CN111381166A - Method and system for monitoring battery state - Google Patents
Method and system for monitoring battery state Download PDFInfo
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- CN111381166A CN111381166A CN201811647272.5A CN201811647272A CN111381166A CN 111381166 A CN111381166 A CN 111381166A CN 201811647272 A CN201811647272 A CN 201811647272A CN 111381166 A CN111381166 A CN 111381166A
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Abstract
The application discloses a method and a system for monitoring a battery state. The method comprises the following steps: obtaining at least one battery parameter of a battery on a vehicle; the at least one battery parameter comprises a current battery parameter and/or a historical battery parameter; judging the state of the battery at least based on the at least one battery parameter, wherein the state of the battery at least comprises a normal state and an abnormal state; executing a set operation in response to the judgment result that the battery is in the abnormal state; wherein the state of the battery is associated with the at least one battery parameter. The state of the battery can be judged in time by obtaining the battery parameters, and the battery in different states is managed in response.
Description
Technical Field
The present disclosure relates to battery management systems, and more particularly, to a method and system for monitoring battery status.
Background
The sharing concept and the low-carbon life concept are gradually accepted by the general public, and the selection of a sharing vehicle for going out becomes a trend. Shared bicycles are a new type of shared economy and are increasingly favored by people for their convenience and environmental protection. Especially, compare ordinary bicycle, electric bicycle can make people's trip convenient and fast more. However, batteries used in electric bicycles are susceptible to danger from external factors. For example, when a vehicle bumps during driving, a battery is easily squeezed and falls, and external factors such as squeezing or falling of the battery and excessive temperature may cause damage to a battery cell, resulting in safety accidents (e.g., explosion, fire, etc.). Therefore, a method and system for monitoring the state of a battery are needed to determine whether the battery is in an abnormal state, so that the battery can be maintained, repaired and replaced in a timely manner.
Disclosure of Invention
The embodiment of the application provides a safety monitoring method, a safety monitoring system, a safety monitoring device and a computer readable storage medium. The method specifically comprises the following aspects:
in a first aspect, a method of monitoring a state of a battery is disclosed. The method comprises the following steps: obtaining at least one battery parameter of a battery on a vehicle; the at least one battery parameter comprises a current battery parameter and/or a historical battery parameter; judging the state of the battery at least based on the at least one battery parameter, wherein the state of the battery at least comprises a normal state and an abnormal state; executing a set operation in response to the judgment result that the battery is in the abnormal state; wherein the state of the battery is associated with the at least one battery parameter.
In some embodiments, the abnormal state includes at least one of: maintenance, suspected fault, fault status.
In some embodiments, the current battery parameter includes at least one of: the battery comprises an output voltage, a voltage difference between battery cores, an output current, external environment humidity of the battery, internal resistance of the battery, temperature of the battery, acceleration of the battery, accumulated discharge capacity of the battery, service time, service duration, leakage of the battery and residual electric quantity of the battery. .
In some embodiments, said determining the state of the battery based on at least the at least one battery parameter further comprises: and when the battery leaks or the voltage difference between the battery cores exceeds a preset range, determining that the battery is in an abnormal state.
In some embodiments, determining the state of the battery based at least on the at least one battery parameter further comprises: constructing a feature vector based on at least the current battery parameters and/or the historical battery parameters; and inputting the characteristic vector into a trained battery state detection model, and acquiring a state detection result of the battery at the current moment.
In some embodiments, training the battery state detection model comprises the steps of: acquiring sample data of at least one positive sample; the sample data of each positive sample at least comprises battery parameters corresponding to at least one acquisition moment when the sample battery is in a normal state; acquiring sample data of at least one negative sample; the sample data of each negative sample at least comprises battery parameters corresponding to at least one acquisition moment before the sample battery is in an abnormal state; constructing a feature vector corresponding to each sample based on the sample data of the at least one positive sample and the sample data of the at least one negative sample; inputting the feature vector corresponding to each sample into an initialized detection model to obtain a battery state detection result of the sample; and training the initialized detection model according to the battery state detection result of each sample and the actual battery state corresponding to the sample to obtain the battery state detection model.
In some embodiments, said constructing the feature vector corresponding to each sample based on the sample data of the at least one positive sample and the sample data of the at least one negative sample comprises: according to the battery parameters corresponding to the at least one acquisition moment, constructing a feature vector corresponding to the at least one acquisition moment; and carrying out weighted summation on the characteristic vectors corresponding to the at least one acquisition moment to generate the characteristic vector of the sample.
In some embodiments, when the battery is in the abnormal state in response to the determination result, the performing the set operation further includes performing at least one of: sending an instruction to the vehicle to trigger an alarm mechanism; sending an instruction for executing locking to the vehicle; sending prompt information of the battery state to a user terminal; and sending the identification information of the battery and/or the identification information of the vehicle to a maintenance terminal.
In some embodiments, the vehicle is an electric bicycle.
In a second aspect, a system for monitoring a state of a battery is disclosed. The system comprises: an acquisition module for acquiring at least one battery parameter of a battery on a vehicle; the at least one battery parameter comprises a current battery parameter and/or a historical battery parameter; the judging module is used for judging the state of the battery at least based on the at least one battery parameter, and the state of the battery comprises a normal state and an abnormal state; the control module is used for responding to the judgment result that the battery is in the abnormal state and executing the set operation; wherein the state of the battery is associated with the at least one battery parameter.
In some embodiments, the abnormal state includes at least one of: maintenance, suspected fault, fault status.
In some embodiments, the current battery parameter includes at least one of: the battery comprises an output voltage, a voltage difference between battery cores, an output current, external environment humidity of the battery, internal resistance of the battery, temperature of the battery, acceleration of the battery, accumulated discharge capacity of the battery, service time, service duration, leakage of the battery and residual electric quantity of the battery.
In some embodiments, the determining module is further configured to: and when the battery leaks or the voltage difference between the battery cores exceeds a preset range, determining that the battery is in a damaged state.
In some embodiments, the determining module is further configured to: constructing a feature vector based on at least the current battery parameters and/or the historical battery parameters; and inputting the characteristic vector into a trained battery state detection model, and acquiring a state detection result of the battery at the current moment.
In some embodiments, the system further comprises a training module for training the battery state detection model using the steps of: acquiring sample data of at least one positive sample; the sample data of each positive sample at least comprises battery parameters corresponding to at least one acquisition moment when the sample battery is in a normal state; acquiring sample data of at least one negative sample; the sample data of each negative sample at least comprises battery parameters corresponding to at least one acquisition moment before the sample battery is in an abnormal state; constructing a feature vector corresponding to each sample based on the sample data of the at least one positive sample and the sample data of the at least one negative sample; inputting the feature vector corresponding to each sample into an initialized detection model to obtain a battery state detection result of the sample; and training the initialized detection model according to the battery state detection result of each sample and the actual battery state corresponding to the sample to obtain the battery state detection model.
In some embodiments, the training module constructs the feature vector corresponding to each sample by: according to the battery parameters corresponding to the at least one acquisition moment, constructing a feature vector corresponding to the at least one acquisition moment; and carrying out weighted summation on the characteristic vectors corresponding to the at least one acquisition moment to generate the characteristic vector of the sample.
In some embodiments, the control module is configured to perform at least one of the following operations in response to the determination result indicating that the battery is in the abnormal state: sending an instruction to the vehicle to trigger an alarm mechanism; sending an instruction for executing locking to the vehicle; sending prompt information of the battery state to a user terminal; and sending the identification information of the battery and/or the identification information of the vehicle to a maintenance terminal.
In some embodiments, the vehicle is an electric bicycle.
In a third aspect, an apparatus for monitoring the condition of a battery is disclosed. The apparatus comprises at least one processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement the method of monitoring battery status.
In a fourth aspect, a computer-readable storage medium is disclosed. The storage medium stores computer instructions that, when executed by a processor, implement the method of monitoring battery status.
Additional features of the present application will be set forth in part in the description which follows. Additional features of some aspects of the present application will be apparent to those of ordinary skill in the art in view of the following description and accompanying drawings, or in view of the production or operation of the embodiments. The features of the present application may be realized and attained by practice or use of the methods, instrumentalities and combinations of the various aspects of the specific embodiments described below.
Drawings
FIG. 1 is a schematic diagram of an on-demand service system 100 according to some embodiments of the present application.
Fig. 2 is a flow chart illustrating an exemplary method 200 of monitoring battery conditions in accordance with an embodiment of the present application.
Fig. 3 is a flow chart of another exemplary method 300 of monitoring a state of a battery according to some embodiments of the present application.
FIG. 4 is a flow diagram illustrating a method 400 of training a battery state detection model according to an embodiment of the present application.
Fig. 5 is a block diagram illustrating an exemplary apparatus 500 for monitoring a state of a battery according to an embodiment of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Although various references are made herein to certain modules or units in a system according to embodiments of the present application, any number of different modules or units may be used and run on a client and/or server. The modules are merely illustrative and different aspects of the systems and methods may use different modules.
Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Embodiments of the present application may be applied to different transportation systems including, but not limited to, one or a combination of terrestrial, marine, aeronautical, aerospace, and the like. For example, taxis, special cars, tailplanes, buses, designated drives, trains, railcars, high-speed rail, unmanned vehicles, receiving/sending couriers, and the like employ managed and/or distributed transportation systems. The application scenarios of the different embodiments of the present application include, but are not limited to, one or a combination of several of a web page, a browser plug-in, a client, a customization system, an intra-enterprise analysis system, an artificial intelligence robot, and the like. It should be understood that the application scenarios of the system and method of the present application are merely examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios without inventive effort based on these figures.
The terms "passenger side," "passenger side user," "passenger side device," "client side," "client device," "client user," "user side," "mobile terminal," "mobile device," and the like, described herein are interchangeable, refer to a party that needs or orders a service, either a person or a tool. In addition, a "user" as described herein may be a party that needs or subscribes to a service, or a party that provides or assists in providing a service
FIG. 1 is a schematic diagram of an on-demand service system 100 according to some embodiments of the present application.
The on-demand service system 100 may include a server 110, a network 120, a customer premises 130, a vehicle 140, and a memory 150.
The server 110 may be local or remote. Server 110 may process information and/or data. In some embodiments, the server 110 may be used in a system that performs analytical processing on the collected information to generate analytical results. The server 110 may be a terminal device, a server, or a server group. The server farm may be centralized, such as a data center. The server farm may also be distributed, such as a distributed system. When the user is riding, the server 110 may obtain the location information of the user terminal 130 via the network 120, and the location information may also be regarded as the location information of the vehicle 140 or the location information of the battery.
The network 120 may provide a conduit for the exchange of information. The network 120 may be a single network or a combination of networks. In some embodiments, network 120 may include at least one network access point. For example, network 120 may include wired or wireless network access points, such as base stations and/or Internet switching points 120-1, 120-2, … …, through which at least one component of system 100 may connect to network 120 to exchange data and/or information.
The user terminal 130 may also refer to an individual, tool, or other entity that issues a service order. In some embodiments, the user terminal 130 includes, but is not limited to, one or a combination of mobile device 130-1 (e.g., a mobile phone), a vehicle built-in device 130-2, a laptop computer 130-3, a desktop computer 130-4, and the like. The user terminal 130 can process information and/or data. The user terminal can be used for analyzing and processing the collected information to generate an analysis result. The user terminal 130 may display the status information of the vehicle 140 and the battery parameters corresponding to the vehicle 140. The user may view status information of the vehicle 140, such as the current speed, acceleration, etc. of the vehicle 140 through the user terminal 130. The user may view the battery parameters through the user terminal 139, for example, the remaining capacity of the battery, the estimated driving range, the battery voltage, the battery current, the battery temperature, etc. may be viewed.
The vehicle 140 may include, but is not limited to, a bicycle, an electric bicycle, a bicycle, an electric vehicle, an electric motorcycle, an electric bicycle, and the like. The vehicle 140 may be equipped with a battery to provide power to the vehicle, such as to power the vehicle's drive system. The battery may be a single battery, or may be a battery pack including a plurality of single batteries, for example, a plurality of battery cells are included in the battery. Each battery has a corresponding battery identification. The battery may be a lithium battery, a lead storage battery, a solar battery, a fuel cell, or other types of batteries. In some embodiments, the battery may include a monitoring module, a processing module, a communication module, a transmission module, and the like. The battery may upload monitored battery parameters to the server 110 via the network 120. The vehicle 140 may include a control module, a GPS module, a communication module, a transmission module, and the like. In some embodiments, an alarm device may be mounted on the battery or on the vehicle, and may trigger an alarm to prompt a user when the battery is in an abnormal state (e.g., requiring maintenance, suspected of being faulty, in a faulty state). In some embodiments, the vehicle 140 may also upload monitored vehicle information to the server 110.
The server 110 can directly access the data information stored in the memory 150, or can directly access the information of the access client 130 through the network 120. For example, the server 110 may access user information of the user terminal 130, such as user account information, historical data of the user using the vehicle, historical status information of the vehicle-mounted battery. The memory 150 may generally refer to a device having a storage function. The memory 150 is mainly used for storing data collected from the user terminal 130 and various data generated in the operation of the on-demand service system 100. The memory 150 may be local or remote. The connection or communication between the system database and other modules of the system may be wired or wireless.
Fig. 2 is a flow chart illustrating an exemplary method 200 of monitoring battery conditions in accordance with an embodiment of the present application.
In some embodiments, the method 200 of monitoring the state of a battery may be performed by a device having processing, computing, capabilities, such as the server 110. In some embodiments, the method 200 for monitoring the state of the battery may be performed by a device having processing and computing capabilities, such as a mobile terminal (e.g., the user terminal 130).
In some embodiments, the at least battery parameter may include a battery parameter collected at a current time, and may also include a historical battery parameter. The battery parameters of the battery may be collected in real time by a collection device installed inside the battery or on the vehicle, and the collected battery parameters may be uploaded to the server 110. For example, during riding of the electric bicycle, a battery on the electric bicycle may monitor battery parameters of the battery via sensors in the acquisition device. For example, the sensor may include a voltage sensor, a current sensor, a smoke sensor, a temperature sensor, a humidity sensor, a photosensor, a pressure sensor, a vibration sensor, a displacement sensor, a gyroscope, an acceleration sensor, a velocity sensor, a controller, a relay, a camera, and the like. In some embodiments, the battery parameters may include current battery parameters, for example, a moving speed of the battery, an acceleration of the battery, an output voltage of the battery, an output current of the battery, a temperature of the battery, external environment data of the battery (e.g., an external temperature, an external humidity, a road state, etc.), a remaining capacity of the battery, an internal resistance of the battery, position information of the battery (or position information of a vehicle), whether leakage occurs in the battery, a dropping height of the battery (or a height difference between upper and lower bumps of the battery), whether the dropping height of the battery exceeds a preset safe dropping height, and the like. In some embodiments, the battery parameters may also include historical battery parameters, for example, a moving speed of the battery, an acceleration of the battery, an output voltage of the battery, a voltage difference between battery cells, an output current of the battery, a temperature of the battery, external environment data of the battery (e.g., an external temperature, an external humidity, a road state, etc.), an accumulated discharge amount of the battery, a usage time, a usage duration, a remaining battery capacity, an internal battery resistance, location information of the battery (or location information of the vehicle), whether leakage occurs in the battery, a dropping height of the battery, whether the dropping height of the battery exceeds a preset safe dropping height, and the like, which are collected at a historical time. In some embodiments, the battery parameters may also include a cumulative total of battery drop heights counted based on historical battery parameters, a sum of times that the battery drop height exceeds a preset safety drop height, a number of charged and discharged times of the battery, a cumulative used time of the battery, a predicted remaining service life of the battery, and the like. In some embodiments, the falling height (or the height difference between the battery and the upper and lower bumps) of the battery or the falling height can be determined by the variation of the acceleration of the battery, for example, the variation of the acceleration collected at the current moment and the variation of the acceleration collected at the previous moment. In some embodiments, the battery may include a transmission module that uploads the battery parameters to the server 110 via the network 120. In some embodiments, the vehicle 140 may also upload battery parameters to the server 110 by installing a transmission module.
Step 202, judging the state of the battery at least based on the at least one battery parameter, wherein the state of the battery at least comprises a normal state and an abnormal state.
In some embodiments, the abnormal state may include an abnormal state such as maintenance, suspected fault, fault state, etc. The server 110 may determine the state of the battery based on the acquired at least one battery parameter. The state of the battery is associated with the at least one battery parameter. In some embodiments, the server 110 may determine whether the battery is in an abnormal state based on some important parameter (e.g., output voltage, whether leakage occurs, etc.) in the current battery parameters. For example, when the battery is in a leakage state, it can be directly determined that the battery is in an abnormal state, and the battery is considered to be in a failure state and needs to be replaced. For another example, when the output voltage of the battery exceeds a preset range (for example, is lower than a certain voltage threshold or higher than a certain voltage threshold), it may be directly determined that the battery is in an abnormal state, and it may be considered that the battery is in a failure state and needs to be replaced. For another example, when the smoke sensor of the battery detects smoke in the battery, it indicates that the battery may spontaneously ignite, and may directly determine that the battery is in an abnormal state, and may consider that the battery is in a failure state and needs to be replaced. In some embodiments, the server 110 may determine that the battery is in a particular one of the abnormal states, e.g., maintenance, suspected failure, failed state, etc., based on the current battery parameters.
In some embodiments, the server 110 cannot directly determine whether the battery is in an abnormal state based on the core parameters of the acquired battery parameters, and the server 110 may determine based on a detection model counted by big data and the acquired battery parameters. Further reference may be made to the detailed description below (e.g., fig. 3) regarding determining the state of the battery according to the detection model and the acquired battery parameters.
Step 203, responding to the judgment result that the battery is in an abnormal state, and executing set operation; and executing the set operation when the battery is in the abnormal state according to the judgment result.
In some embodiments, server 110 may send an instruction to the vehicle or battery via network 120 to trigger an alarm mechanism when it determines that the battery is in an abnormal state. In some embodiments, the server 110 may send an instruction to an alert device installed on the battery or vehicle over the network 120 to trigger an alert mechanism to alert the user that the battery of the vehicle is in an abnormal state. In some embodiments, the server 110 may send an instruction to the vehicle to perform lock-up via the network 120 when it is determined that the battery is in an abnormal state. The vehicle may execute a lock-off command that disables the user from continuing to ride or drive.
In some embodiments, when the next user is to ride or drive the vehicle, the server 110 may control the vehicle such that the vehicle cannot be unlocked, and may send a prompt message to the mobile terminal of the next user to indicate that the vehicle is in a state of not working properly, thereby preventing the battery of the vehicle from being further damaged. In some embodiments, when determining that the battery is in an abnormal state, the server 110 may send a prompt message of the battery state to the user terminal through the network 120, so that the user may know the current battery usage state and may replace the vehicle in time. In some embodiments, the server 110 may send the identification information of the battery and/or the identification information of the vehicle to the maintenance terminal through the network 120 when determining that the battery is in an abnormal state, for example, needs maintenance, suspected fault, fault state, etc. The maintenance personnel can quickly find the battery according to the identification information of the battery, the identification information of the vehicle, the positioning of the battery and other information, and can maintain, maintain or replace the battery.
Fig. 3 is a flow chart of another exemplary method 300 of monitoring a state of a battery according to some embodiments of the present application.
In some embodiments, the method 300 of monitoring the state of a battery may be performed by a device having processing, computing, capabilities, such as the server 110. In some embodiments, the method 300 of monitoring the state of the battery may be performed by a device having processing and computing capabilities, such as a mobile terminal (e.g., the user terminal 130).
Step 301, obtaining at least one battery parameter of a battery on a vehicle; the at least one battery parameter includes a current battery parameter and/or a historical battery parameter.
Step 301 is similar to step 201 in fig. 2, and the detailed description may refer to the description about step 201 in fig. 2.
At step 302, a feature vector is constructed based at least on current battery parameters and/or historical battery parameters.
In some embodiments, the server 110 may construct a feature vector based on the battery parameters collected at the current time. The characteristic values of the battery parameters may include one or more of output voltage, voltage difference between the battery cells, output current, battery temperature, external environment humidity of the battery, accumulated discharge capacity of the battery, service time, service duration, speed of the battery, acceleration of the battery, drop height (or bumping amplitude), residual capacity of the battery, whether the drop height of the battery exceeds a preset safe drop height, and the like, which are acquired at the current moment. Among them, the falling height (amplitude of the bumping) can be obtained based on the change in the acceleration of the battery. And for the numerical characteristics, directly using the numerical values corresponding to the numerical characteristics. For example, if the output voltage is 20V, the characteristic value corresponding to the output voltage characteristic is "20". For another example, if the battery temperature is 30 ℃, the characteristic value corresponding to the battery temperature characteristic is "30". For another example, if the drop height (or the amplitude of the pitch) is 20 cm, the drop height (or the amplitude of the pitch) corresponds to a characteristic value of "20". For another example, if the battery drop height exceeds the preset safe drop height, whether the battery drop height exceeds the preset safe drop height is represented by a characteristic value "1", and if the battery drop height does not exceed the preset safe drop height, whether the battery drop height exceeds the preset safe drop height is represented by a characteristic value "0". In some embodiments, the server 110 may input the current battery parameters into a machine learning model, generating corresponding feature vectors.
In some embodiments, the server 110 may construct a feature vector based on the battery parameters collected at the current time and historical battery parameters. The feature vector not only includes the battery parameters at the current acquisition time, but also includes the battery parameters at the historical acquisition time. For example, the characteristic vector not only relates to the voltage of the current battery, the voltage difference between the battery cells, the current, the temperature, the humidity, the speed, the acceleration, the drop height (or the amplitude of bumping), the accumulated discharge capacity of the battery, the service time and the residual capacity, but also relates to the accumulated sum of the drop heights of the battery counted based on historical battery parameters, the sum of the times that the drop height of the battery exceeds the preset safe drop height, the charged and discharged times of the battery, the used accumulated time of the battery, the predicted residual service life of the battery and the like. For example only, the server 110 may construct the feature vector based on the battery parameters at the current acquisition time and the battery parameters acquired a certain period of time before the current acquisition time. For example, the server 110 may construct a plurality of historical feature vectors corresponding to the plurality of historical acquisition times according to the battery parameters corresponding to the historical acquisition times. Further, the server 110 may construct a current feature vector corresponding to the current collection time according to the battery parameter corresponding to the current collection time. Further, the server 110 may perform a weighted summation of the plurality of historical feature vectors and the current feature vector to determine a final feature vector.
For another example, the server 110 may construct a plurality of historical feature vectors corresponding to a plurality of historical collection times according to battery parameters corresponding to the plurality of historical collection times, and further determine a final historical feature vector based on the plurality of historical feature vectors (e.g., perform a weighted summation on the plurality of historical feature vectors). Further, the server 110 may construct a current feature vector corresponding to the current collection time according to the battery parameter corresponding to the current collection time. Further, the server 110 may concatenate the final historical feature vector and the current feature vector to determine a final feature vector. For example, the feature vector corresponding to the current acquisition time is X1(D1, D2, D3), the final historical feature vector determined based on the historical battery parameters is X2(D1, D2, D3), and the final feature vector obtained after splicing is (D1, D2, D3, D1, D2, D3). In some embodiments, the server 110 may input the current battery parameters and the historical battery parameters into a machine learning model, generating corresponding feature vectors.
Step 303, inputting the feature vector into a trained battery state detection model, and obtaining a state detection result of the battery at the current moment.
In some embodiments, the feature vector determined in step 302 is input into the trained battery state detection model, so as to obtain a probability that the battery is in an abnormal state, thereby being able to determine a state detection result of the battery at the current time. When the probability that the battery is in the abnormal state is greater than a certain threshold, the server 110 may confirm that the battery is in the abnormal state. In some embodiments, the trained battery state detection model may also determine a specific state of the battery in an abnormal state, such as a need for maintenance, a suspected fault, a fault condition, etc.
In some embodiments, the battery state detection model may be comprised of a neural network and a classifier. The characteristic vector of the battery to be detected is input into the neural network to obtain a target characteristic vector, and the target characteristic vector is input into the classifier to obtain a state detection result of the battery, for example, an output value of "1" indicates that the battery is in an abnormal state, and an output value of "0" indicates that the battery is in a normal state.
Fig. 4 is a flow chart illustrating a method 400 of training a battery state detection model in an exemplary method of monitoring a battery state according to an embodiment of the present application.
In some embodiments, the training process 400 for the battery state detection model may be performed at a server. The process of the server 110 training the battery state detection model may include steps 401 to 405.
Step 401, obtaining sample data of a plurality of positive samples; the sample data of each positive sample at least comprises battery parameters corresponding to at least one acquisition moment when the sample battery is in a normal state.
In some embodiments, the server 110 may record battery parameters at different collection times of the battery under normal operating conditions. For example, when a maintenance worker detects that a battery is in a normal state while charging the battery, the server 110 may extract battery parameters corresponding to different times of the battery during a historical operation (e.g., during the current day). The battery parameter in the normal state may be taken as sample data of the positive sample. In some embodiments, the sample data for each positive sample may include battery parameters corresponding to one or more acquisition moments when the sample battery is in a normal state. In some embodiments, the sample data of each battery sample may also include only partial data of the battery parameters, such as output voltage, voltage difference between cells, output current, battery temperature, acceleration of the battery, battery drop height, accumulated battery discharge amount, usage time, usage duration, remaining battery capacity, and the like. For example, by obtaining battery parameters at a plurality of collection times of different batteries, sample data of each battery sample is obtained. For another example, for a single battery, battery parameters at a plurality of collection times of the battery may be obtained, and the battery parameter at each of the plurality of collection times is taken as one sample data.
Step 402, obtaining sample data of a plurality of negative samples; the sample data of each negative sample at least comprises battery parameters corresponding to at least one acquisition moment before the sample battery is in the abnormal state.
When a service worker finds a battery failure (e.g., failure to properly charge, damage to the battery) while charging the battery, the server 110 may retrieve battery parameters for historical use of the battery. For example, the server 110 may retrieve from the memory 150 the battery parameters of the battery from the last charge, or from the last ride or drive. In some embodiments, the sample data for each negative sample may include battery parameters corresponding to one or more acquisition moments before the sample battery is in an abnormal state. In some embodiments, the sample data of each battery sample may also include only partial data of the battery parameters, such as output voltage, voltage difference between cells, output current, battery temperature, acceleration of the battery, battery drop height, accumulated battery discharge amount, usage time, usage duration, remaining battery capacity, and the like. For example, the sample data of each battery sample may be obtained by obtaining battery parameters at a plurality of collection times for different batteries. For another example, for a single battery, battery parameters at a plurality of collection times of the battery may be obtained, and the battery parameter at each of the plurality of collection times is taken as one sample data.
For example, the server 110 may construct the feature vectors corresponding to the multiple collection times according to the battery parameters corresponding to the multiple collection times. The feature vector corresponding to each collection time may include an output voltage, a voltage difference between battery cores, an output current, a battery temperature, a humidity of an external environment of the battery, a speed of the battery, an acceleration of the battery, a battery falling height (or a bumpy amplitude), a battery accumulated discharge amount, a service time, a service duration, and a battery remaining capacity corresponding to each collection time, and may further include an accumulated sum of battery falling heights counted based on historical battery parameters before each collection time, a sum of times that the battery falling height exceeds a preset safe falling height, a number of charged and discharged times of the battery, an accumulated used duration of the battery, a predicted remaining service life of the battery, and the like. In some embodiments, the server 110 may perform a weighted summation of the feature vectors corresponding to the multiple acquisition time instants to generate the feature vector of the sample. For example, the server 110 may generate the feature vector of the sample by adding the feature vectors corresponding to the plurality of acquisition times and dividing the sum by the number of feature vectors corresponding to the plurality of acquisition times. For example, the server 110 may add feature vectors corresponding to a plurality of acquisition times, and multiply each feature value of the added feature vectors by a certain weight to generate a feature vector of the sample.
The server 110 may input the feature vector corresponding to each sample into the initialized detection model, and may obtain a battery state detection result of the sample.
In some embodiments, the initialized detection model may be composed of a neural network, or a neural network and a classifier, and the battery state detection result can be obtained by inputting the feature vectors corresponding to the samples into the initialized detection model.
According to the battery state detection result of each sample and the actual battery state (for example, the battery state detection result may include a normal state, an abnormal state, and a specific abnormal state, for example, a state requiring maintenance, suspected fault, or fault) corresponding to the sample, the training parameters of the initialized detection model may be continuously adjusted, and the trained battery state detection model may be obtained through multiple rounds of training. The server 110 may perform a round of training on the initialized detection model according to the battery state detection result of each sample and the corresponding actual battery state, adjust the training parameters of the initialized detection model, and perform the next round of training. The server 110 may determine the detection model after multiple rounds of training as a trained battery state detection model.
Fig. 5 is a block diagram illustrating an exemplary apparatus 500 for monitoring a state of a battery according to an embodiment of the present application.
The means for monitoring battery status 500 may include an acquisition module 510, a determination module 520, and a control module 530.
The obtaining module 510 may be configured to obtain at least one battery parameter of a battery on a vehicle; the at least one battery parameter includes a current battery parameter and/or a historical battery parameter.
The determining module 520 may be configured to determine a state of the battery based on at least the at least one battery parameter, where the state of the battery includes a normal state and an abnormal state. The state of the battery is associated with the at least one battery parameter.
The control module 530 may be configured to perform a set operation in response to the determination result being that the battery is in the abnormal state.
In some embodiments, the abnormal state includes at least one of: maintenance, suspected fault, fault status.
In some embodiments, the current battery parameter includes at least one of: output voltage, output current, external environment humidity of the battery, internal resistance of the battery, temperature of the battery, acceleration of the battery, leakage of the battery and residual capacity of the battery.
In some embodiments, the determining module 520 may be further configured to determine that the battery is in an abnormal state when leakage occurs in the battery or a voltage difference between the battery cells exceeds a preset range.
In some embodiments, the determining module 520 may be further configured to construct a feature vector based on at least the current battery parameters and/or the historical battery parameters; and inputting the characteristic vector into a trained battery state detection model, and acquiring a state detection result of the battery at the current moment.
In some embodiments, the means for monitoring battery status 500 may also include a training module. The training module may be configured to train the battery state detection model using:
acquiring sample data of at least one positive sample; the sample data of each positive sample at least comprises battery parameters corresponding to at least one acquisition moment when the sample battery is in a normal state;
acquiring sample data of at least one negative sample; the sample data of each negative sample at least comprises battery parameters corresponding to at least one acquisition moment before the sample battery is in an abnormal state;
constructing a feature vector corresponding to each sample based on the sample data of the at least one positive sample and the sample data of the at least one negative sample;
inputting the feature vector corresponding to each sample into an initialized detection model to obtain a battery state detection result of the sample;
and training the initialized detection model according to the battery state detection result of each sample and the actual battery state corresponding to the sample to obtain the battery state detection model.
In some embodiments, the training module may construct the feature vector corresponding to each sample in the following manner:
according to the battery parameters corresponding to the at least one acquisition moment, constructing a feature vector corresponding to the at least one acquisition moment;
and carrying out weighted summation on the characteristic vectors corresponding to the at least one acquisition moment to generate the characteristic vector of the sample.
In some embodiments, the control module 530 may be configured to perform at least one of the following operations in response to the determination result indicating that the battery is in the abnormal state:
sending an instruction to the vehicle to trigger an alarm mechanism;
sending an instruction for executing locking to the vehicle;
sending prompt information of the battery state to a user terminal;
and sending the identification information of the battery and/or the identification information of the vehicle to a maintenance terminal.
In some embodiments, the vehicle may be an electric bicycle.
In some embodiments, the present application also relates to an apparatus for monitoring the state of a battery. The apparatus comprises at least one processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement a method of monitoring battery status.
In some embodiments, the present application also relates to a computer-readable storage medium. The storage medium stores computer instructions that, when executed by a processor, implement a method of monitoring battery status.
The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: according to the method and the device, the battery parameters are obtained, whether the battery is abnormal or not (for example, the battery is in a fault) is judged based on one or more important parameters in the battery parameters, or whether the battery is abnormal or not is judged according to the battery state detection model based on big data statistics, specific abnormal states such as maintenance, suspected fault and fault state can be further determined, and battery information is sent to a maintenance person of a maintenance terminal for timely maintenance, maintenance and replacement.
It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of at least one embodiment of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in at least one computer readable medium.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of at least one embodiment of the invention. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on at least one computer-usable storage medium (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The foregoing is a general idea of the present application, which is presented by way of example only, and it will be apparent to those skilled in the art that various changes, modifications or improvements may be made in accordance with the present application. Such alterations, modifications, and improvements are intended to be suggested or suggested by the present application and are intended to be within the spirit and scope of the embodiments of the present application.
Claims (20)
1. A method of monitoring a state of a battery, comprising:
obtaining at least one battery parameter of a battery on a vehicle; the at least one battery parameter comprises a current battery parameter and/or a historical battery parameter;
judging the state of the battery at least based on the at least one battery parameter, wherein the state of the battery at least comprises a normal state and an abnormal state;
executing a set operation in response to the judgment result that the battery is in the abnormal state;
wherein the state of the battery is associated with the at least one battery parameter.
2. The method of claim 1, wherein the abnormal condition includes at least one of: maintenance, suspected fault, fault status.
3. The method of claim 1, wherein the current battery parameters include at least one of: the battery comprises an output voltage, a voltage difference between battery cores, an output current, external environment humidity of the battery, internal resistance of the battery, temperature of the battery, acceleration of the battery, accumulated discharge capacity of the battery, service time, service duration, leakage of the battery and residual electric quantity of the battery.
4. The method of claim 3, wherein said determining the state of the battery based on at least the at least one battery parameter further comprises:
and when the battery leaks or the voltage difference between the battery cores exceeds a preset range, determining that the battery is in an abnormal state.
5. The method of claim 1, wherein determining the state of the battery based at least on the at least one battery parameter further comprises:
constructing a feature vector based on at least the current battery parameters and/or the historical battery parameters;
and inputting the characteristic vector into a trained battery state detection model, and acquiring a state detection result of the battery at the current moment.
6. The method of claim 5, wherein training the battery state detection model comprises the steps of:
acquiring sample data of at least one positive sample; the sample data of each positive sample at least comprises battery parameters corresponding to at least one acquisition moment when the sample battery is in a normal state;
acquiring sample data of at least one negative sample; the sample data of each negative sample at least comprises battery parameters corresponding to at least one acquisition moment before the sample battery is in an abnormal state;
constructing a feature vector corresponding to each sample based on the sample data of the at least one positive sample and the sample data of the at least one negative sample;
inputting the feature vector corresponding to each sample into an initialized detection model to obtain a battery state detection result of the sample;
and training the initialized detection model according to the battery state detection result of each sample and the actual battery state corresponding to the sample to obtain the battery state detection model.
7. The method of claim 6, wherein the constructing the feature vector for each sample based on the sample data for the at least one positive sample and the sample data for the at least one negative sample comprises:
according to the battery parameters corresponding to the at least one acquisition moment, constructing a feature vector corresponding to the at least one acquisition moment;
and carrying out weighted summation on the characteristic vectors corresponding to the at least one acquisition moment to generate the characteristic vector of the sample.
8. The method of claim 1, wherein performing the set operation in response to determining that the battery is in the abnormal state further comprises performing at least one of:
sending an instruction to the vehicle to trigger an alarm mechanism;
sending an instruction for executing locking to the vehicle;
sending prompt information of the battery state to a user terminal;
and sending the identification information of the battery and/or the identification information of the vehicle to a maintenance terminal.
9. The method of claim 1, wherein the vehicle is an electric bicycle.
10. A system for monitoring the condition of a battery, comprising:
an acquisition module for acquiring at least one battery parameter of a battery on a vehicle; the at least one battery parameter comprises a current battery parameter and/or a historical battery parameter;
the judging module is used for judging the state of the battery at least based on the at least one battery parameter, and the state of the battery comprises a normal state and an abnormal state;
the control module is used for responding to the judgment result that the battery is in the abnormal state and executing the set operation; wherein the state of the battery is associated with the at least one battery parameter.
11. The system of claim 10, wherein the abnormal condition includes at least one of: maintenance, suspected fault, fault status.
12. The system of claim 11, wherein the current battery parameters include at least one of: the battery comprises an output voltage, a voltage difference between battery cores, an output current, external environment humidity of the battery, internal resistance of the battery, temperature of the battery, accumulated discharge capacity of the battery, service time, service duration, acceleration of the battery, leakage of the battery and residual electric quantity of the battery.
13. The system of claim 12, wherein the determination module is further configured to:
and when the battery leaks or the voltage difference between the battery cores exceeds a preset range, determining that the battery is in an abnormal state.
14. The system of claim 10, wherein the determination module is further configured to:
constructing a feature vector based on at least the current battery parameters and/or the historical battery parameters;
and inputting the characteristic vector into a trained battery state detection model, and acquiring a state detection result of the battery at the current moment.
15. The system of claim 14, further comprising a training module to train the battery state detection model using the steps of:
acquiring sample data of at least one positive sample; the sample data of each positive sample at least comprises battery parameters corresponding to at least one acquisition moment when the sample battery is in a normal state;
acquiring sample data of at least one negative sample; the sample data of each negative sample at least comprises battery parameters corresponding to at least one acquisition moment before the sample battery is in an abnormal state;
constructing a feature vector corresponding to each sample based on the sample data of the at least one positive sample and the sample data of the at least one negative sample;
inputting the feature vector corresponding to each sample into an initialized detection model to obtain a battery state detection result of the sample;
and training the initialized detection model according to the battery state detection result of each sample and the actual battery state corresponding to the sample to obtain the battery state detection model.
16. The system of claim 15, wherein the training module constructs the feature vector for each sample by:
according to the battery parameters corresponding to the at least one acquisition moment, constructing a feature vector corresponding to the at least one acquisition moment;
and carrying out weighted summation on the characteristic vectors corresponding to the at least one acquisition moment to generate the characteristic vector of the sample.
17. The system of claim 10, wherein the control module is configured to perform at least one of the following operations in response to determining that the battery is in an abnormal state:
sending an instruction to the vehicle to trigger an alarm mechanism;
sending an instruction for executing locking to the vehicle;
sending prompt information of the battery state to a user terminal;
and sending the identification information of the battery and/or the identification information of the vehicle to a maintenance terminal.
18. The system of claim 10, wherein the vehicle is an electric bicycle.
19. An apparatus for monitoring battery status, the apparatus comprising at least one processor and at least one memory;
the at least one memory is for storing computer instructions;
the at least one processor is configured to execute at least some of the computer instructions to implement the operations of any of claims 1 to 9.
20. A computer-readable storage medium, characterized in that the storage medium stores computer instructions which, when executed by a processor, implement the operations of any one of claims 1 to 9.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112488340A (en) * | 2020-12-18 | 2021-03-12 | 无锡挚达物联科技有限公司 | Intelligent energy storage control method and device based on deep learning and storage medium |
| CN112583100A (en) * | 2020-11-26 | 2021-03-30 | 安徽中皖自动化科技有限公司 | Solar controller control system |
| CN112652827A (en) * | 2020-11-30 | 2021-04-13 | 嘉兴东大智宇科技有限公司 | Electric fork-lift uses battery management system based on end limit cloud |
| CN113344352A (en) * | 2021-05-28 | 2021-09-03 | 重庆长安汽车股份有限公司 | Storage battery feed risk identification method based on machine learning algorithm |
| CN114520822A (en) * | 2022-02-16 | 2022-05-20 | 东集技术股份有限公司 | Monitoring system, method, equipment and storage medium for equipment battery |
| WO2022134988A1 (en) * | 2020-12-26 | 2022-06-30 | 华为技术有限公司 | Battery fault detection method, system and apparatus, and device |
| WO2022174679A1 (en) * | 2021-02-18 | 2022-08-25 | 中国第一汽车股份有限公司 | Method and apparatus for predicting voltage inconsistency fault of battery cells, and server |
| CN115963423A (en) * | 2022-12-21 | 2023-04-14 | 广州辰创科技发展有限公司 | Power health state monitoring system |
| WO2023125382A1 (en) * | 2021-12-30 | 2023-07-06 | 奥动新能源汽车科技有限公司 | Battery management method and apparatus, electronic device, and storage medium |
| CN116424096A (en) * | 2023-03-28 | 2023-07-14 | 浙江新富尔电子有限公司 | New energy automobile battery acquisition assembly method and system for dynamic resource optimization configuration |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102024999A (en) * | 2010-11-16 | 2011-04-20 | 上海交通大学 | Electric car running power management system |
| CN103580073A (en) * | 2012-07-23 | 2014-02-12 | 群力电能科技股份有限公司 | Battery monitoring and protecting device and system and method thereof |
| CN104714187A (en) * | 2015-03-23 | 2015-06-17 | 重庆长安汽车股份有限公司 | Power battery abnormity judging method and system |
| CN106114259A (en) * | 2016-07-11 | 2016-11-16 | 安庆新景技电子科技有限公司 | Vehicular battery management method |
| CN106168799A (en) * | 2016-06-30 | 2016-11-30 | 常伟 | A kind of method carrying out batteries of electric automobile predictive maintenance based on big data machine learning |
| US20170261473A1 (en) * | 2015-03-09 | 2017-09-14 | Samsung Electronics Co., Ltd. | Method and apparatus for monitoring battery state |
| CN107329088A (en) * | 2016-04-29 | 2017-11-07 | 株式会社日立制作所 | The health status diagnostic device and method of battery |
| CN107380004A (en) * | 2017-09-07 | 2017-11-24 | 苏州易信安工业技术有限公司 | A kind of electric vehicle battery management method, apparatus and system |
| CN108357361A (en) * | 2018-05-07 | 2018-08-03 | 中国科学技术大学 | A kind of electric automobile power battery safety monitoring device and method |
| CN108674216A (en) * | 2018-04-23 | 2018-10-19 | 爱驰汽车有限公司 | A kind of the battery status monitoring method and system, car-mounted terminal of electric vehicle |
-
2018
- 2018-12-29 CN CN201811647272.5A patent/CN111381166A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102024999A (en) * | 2010-11-16 | 2011-04-20 | 上海交通大学 | Electric car running power management system |
| CN103580073A (en) * | 2012-07-23 | 2014-02-12 | 群力电能科技股份有限公司 | Battery monitoring and protecting device and system and method thereof |
| US20170261473A1 (en) * | 2015-03-09 | 2017-09-14 | Samsung Electronics Co., Ltd. | Method and apparatus for monitoring battery state |
| CN104714187A (en) * | 2015-03-23 | 2015-06-17 | 重庆长安汽车股份有限公司 | Power battery abnormity judging method and system |
| CN107329088A (en) * | 2016-04-29 | 2017-11-07 | 株式会社日立制作所 | The health status diagnostic device and method of battery |
| CN106168799A (en) * | 2016-06-30 | 2016-11-30 | 常伟 | A kind of method carrying out batteries of electric automobile predictive maintenance based on big data machine learning |
| CN106114259A (en) * | 2016-07-11 | 2016-11-16 | 安庆新景技电子科技有限公司 | Vehicular battery management method |
| CN107380004A (en) * | 2017-09-07 | 2017-11-24 | 苏州易信安工业技术有限公司 | A kind of electric vehicle battery management method, apparatus and system |
| CN108674216A (en) * | 2018-04-23 | 2018-10-19 | 爱驰汽车有限公司 | A kind of the battery status monitoring method and system, car-mounted terminal of electric vehicle |
| CN108357361A (en) * | 2018-05-07 | 2018-08-03 | 中国科学技术大学 | A kind of electric automobile power battery safety monitoring device and method |
Non-Patent Citations (3)
| Title |
|---|
| 刘金: "碰撞工况下汽车动力电池包动力学响应分析及安全评价", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 * |
| 樊彬等: "一种电池包跌落实验装置的研制及应用", 《电源技术研究与设计》 * |
| 蒲雪: "基于ANSYS/LS-DYNA的电子产品跌落仿真研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112583100A (en) * | 2020-11-26 | 2021-03-30 | 安徽中皖自动化科技有限公司 | Solar controller control system |
| CN112652827A (en) * | 2020-11-30 | 2021-04-13 | 嘉兴东大智宇科技有限公司 | Electric fork-lift uses battery management system based on end limit cloud |
| CN112652827B (en) * | 2020-11-30 | 2022-02-11 | 嘉兴东大智宇科技有限公司 | Electric fork-lift uses battery management system based on end limit cloud |
| CN112488340A (en) * | 2020-12-18 | 2021-03-12 | 无锡挚达物联科技有限公司 | Intelligent energy storage control method and device based on deep learning and storage medium |
| WO2022134988A1 (en) * | 2020-12-26 | 2022-06-30 | 华为技术有限公司 | Battery fault detection method, system and apparatus, and device |
| WO2022174679A1 (en) * | 2021-02-18 | 2022-08-25 | 中国第一汽车股份有限公司 | Method and apparatus for predicting voltage inconsistency fault of battery cells, and server |
| CN113344352A (en) * | 2021-05-28 | 2021-09-03 | 重庆长安汽车股份有限公司 | Storage battery feed risk identification method based on machine learning algorithm |
| WO2023125382A1 (en) * | 2021-12-30 | 2023-07-06 | 奥动新能源汽车科技有限公司 | Battery management method and apparatus, electronic device, and storage medium |
| CN116416083A (en) * | 2021-12-30 | 2023-07-11 | 奥动新能源汽车科技有限公司 | Battery management method, device, electronic equipment and storage medium |
| CN114520822A (en) * | 2022-02-16 | 2022-05-20 | 东集技术股份有限公司 | Monitoring system, method, equipment and storage medium for equipment battery |
| CN115963423A (en) * | 2022-12-21 | 2023-04-14 | 广州辰创科技发展有限公司 | Power health state monitoring system |
| CN115963423B (en) * | 2022-12-21 | 2023-11-14 | 广州辰创科技发展有限公司 | Power supply health state monitoring system |
| CN116424096A (en) * | 2023-03-28 | 2023-07-14 | 浙江新富尔电子有限公司 | New energy automobile battery acquisition assembly method and system for dynamic resource optimization configuration |
| CN116424096B (en) * | 2023-03-28 | 2023-09-29 | 浙江新富尔电子有限公司 | New energy automobile battery acquisition assembly method and system for dynamic resource optimization configuration |
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Application publication date: 20200707 |