server; specifically, when the service life of the storage battery is predicted, the storage battery sensor collects battery characteristic data of the storage battery and sends the collected battery characteristic data of the storage battery to the gateway; the vehicle information acquisition module acquires vehicle state data and sends the acquired vehicle state data to the gateway; the gateway determines an initial value of the capacity SOH of the storage battery according to the battery characteristic data, and then sends the initial value of the SOH and the vehicle state data to the server through the remote information processor; and correspondingly, the server calculates the SOH correction value according to the SOH initial value and the vehicle state data, judges whether the SOH correction value is smaller than the calibration correction value, and sends a storage battery replacement reminding message to the terminal equipment to remind a user to replace the storage battery if the SOH correction value is smaller than the calibration correction value.
According to the storage battery service life prediction method, the server corrects the SOH initial value by further combining the vehicle state data capable of reflecting the user's vehicle using habits and the vehicle running conditions on the basis of the SOH initial value calculated by the gateway based on the battery characteristic parameters of the storage battery to obtain the SOH correction value, compared with the SOH value directly calculated based on the battery characteristic data of the storage battery, the SOH correction value is more accurate, the SOH correction value is used as a basis for judging whether the storage battery needs to be replaced, the fact that the user needs to be replaced by the storage battery can be further guaranteed to be timely and accurately reminded, and the vehicle using experience of the user is prevented from being influenced due to the fact that the storage battery is not replaced timely.
In order to further understand the method for predicting the life of the automobile battery provided in the embodiment of the present application, a system for predicting the life of the automobile battery, to which the method for predicting the life of the automobile battery is applied, is described below with reference to fig. 1.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a battery life prediction system according to an embodiment of the present disclosure. As shown in fig. 1, the battery life prediction system includes a battery 101, a battery sensor 102, a vehicle information collection module 103, a Gateway (GW) 104, a telematics processor 105(telematics box), and a server 106.
The storage battery sensor 102 collects the battery characteristic data of the storage battery 101 in real time and sends the battery characteristic data to the gateway 104; the vehicle information acquisition module 103 acquires vehicle state data capable of reflecting user usage habits and vehicle running conditions in real time and sends the vehicle state data to the gateway 104; the gateway 104 determines an initial SOH value of the storage battery 101 according to the battery characteristic data of the storage battery 101, and then sends the initial SOH value and the vehicle state data to the T-box105, and the T-box105 further forwards the initial SOH value and the vehicle state data to the server; the server 106 further calculates the SOH correction value of the storage battery 101 based on the SOH initial value and the vehicle state data, and determines whether it is necessary to send a storage battery replacement warning message to the terminal device based on the SOH correction.
Optionally, in an automobile powered by a gasoline Engine or a diesel Engine, the vehicle information acquisition module generally includes an Engine Management System (EMS), a generator, and a load; on an electric Vehicle or a hybrid Vehicle, the Vehicle information acquisition module generally includes a Vehicle Control Unit (VCU), a high voltage direct current dc converter HVDCDC and a load.
The method for predicting the service life of the automobile storage battery provided by the embodiment of the application is introduced in the following way by the embodiment:
referring to fig. 2, fig. 2 is a schematic flow chart of a method for predicting the life of an automobile battery according to an embodiment of the present disclosure. As shown in fig. 2, the method for predicting the life of the automobile storage battery comprises the following steps:
step 201: the storage battery sensor collects battery characteristic data of the storage battery and sends the battery characteristic data to the gateway.
The storage battery sensor monitors the working state of the storage battery in real time, collects battery characteristic data of the storage battery at the same time, and transmits the collected battery characteristic data to the gateway through a serial interconnection Network (LIN) after collecting the battery characteristic data of the storage battery.
In general, battery characteristic data of a secondary battery includes battery startability (Bat _ SOF), internal resistance (Bat _ Ri), active material loss (Bat _ Lam), degree of vulcanization (Bat _ Sul), degree of plate corrosion (Bat _ Cor), temperature (Bat _ Temp), charge-discharge rate (Bat _ Crnt), and the like.
Step 202: the vehicle information acquisition module acquires vehicle state data and sends the vehicle state data to the gateway, and the vehicle state data can reflect the user's habit of using the vehicle and the vehicle driving condition.
The vehicle information acquisition module acquires vehicle state data generated in the current running process of a vehicle, the vehicle state data CAN reflect the vehicle using habit of a user on one hand and the current running working condition of the vehicle on the other hand, and after the vehicle state data are acquired by the vehicle information acquisition module, the acquired vehicle state data are sent to a gateway through LIN or a Controller Area Network (CAN).
It should be noted that, in an automobile powered by a gasoline engine and a diesel engine, the vehicle information acquisition module generally includes an EMS, a generator, and a load; correspondingly, the vehicle state data comprises vehicle running state information, whole vehicle charging voltage, load rate and load state signals; the vehicle state information is collected by an EMS (energy management system), the vehicle state information is sent to a gateway by the EMS through a CAN (controller area network), the charging voltage and the load rate of the whole vehicle are collected by a generator, the charging voltage and the load rate of the whole vehicle are sent to the gateway by the generator through an LIN, a load state signal is collected by load feedback, the load state signal is sent to the gateway by a load through the CAN or the LIN, and the load state signal mainly refers to the on-off state of some electric appliances on the vehicle.
It should be noted that, in an electric vehicle or a hybrid vehicle, the vehicle information acquisition module generally includes a VCU, an HVDCDC and a load; correspondingly, the vehicle state data comprises vehicle running state information, whole vehicle charging voltage, load rate and load state signals; the vehicle state information is collected by a VCU, the vehicle state information is sent to a gateway by the VCU through a CAN, the charging voltage and the load rate of the whole vehicle are collected by HVDCDC, the charging voltage and the load rate of the whole vehicle are sent to the gateway by the HVDCDC through LIN, a load state signal is collected by a load, the load state signal is sent to the gateway by the load through the CAN, and the load state signal mainly refers to the on-off state of some electric appliances on the vehicle.
Step 203: and the gateway determines the initial value of the SOH of the storage battery according to the battery characteristic data.
And after receiving the battery characteristic data sent by the storage battery sensor, the gateway determines the initial value of the SOH of the storage battery according to the received battery characteristic data.
It should be noted that the gateway may determine the initial value of SOH by querying the parameter table of the storage battery according to the battery characteristic data; the storage battery parameter table comprises battery characteristic data recorded when the storage battery with the same model as the storage battery is subjected to aging test.
That is, the gateway needs to obtain a storage battery parameter table of the same model in advance, the storage battery parameter table records the corresponding relationship between the SOH value of each stage and the battery characteristic parameter of the storage battery in the aging test process of the storage battery of the model, and the gateway can search the SOH value corresponding to the battery characteristic parameter in the storage battery parameter table according to the battery characteristic parameter received by the gateway, and further, the SOH value is used as the SOH initial value.
Step 204: the gateway sends the SOH initial value and the vehicle state data to the server through the telematics processor.
After determining the SOH initial value and obtaining the vehicle state data, the gateway performs operation processing on the SOH initial value and the vehicle state data to obtain packed data of the SOH initial value and the vehicle state data, and then sends the packed data to the server through the vehicle-mounted T-box through the network.
Step 205: the server calculates an SOH correction value according to the SOH initial value and the vehicle state data; and judging whether the SOH correction value is smaller than the calibration correction value, and if so, sending a storage battery replacement reminding message to the terminal equipment.
And after receiving the packaged data sent by the vehicle-mounted T-box, the server processes the packaged data to obtain an SOH initial value and vehicle state data of the storage battery, and further calculates an SOH correction value according to the SOH initial value and the vehicle state data. And then, the server judges whether the SOH correction value is smaller than the calibration correction value SOH _ Critical, if so, the storage battery is irreversibly aged and needs to be replaced, and correspondingly, the server sends a storage battery replacement reminding message to the terminal equipment.
It should be noted that the terminal device may specifically include an intelligent terminal, an internet large screen and a vehicle instrument, that is, the server may send the battery replacement reminding message to a client (APP), an internet large screen (FICM) and a vehicle instrument running on the intelligent terminal, so as to notify the user that the battery may be replaced.
When the server calculates the SOH correction value according to the SOH initial value and the vehicle state data, a weighting algorithm may be first adopted to calculate the SOH correction coefficient according to the vehicle state data, and then the SOH correction coefficient is used to correct the SOH initial value to obtain the SOH correction value.
When the server calculates the SOH correction coefficient, the SOH correction coefficient may be calculated by using a formula shown in formula (1):
f(x)＝x1·λ1+x2·λ2+x3·λ3+x4·λ4+.....(1)
where f (x) is an SOH correction coefficient, x1, x2, x3, x4, and the like are all various vehicle state data, specifically, the vehicle state data may be mileage, vehicle speed, vehicle operating time, ambient temperature, and the like, and λ 1, λ 2, λ 3, and λ 4 are weights corresponding to different vehicle state data, respectively. It should be understood that the vehicle state data having a greater effect on the service life of the battery is weighted more heavily, whereas the vehicle state data having a lesser effect on the service life of the battery is weighted less heavily.
When the server corrects the SOH initial value by using the SOH correction coefficient, the calculation may be performed by using a formula shown in formula (2):
SOH_Dsp＝SOH_GW*f(x) (2)
wherein SOH _ Dsp is an SOH correction value, SOH _ GW is an SOH initial value, and f (x) is an SOH correction coefficient.
It should be noted that, the server may also send a vehicle usage habit guidance prompt message to the terminal device according to the vehicle state data, for example, send a prompt to avoid parking for a long time and turning on the electrical equipment, and prompt the battery to be aged to select a large-current charging mode to activate the battery, so as to prompt the user to adopt a correct vehicle usage habit, so that the user may use the vehicle based on the correct vehicle usage habit, thereby ensuring that the service life of the storage battery is prolonged as much as possible.
In addition, the server can also preset the prompting times of the storage battery replacement prompting message, and correspondingly, after the server sends the storage battery replacement prompting message to the terminal equipment, the terminal equipment reminds the storage battery replacement according to the prompting times.
In general, the server sends a battery replacement reminding message to the terminal device after judging that the vehicle is started. Specifically, the server may obtain a Power Mode of the entire vehicle, and send a battery replacement reminding message to the terminal device when the Power Mode is run.
The method for predicting the service life of the automobile storage battery is applied to a storage battery service life prediction system, and the storage battery service life prediction system specifically comprises a storage battery, a storage battery sensor, a vehicle information acquisition module, a gateway, a remote information processor and a server; specifically, when the service life of the storage battery is predicted, the storage battery sensor collects battery characteristic data of the storage battery and sends the collected battery characteristic data of the storage battery to the gateway; the vehicle information acquisition module acquires vehicle state data and sends the acquired vehicle state data to the gateway; the gateway determines an initial value Of the capacity (SOH) Of the storage battery according to the battery characteristic data, and then sends the initial value Of the SOH and the vehicle state data to the server through the remote information processor; and correspondingly, the server calculates the SOH correction value according to the SOH initial value and the vehicle state data, judges whether the SOH correction value is smaller than the calibration correction value, and sends a storage battery replacement reminding message to the terminal equipment to remind a user to replace the storage battery if the SOH correction value is smaller than the calibration correction value. According to the storage battery service life prediction method, the server corrects the SOH initial value by further combining the vehicle state data capable of reflecting the user's vehicle using habits and the vehicle running conditions on the basis of the SOH initial value calculated by the gateway based on the battery characteristic parameters of the storage battery to obtain the SOH correction value, compared with the SOH value directly calculated based on the battery characteristic data of the storage battery, the SOH correction value is more accurate, the SOH correction value is used as a basis for judging whether the storage battery needs to be replaced, the fact that the user needs to be replaced by the storage battery can be further guaranteed to be timely and accurately reminded, and the vehicle using experience of the user is prevented from being influenced due to the fact that the storage battery is not replaced timely.
It should be noted that, in the present specification, all the embodiments are described in a progressive manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus and system embodiments, since they are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only one specific embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The method for predicting the service life of the automobile storage battery is applied to a storage battery service life prediction system, wherein the storage battery service life prediction system comprises a storage battery, a storage battery sensor, a vehicle information acquisition module, a gateway, a remote information processor and a server, and the method comprises the following steps:
the storage battery sensor collects battery characteristic data of the storage battery and sends the battery characteristic data to the gateway;
the vehicle information acquisition module acquires vehicle state data and sends the vehicle state data to the gateway, and the vehicle state data can reflect the user's vehicle using habits and vehicle running conditions;
the gateway determines the initial value of the capacity SOH of the storage battery according to the battery characteristic data;
the gateway sending the SOH initial value and the vehicle state data to the server through the telematics processor;
the server calculates an SOH correction value according to the SOH initial value and the vehicle state data; judging whether the SOH correction value is smaller than a calibration correction value or not, and if so, sending a storage battery replacement reminding message to the terminal equipment;
the server calculates an SOH correction value based on the SOH initial value and the vehicle state data, including:
the server calculates an SOH correction coefficient according to the vehicle state data by adopting a weighting algorithm;
when the server calculates the SOH correction coefficient, the following formula is adopted for calculation:
f(x)＝x1·λ1+x2·λ2+x3·λ3+x4·λ4+……
(x) is an SOH correction coefficient, x1, x2, x3, x4 and the like are all various vehicle state data, specifically, the vehicle state data may be mileage, vehicle speed, vehicle running time, ambient temperature and the like, λ 1, λ 2, λ 3 and λ 4 are weights corresponding to different vehicle state data, and the weight corresponding to the vehicle state data having a greater influence on the service life of the battery is greater, and the weight corresponding to the vehicle state data having a smaller influence on the service life of the battery is smaller;
correcting the initial SOH value by using the SOH correction coefficient to obtain the SOH correction value, wherein the step of correcting the initial SOH value by using the SOH correction coefficient comprises the following steps:
when the server corrects the SOH initial value by using the SOH correction coefficient, the following formula is adopted for calculation:
SOH_Dsp＝SOH_GW*f(x)
SOH _ Dsp is an SOH correction value, SOH _ GW is an SOH initial value, and f (x) is an SOH correction coefficient.
2. The method of claim 1, wherein the vehicle information collection module comprises an engine management system, a generator, and a load;
the vehicle state data comprises vehicle running state information, vehicle charging voltage, load rate and load state signals; the vehicle state information is collected by the engine management system, the charging voltage and the load rate of the whole vehicle are output by the generator, and the load state signal is fed back by the load.
3. The method according to claim 1, wherein the vehicle information acquisition module comprises a vehicle control unit, a high voltage direct current (HVDCDC) converter and a load;
the vehicle state data comprises vehicle running state information, vehicle charging voltage, load rate and load state signals; the vehicle state information is collected by the vehicle controller, the charging voltage and the load rate of the whole vehicle are output by the high-voltage direct-current converter, and the load state signal is fed back by the load.
4. The method of claim 1, wherein the gateway determines an initial value of the capacity SOH of the battery based on the battery characteristic data, comprising:
the gateway determines an initial SOH value by inquiring a storage battery parameter table according to the battery characteristic data; the storage battery parameter table comprises battery characteristic data recorded when the storage battery with the same model as the storage battery is subjected to aging test.
5. The method of claim 1, wherein the terminal device comprises: intelligent terminal, internet large-size screen and vehicle instrument.
6. The method of claim 1, further comprising:
and the server sends a vehicle using habit guidance prompt message to the terminal equipment according to the vehicle state data.
7. The method according to claim 1 or 5, wherein the server sends a battery replacement reminding message to the terminal device, and the method comprises the following steps:
and after judging that the vehicle is started, the server sends the storage battery replacement reminding message to the terminal equipment.
8. The method according to claim 1, wherein the server sets a number of times of prompting of the battery replacement prompting message;
and the terminal equipment displays the storage battery replacement reminding message according to the prompting times.
9. A system for predicting the service life of a storage battery is characterized by comprising the storage battery, a storage battery sensor, a vehicle information acquisition module, a gateway, a remote information processor and a server;
the storage battery sensor is used for acquiring battery characteristic data of the storage battery and sending the battery characteristic data to the gateway;
the vehicle information acquisition module is used for acquiring vehicle state data and sending the vehicle state data to the gateway, and the vehicle state data can reflect the vehicle using habits of users and the vehicle running conditions;
the gateway is used for determining an initial value of the capacity SOH of the storage battery according to the battery characteristic data, and sending the initial value of the SOH and the vehicle state data to the server through the remote information processor;
the server is used for calculating an SOH correction value according to the SOH initial value and the vehicle state data; judging whether the SOH correction value is smaller than a calibration correction value or not, and if so, sending a storage battery replacement reminding message to the terminal equipment;
the server calculates an SOH correction value based on the SOH initial value and the vehicle state data, including:
the server calculates an SOH correction coefficient according to the vehicle state data by adopting a weighting algorithm;
when the server calculates the SOH correction coefficient, the following formula is adopted for calculation:
f(x)＝x1·λ1+x2·λ2+x3·λ3+x4·λ4+……
(x) is an SOH correction coefficient, x1, x2, x3, x4 and the like are all various vehicle state data, specifically, the vehicle state data may be mileage, vehicle speed, vehicle running time, ambient temperature and the like, λ 1, λ 2, λ 3 and λ 4 are weights corresponding to different vehicle state data, and the weight corresponding to the vehicle state data having a greater influence on the service life of the battery is greater, and the weight corresponding to the vehicle state data having a smaller influence on the service life of the battery is smaller;
correcting the initial SOH value by using the SOH correction coefficient to obtain the SOH correction value, wherein the step of correcting the initial SOH value by using the SOH correction coefficient comprises the following steps:
when the server corrects the SOH initial value by using the SOH correction coefficient, the following formula is adopted for calculation:
SOH_Dsp＝SOH_GW*f(x)
SOH _ Dsp is an SOH correction value, SOH _ GW is an SOH initial value, and f (x) is an SOH correction coefficient.
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Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113954689A (en) * | 2020-07-17 | 2022-01-21 | 奇瑞新能源汽车股份有限公司 | Pure electric vehicle storage battery electricity supplementing system and electricity supplementing control method thereof |
| CN112698232A (en) * | 2020-12-14 | 2021-04-23 | 尼讯(上海)科技有限公司 | Battery health state tracking cloud system based on battery detection |
| CN112802230A (en) * | 2021-02-04 | 2021-05-14 | 江西江铃集团新能源汽车有限公司 | Power consumption rate calculation method and device, readable storage medium and vehicle-mounted system |
| CN113147631B (en) * | 2021-05-06 | 2023-03-31 | 重庆金康赛力斯新能源汽车设计院有限公司 | Output power determination method of low-voltage converter and related equipment |
| CN114755596B (en) * | 2021-12-15 | 2023-07-14 | 广州汽车集团股份有限公司 | Battery aging prediction method and automotive |
| CN115037997A (en) * | 2022-05-31 | 2022-09-09 | 中国第一汽车股份有限公司 | A calibration result detection method, device, equipment and storage medium |
| CN115107571B (en) * | 2022-06-27 | 2025-09-12 | 株式会社Iat | Battery management system, replacement system, and battery replacement judgment system for vehicle batteries |
| CN115472924B (en) * | 2022-08-25 | 2025-09-09 | 中国第一汽车股份有限公司 | System, method, device, terminal and medium for modifying SOH (self-contained identification) of battery pack for battery replacement |
| CN115421062A (en) * | 2022-08-30 | 2022-12-02 | 重庆长安汽车股份有限公司 | Storage battery monitoring method and system, electronic equipment and storage medium |
| CN116118567A (en) * | 2022-11-17 | 2023-05-16 | 深圳市元征科技股份有限公司 | Battery management method, device, terminal equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102590760A (en) * | 2012-02-27 | 2012-07-18 | 力帆实业(集团)股份有限公司 | Storage battery state detection device and detection method thereof |
| CN204028667U (en) * | 2014-07-25 | 2014-12-17 | 北汽福田汽车股份有限公司 | Vehicular accumulator cell condition monitoring system, supervising device and vehicle |
| EP2940780A2 (en) * | 2014-05-02 | 2015-11-04 | Samsung SDI Co., Ltd. | Battery management apparatus |
| CN105717457A (en) * | 2016-02-03 | 2016-06-29 | 惠州市蓝微新源技术有限公司 | Method for utilizing big database analysis to carry out battery pack health state estimation |
| CN106461734A (en) * | 2014-06-04 | 2017-02-22 | 罗伯特·博世有限公司 | Method for estimating an electrical capacitance of a secondary battery |
| CN108089135A (en) * | 2017-12-22 | 2018-05-29 | 广州市香港科大霍英东研究院 | A kind of battery status forecasting system and its implementation based on limit learning model |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9461490B2 (en) * | 2013-03-13 | 2016-10-04 | GM Global Technology Operations LLC | Method and apparatus for evaluating a rechargeable battery |
| CN106610475B (en) * | 2015-10-21 | 2019-08-20 | 深圳市沃特玛电池有限公司 | A kind of battery pack health degree appraisal procedure |
-
2018
- 2018-10-24 CN CN201811245503.XA patent/CN111091632B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102590760A (en) * | 2012-02-27 | 2012-07-18 | 力帆实业(集团)股份有限公司 | Storage battery state detection device and detection method thereof |
| EP2940780A2 (en) * | 2014-05-02 | 2015-11-04 | Samsung SDI Co., Ltd. | Battery management apparatus |
| CN106461734A (en) * | 2014-06-04 | 2017-02-22 | 罗伯特·博世有限公司 | Method for estimating an electrical capacitance of a secondary battery |
| CN204028667U (en) * | 2014-07-25 | 2014-12-17 | 北汽福田汽车股份有限公司 | Vehicular accumulator cell condition monitoring system, supervising device and vehicle |
| CN105717457A (en) * | 2016-02-03 | 2016-06-29 | 惠州市蓝微新源技术有限公司 | Method for utilizing big database analysis to carry out battery pack health state estimation |
| CN108089135A (en) * | 2017-12-22 | 2018-05-29 | 广州市香港科大霍英东研究院 | A kind of battery status forecasting system and its implementation based on limit learning model |
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