CN110324383B - Cloud server, electric automobile and management system and method of power battery in electric automobile - Google Patents
Cloud server, electric automobile and management system and method of power battery in electric automobile Download PDFInfo
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Abstract
The invention discloses a cloud server, an electric automobile and a management system and method of a power battery in the electric automobile. The battery management system comprises a cloud server and a BMS arranged on the electric automobile, wherein the BMS is used for acquiring state information of a power battery in the electric automobile, acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the state information of the power battery and the plurality of first reference curve clusters to the cloud server, and receiving and storing a second reference curve cluster sent by the cloud server; the cloud server is used for storing historical data of the power battery and generating a second reference curve cluster according to the historical data and the plurality of first reference curve clusters, so that the reference curve clusters in the BMS can be continuously updated, various state information of the power battery can be accurately estimated through the updated reference curve clusters, the power battery can be effectively managed conveniently, and the service life of the power battery can be prolonged.
Description
Technical Field
The invention relates to the technical field of electric automobiles, in particular to a management system of a power battery in an electric automobile, a management method of the power battery in the electric automobile, the electric automobile and a cloud server.
Background
The lithium ion battery has become the most common energy storage device on the current electric automobile due to the advantages of high specific energy, long cycle life, strong charge retention capacity, low environmental pollution, no memory effect and the like, so the performance and the working state of the lithium ion battery are very important for the whole automobile. In order to ensure good performance of the power battery pack, fully utilize energy of the power battery, and prolong service life of the battery, it is important to effectively manage and control the power battery pack.
Currently, a Battery Management System (BMS) is composed of a Battery Control Unit (BCU) and a Battery Information Collector (BIC), and each Battery pack is provided with the BIC and the BCU. The BCU is used for bus current detection, system insulation monitoring, battery system power-on/power-off management, battery system thermal management, battery state of charge estimation, battery state of health estimation, battery power state estimation, fault diagnosis, vehicle communication and online program updating, data recording and the like.
According to the technology, when the battery State is estimated, the OCV (Open Circuit Voltage) -SOC (State of Charge) curve table-lookup prestored in the BCU is called to correct the battery State, and then the State parameters of the power battery including the kilometer of the electric vehicle are obtained according to the prestored reference curve table-lookup, and meanwhile, the functions of State monitoring, Charge and discharge control, fault diagnosis, CAN communication and the like are achieved.
The battery state parameters are influenced by factors such as temperature, charge-discharge rate, aging degree and battery use history. However, the OCV-SOC curve pre-stored in the BCU in the above-mentioned technology is usually a curve measured under a specific temperature and a specific charging rate by using batteries of the same type under a laboratory condition, and since the introduced influencing factors are solidified into a constant rather than a reference variable, the curve cannot reflect the change relationship among the battery state parameters along with the temperature, the charging and discharging rate, the aging degree and the battery use history, and cannot estimate the state parameter change trend of the battery, so that the estimated battery state parameters of the electric vehicle in the full working condition range have a large error. And as the attenuation degree of the battery pack is deepened, the error is continuously accumulated and enlarged, so that the problems of SOC jump and inaccurate driving range of the vehicle occur in the driving process.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the art described above. Therefore, an object of the present invention is to provide a management system for a power battery in an electric vehicle, so as to accurately estimate various status information of the power battery, and to implement effective management on the power battery.
The second purpose of the invention is to provide a management method of a power battery in an electric automobile.
The third purpose of the invention is to provide an electric automobile.
The fourth purpose of the invention is to provide a cloud server.
In order to achieve the above object, a management system for a power battery in an electric vehicle according to an embodiment of a first aspect of the present invention includes a cloud server and a battery management system BMS arranged on the electric vehicle, where the BMS is configured to collect state information of the power battery in the electric vehicle, obtain a plurality of first reference curve clusters of the power battery under a plurality of operating conditions according to the state information of the power battery, send the state information of the power battery and the plurality of first reference curve clusters to the cloud server, and receive and save a second reference curve cluster sent by the cloud server to update the reference curve clusters in the BMS; the cloud server is used for storing historical data of the power battery and generating the second reference curve cluster according to the historical data and the plurality of first reference curve clusters.
According to the management system of the power battery in the electric vehicle, the BMS acquires the plurality of first reference curve clusters of the power battery under the plurality of working conditions according to the state information of the power battery, the server generates the second reference curve cluster according to the historical data and the plurality of first reference curve clusters, and the BMS receives and stores the second reference curve cluster, so that the reference curves in the BMS can be continuously updated, the current state information of the power battery can be accurately estimated, the effective management of the power battery is facilitated, and the service life of the power battery is prolonged.
In addition, the management system for the power battery in the electric vehicle according to the above embodiment of the present invention may further have the following additional technical features:
according to one embodiment of the present invention, the BMS estimates fit the plurality of first clusters of reference curves according to a multiple-fit algorithm.
According to one embodiment of the invention, the cloud server is further configured to generate a prediction curve of the power battery according to the historical data and send the prediction curve to the BMS so as to update the reference curve cluster in the BMS.
According to one embodiment of the present invention, the BMS includes: the battery information collectors BIC correspond to the battery monomers in the power battery respectively; the BCU is used for acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the plurality of first reference curve clusters to the cloud server, and receiving and storing a second reference curve cluster sent by the cloud server.
According to one embodiment of the present invention, the BCU includes: the first controller is used for controlling the whole vehicle according to the state information of the power battery; the second controller is used for communicating with the cloud server, acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the plurality of first reference curve clusters to the cloud server, and receiving and storing a second reference curve cluster sent by the cloud server.
According to one embodiment of the invention, the plurality of operating conditions comprise a plurality of battery temperatures, a plurality of charge and discharge rates or a plurality of aging degrees.
In order to achieve the above object, a second embodiment of the present invention provides a method for managing a power battery in an electric vehicle, wherein a battery management system BMS is disposed above the electric vehicle, and the method includes the following steps: the BMS acquires state information of a power battery in the electric automobile, acquires a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, and sends the state information of the power battery and the plurality of first reference curve clusters to a cloud server; the cloud server generates the second reference curve cluster according to the historical data of the power battery and the plurality of first reference curve clusters; and the BMS receives and stores the second reference curve cluster sent by the cloud server so as to update the reference curve cluster in the BMS.
According to the management method of the power battery in the electric vehicle, the BMS is used for acquiring the plurality of first reference curve clusters of the power battery under the plurality of working conditions according to the state information of the power battery, the server is further used for generating the second reference curve cluster according to the historical data and the plurality of first reference curve clusters, and the BMS is used for receiving and storing the second reference curve cluster, so that the reference curves in the BMS can be continuously updated, the current state information of the power battery can be accurately estimated, the effective management of the power battery is facilitated, and the service life of the power battery is prolonged.
In addition, the management method for the power battery in the electric vehicle according to the above embodiment of the present invention may further have the following additional technical features:
according to one embodiment of the present invention, the BMS estimates fit the plurality of first clusters of reference curves according to a multiple-fit algorithm.
According to one embodiment of the invention, the cloud server further generates a prediction curve of the power battery according to the historical data and sends the prediction curve to the BMS so as to update a reference curve cluster in the BMS.
According to one embodiment of the invention, the plurality of operating conditions comprise a plurality of battery temperatures, a plurality of charge and discharge rates or a plurality of aging degrees.
In order to achieve the above object, a third aspect of the present invention provides an electric vehicle, on which a battery management system BMS is disposed, wherein the BMS is configured to: acquiring state information of a power battery in the electric automobile, acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, and sending the state information of the power battery and the plurality of first reference curve clusters to the cloud server so that the cloud server generates a second reference curve cluster according to the stored historical data of the power battery and the plurality of first reference curve clusters; and receiving and saving the second reference curve cluster sent by the cloud server to update the reference curve cluster in the BMS.
According to the electric automobile provided by the embodiment of the invention, the BMS acquires the plurality of first reference curve clusters of the power battery under the plurality of working conditions according to the state information of the power battery, so that the server generates the second reference curve cluster according to the historical data and the plurality of first reference curve clusters, and the BMS receives and stores the second reference curve cluster. Therefore, the reference curve in the BMS can be continuously updated, the current state information of each item of the power battery can be accurately estimated, the power battery can be effectively managed, and the service life of the power battery is prolonged.
In addition, the management method for the power battery in the electric vehicle according to the above embodiment of the present invention may further have the following additional technical features:
according to one embodiment of the present invention, the BMS estimates fit the plurality of first clusters of reference curves according to a multiple-fit algorithm.
According to one embodiment of the present invention, the BMS includes: the battery information collectors BIC correspond to the battery monomers in the power battery respectively; the BCU is used for acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the plurality of first reference curve clusters to the cloud server, receiving and storing the second reference curve clusters sent by the cloud server.
According to one embodiment of the present invention, the BCU includes: the first controller is used for controlling the whole vehicle according to the state information of the power battery; the second controller is used for communicating with the cloud server, acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the plurality of first reference curve clusters to the cloud server, and receiving and storing a second reference curve cluster sent by the cloud server.
According to one embodiment of the invention, the plurality of operating conditions comprise a plurality of battery temperatures, a plurality of charge and discharge rates or a plurality of aging degrees.
In order to achieve the above object, a fourth aspect of the present invention provides a cloud server, including: the system comprises a receiving module, a judging module and a judging module, wherein the receiving module is used for receiving state information of a power battery in the electric automobile and a plurality of first reference curve clusters sent by a Battery Management System (BMS) in the electric automobile, and the BMS acquires the plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery; the storage module is used for storing the state information of the power battery as historical data of the power battery; a first generating module, configured to generate a second reference curve cluster according to the historical data and the plurality of first reference curve clusters; and the sending module is used for sending the second reference curve cluster to the BMS so as to update the reference curve cluster in the BMS.
According to the cloud server provided by the embodiment of the invention, the receiving module is used for receiving the state information of the power battery in the electric vehicle and the plurality of first reference curve clusters, the first generating module is used for generating the second reference curve cluster according to the historical data containing the historical state parameters of the power battery and the plurality of first reference curve clusters, and the sending module is used for sending the second reference curve cluster to the BMS, so that the reference curves in the BMS can be continuously updated, the current state information of the power battery can be accurately estimated, the effective management of the power battery is facilitated, and the service life of the power battery is prolonged.
In addition, the cloud server proposed according to the above embodiment of the present invention may further have the following additional technical features:
according to an embodiment of the present invention, the cloud server further includes: and the second generation module is used for generating a prediction curve of the power battery according to the historical data, wherein the sending module is also used for sending the prediction curve to the BMS so as to update the reference curve cluster in the BMS.
Drawings
Fig. 1 is a block diagram of a management system of a power battery in an electric vehicle according to an embodiment of the present invention;
fig. 2 is a block diagram of a management system for a power battery in an electric vehicle according to an embodiment of the present invention;
fig. 3 is a block diagram of a management system of a power battery in an electric vehicle according to another embodiment of the present invention;
FIG. 4 is a flowchart illustrating the operation of a system for managing a power battery in an electric vehicle according to an embodiment of the present invention;
FIG. 5 is a flow chart of a method for managing a power battery in an electric vehicle according to an embodiment of the invention;
fig. 6 is a block diagram of the structure of an electric vehicle according to an embodiment of the present invention;
FIG. 7 is a block diagram of a cloud server according to one embodiment of the present invention; and
fig. 8 is a block diagram of a cloud server according to another embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The electric vehicle, the battery management system and the management method thereof according to the embodiment of the invention are described below with reference to the accompanying drawings.
Fig. 1 is a block diagram of a management system of a power battery in an electric vehicle according to an embodiment of the present invention. As shown in fig. 1, the management system 100 for a power battery in an electric vehicle includes a cloud server 10 and a battery management system BMS20 provided above the electric vehicle.
The BMS20 is used for acquiring state information of a power battery in the electric vehicle, acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the state information of the power battery and the plurality of first reference curve clusters to the cloud server 10, receiving and storing a second reference curve cluster sent by the cloud server 10, and updating the reference curve clusters in the BMS20 according to the second reference curve cluster. The cloud server 10 is configured to store historical data of the power battery, and generate a second reference curve cluster according to the historical data and the plurality of first reference curve clusters.
Optionally, the state information of the power battery includes a total voltage of the power battery, a voltage of the battery cell, a battery equalization condition, a temperature of the battery cell, a bus current, and the like; the historical data includes historical state information of the power battery and historical reference curve clusters of the power battery. The historical state information of the power batteries, that is, the state information of all the power batteries sent by the BMS20, and the historical reference curve cluster may include all the first reference curve clusters sent by the BMS20 received by the cloud server 10 and all the second reference curve clusters generated by the cloud server 10.
In this embodiment, the plurality of operating conditions include a plurality of battery temperatures, a plurality of charge-discharge rates, or a plurality of aging degrees, and the first reference curve cluster and the second reference curve cluster both refer to a plurality of state parameter variation curves of the battery cell at different temperatures, different charge-discharge rates, or different aging degrees.
Specifically, the BMS20 may collect the state information of the power battery in the electric vehicle at preset time t, and if the state information of the power battery is collected at time t (for the first time), obtain a plurality of first reference curve clusters of the corresponding power battery under a plurality of operating conditions according to the state information of the power battery, and then send the state information of the power battery at time t and the corresponding plurality of first reference curve clusters to the cloud server 10. The cloud server 10 receives the state information of the power battery at the time t and the corresponding multiple first reference curve clusters, puts the state information of the power battery at the time t into a historical database for storage, further generates a second reference curve cluster according to historical data in the current historical database and the multiple first reference curve clusters, and transmits the second reference curve cluster back to the BMS20, and meanwhile, the cloud server 10 can also store the second reference curve cluster into the historical database. The BMS20 receives and saves the second reference curve cluster returned by the cloud server 10, and replaces the current reference curve cluster in the BMS20 with the received second reference curve cluster as a reference curve for battery prediction management.
Further, the BMS20 collects the state information of the power battery at the time 2 × t, acquires a plurality of first reference curve clusters of the corresponding power battery under a plurality of working conditions according to the state information of the power battery, and further sends the state information of the power battery at the time 2 × t and the corresponding plurality of first reference curve clusters to the cloud server 10. The cloud server 10 receives the state information of the power battery at the time 2 × t and the corresponding multiple first reference curve clusters, puts the state information of the power battery at the time 2 × t into a historical database for storage, further generates a second reference curve cluster according to historical data in the current historical database and the multiple first reference curve clusters, and transmits the second reference curve cluster back to the BMS20, and meanwhile, the cloud server 10 can also store the second reference curve cluster into the historical database. The BMS20 receives and saves the second reference curve cluster returned by the cloud server 10, and replaces the current reference curve cluster in the BMS20 with the received second reference curve cluster as a reference curve for battery prediction management.
Therefore, as the power battery is charged and discharged in a cyclic depth, the BMS20 continuously fits and estimates to obtain a new first reference curve cluster and uploads the new first reference curve cluster to the cloud server 10, the cloud server 10 continuously generates a new second reference curve cluster according to historical data and transmits the new second reference curve cluster back to the BMS10, and continuous cyclic iteration is performed, so that the prediction result of the whole battery system is closer to the real state of the power battery, the effective management of the power battery is facilitated, and the service life of the power battery is prolonged.
In one embodiment of the invention, BMS20 estimates fit a plurality of first reference curve clusters according to a multiple fit algorithm.
For example, the BMS20 estimates fit a plurality of first reference curve clusters based on a target tracking filtering algorithm and a BMS algorithm of a dual or joint estimation architecture constructed by a battery model.
Wherein, the target tracking filter algorithm can be a Kalman filter algorithm; the initial values of the reference curves of the battery model, i.e. the power battery, may be a plurality of reference curve clusters measured under laboratory conditions, i.e. functional curves of influencing factors (e.g. current I, temperature T, state of charge SOC, state of health SOH) as a function of the battery model parameters (internal battery resistance DCIR, resistance R0, resistance R1, capacitance C1) and of the battery state quantities (Cap capacity, SOC, SOH, SOP, SOE). Alternatively, the reference curve cluster may be pre-stored in the BMS20 at the time of factory shipment of the electric vehicle.
For example, the BMS20 may obtain a plurality of first reference curve clusters under a plurality of operating conditions according to the collected current state information of the power battery and the battery model and by using a kalman filtering algorithm estimation fitting. Further, the server 10 may generate a plurality of second reference curve clusters according to the historical data of the power battery (at least including the historical state information of the power battery) and the plurality of first reference curve clusters, and send the plurality of second reference curve clusters to the BMS20, the BMS20 receives the second reference curve clusters, updates the reference curve clusters currently stored in the BMS20, and uses the updated reference curve clusters for table look-up input of the battery state parameter estimation algorithm. It should be noted that the reference curve cluster in BMS20 is continuously updated.
In one embodiment of the present invention, a fitting algorithm combining a polynomial, a neural network model, and the like may be further used to estimate the fitting to obtain the second reference curve cluster.
It is understood that the initial values of the reference curves stored in the BMS20 are always present and are used to estimate the fit to the first cluster of reference curves, and the stored clusters of reference curves are updated after receiving the second cluster of reference curves sent by the cloud server 10. And when the cloud server 10 fits the data uploaded by the BMS20 to obtain a second reference curve cluster, continuously transmitting the second reference curve cluster back to the BMS for updating the reference curve cluster currently stored in the BMS 20.
Further, the BMS20 may estimate the sop (state of power) of the power battery according to the updated reference curve cluster, and estimate the maximum power of the power battery under the current operating condition, so as to improve the discharging efficiency of the power battery. The BMS20 can also estimate the energy state SOE (State of energy) of the power battery according to the updated reference curve cluster, and provides a direct reference for accurately estimating the remaining mileage of the electric vehicle.
In one embodiment of the present invention, the cloud server 10 is further configured to generate a prediction curve of the power battery according to the historical data, and send the prediction curve to the BMS 20.
Specifically, the cloud server 10 may perform big data analysis on historical data of the power battery (e.g., historical state parameters of the power battery, a historical reference curve cluster, etc.), for example, if the power vehicle is currently driving on a highway, all historical data under the operating condition may be obtained, and a future state of the power battery, i.e., a prediction curve, may be predicted according to the historical data, so as to provide an important reference basis for a control strategy of the BMS20 (e.g., predicting a power state SOP, an energy state SOE, etc. of the power battery).
Compared with the battery management system in the related art, the management system of the power battery in the electric vehicle has the battery state management function of the traditional BMS and the battery state prediction function, and can accurately monitor the current state of the power battery and accurately predict the future state of the power battery by analyzing the historical change curve of the state information of the power battery.
In this embodiment, the BMS20 has the capability of quickly recognizing, accurately tracking and monitoring the state information and the state parameters of each battery cell, and the cloud server 10 has the functions of cloud data collection, big data statistical analysis, personalized real-time update, and the like. Therefore, the system 100 can record and monitor the historical state and the current state of all the battery cell packs, estimate the future state according to a mathematical statistical algorithm, optimize the performance and the service life of the power battery and provide data support for the hierarchical utilization of the power battery.
In one embodiment of the present invention, as shown in fig. 2, the BMS20 includes a plurality of battery information collectors BIC21 and a battery control unit BCU 22.
Wherein, a plurality of BICs 21 correspond to a plurality of battery cells in the power battery respectively; the BCU22 is connected to the plurality of BIC21 and communicates with the cloud server 10, and the BCU22 is configured to acquire a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, send the plurality of first reference curve clusters to the cloud server 10, and receive and store a second reference curve cluster sent by the cloud server 10. It should be noted that the BCU22 may save the second reference curve cluster to a position corresponding to the current reference curve cluster in the BMS20 to update the reference curve cluster in the BMS 20.
Alternatively, each BIC21 may send data to BCU22 via CAN (Controller Area Network), FlexRay or Daisy Chain.
In this embodiment, BCU22 and all BIC21 may be assembled with all battery packs inside the cabin of an electric vehicle.
Specifically, the BIC21 is used for sampling and monitoring the voltage of a battery cell, balancing the battery, sampling and monitoring the temperature of a battery pack, and the BCU22 is used for detecting the current of a bus, monitoring the insulation of a system, managing the power on/off of a battery system, managing the thermal management of the battery system, estimating the state of charge (soc) (state of charge), estimating the state of health (soh) (state of health), estimating the state of power (sop) (state of power), diagnosing faults, communicating the whole vehicle and updating an online program, recording data and the like.
Further, as shown in fig. 3, BCU22 includes a first controller 22a and a second controller 22 b. The first controller 22a is used for performing vehicle control according to the state information of the power battery; the second controller 22b is configured to communicate with the cloud server 10, acquire a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, send the plurality of first reference curve clusters to the cloud server 10, and receive and store a second reference curve cluster sent by the cloud server. It should be noted that the second controller 22b may save the second reference curve cluster to a position corresponding to the current reference curve cluster in the BMS20 to update the reference curve cluster in the BMS 20.
In this embodiment, the BCU22 has a powerful data storage space and a dual MCU (i.e., the first controller 22a and the second controller 22b) with high data processing speed, has an offline data processing capability, and can interact with the cloud server 10 through a wireless communication module by means of wireless communication. And then the cloud server 10 performs cloud computing and big data analysis on the battery state information and state parameters of the whole life cycle of the power battery, so as to realize current state management and future state prediction of the power battery.
To facilitate understanding of the work flow of the management system for a power battery in an electric vehicle according to an embodiment of the present invention, the following description may be made with reference to fig. 4:
as shown in fig. 4, first, the start BMS20 collects the state information of the power battery, including the voltage V of the power battery collected by the BIC21General assemblyVoltage V of each battery cellcellTemperature T of power battery and bus current I collected by BCU22General assemblyAnd BIC21 converts VGeneral assembly、VcellAnd T to BCU 22.
Then, the first controller 22a in BCU22 is dependent on VGeneral assembly、VcellExecuting a vehicle control strategy, including controlling the action of external high-voltage and low-voltage components, fault diagnosis, overcharge protection, overdischarge protection, over-temperature protection, balance control and the like; the second controller 22b in BCU22 may utilize a dual model algorithm, on the one hand, based on VGeneral assembly、VcellT and IGeneral assemblyEstimating and fitting the first reference curve cluster under different working conditions, and on the other hand, estimating and fitting the first reference curve cluster under different working conditions according to VGeneral assembly、VcellT and IGeneral assemblyAnd estimating and predicting state parameters such as SOC, SOH, SOP, SOE, RM and the like. Furthermore, the first reference curve cluster and the state parameter estimation result may be uploaded to the cloud server 10 by a wireless communication technology such as 2/3/4/5G or bluetooth.
Further, the cloud server 10 integrates the first reference curve cluster and the estimation result of the state parameter, and fits a second reference curve cluster closest to the current state of the power battery according to the first reference curve cluster and the stored historical data (such as the previously received state information and the state parameter) of the power battery, and then transmits the second reference curve cluster back to the BCU22 through wireless communication technology such as 2/3/4/5G or bluetooth for updating the reference curve cluster stored in the second controller 22 b.
In this way, the reference curve cluster in the BMS20 can be continuously updated iteratively in the above manner, so that the prediction result of the battery system is closer to the real state of the battery, and accurate management of the power battery is facilitated.
Meanwhile, the cloud server 10 may also analyze historical state information and state parameters of the power battery to predict a future state of the battery, and provide an important reference for the BMS control strategy.
It can be understood that after the data processing is finished, the next cycle can be entered.
In summary, according to the management system of the power battery in the electric vehicle in the embodiment of the invention, not only can the state parameters of the battery, including the maximum values of SOC, SOH, SOE, SOP and SOP, be more accurately estimated, the remaining mileage of the electric vehicle can be conveniently estimated, the vehicle can be guided to release energy with the maximum power in the discharging stage or the maximum power releasable by the vehicle at the current moment can be limited, so as to effectively protect the power battery and improve the service life of the power battery, but also the future state of the battery can be predicted, so that a predictive maintenance suggestion or maintenance can be conveniently provided for the states of the electric vehicle and the power battery. In addition, through cloud computing and big data analysis, the performance of the power battery characteristics under different types, different batches and different proportioning conditions can be accurately known, and important design reference is provided for the power battery design; and when the power battery reaches the decommissioning condition on the electric automobile, the decommissioned battery pack can be analyzed and screened by utilizing the cloud data, so that grading and gradient utilization of the decommissioned power battery are facilitated.
Fig. 5 is a flowchart of a management method of a power battery in an electric vehicle according to an embodiment of the present invention.
In an embodiment of the present invention, a BMS is provided above the electric vehicle.
As shown in fig. 5, the method for managing the power battery in the electric vehicle includes the following steps:
s101, the BMS acquires state information of a power battery in the electric automobile, acquires a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, and sends the state information of the power battery and the plurality of first reference curve clusters to a cloud server.
The plurality of working conditions comprise a plurality of battery temperatures, a plurality of charge and discharge rates or a plurality of aging degrees.
In one embodiment of the invention, the BMS models the plurality of first reference curve clusters according to a multiple-fit algorithm estimation.
And S102, the cloud server generates a second reference curve cluster according to the historical data of the power battery and the plurality of first reference curve clusters.
And S103, the BMS receives and stores the second reference curve cluster sent by the cloud server so as to update the reference curve cluster in the BMS.
In one embodiment of the invention, the cloud server further generates a prediction curve of the power battery according to the historical data and transmits the prediction curve to the BMS to update the reference curve cluster in the BMS.
In addition, as another specific implementation of the method for managing a power battery in an electric vehicle according to an embodiment of the present invention, reference may be made to the specific implementation of the system for managing a power battery in an electric vehicle according to the above-described embodiment.
According to the management method of the power battery in the electric automobile, provided by the embodiment of the invention, various state parameters of the battery, including the maximum values of SOC, SOH, SOE, SOP and SOP, can be estimated more accurately, so that the residual mileage of the electric automobile can be estimated conveniently, the energy can be released by the vehicle with the maximum power in the discharging stage or the maximum power released by the vehicle at the current moment can be limited, the power battery can be effectively protected, the service life of the power battery can be prolonged, the future state of the battery can be predicted, and the predictive maintenance suggestion or maintenance can be provided for the states of the electric automobile and the power battery conveniently. In addition, through cloud computing and big data analysis, the performance of the power battery characteristics under different types, different batches and different proportioning conditions can be accurately known, and important design reference is provided for the power battery design; and when the power battery reaches the decommissioning condition on the electric automobile, the decommissioned battery pack can be analyzed and screened by utilizing the cloud data, so that grading and gradient utilization of the decommissioned power battery are facilitated.
Fig. 6 is a block diagram of the electric vehicle according to the embodiment of the present invention.
As shown in fig. 6, a BMS20 is disposed above the electric vehicle 200, wherein the BMS20 is configured to collect state information of a power battery in the electric vehicle, acquire a plurality of first reference curve clusters of the power battery under a plurality of operating conditions according to the state information of the power battery, and send the state information of the power battery and the plurality of first reference curve clusters to a cloud server, so that the cloud server generates a second reference curve cluster according to stored historical data of the power battery and the plurality of first reference curve clusters, and the BMS20 further receives and stores the second reference curve cluster sent by the cloud server to update the reference curve clusters in the BMS.
The plurality of working conditions comprise a plurality of battery temperatures, a plurality of charge and discharge rates or a plurality of aging degrees.
Specifically, in one embodiment of the present invention, BMS20 estimates fit a plurality of first clusters of reference curves according to a multiple fit algorithm.
In one embodiment of the present invention, referring to fig. 2, the BMS20 includes: a plurality of battery information collectors BIC21 and a battery control unit BCU 22.
Wherein, a plurality of BIC21 correspond to a plurality of battery monomer in the power battery respectively. The BCU22 is connected with the BICs 21 and is in communication with the cloud server, and the BCU22 is used for acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the plurality of first reference curve clusters to the cloud server, and receiving and storing a second reference curve cluster sent by the cloud server.
Further, referring to fig. 3, BCU22 includes: a first controller 22a and a second controller 22 b.
The first controller 22a is configured to perform vehicle control according to the state information of the power battery. The second controller 22b is configured to communicate with the cloud server, acquire a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, send the plurality of first reference curve clusters to the cloud server, and receive and store a second reference curve cluster sent by the cloud server.
In addition, as another specific implementation of the electric vehicle according to the embodiment of the present invention, reference may be made to the specific implementation of the management system of the power battery in the electric vehicle according to the above-described embodiment.
According to the electric vehicle disclosed by the embodiment of the invention, various state information of the battery, including the maximum values of SOC, SOH, SOE, SOP and SOP, can be estimated more accurately, so that the remaining mileage of the electric vehicle can be estimated conveniently, the energy of the vehicle is released by the maximum power in the discharging stage or the maximum power released by the vehicle at the current moment is limited, the power battery is effectively protected, and the service life of the power battery is prolonged.
In addition, other structures and functions of the electric vehicle according to the embodiment of the present invention are known to those skilled in the art, and are not described herein in detail to reduce redundancy.
Fig. 7 is a block diagram of a cloud server according to an embodiment of the present invention. As shown in fig. 7, the cloud server 10 includes: a receiving module 11, a storage module 12, a first generating module 13 and a transmitting module 14.
The receiving module 11 is configured to receive state information of a power battery in the electric vehicle and a plurality of first reference curve clusters sent by a BMS in the electric vehicle, where the BMS acquires the plurality of first reference curve clusters of the power battery under a plurality of operating conditions according to the state information of the power battery. The storage module 12 is used for storing the state information of the power battery as historical data of the power battery. The first generating module 13 is configured to generate a second reference curve cluster according to the historical data and the plurality of first reference curve clusters. The sending module 14 is configured to send the second reference curve cluster to the BMS to update the reference curve cluster in the BMS.
In one embodiment of the present invention, as shown in fig. 8, the cloud server 10 further includes a second generation module 15. The second generation module 15 is used for generating a prediction curve of the power battery according to the historical data.
In this embodiment, the sending module 14 also sends the prediction curves to the BMS to update the clusters of reference curves in the BMS.
It should be noted that, as another specific implementation of the cloud server 10 according to the embodiment of the present invention, reference may be made to a specific implementation of the cloud server 10 in the management system of a power battery in an electric vehicle according to the above embodiment of the present invention.
According to the cloud server provided by the embodiment of the invention, the second reference curve cluster is generated by continuously analyzing the historical state parameters of the battery so as to continuously update the reference curve cluster in the BMS, so that the BMS can accurately estimate various current state information of the power battery, the power battery can be effectively managed, and the service life of the power battery can be prolonged. In addition, through cloud computing and big data analysis, the performance of the power battery characteristics under different types, different batches and different proportioning conditions can be accurately known, and important design reference is provided for the power battery design; and when the power battery reaches the decommissioning condition on the electric automobile, the decommissioned battery pack can be analyzed and screened by utilizing the cloud data, so that grading and gradient utilization of the decommissioned power battery are facilitated.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (14)
1. A management system of a power battery in an electric vehicle is characterized by comprising a cloud server and a battery management system BMS arranged on the electric vehicle, wherein,
the BMS is used for acquiring state information of a power battery in the electric automobile, acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the state information of the power battery and the plurality of first reference curve clusters to the cloud server, and receiving and storing a second reference curve cluster sent by the cloud server to update the reference curve clusters in the BMS, wherein the BMS estimates and fits the plurality of first reference curve clusters according to the state information of the power battery, an initial reference curve cluster and a multiple fitting algorithm;
the cloud server is used for storing historical data of the power battery and generating the second reference curve cluster according to the historical data and the plurality of first reference curve clusters.
2. The system for managing the power battery in the electric vehicle according to claim 1, wherein the cloud server is further configured to generate a prediction curve of the power battery according to the historical data and send the prediction curve to the BMS so as to update the reference curve cluster in the BMS.
3. The system for managing a power battery in an electric vehicle according to claim 1, wherein the BMS includes:
the battery information collectors BIC correspond to the battery monomers in the power battery respectively;
the BCU is used for acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the plurality of first reference curve clusters to the cloud server, receiving and storing the second reference curve clusters sent by the cloud server.
4. The system for managing a power battery in an electric vehicle according to claim 3, wherein the BCU comprises:
the first controller is used for controlling the whole vehicle according to the state information of the power battery;
the second controller is used for communicating with the cloud server, acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the plurality of first reference curve clusters to the cloud server, and receiving and storing a second reference curve cluster sent by the cloud server.
5. The system of claim 1, wherein the plurality of operating conditions comprise a plurality of battery temperatures, a plurality of charge and discharge rates, or a plurality of aging levels.
6. A management method of a power battery in an electric vehicle, wherein a battery management system BMS is provided above the electric vehicle, the method comprising the steps of:
the BMS acquires state information of a power battery in the electric automobile, acquires a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, and sends the plurality of first reference curve clusters to a cloud server, wherein the BMS estimates and fits the plurality of first reference curve clusters according to the state information of the power battery, an initial reference curve cluster and a multiple fitting algorithm;
the cloud server generates a second reference curve cluster according to the historical data of the power battery and the plurality of first reference curve clusters;
and the BMS receives and stores the second reference curve cluster sent by the cloud server so as to update the reference curve cluster in the BMS.
7. The method for managing the power battery in the electric vehicle according to claim 6, wherein the cloud server further generates a prediction curve of the power battery according to the historical data and transmits the prediction curve to the BMS so as to update a reference curve cluster in the BMS.
8. The method for managing the power battery in the electric vehicle according to claim 6, wherein the plurality of working conditions comprise a plurality of battery temperatures, a plurality of charge and discharge rates or a plurality of aging degrees.
9. An electric vehicle, characterized in that a battery management system BMS is provided above the electric vehicle, wherein the BMS is configured to:
acquiring state information of a power battery in the electric vehicle, acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, and sending the plurality of first reference curve clusters to a cloud server, so that the cloud server generates a second reference curve cluster according to stored historical data of the power battery and the plurality of first reference curve clusters, wherein the BMS estimates and fits the plurality of first reference curve clusters according to the state information of the power battery, an initial reference curve cluster and a multiple fitting algorithm; and
and receiving and saving the second reference curve cluster sent by the cloud server so as to update the reference curve cluster in the BMS.
10. The electric vehicle of claim 9, wherein the BMS comprises:
the battery information collectors BIC correspond to the battery monomers in the power battery respectively;
the BCU is used for acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the plurality of first reference curve clusters to the cloud server, receiving and storing the second reference curve clusters sent by the cloud server.
11. The electric vehicle of claim 10, wherein the BCU comprises:
the first controller is used for controlling the whole vehicle according to the state information of the power battery;
the second controller is used for communicating with the cloud server, acquiring a plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, sending the plurality of first reference curve clusters to the cloud server, and receiving and storing a second reference curve cluster sent by the cloud server.
12. The electric vehicle of claim 9, wherein the plurality of operating conditions comprise a plurality of battery temperatures, a plurality of charge and discharge rates, or a plurality of aging degrees.
13. A cloud server, comprising:
the system comprises a receiving module, a judging module and a judging module, wherein the receiving module is used for receiving state information of a power battery in the electric automobile and a plurality of first reference curve clusters sent by a battery management system BMS in the electric automobile, the BMS acquires the plurality of first reference curve clusters of the power battery under a plurality of working conditions according to the state information of the power battery, and the BMS estimates and fits the plurality of first reference curve clusters according to the state information of the power battery, an initial reference curve cluster and a multiple fitting algorithm;
the storage module is used for storing the state information of the power battery as historical data of the power battery;
a first generating module, configured to generate a second reference curve cluster according to the historical data and the plurality of first reference curve clusters;
and the sending module is used for sending the second reference curve cluster to the BMS.
14. The cloud server of claim 13, further comprising:
a second generation module for generating a prediction curve of the power battery according to the historical data, wherein,
the transmission module also transmits the prediction curve to the BMS.
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| CN113848430A (en) * | 2021-10-20 | 2021-12-28 | 广东电网有限责任公司广州供电局 | Electric energy fault monitoring method and device |
| CN114035098A (en) * | 2021-12-14 | 2022-02-11 | 北京航空航天大学 | Lithium battery health state prediction method integrating future working condition information and historical state information |
| CN114629905B (en) * | 2022-02-16 | 2022-10-28 | 福建时代星云科技有限公司 | Energy storage system SOP optimization method and device based on cloud data |
| DE102022115777A1 (en) | 2022-06-24 | 2024-01-04 | Accure Battery Intelligence Gmbh | Method and system for the early detection of critical errors in a battery |
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