CN115799673A - Energy storage battery management system with multidimensional sensing - Google Patents
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Abstract
The invention relates to the field of energy storage system battery management, in particular to a multidimensional sensing energy storage battery management system. The invention discloses a multidimensional sensing energy storage battery management system, which comprises: the system comprises a battery management module, a perception detection module and a battery cluster management module; the battery management unit is used for managing the battery according to the external parameters of the battery; the battery management module is used for detecting the self-perception information of the battery and transmitting the self-perception information and the perception information transmitted by the perception detection module to the battery cluster management unit together; and the battery cluster management module is used for integrating all the perception information transmitted by the battery management module, and performing data processing and analysis to further estimate the safety state of the battery. The method solves the problems that the existing battery management system can only collect the voltage, the current and the external temperature of the battery, and the measurement of the battery state is inaccurate. The reliability and safety of battery management are improved.
Description
Technical Field
The invention relates to the field of energy storage system battery management, in particular to a multidimensional sensing energy storage battery management system.
Background
1.1 background of the invention
Due to the inherent intermittent and random characteristics of new energy power generation such as wind energy, solar energy and the like, adverse effects are brought to the safe and stable operation of a power grid. The running quality can be improved by developing an energy storage technology. In electrochemical energy storage power stations, the battery management system plays a crucial role in the life of the stack and the safety of the entire energy storage system.
Most of the existing battery management systems are directly introduced from the battery management system of the electric automobile. Compared with an electric automobile, the single battery of the megawatt-level energy storage system has the advantages of larger capacity, more batteries, higher series voltage, higher requirement on the consistency of the batteries and more outstanding complete problems.
The safety problem of the energy storage system is the pain point and the difficulty point of the development of the energy storage industry, and is not solved all the time, so the analysis of the safety state of the battery is an extremely important requirement for a battery management system. But the bottlenecks currently faced are: the conventional battery management system can only collect the voltage, the current and the external temperature of the battery, has narrow sensing dimension and range, and cannot really sense the change inside the battery, so that the internal thermal runaway of the battery core occurs, and the safety warning cannot be given in time under the short-circuit condition.
1.2 Prior Art related to the invention
1.2.1 technical solution of the prior art one
Chinese patent publication No. CN 103227494B discloses an energy storage battery management system: the technical scheme adopts a three-layer management framework, and a BMU (bottom battery management unit) is responsible for acquiring the voltage and the temperature of a single battery; the middle layer cluster management unit BCU is responsible for calculating a battery control strategy and a battery SOC (state of charge)/SOH (state of health); the top battery information management unit BAU is responsible for communication and system information processing.
1.2.2 disadvantages of the first prior art
The bottom battery management unit BMU of this technique can only gather battery cell's voltage and battery external temperature, and the temperature point is generally gathered on the copper bar is connected to the battery, and the copper bar is good radiator, can dissipate some heat, gathers each section battery of temperature point unable cover battery box in addition, leads to temperature measurement inaccurate, can't reflect the inside true temperature of battery, just also can't accurately calculate battery capacity, battery health degree and battery safety state.
Disclosure of Invention
The invention aims to solve the problems of an energy storage battery management system, and provides a multidimensional sensing energy storage battery management system in order to improve the capacity of an energy storage battery and the detection accuracy of the health degree of the battery and effectively carry out battery safety early warning.
In order to achieve the above purpose, the present invention is realized by the following technical scheme.
The invention provides a multidimensional sensing energy storage battery management system, which comprises: the system comprises a battery management module, a perception detection module and a battery cluster management module; wherein, the first and the second end of the pipe are connected with each other,
the perception detection module is used for detecting perception information of external parameters of the battery and transmitting the perception information to the battery management unit;
the battery management module is used for detecting the self-perception information of the battery and transmitting the self-perception information and the perception information transmitted by the perception detection module to the battery cluster management unit;
the battery cluster management module comprises a data processing unit; the data processing unit is used for integrating all sensing information transmitted by the battery management module, and performing data processing analysis, so as to estimate the SOC and/or SOH of the battery and evaluate the safety state of the battery.
As an improvement of the above technical solution, the sensing detection module includes: the system comprises a safety detection unit and an infrared detection unit; wherein the content of the first and second substances,
the safety detection unit is used for detecting battery gas and battery pressure;
and the infrared detection unit is used for detecting the temperature of the battery.
As an improvement of the above technical solution, the security detection unit includes: a battery gas detection sensor and a battery pressure detection sensor; wherein the content of the first and second substances,
the battery gas detection sensor is used for detecting gases generated by the battery in the thermal runaway process, including CO and H2;
the battery pressure detection sensor is used for detecting the pressure change of the battery pressure reducing valve.
As one improvement of the above technical solution, the safety detection unit is installed in the battery box; the battery gas detection sensor and the battery pressure detection sensor are both arranged at the pressure reducing valve of each single battery in the battery box.
As an improvement of the above technical solution, the infrared detection unit is mounted on the battery rack and used for detecting the internal temperature of the battery by using an infrared technology.
As an improvement of the above technical solution, a battery management module includes: an internal resistance detection unit and a voltage detection unit; wherein the content of the first and second substances,
the internal resistance detection unit is used for detecting the internal impedance of the battery;
the voltage detection unit is used for detecting the voltage of the battery.
As one improvement of the above technical solution, the estimating SOC and/or SOH of the battery specifically includes:
constructing a lithium battery discrete SOC and/or SOH model based on a neural network;
extracting historical voltage, current, temperature, internal resistance and SOC and/or SOH, establishing a training data set, training a discrete SOC and/or SOH model of the lithium battery by adopting an ELM algorithm, and simulating a nonlinear relation between approximate voltage, current, temperature, internal resistance and SOC and/or a nonlinear relation between voltage, current, temperature, internal resistance and SOH;
and inputting the acquired voltage, current, temperature and internal resistance into the trained discrete lithium battery model, and outputting the SOC and/or SOH.
As an improvement of the above technical solution, the estimating the SOC and/or SOH of the battery further includes:
and correcting the output current SOC and/or SOH of the battery by using an unscented Kalman filter.
As one improvement of the above technical solution, the performing of the battery safety state evaluation specifically includes:
the method comprises the following steps of carrying out predictive analysis on battery consistency and internal state by utilizing dynamic data of voltage change, current change, temperature change and internal resistance change, and screening out micro-damaged batteries in advance;
according to the gas, pressure and temperature data, early warning is carried out at the early stage of thermal runaway of the battery;
and acquiring the consistency of the voltage, the temperature, the capacity, the charge-discharge rate and the charge-discharge depth of the battery according to the multidimensional parameters of the voltage, the current, the temperature, the internal resistance, the SOC and the SOH, and further comprehensively evaluating the safety state of the battery.
As an improvement of the above technical solution, the battery cluster management module further includes a battery current detection unit for detecting a battery current.
Compared with the prior art, the invention has the advantages that:
1. the battery management system can not only collect the voltage, the current and the external temperature of the battery, but also collect the sensing information in the battery, thereby realizing the real-time accurate detection of the battery state;
2. according to the acquired dynamic information, the battery management system gives safety early warning and emergency treatment when the battery cell is subjected to abnormal conditions such as micro-damage, short circuit or thermal runaway;
3. the battery management system provided by the invention also can evaluate the safety state of the battery in an all-around manner according to the collected multidimensional sensing information, and is matched with the energy storage converter to carry out charging and discharging control, so that battery equalization is carried out in real time, and the service life and charging and discharging efficiency of the battery are improved.
Drawings
Fig. 1 is a multi-dimensional sensing energy storage battery management system architecture diagram.
Detailed Description
The technical scheme provided by the invention is further illustrated by combining the following embodiments.
Fig. 1 is a multi-dimensional sensing energy storage battery management unit system architecture, a sensing range is expanded, and single battery gas detection, single battery pressure detection, single battery internal resistance detection and single battery internal temperature detection are added to a sensing object from previous voltage, current and external temperature acquisition.
As can be seen from fig. 1, the system architecture includes four major parts: the device comprises a safety detection module, a battery management unit, an infrared detection module and a battery cluster management unit. The logical relationship between them is: the battery management unit collects the self-sensing information and the two modules of sensing information and sends the information to the battery cluster management unit in a unified way, and the battery cluster management unit integrates all the sensing information and carries out data processing and analysis, battery SOC/SOH estimation and battery safety state evaluation.
The safety detection module is arranged in the battery box, and the battery gas detection sensor and the battery pressure detection sensor of the safety detection module are arranged at the pressure reducing valve of each single battery in the battery box.
The gas detection sensor can detect CO and H generated in the thermal runaway process of the battery 2 When gases are used, the thermal runaway characteristic of the lithium ion battery is researched, the fact that the temperature measured on the surface of the battery is 120.4 ℃ when the battery is on fire is found, the smoking phenomenon can be generated in the early stage of thermal runaway of the battery, the concentration of the generated characteristic gases can be suddenly increased from zero to hundreds or even thousands of milligrams per cubic meter, and the lithium ion battery is suitable for being used as a judgment basis for battery safety early warning.
The pressure sensor may detect a pressure change of the battery pressure reducing valve. The battery thermal runaway process relates to the reaction of electrolyte and adhesive with positive and negative electrodes and the self decomposition reaction, a large amount of gas and smog are generated, the gas can cause the pressure of the battery to change, the battery can bulge and finally spray the gas through a pressure reducing valve, and the safety warning of the battery thermal runaway can be realized by monitoring the change of the pressure of a battery case before the gas is sprayed out from the pressure reducing valve.
The infrared detection module is installed on the battery frame, and the internal temperature of each single battery of the battery pack is monitored through an infrared technology. The battery is an electrochemical reaction, the temperature is an important parameter influencing the electrochemical reaction, the problem that the surface temperature of the battery is measured to be the maximum through a thermocouple or a temperature sensor in the prior art is that the internal temperature of the battery cannot be measured accurately, the measurement is inaccurate, the internal temperature of the battery can be detected through infrared detection, and powerful guarantee is provided for SOC/SOH calculation and safety state evaluation of the battery.
The battery management unit is generally hung on the battery box shell, and the cell voltage detection is the basic function of the battery management unit, which is not described herein again. The internal resistance detection module is mainly used for detecting the internal impedance of the battery. The internal resistance is a very important parameter of the lithium ion battery, can change along with the conditions of charge and discharge states, working environment temperature and the like, is used for battery life evaluation, health state evaluation and performance detection, and is an important parameter for detecting whether the battery is abnormal or not.
The battery cluster management unit is installed in the high-voltage box, and the current detection is the basic function of the battery cluster management unit, and is not described in detail herein. On one hand, various kinds of perception data are gathered, and on the other hand, the data are processed and analyzed. Under the normal charging and discharging working condition, the current SOC of the battery is calculated by collecting voltage, current, temperature and internal resistance, charging and discharging control is carried out by matching with a Power Conversion System (PCS) according to the System scheduling Power requirement, and battery equalization is carried out in real time, so that the service life of the battery and the charging and discharging efficiency are improved; under normal working conditions, dynamic data such as voltage change, current change, temperature change, internal resistance change and the like are utilized to carry out predictive analysis on battery consistency and internal state, and micro-damage batteries are screened out in advance; in the early stage of thermal runaway of the battery, faults are timely found through data such as a gas sensor, a pressure sensor and temperature.
In order to establish an accurate discrete lithium battery model, an ELM (Extreme Learning Machine) model is trained by using experimental data to establish an SOC (state of charge), an SOH (state of health) estimation method is established, and a model result is further corrected by using an unscented Kalman filter. For the SOC model, the inputs and outputs of the model are first determined. In step K, SOC (K) is sampled SOC as a model input, which represents the current state of the battery. SOC has a nonlinear relationship with battery voltage, current, and other factors. As a direct measurement variable, the current I (K) is taken as input and the battery terminal voltage V (K) is defined as output. In addition, the terminal voltage at sampling step K-1,V (K-1) is used as the third input to the model. And (3) learning and training a corresponding NN (neural network) model according to the input and output data, and simulating an approximation function relation. Before model training, input and output samples are obtained through experiments, in order to approximate to input and output mapping of the training samples, an ELM algorithm with high learning speed and good generalization is adopted, model parameters do not need to be adjusted in the learning process, and dependency relationships do not exist between weights and deviations, between the parameters and training data. For the SOH model, the modeling method is the same as above, and the model input variables are the charge-discharge depth, the temperature, the charge-discharge magnification and the internal resistance.
Battery behavior can affect the life and safety of the battery, and many behaviors are potentially immaterial and affect the health of the battery. Therefore, the battery behaviors of the station, the cluster and the module are analyzed, the health condition of the battery is quantized, and a user can conveniently control the safety state of the battery. The safety state of the battery is comprehensively evaluated from the aspects of battery voltage consistency expression, temperature consistency expression, capacity consistency expression, charge-discharge rate consistency expression, charge-discharge depth expression consistency and the like by monitoring the voltage, the current, the temperature and the internal resistance of the battery and combining multidimensional parameters of SOC and SOH.
In addition, the battery runaway may be caused by external environments such as electricity and external impacts besides the battery itself, and therefore, the battery is also required to be monitored for thermal runaway. Once thermal runaway happens, the thermal runaway can be irreversible, but the time from the thermal runaway to thermal spread is several minutes, and by utilizing the characteristic gas generated by the electrochemical reaction of the battery during the thermal runaway, the timely warning, accurate positioning and emergency treatment of the thermal runaway can be realized through gas and pressure monitoring.
The invention discloses a multidimensional sensing energy storage battery management system, which comprises four parts: the device comprises a safety detection module, a battery management unit, an infrared detection module and a battery cluster management unit. The logical relationship between them is: the safety detection module and the infrared detection module send the sensing information to the battery management unit, the battery management unit collects the self sensing information and the sensing information of the two modules and sends the information and the information to the battery cluster management unit in a unified mode, the battery cluster management unit integrates all the sensing information, data processing and analysis are carried out, and battery SOC/SOH and battery safety state evaluation are estimated. To sum up, through the multidimensional perception, the management capability of the energy storage battery is further improved, and the omnibearing protection of the battery is realized.
As can be seen from the above detailed description of the present invention, the present invention synchronously collects the internal and external sensing information of the battery, and accurately calculates the battery capacity, the battery health degree, the battery safety state, and the like according to the collected information, thereby performing control management and abnormality warning on the battery.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A multidimensional sensing energy storage battery management system, the system comprising: the system comprises a battery management module, a perception detection module and a battery cluster management module; wherein the content of the first and second substances,
the perception detection module is used for detecting perception information of external parameters of the battery and transmitting the perception information to the battery management unit;
the battery management module is used for detecting the self-perception information of the battery and transmitting the self-perception information and the perception information transmitted by the perception detection module to the battery cluster management unit;
the battery cluster management module comprises a data processing unit; the data processing unit is used for integrating all sensing information transmitted by the battery management module, and performing data processing analysis, so as to estimate the SOC and/or SOH of the battery and evaluate the safety state of the battery.
2. The multidimensional aware energy storage battery management system of claim 1, wherein the awareness detection module comprises: the system comprises a safety detection unit and an infrared detection unit; wherein the content of the first and second substances,
the safety detection unit is used for detecting battery gas and battery pressure;
and the infrared detection unit is used for detecting the temperature of the battery.
3. The multi-dimensional aware energy storage battery management system of claim 2, wherein the safety detection unit comprises: a battery gas detection sensor and a battery pressure detection sensor; wherein, the first and the second end of the pipe are connected with each other,
the battery gas detection sensor is used for detecting gases including CO and H generated by the battery in the thermal runaway process 2 ；
And the battery pressure detection sensor is used for detecting the pressure change of the battery pressure reducing valve.
4. The multi-dimensional aware energy storage battery management system of claim 3, wherein the safety detection unit is installed in a battery box; the battery gas detection sensor and the battery pressure detection sensor are both arranged at the pressure reducing valve of each single battery in the battery box.
5. The multi-dimensional sensing energy storage battery management system according to claim 2, wherein the infrared detection unit is mounted on the battery rack and used for detecting the internal temperature of the battery through an infrared technology.
6. The multidimensional aware energy storage battery management system of claim 1, wherein the battery management module comprises: an internal resistance detection unit and a voltage detection unit; wherein, the first and the second end of the pipe are connected with each other,
the internal resistance detection unit is used for detecting the internal impedance of the battery;
the voltage detection unit is used for detecting the voltage of the battery.
7. The multidimensional sensing energy storage battery management system according to claim 1, wherein the estimating the SOC and/or SOH of the battery specifically comprises:
constructing a lithium battery discrete SOC and/or SOH model based on a neural network;
extracting historical voltage, current, temperature, internal resistance and SOC and/or SOH, establishing a training data set, training a discrete SOC and/or SOH model of the lithium battery by adopting an ELM algorithm, and simulating a nonlinear relation between approximate voltage, current, temperature, internal resistance and SOC and/or a nonlinear relation between voltage, current, temperature, internal resistance and SOH;
and inputting the acquired voltage, current, temperature and internal resistance into the trained discrete lithium battery model, and outputting the SOC and/or SOH.
8. The multidimensional sensing energy storage battery management system of claim 7, wherein the estimating battery SOC and/or SOH further comprises:
and correcting the output current SOC and/or SOH of the battery by using an unscented Kalman filter.
9. The multidimensional sensing energy storage battery management system according to claim 1, wherein the performing battery safety state assessment specifically comprises:
the method comprises the following steps of carrying out predictive analysis on battery consistency and internal state by utilizing dynamic data of voltage change, current change, temperature change and internal resistance change, and screening out micro-damaged batteries in advance;
according to the gas, pressure and temperature data, early warning is carried out at the early stage of thermal runaway of the battery;
and acquiring the consistency of the voltage, the temperature, the capacity, the charge-discharge multiplying power and the charge-discharge depth of the battery according to the multidimensional parameters of the voltage, the current, the temperature, the internal resistance, the SOC and the SOH, and further comprehensively evaluating the safety state of the battery.
10. The multidimensional aware energy storage battery management system of claim 1, wherein the battery cluster management module further comprises a battery current detection unit for detecting a battery current.
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