WO2019184841A1

WO2019184841A1 - Electric vehicle, and management system and method for power battery therein

Info

Publication number: WO2019184841A1
Application number: PCT/CN2019/079446
Authority: WO
Inventors: 邓林旺; 冯天宇; 林思岐; 吕纯; 杨子华
Original assignee: 比亚迪股份有限公司
Priority date: 2018-03-30
Filing date: 2019-03-25
Publication date: 2019-10-03
Also published as: CN110324383B; CN110324383A

Abstract

Disclosed are an electric vehicle, and a management system and method for a power battery therein. The battery management system comprises a cloud server and a BMS disposed on an electric vehicle, wherein the BMS is used to collect state information of a power battery in the electric vehicle, acquire a plurality of first reference curve clusters of the power battery in 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; and the cloud server is used to save historical data of the power battery, and generate the second reference curve cluster according to the historical data and the plurality of first reference curve clusters.

Description

Electric vehicle and management system and method thereof for power battery

Cross-reference to related applications

The present application claims the priority of the Chinese patent application No. "201810287724.7", which is filed on March 30, 2018, and is entitled "Management System and Method for Cloud Server, Electric Vehicle and Its Medium Power Battery".

Technical field

The present application relates to the field of electric vehicle technology, and in particular, to a power battery management system for an electric vehicle, a power battery management method for the electric vehicle, and an electric vehicle.

Background technique

Lithium-ion batteries have become the most commonly used energy storage devices in electric vehicles because of their high specific energy, long cycle life, strong charge retention, low environmental pollution, and no memory effect. Therefore, their performance and working status are correct. The whole car is crucial. In order to ensure the good performance of the power battery pack, make full use of the energy of the power battery, and extend the life of the battery, it is particularly important to effectively manage and control the battery.

The current BMS (Battery Management System) consists of a BCU (Battery Control Unit) and a BIC (Battery Information Collector), and each battery pack is equipped with a BIC. With the BCU. Among them, BIC is used for sampling and monitoring of battery cell voltage, battery equalization, battery pack temperature sampling and monitoring, BCU for bus current detection, system insulation monitoring, battery system up/down management, battery system thermal management, battery charge Electrical status estimation, battery health status estimation, battery power status estimation, fault diagnosis, vehicle communication and online program update, data recording, etc.

In this technique, when estimating the state of the battery, it is corrected by calling the OCV (Open Circuit Voltage)-SOC (State of Charge) curve table stored in the BCU, and then according to the pre-stored reference. The curve look-up table shows the state parameters of the power battery, including the remaining mileage of the electric vehicle (km km), and at the same time realizes functions such as condition monitoring, charge and discharge control, fault diagnosis, and CAN communication.

Battery status parameters are affected by factors such as temperature, charge and discharge rate, ageing, and battery usage history. The OCV-SOC curve pre-stored in the BCU in the above technology is usually a curve measured under a laboratory condition using a battery of the same type at a specific temperature and a specific charging rate, and the influencing factor is solidified into a constant instead of Reference variable, so the curve does not reflect the relationship between battery state parameters with temperature, charge and discharge rate, aging degree and battery usage history, and can not predict the trend of battery state parameters, so the estimated electric car There is a large error in the battery state parameters over the full operating range. And as the attenuation of the battery pack deepens, the error will continue to accumulate and expand, causing the SOC to jump during driving and the problem of inexhaustible mileage.

Summary of the invention

The present application aims to solve at least one of the technical problems in the above-mentioned techniques to some extent. To this end, an object of the present application is to provide a power battery management system for an electric vehicle to accurately estimate various status information of the power battery and achieve effective management of the power battery.

A second object of the present application is to provide a method of managing a power battery in an electric vehicle.

A third object of the present application is to propose an electric vehicle.

A fourth object of the present application is to propose a cloud server.

In order to achieve the above objective, the first aspect of the present application provides a management system for a power battery in an electric vehicle, including a cloud server and a battery management system BMS disposed on the electric vehicle, wherein the BMS is used. Collecting state information of the power battery in the electric vehicle, and acquiring a plurality of first reference curve clusters of the power battery under a plurality of operating conditions according to state information of the power battery, and the power battery Status information and the plurality of first reference curve clusters are sent to the cloud server, and receiving and saving a second reference curve cluster sent by the cloud server to update a reference curve cluster in the BMS; the cloud And a server, configured to save historical data of the power battery, and generate 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 according to the embodiment of the present application, the BMS obtains 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 then passes the server according to historical data and The plurality of first reference curve clusters generate a second reference curve cluster, and receive and save the second reference curve cluster through the BMS, thereby continuously updating the reference curve in the BMS, so as to accurately estimate current status information of the power battery. It is beneficial to effectively manage the power battery and improve the service life of the power battery.

In addition, the management system of the power battery in the electric vehicle according to the above embodiment of the present application may further have the following additional technical features:

The BMS estimates the fit of the plurality of first reference curve clusters according to a multiple fitting algorithm.

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 to update a reference curve cluster in the BMS.

The BMS includes: a plurality of battery information collectors BIC respectively corresponding to a plurality of battery cells in the power battery; a battery control unit BCU, a BCU connected to the plurality of BICs, and the Communicating by the cloud server, the BCU is configured to acquire, according to the state information of the power battery, a plurality of first reference curve clusters of the power battery under a plurality of operating conditions, and the plurality of first reference curves The cluster is sent to the cloud server, and the second reference curve cluster sent by the cloud server is received and saved.

The BCU includes: a first controller, configured to perform vehicle control according to status information of the power battery; a second controller, configured to communicate with the cloud server, and obtain according to status information of the power battery a plurality of first reference curve clusters of the power battery under a plurality of operating conditions, and transmitting the plurality of first reference curve clusters to the cloud server, and receiving and saving the number sent by the cloud server Two reference curve clusters.

The plurality of operating conditions includes a plurality of battery temperatures, a plurality of charge and discharge rates, or a plurality of degrees of aging.

In order to achieve the above object, a second aspect of the present application 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 collects state information of the power battery in the electric vehicle, and acquires 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 the power battery The status information and the plurality of first reference curve clusters are sent to the 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; The BMS receives and saves a second reference curve cluster sent by the cloud server to update a reference curve cluster in the BMS.

According to the method for managing a power battery in an electric vehicle according to an embodiment of the present application, a plurality of first reference curve clusters of the power battery under a plurality of operating conditions are acquired by the BMS according to state information of the power battery, and then the server is based on historical data and The plurality of first reference curve clusters generate a second reference curve cluster, and receive and save the second reference curve cluster through the BMS, thereby continuously updating the reference curve in the BMS, so as to accurately estimate current status information of the power battery. It is beneficial to effectively manage the power battery and improve the service life of the power battery.

In addition, the method for managing a power battery in an electric vehicle according to the above embodiment of the present application may further have the following additional technical features:

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 to update a reference curve cluster in the BMS.

In order to achieve the above object, an embodiment of the third aspect of the present application provides an electric vehicle, and a battery management system BMS is disposed on the electric vehicle, wherein the BMS is used to: collect a power battery in the electric vehicle. State information, and acquiring a plurality of first reference curve clusters of the power battery under a plurality of operating conditions according to state information of the power battery, and state information of the power battery and the plurality of first Sending a reference cluster to the cloud server, so that the cloud server generates a second reference curve cluster according to the saved historical data of the power battery and the plurality of first reference curve clusters; and receiving and saving the cloud The second reference curve cluster sent by the server to update the reference curve cluster in the BMS.

According to the electric vehicle of the embodiment of the present application, a plurality of first reference curve clusters of the power battery under a plurality of operating conditions are acquired by the BMS according to the state information of the power battery to pass the server according to the historical data and the plurality of first reference curves. The cluster generates a second reference curve cluster, and then receives and saves the second reference curve cluster through the BMS. Thereby, the reference curve in the BMS can be continuously updated, which is convenient for accurately estimating the current state information of the power battery, thereby facilitating effective management of the power battery and improving the service life of the power battery.

The BMS includes: a plurality of battery information collectors BIC, each of which corresponds to a plurality of battery cells in the power battery; a battery control unit BCU, the BCU is connected to the plurality of BICs, and The cloud server performs communication, and the BCU is configured to acquire, according to status information of the power battery, a plurality of first reference curve clusters of the power battery under multiple operating conditions, and the plurality of first The reference curve cluster is sent to the cloud server, and the second reference curve cluster sent by the cloud server is received and saved.

In order to achieve the above objective, a fourth embodiment of the present application provides a cloud server, including: a receiving module, configured to receive status information and multiple firsts of a power battery in the electric vehicle sent by a battery management system BMS in an electric vehicle a reference curve cluster, wherein the BMS acquires the plurality of first reference curve clusters of the power battery under a plurality of operating conditions according to state information of the power battery; and a storage module configured to store the power Status information of the 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 a sending module, configured to: The second reference curve cluster is sent to the BMS to update a reference curve cluster in the BMS.

The cloud server of the embodiment of the present application receives, by the receiving module, status information of the power battery in the electric vehicle sent by the BMS in the electric vehicle and a plurality of first reference curve clusters, and the history of the historical state parameters including the power battery is adopted by the first generation module. The data and the plurality of first reference curve clusters generate a second reference curve cluster, and send the second reference curve cluster to the BMS through the sending module, thereby continuously updating the reference curve in the BMS, so as to accurately estimate the current power battery The item status information is beneficial to effectively manage the power battery and improve the service life of the power battery.

In addition, the cloud server proposed according to the foregoing embodiment of the present application may further have the following additional technical features:

The cloud server further includes: a second generating module, configured to generate a prediction curve of the power battery according to the historical data, wherein the sending module further sends the prediction curve to the BMS to update the A reference curve cluster in the BMS.

DRAWINGS

1 is a block diagram showing the structure of a power battery management system in an electric vehicle according to an embodiment of the present application;

2 is a structural block diagram of a power battery management system in an electric vehicle according to an embodiment of the present application;

3 is a structural block diagram of a power battery management system in an electric vehicle according to another embodiment of the present application;

4 is a flowchart showing the operation of a power battery management system in an electric vehicle according to an embodiment of the present application;

5 is a flowchart of a method of managing a power battery in an electric vehicle according to an embodiment of the present application;

6 is a structural block diagram of an electric vehicle according to an embodiment of the present application;

7 is a structural block diagram of a cloud server according to an embodiment of the present application;

FIG. 8 is a structural block diagram of a cloud server according to another embodiment of the present application.

detailed description

The embodiments of the present application are described in detail below, and the examples of the embodiments are illustrated in the drawings, wherein the same or similar reference numerals are used to refer to the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are intended to be illustrative, and are not to be construed as limiting.

An electric vehicle and a battery management system and management method thereof according to embodiments of the present application will be described below with reference to the accompanying drawings.

1 is a block diagram of a management system of a power battery in an electric vehicle according to an embodiment of the present application. As shown in FIG. 1, the power battery management system 100 in the electric vehicle includes a cloud server 10 and a battery management system BMS20 disposed above the electric vehicle.

The BMS 20 is used for collecting state information of the power battery in the electric vehicle, and 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 the state information of the power battery and The plurality of first reference curve clusters are sent to the cloud server 10, and the second reference curve clusters sent by the cloud server 10 are received and saved, and the reference curve clusters in the BMS 20 are updated according to the second reference curve cluster. The cloud server 10 is configured to save 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 status information of the power battery includes a total voltage of the power battery, a voltage of the battery unit, a battery balance, a temperature of the battery unit, a bus current, and the like; the historical data includes historical status information of the power battery and history of the power battery. Reference curve cluster. The historical status information of the power battery, that is, the status information of all the power batteries sent by the BMS 20, the historical reference curve cluster may include all the first reference curve clusters sent by the BMS 20 received by the cloud server 10 and all the second references generated by the cloud server 10. Curve cluster.

In this embodiment, the plurality of operating conditions include a plurality of battery temperatures, a plurality of charge and discharge rates, or a plurality of degrees of aging. The first reference curve cluster and the second reference curve cluster refer to the battery cells at different temperatures and different charges. Multiple state parameter curves for discharge rate or different degrees of aging.

The BMS 20 can collect state information of the power battery in the electric vehicle every preset time t. For example, when the state information of the power battery is collected at time t (first time), the corresponding power battery is obtained according to the state information of the power battery. The plurality of first reference curve clusters under the working condition further 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 time t and the corresponding plurality of first reference curve clusters, and stores the state information of the power battery at time t in the historical database, and further according to the historical data in the current history database. The first reference curve cluster generates a second reference curve cluster, and the second reference curve cluster is transmitted back to the BMS 20, and the cloud server 10 can also save the second reference curve cluster to the history database. The BMS 20 receives and saves the second reference curve cluster returned by the cloud server 10, and replaces the current reference curve cluster in the BMS 20 with the received second reference curve cluster as a reference curve for battery prediction management.

The BMS 20 collects state information of the power battery at 2*t time, and obtains 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 further 2*t The state information of the power battery at the moment and the corresponding plurality of first reference curve clusters are sent to the cloud server 10. The cloud server 10 receives the state information of the power battery at the time of 2*t and the corresponding plurality of first reference curve clusters, and stores the state information of the power battery at the time of 2*t in the historical database, and then according to the current history database. The historical data and the plurality of first reference curve clusters generate a second reference curve cluster, and the second reference curve cluster is transmitted back to the BMS 20, and the cloud server 10 may also save the second reference curve cluster to the history database. The BMS 20 receives and saves the second reference curve cluster returned by the cloud server 10, and replaces the current reference curve cluster in the BMS 20 with the received second reference curve cluster as a reference curve for battery prediction management.

Thus, as the power battery charge and discharge cycle deepens, the BMS 20 continuously estimates and estimates a new first reference curve cluster and uploads it to the cloud server 10, and the cloud server 10 continuously generates a new second reference curve cluster based on the historical data and returns it. To BMS10, continuous loop iteration, thereby making the overall battery system prediction result closer to the real state of the power battery, which is beneficial to the effective management of the power battery and the service life of the power battery.

Wherein, the BMS 20 estimates a plurality of first reference curve clusters according to a multiple fitting algorithm.

For example, the BMS 20 estimates a plurality of first reference curve clusters based on a BMS algorithm of a dual estimation or joint estimation architecture constructed by a target tracking filtering algorithm and a battery model.

The target tracking filtering algorithm may be a Kalman filtering algorithm; a battery model, that is, an initial value of a reference curve of the power battery, and the initial value may be a plurality of reference curve clusters measured under laboratory conditions, and the reference curve cluster is Influencing factors (such as current I, temperature T, charge state SOC, health state SOH) versus battery model parameters (battery internal resistance DCIR, resistance R0, resistance R1, capacitance C1) and battery state quantities (Cap capacity, SOC, SOH, SOP) , SOE) function relationship curve. The reference curve cluster may be pre-stored in the BMS 20 when the electric vehicle is shipped from the factory.

For example, the BMS 20 can estimate the plurality of first reference curve clusters under multiple operating conditions according to the current state information and the battery model of the collected power battery and using the Kalman filtering algorithm. The server 10 may generate a plurality of second reference curve clusters corresponding to the historical data of the power battery (including at least 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 BMS 20, and the BMS 20 receives the first The reference curve cluster is updated, and the reference curve cluster currently stored in the BMS 20 is updated, and the updated reference curve cluster is used for the look-up table input of the battery state parameter estimation algorithm. It should be noted that the reference curve cluster in the BMS 20 is continuously updated.

In addition, a combination of a polynomial, a neural network model, and the like may be used to estimate the fitting to obtain a second reference curve cluster.

It can be understood that the initial value of the reference curve stored in the BMS 20 is always present for estimating the cluster of the first reference curve, and the stored reference curve cluster is after receiving the second reference curve cluster sent by the cloud server 10. updated. When the cloud server 10 fits the second reference curve cluster according to the data uploaded by the BMS 20, the second reference curve cluster is continuously transmitted back to the BMS for updating the reference curve cluster currently stored in the BMS 20.

The BMS 20 can estimate the power state 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 working condition to improve the discharge efficiency of the power battery. The BMS20 can also estimate the state of energy (SOE) of the power battery based on the updated reference curve cluster, providing a direct reference for accurately estimating the remaining mileage of the electric vehicle.

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.

The cloud server 10 can perform big data analysis on historical data of the power battery (such as historical state parameters of the power battery, historical reference curve clusters, etc.), for example, if the power car is currently traveling on a highway, all the history under the working condition can be obtained. The data, and based on this, predicts the future state of the power battery, that is, the prediction curve, and provides an important reference for the BMS20 control strategy (such as estimating the power state SOP of the power battery, energy state SOE, etc.).

It can be seen that, compared with the battery management system in the related art, the management system of the power battery in the electric vehicle of the present application not only has the battery state management function of the traditional BMS, but also has the battery state prediction function, that is, the power battery can be analyzed. The historical change curve of the status information can accurately predict the future state of the power battery while accurately monitoring the current state of the power battery.

In this embodiment, the BMS 20 has the capability of quickly identifying, accurately tracking, and monitoring each battery cell status information and status parameters. The cloud server 10 has functions of cloud data collection, big data statistical analysis, personalized real-time update, and the like. Thus, the system 100 is capable of recording and monitoring the historical and current status of all battery packs and estimating future states based on mathematical statistics algorithms to optimize performance and life of the power battery and provide data support for its hierarchical utilization.

As shown in FIG. 2, the BMS 20 includes a plurality of battery information collectors BIC21 and a battery control unit BCU22.

The plurality of BICs 21 respectively correspond to a plurality of battery cells in the power battery; the BCU 22 is connected to the plurality of BICs 21 and communicates with the cloud server 10, and the BCU 22 is configured to acquire the power battery according to the state information of the power battery. A plurality of first reference curve clusters under the working condition, and sending the plurality of first reference curve clusters to the cloud server 10, and receiving and saving the second reference curve clusters sent by the cloud server 10. It should be noted that the BCU 22 may save the second reference curve cluster to the position corresponding to the current reference curve cluster in the BMS 20 to update the reference curve cluster in the BMS 20.

Each BIC21 can send data to the BCU 22 via CAN (Controller Area Network), in-vehicle network FlexRay or Daisy Chain (daisy chain).

In this embodiment, the BCU 22 and all of the BICs 21 can be assembled with all of the battery cells pack inside the cabin of an electric vehicle.

BIC21 is used for battery cell voltage sampling and monitoring, battery equalization, battery pack temperature sampling and monitoring, BCU22 for bus current detection, system insulation monitoring, battery system up/down management, battery system thermal management, battery state of charge SOC (State of Charge) estimation, battery health state SOH (State of Health) estimation, battery power state SOP (State of Power) estimation, fault diagnosis, vehicle communication and online program update, data recording.

As shown in FIG. 3, the BCU 22 includes a first controller 22a and a second controller 22b. 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 10, and obtain the power battery in multiple operating conditions according to the state information of the power battery. The plurality of first reference curve clusters are below, and the plurality of first reference curve clusters are sent to the cloud server 10, and the second reference curve cluster sent by the cloud server is received and saved. 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 BMS 20 to update the reference curve cluster in the BMS 20.

In this embodiment, the BCU 22 has a powerful data storage space and a high-speed data processing speed dual MCU (ie, the first controller 22a and the second controller 22b), has off-line data processing capability, and can be accessed through a wireless communication module. The wireless communication method performs data interaction with the cloud server 10. Further, the cloud server 10 performs cloud computing and big data analysis on the battery state information and the state parameters of the entire life cycle of the power battery to realize current state management and future state prediction of the power battery.

To facilitate understanding of the workflow of the power battery management system in the electric vehicle of the embodiment of the present application, it can be explained in conjunction with FIG. 4:

As shown in FIG 4, first, the start BMS20 status information collecting power battery, including a voltage V _Total BIC21 acquired power battery voltage V _Cell, power battery temperature T, the and the bus current BCU22 collected each cell I _total , and BIC21 sends V _total , V _cell , T to BCU22.

Then, the first controller 22a in the BCU 22 executes the vehicle control strategy according to the V _total , V _cell , T, including controlling the action of the external high and low voltage components, fault diagnosis, overcharge protection, over discharge protection, over temperature protection, and equalization control. The second controller 22b in the BCU 22 can use the dual model algorithm to fit the first reference curve cluster under different working conditions according to the _total estimation of V _total , V _cell , T and I on the one hand, and on the other hand, according to the V _total V _cell , T and I _always estimate and predict the state parameters such as SOC, SOH, SOP, SOE and RM. Further, the first reference curve cluster and the state parameter estimation result can be uploaded to the cloud server 10 by wireless communication technologies such as 2/3/4/5G or bluetooth.

The cloud server 10 integrates the first reference curve cluster and the state parameter estimation result, and fits the most according to the first reference curve cluster and the stored historical data of the power battery (such as the previously received state information and state parameters). a second reference curve cluster close to the current state of the power battery, and then transmitting the second reference curve cluster to the BCU 22 via a wireless communication technology such as 2/3/4/5G or bluetooth for updating the second controller 22b Reference curve cluster.

In this way, the reference curve cluster in the BMS 20 can be iteratively updated in the above manner, so that the predicted result of the battery system is closer to the real state of the battery, and the accurate management of the power battery is facilitated.

The cloud server 10 can also analyze historical state information and state parameters of the power battery to predict the future state of the battery, and provide an important reference for the BMS control strategy.

It can be understood that after the end of this data processing, you can enter the next cycle.

In summary, the power battery management system of the electric vehicle according to the embodiment of the present application can not only accurately estimate the state parameters of the battery, including the maximum values of SOC, SOH, SOE, SOP, and SOP, and is convenient for estimating the electric vehicle. The remaining mileage, guiding the vehicle to release energy at maximum power during the discharge phase or limiting the maximum power that the vehicle can release at the current time, effectively protecting the power battery, improving the service life of the power battery, and predicting the future state of the battery, facilitating the electric vehicle and Power battery status for predictive maintenance advice or repair. In addition, through cloud computing and big data analysis, it is possible to accurately understand the performance characteristics of power batteries under different types, different batches, and different ratios, providing an important design reference for power battery design; and when the power battery is on an electric vehicle. When the decommissioning conditions are reached, the cloud data can be used to analyze and screen the decommissioned battery packs, which facilitates the grading and ladder utilization of the decommissioned power batteries.

FIG. 5 is a flowchart of a method of managing a power battery in an electric vehicle according to an embodiment of the present application.

In an embodiment of the present application, a BMS is disposed above the electric vehicle.

As shown in FIG. 5, the method for managing a power battery in the electric vehicle includes the following steps:

S101. The BMS collects state information of the power battery in the electric vehicle, and obtains 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 the state information of the power battery and the plurality of The first reference curve cluster is sent to the cloud server.

Wherein, the plurality of operating conditions include a plurality of battery temperatures, a plurality of charge and discharge rates, or a plurality of degrees of aging.

The BMS estimates a plurality of first reference curve clusters according to a multiple fitting algorithm.

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.

S103. The BMS receives and saves a second reference curve cluster sent by the cloud server to update a reference curve cluster in the BMS.

The cloud server also generates a prediction curve of the power battery based on the historical data, and sends the prediction curve to the BMS to update the reference curve cluster in the BMS.

It should be noted that other specific embodiments of the method for managing the power battery in the electric vehicle according to the embodiment of the present application can refer to the specific embodiment of the power battery management system in the electric vehicle of the above embodiment.

According to the method for managing a power battery in an electric vehicle according to an embodiment of the present application, not only can the battery state parameters be accurately estimated, including the maximum values of SOC, SOH, SOE, SOP, and SOP, thereby facilitating estimation of the remaining mileage of the electric vehicle, Instruct the vehicle to release energy at maximum power during the discharge phase or limit the maximum power that the vehicle can release at the current time, to effectively protect the power battery, improve the service life of the power battery, and predict the future state of the battery, facilitating the status of the electric vehicle and the power battery. Propose predictive maintenance advice or repairs. In addition, through cloud computing and big data analysis, it is possible to accurately understand the performance characteristics of power batteries under different types, different batches, and different ratios, providing an important design reference for power battery design; and when the power battery is on an electric vehicle. When the decommissioning conditions are reached, the cloud data can be used to analyze and screen the decommissioned battery packs, which facilitates the grading and ladder utilization of the decommissioned power batteries.

FIG. 6 is a structural block diagram of an electric vehicle according to an embodiment of the present application.

As shown in FIG. 6 , a BMS 20 is disposed on the electric vehicle 200 , wherein the BMS 20 is used to collect state information of the power battery in the electric vehicle, and obtain the power battery under multiple working conditions according to the state information of the power battery. And a plurality of first reference curve clusters, and sending status information of the power battery and the plurality of first reference curve clusters to the cloud server, so that the cloud server generates the historical data according to the saved power battery and the plurality of first reference curve clusters The second reference curve cluster, and the BMS 20 further receives and saves the second reference curve cluster sent by the cloud server to update the reference curve cluster in the BMS.

The BMS 20 estimates a plurality of first reference curve clusters to be fitted according to a multiple fitting algorithm.

Referring to FIG. 2, the BMS 20 includes a plurality of battery information collectors BIC21 and a battery control unit BCU22.

Wherein, the plurality of BICs 21 respectively correspond to a plurality of battery cells in the power battery. The BCU 22 is connected to the plurality of BICs 21 and communicates with the cloud server, and the BCU 22 is configured to acquire a plurality of first reference curve clusters of the power battery under the plurality of operating conditions according to the state information of the power battery, and the plurality of first reference The curve cluster is sent to the cloud server, and the second reference curve cluster sent by the cloud server is received and saved.

Referring to FIG. 3, the BCU 22 includes a first controller 22a and a second controller 22b.

The first controller 22a is configured to perform vehicle control according to status information of the power battery. The second controller 22b is configured to communicate with the cloud server, and obtain a plurality of first reference curve clusters of the power battery under the plurality of operating conditions according to the state information of the power battery, and send the plurality of first reference curve clusters To the cloud server, and receive and save the second reference curve cluster sent by the cloud server.

It should be noted that, in other specific embodiments of the electric vehicle according to the embodiment of the present application, reference may be made to the specific embodiment of the power battery management system in the electric vehicle of the above embodiment.

According to the electric vehicle of the embodiment of the present application, not only can the battery state information be accurately estimated, including the maximum values of SOC, SOH, SOE, SOP, and SOP, it is convenient to estimate the remaining mileage of the electric vehicle, and the vehicle is guided to the discharge stage. The maximum power release energy or limit the maximum power that the vehicle can release at the current moment to effectively protect the power battery and improve the service life of the power battery.

In addition, other configurations of the electric vehicle according to the embodiment of the present application and their effects are known to those skilled in the art, and redundancy is not described herein.

FIG. 7 is a structural block diagram of a cloud server according to an embodiment of the present application. 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 sending module 14.

The receiving module 11 is configured to receive state information of the power battery in the electric vehicle sent by the BMS in the electric vehicle and a plurality of first reference curve clusters, wherein the BMS obtains the power battery in multiple operating conditions according to the state information of the power battery. A plurality of first reference curve clusters. The storage module 12 is configured to store 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 transmitting module 14 is configured to send the second reference curve cluster to the BMS to update the reference curve cluster in the BMS.

As shown in FIG. 8, the cloud server 10 further includes a second generation module 15. The second generation module 15 is configured to generate a prediction curve of the power battery based on the historical data.

In this embodiment, the transmitting module 14 also sends a prediction curve to the BMS to update the reference curve clusters in the BMS.

It should be noted that, in other specific implementation manners of the cloud server 10 of the embodiment of the present application, reference may be made to the specific implementation manner of the cloud server 10 in the power battery management system of the electric vehicle in the above embodiment of the present application.

According to the cloud server of the embodiment of the present application, the second reference curve cluster is generated by continuously analyzing the battery historical state parameter to continuously update the reference curve cluster in the BMS, so that the BMS can accurately estimate the current state information of the power battery, which is beneficial to the BMS. Effective management of the power battery to improve the service life of the power battery. In addition, through cloud computing and big data analysis, it is possible to accurately understand the performance characteristics of power batteries under different types, different batches, and different ratios, providing an important design reference for power battery design; and when the power battery is on an electric vehicle. When the decommissioning conditions are reached, the cloud data can be used to analyze and screen the decommissioned battery packs, which facilitates the grading and ladder utilization of the decommissioned power batteries.

In the description of the present application, it is to be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", " Rear, Left, Right, Vertical, Horizontal, Top, Bottom, Inner, Out, Clockwise, Counterclockwise, Axial The orientation or positional relationship of the "radial", "circumferential" and the like is based on the orientation or positional relationship shown in the drawings, and is merely for the convenience of describing the present invention and simplifying the description, and does not indicate or imply the indicated device or The elements must have a particular orientation, are constructed and operated in a particular orientation and are therefore not to be construed as limiting.

Moreover, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include one or more of the features either explicitly or implicitly. In the description of the present invention, the meaning of "a plurality" is two or more unless specifically and specifically defined otherwise.

In the present invention, the terms "installation", "connected", "connected", "fixed" and the like shall be understood broadly, and may be either a fixed connection or a detachable connection, unless explicitly stated and defined otherwise. , or integrated; can be mechanical connection, or can be electrical connection; can be directly connected, or can be indirectly connected through an intermediate medium, can be the internal communication of two elements or the interaction of two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood on a case-by-case basis.

In the present invention, the first feature "on" or "under" the second feature may be a direct contact of the first and second features, or the first and second features may be indirectly through an intermediate medium, unless otherwise explicitly stated and defined. contact. Moreover, the first feature "above", "above" and "above" the second feature may be that the first feature is directly above or above the second feature, or merely that the first feature level is higher than the second feature. The first feature "below", "below" and "below" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely that the first feature level is less than the second feature.

In the description of the present specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means a specific feature described in connection with the embodiment or example. A structure, material or feature is included in at least one embodiment or example of the invention. In the present specification, the schematic representation of the above terms is not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, various embodiments or examples described in the specification, as well as features of various embodiments or examples, may be combined and combined.

Although the embodiments of the present invention have been shown and described, it is understood that the above-described embodiments are illustrative and are not to be construed as limiting the scope of the invention. The embodiments are subject to variations, modifications, substitutions and variations.

Claims

A management system for a power battery in an electric vehicle, comprising: a cloud server; and a battery management system BMS disposed on the electric vehicle, wherein

The BMS is configured to collect state information of a power battery in the electric vehicle, and acquire a plurality of first reference curve clusters of the power battery under multiple operating conditions according to state information of the power battery, and Sending status information of the power battery and the plurality of first reference curve clusters to the cloud server, and receiving and saving a second reference curve cluster sent by the cloud server to update a reference curve in the BMS cluster;

The cloud server is configured to save historical data of the power battery, and generate the second reference curve cluster according to the historical data and the plurality of first reference curve clusters.
The power battery management system for an electric vehicle according to claim 1, wherein said BMS estimates said fitting said plurality of first reference curve clusters according to a multiple fitting algorithm.
The power battery management system for an 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 to update a reference curve cluster in the BMS.
The power battery management system for an electric vehicle according to any one of claims 1 to 3, wherein the BMS comprises:

a plurality of battery information collectors BIC, the plurality of BICs respectively corresponding to the plurality of battery cells in the power battery;

a battery control unit BCU, the BCU is connected to the plurality of BICs, and is in communication with the cloud server, wherein the BCU is configured to acquire, according to the state information of the power battery, the plurality of operating conditions of the power battery. And a plurality of first reference curve clusters, and sending the plurality of first reference curve clusters to the cloud server, and receiving and saving a second reference curve cluster sent by the cloud server.
The power battery management system for an electric vehicle according to claim 4, wherein the BCU comprises:

a first controller, configured to perform vehicle control according to status information of the power battery;

a second controller, configured to communicate with the cloud server, and acquire, according to status information of the power battery, a plurality of first reference curve clusters of the power battery under multiple operating conditions, and A plurality of first reference curve clusters are sent to the cloud server, and a second reference curve cluster sent by the cloud server is received and saved.
A power battery management system for an electric vehicle according to any one of claims 1 to 5, wherein said plurality of operating conditions include a plurality of battery temperatures, a plurality of charge and discharge rates, or a plurality of degrees of aging.
A method for managing a power battery in an electric vehicle, wherein a battery management system BMS is disposed above the electric vehicle, the method comprising the steps of:

The BMS collects state information of the power battery in the electric vehicle, and acquires a plurality of first reference curve clusters of the power battery under a plurality of operating conditions according to state information of the power battery, and the Sending a plurality of first reference curve clusters to the cloud server;

The cloud server generates the second reference curve cluster according to historical data of the power battery and the plurality of first reference curve clusters;

The BMS receives and saves a second reference curve cluster sent by the cloud server to update a reference curve cluster in the BMS.
The method of managing a power battery in an electric vehicle according to claim 7, wherein the BMS estimates the fitting of the plurality of first reference curve clusters according to a multiple fitting algorithm.
The method for managing a power battery in an electric vehicle according to claim 7 or 8, wherein the cloud server further generates a prediction curve of the power battery based on the historical data, and transmits the prediction curve to the The BMS is described to update the reference curve clusters in the BMS.
The method of managing a power battery in an electric vehicle according to any one of claims 7-9, wherein the plurality of operating conditions include a plurality of battery temperatures, a plurality of charge and discharge rates, or a plurality of degrees of aging.
An electric vehicle, characterized in that a battery management system BMS is arranged above the electric vehicle, wherein the BMS is used for:

Collecting state information of the power battery in the electric vehicle, and acquiring a plurality of first reference curve clusters of the power battery under a plurality of operating conditions according to state information of the power battery, and Sending a reference curve cluster to the cloud server, so that the cloud server generates a second reference curve cluster according to the saved historical data of the power battery and the plurality of first reference curve clusters;

Receiving and saving the second reference curve cluster sent by the cloud server to update a reference curve cluster in the BMS.
The electric vehicle according to claim 11, wherein said BMS estimates the fitting of said plurality of first reference curve clusters according to a multiple fitting algorithm.
The electric vehicle according to claim 11 or 12, wherein the BMS comprises:

a plurality of battery information collectors BIC, the plurality of BICs respectively corresponding to the plurality of battery cells in the power battery;

a battery control unit BCU, the BCU is connected to the plurality of BICs, and is in communication with the cloud server, wherein the BCU is configured to acquire, according to the state information of the power battery, the plurality of operating conditions of the power battery. And a plurality of first reference curve clusters, and sending the plurality of first reference curve clusters to the cloud server, and receiving and saving a second reference curve cluster sent by the cloud server.
The electric vehicle according to claim 13, wherein said BCU comprises:

a first controller, configured to perform vehicle control according to status information of the power battery;

a second controller, configured to communicate with the cloud server, and acquire, according to status information of the power battery, a plurality of first reference curve clusters of the power battery under multiple operating conditions, and A plurality of first reference curve clusters are sent to the cloud server, and a second reference curve cluster sent by the cloud server is received and saved.
The electric vehicle according to any one of claims 11 to 14, wherein the plurality of operating conditions include a plurality of battery temperatures, a plurality of charge and discharge rates, or a plurality of degrees of aging.