CN115810814A - Protection type BMS battery management system - Google Patents
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- CN115810814A CN115810814A CN202211608621.9A CN202211608621A CN115810814A CN 115810814 A CN115810814 A CN 115810814A CN 202211608621 A CN202211608621 A CN 202211608621A CN 115810814 A CN115810814 A CN 115810814A
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Abstract
The invention provides a protective BMS battery management system.A server is internally provided with a data processing module and a wireless communication module, and a balancing device is internally provided with a data acquisition module for acquiring battery pack data and a state regulation and control module for regulating the state of a battery pack; the data acquisition module is used for acquiring the temperature of the battery pack, the humidity of the environment where the battery pack is located and the current value of the battery pack; the state regulation and control module is connected with a heat dissipation device and a dehumidification device which are arranged on the battery pack, the data processing module is used for acquiring data acquired by the data acquisition module, respectively predicting temperature, humidity and current values through an established LSTM neural network prediction model, and sending an instruction to the state regulation and control module to regulate and control the state of the battery pack according to the predicted temperature, humidity and current values; the invention can conveniently monitor the state of the battery pack, realizes the protection of the battery pack and prolongs the service life of the battery pack.
Description
Technical Field
The invention relates to the technical field of battery management, in particular to a protective BMS battery management system.
Background
The BMS battery system is commonly called a battery caregiver or a battery manager, and is mainly used for intelligently managing and maintaining each battery unit, preventing overcharge and overdischarge of the battery, prolonging the service life of the battery, and monitoring the state of the battery. BMS battery management system unit includes BMS battery management system, control module group, display module group, wireless communication module group, electrical equipment, is used for the group battery of electrical equipment power supply and is used for gathering the collection module of the battery information of group battery, BMS battery management system passes through communication interface and is connected with wireless communication module group and display module group respectively, the output of gathering the module is connected with BMS battery management system's input, BMS battery management system's output is connected with the input of control module group, the control module group is connected with group battery and electrical equipment respectively, BMS battery management system passes through wireless communication module and is connected with the Server end.
The battery pack has large working current and large heat generation quantity, and meanwhile, the battery pack is in a relatively closed environment, so that the temperature of the battery is easily increased, because the electrolyte in the lithium battery plays a charge conduction role in the lithium battery. At present, most of lithium batteries are composed of flammable and volatile non-aqueous solutions, and compared with batteries composed of aqueous solutions, the composition system has higher specific energy and voltage output and meets the higher energy requirements of users. Since the nonaqueous electrolyte is inflammable and volatile, and is soaked in the battery, the non-aqueous electrolyte also forms a combustion source of the battery. The temperature sensor and the humidity sensor are directly arranged on the battery pack according to the prior art to collect the temperature of the battery pack and the humidity of the environment, the temperature and the humidity of the battery pack are adjusted in real time when the temperature and the humidity of the battery pack are obtained, but when the temperature of the battery pack and the humidity of the environment reach alarm values, the temperature of the battery pack and the environment of the battery pack reach warning points or even exceed standards, the temperature of the battery pack and the humidity of the environment of the battery pack can also exceed standards even if the battery pack is subjected to real-time heat dissipation and dehumidification through the heat dissipation device and the dehumidification device in real time, the aging of the battery pack is accelerated, the service life of the battery pack is reduced, and the battery pack is even damaged.
Disclosure of Invention
In view of the above technical problems, the present invention provides a protection type BMS battery management system that can conveniently monitor the state of a battery pack, realize protection of the battery pack, and improve the service life of the battery pack.
In order to achieve the purpose, the invention provides the following technical scheme: a protection type BMS battery management system comprises a server, a mobile terminal and a balancing device which are connected in a two-way mode, wherein a data processing module and a wireless communication module are arranged in the server, and a data acquisition module for acquiring data of a battery pack and a state regulation and control module for regulating the state of the battery pack are arranged in the balancing device;
the data acquisition module is used for acquiring the temperature of the battery pack, the humidity of the environment where the battery pack is located and the current value of the battery pack;
the state regulation and control module is connected with a heat dissipation device and a dehumidification device which are arranged on the battery pack, and the heat dissipation device is used for accelerating the dissipation of heat of the battery pack; the dehumidifying device is used for removing moisture in the ambient air in which the battery pack is positioned;
the data processing module is used for acquiring data acquired by the data acquisition module, respectively predicting temperature, humidity and current values through the established LSTM neural network prediction model, and sending an instruction to the state regulation and control module to regulate and control the state of the battery pack according to the predicted temperature, humidity and current values.
Preferably, the data acquisition module is connected with a temperature sensor, a humidity sensor and a current sensor which are arranged on the battery pack, and acquires the temperature of the battery pack, the humidity of the environment where the battery pack is located and the current value of the battery pack through the temperature sensor, the humidity sensor and the current sensor respectively.
Preferably, the LSTM neural network prediction model is constructed as follows:
the method comprises the following steps: acquiring historical data acquired by a data acquisition module, wherein the historical data is divided into training historical data and inspection historical data, and the historical data is divided into temperature data, humidity data and current data;
step two: designing an LSTM neural network prediction model;
step three: acquiring training historical data in the step one, and training the LSTM neural network prediction model designed in the step two through the training historical data;
the fourth step: and (4) testing the trained LSTM neural network prediction model through testing historical data, and repeating the third step when the similarity between the prediction result and the result in the testing historical data is less than a preset value X.
Preferably, the process of sending the regulation instruction to the state regulation module by the data processing module is as follows:
the first step is as follows: the data processing module acquires the real-time temperature of the battery pack, the real-time humidity of the environment where the battery pack is located and the real-time current value of the battery pack through the data acquisition module;
the second step is that: inputting real-time temperature, real-time humidity and real-time current values to obtain predicted temperature, predicted humidity and predicted current values through an LSTM neural network prediction model;
the third step: and comparing the predicted temperature, the predicted humidity and the predicted current value with a preset temperature T, a preset humidity R and a preset current A respectively, and when at least one of the predicted temperature, the predicted humidity and the predicted current value is greater than the preset value, sending an instruction to the state regulation and control module through the data processing module.
Preferably, the state regulation module regulates the state of the battery pack as follows:
the first step is as follows: the state regulation and control module does not receive the instruction to regulate and control the state of the battery pack, and normal heat dissipation and dehumidification operations are carried out on the battery pack through the heat dissipation device and the dehumidification device;
the second step is that: the state regulation and control module receives the instruction to regulate and control the state of the battery pack, starts a powerful regulation and control mode to increase the output power of the heat dissipation device and the dehumidification device, and enhances the heat dissipation and dehumidification operations of the battery pack.
The invention has the beneficial effects that: wherein directly be convenient for acquire the temperature of group battery through data acquisition module, humidity, and electric current, and with data transmission to server, the server is handled data through data processing module, predict the temperature through LSTM neural network prediction model, humidity, and electric current, and through carrying out the analysis to the prediction data, send instruction to balancing unit, state regulation and control module through among the balancing unit regulates and control the state of group battery, the high temperature of group battery has been avoided, or the group battery is located the environment humidity too high, or the electric current of group battery too high leads to accelerating the ageing of group battery, the condition that damages appears in the group battery even, thereby be convenient for play the guard action to the group battery, can be convenient monitor the state of group battery, the protection to the group battery has been realized, the life of group battery has been improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention in any way:
fig. 1 is a schematic view illustrating a simplified structure of a protection-type BMS battery management system according to the present invention.
FIG. 2 is a schematic view of the construction process of the prediction model of the LSTM neural network of the present invention.
Fig. 3 is a schematic diagram of the LSTM network structure of the present invention.
Fig. 4 is a schematic structural diagram of an LSTM control gate according to the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further explained below by combining the specific embodiments and the attached drawings, but the following embodiments are only the preferred embodiments of the invention, and not all of the embodiments are provided. Based on the embodiments in the implementation, other embodiments obtained by those skilled in the art without any creative efforts belong to the protection scope of the present invention.
Referring to fig. 1-4, a protection-type BMS battery management system includes a server, a mobile terminal, and a balancing device, which are connected two-way to each other, the server is provided with a data processing module and a wireless communication module, the balancing device is provided with a data acquisition module for battery data acquisition and a state regulation module for regulating the state of the battery;
the data acquisition module is used for acquiring the temperature of the battery pack, the humidity of the environment where the battery pack is located and the current value of the battery pack;
the state regulation and control module is connected with a heat dissipation device and a dehumidification device which are arranged on the battery pack, and the heat dissipation device is used for accelerating the dissipation of heat of the battery pack; the dehumidifying device is used for removing moisture in the ambient air where the battery pack is located;
the data processing module is used for acquiring data acquired by the data acquisition module, respectively predicting temperature, humidity and current values through the established LSTM neural network prediction model, and sending instructions to the state regulation and control module to regulate and control the state of the battery pack according to the predicted temperature, humidity and current values.
As shown in fig. 1 to 4, the data acquisition module is used to obtain the temperature of the battery pack, the humidity of the environment where the battery pack is located, and the current data of the battery pack, and send the data to the data processing module, and predict the temperature, humidity, and current of the battery pack after a period of time through the LSTM neural network prediction model, and analyze the predicted data, and send an instruction to the state regulation module to regulate and control the state of the battery pack, i.e., the heat dissipation effect of the battery pack and the moisture in the ambient air where the battery pack is located are accelerated by the heat dissipation device and the dehumidification device, so as to regulate the state of the battery pack in advance, avoid the temperature, the humidity of the environment, and the value of the current reaching preset values, avoid the high temperature accelerating the aging of the battery pack when the temperature reaches the preset values, reduce the service life of the battery pack, and avoid the short circuit of the battery pack caused by the overhigh ambient humidity of the battery pack, so as to protect the battery pack, thereby conveniently monitoring the state of the battery pack, and prolonging the service life of the battery pack.
The data acquisition module is connected with a temperature sensor, a humidity sensor and a current sensor which are arranged on the battery pack, and respectively acquires the temperature of the battery pack, the humidity of the environment where the battery pack is located and the current value of the battery pack through the temperature sensor, the humidity sensor and the current sensor.
The construction of the LSTM neural network prediction model is as follows:
the method comprises the following steps: acquiring historical data acquired by a data acquisition module, wherein the historical data is divided into training historical data and inspection historical data, and the historical data is divided into temperature data, humidity data and current data;
step two: designing an LSTM neural network prediction model;
step three: acquiring training historical data in the step one, and training the LSTM neural network prediction model designed in the step two through the training historical data;
the fourth step: and (4) testing the trained LSTM neural network prediction model through testing historical data, and repeating the third step when the similarity between the prediction result and the result in the testing historical data is less than a preset value X.
Repeatedly training the LSTM neural network prediction model through training historical data, and continuously iterating until the similarity between the predicted data and the actual data is smaller than a preset value X, wherein the calculation formula of the similarity is as follows:
real data, namely data of predicted data time in the inspection data; therefore, the prediction precision of the LSTM neural network prediction model is improved step by step, the temperature, the humidity and the current of the battery pack are predicted accurately, the temperature, the humidity and the current of the battery pack are regulated through the state regulation and control module in advance, the state of the battery pack can be monitored conveniently, the protection of the battery pack is realized, and the service life of the battery pack is prolonged.
On the basis of a common RNN, the long-term memory (LSTM) neural network adds a memory unit in each neural unit of a hidden layer to enable the RNN to have a long-term memory function; the RNN increases the horizontal connection among all units of a hidden layer on the basis of an artificial neural network, and transmits the value of a previous time step of the neural network to the current time step, so that the neural network has a memory function and is applied to the processing of the machine learning problems of natural language identification (NLP) with context connection and time sequence;
for example, at the predicted temperature values as follows:
the hidden layer of the original RNN has only one state s, which is very sensitive to short-term input, wherein the LSTM in the present application adds a cell state (cell state) on the basis of the RNN, so as to facilitate the preservation of the long-term state, and the LSTM neural network structure is shown in fig. 3;
the LSTM adjusts the weights of the historical information and the current information through 3 controllable gates, optimizes and obtains an optimal model, and as shown in fig. 4, on the basis of RNN, the LSTM calculates the current cell state according to the last temperature output value and the current temperature input value:
c t ′＝tanh[W c ·(s t-1 ,T t +b c )]in the formula: c. C t ' is the memory cell state at the current time; (s) t-1 ,T t +b c ) Represents connecting two vectors; w c 、b c Weights and bias parameters for the forgetting gate; s t-1 The neuron output value at the previous moment; by control of the forgetting gate, a new cell state C t The information in the past for a long time can be stored, and the non-important characteristics of the current time step are prevented from entering the memory unit through the control of the input gate; the final output value of LSTM is determined by both output gate and cell state, i.e. o t ＝σ[W o ·(s t-1 ,T t )+b o ]In the formula: o t Is the final temperature output value of the LSTM; w is a group of o 、b o The weights of the output gates, the bias parameters.
The process of sending the regulation and control instruction to the state regulation and control module by the data processing module is as follows:
the first step is as follows: the data processing module acquires the real-time temperature of the battery pack, the real-time humidity of the environment where the battery pack is located and the real-time current value of the battery pack through the data acquisition module;
the second step is that: inputting real-time temperature, real-time humidity and real-time current values to obtain predicted temperature, predicted humidity and predicted current values through an LSTM neural network prediction model;
the third step: and comparing the predicted temperature, the predicted humidity and the predicted current value with the preset temperature T, the preset humidity R and the preset current A respectively, and when at least one of the predicted temperature, the predicted humidity and the predicted current value is greater than the preset value, sending an instruction to the state regulation and control module through the data processing module.
The state regulation and control module regulates and controls the state of the battery pack as follows:
the first step is as follows: the state regulation and control module does not receive the instruction to regulate and control the state of the battery pack, and normal heat dissipation and dehumidification operations are carried out on the battery pack through the heat dissipation device and the dehumidification device;
the second step is that: the state regulation and control module receives the instruction to regulate and control the state of the battery pack, starts a powerful regulation and control mode to increase the output power of the heat dissipation device and the dehumidification device, and enhances the heat dissipation and dehumidification operations of the battery pack.
When the predicted value is reached, the state of the battery pack is adjusted in advance, the heat dissipation and dehumidification operations of the battery pack are enhanced, the condition that the battery pack is damaged or short-circuited due to overhigh temperature and accelerated aging or overhigh humidity or the condition that the current output of the battery pack is overhigh is avoided, the state of the battery pack can be conveniently monitored, the protection of the battery pack is realized, and the service life of the battery pack is prolonged; and the energy consumption can be reduced through different regulation and control modes.
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention, and that various changes and modifications may be made without departing from the spirit and scope of the present invention, which fall within the scope of the claimed invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. A protection type BMS battery management system comprises a server, a mobile terminal and a balancing device which are connected in a two-way mode, and is characterized in that a data processing module and a wireless communication module are arranged in the server, and a data acquisition module for acquiring battery pack data and a state regulation and control module for regulating the state of a battery pack are arranged in the balancing device;
the data acquisition module is used for acquiring the temperature of the battery pack, the humidity of the environment where the battery pack is located and the current value of the battery pack;
the state regulation and control module is connected with a heat dissipation device and a dehumidification device which are arranged on the battery pack, and the heat dissipation device is used for accelerating the dissipation of heat of the battery pack; the dehumidifying device is used for removing moisture in the ambient air in which the battery pack is positioned;
the data processing module is used for acquiring data acquired by the data acquisition module, respectively predicting temperature, humidity and current values through the established LSTM neural network prediction model, and sending instructions to the state regulation and control module to regulate and control the state of the battery pack according to the predicted temperature, humidity and current values.
2. A protected BMS battery management system according to claim 1, characterized in that: the data acquisition module is connected with a temperature sensor, a humidity sensor and a current sensor which are arranged on the battery pack, and respectively acquires the temperature of the battery pack, the humidity of the environment where the battery pack is located and the current value of the battery pack through the temperature sensor, the humidity sensor and the current sensor.
3. A protected BMS battery management system according to claim 1, characterized in that: the construction of the LSTM neural network prediction model is as follows:
the method comprises the following steps: acquiring historical data acquired by a data acquisition module, wherein the historical data is divided into training historical data and inspection historical data, and the historical data is divided into temperature data, humidity data and current data;
step two: designing an LSTM neural network prediction model;
step three: acquiring training historical data in the first step, and training the LSTM neural network prediction model designed in the second step through the training historical data;
the fourth step: and (4) testing the trained LSTM neural network prediction model through testing historical data, and repeating the third step when the similarity between the prediction result and the result in the testing historical data is less than a preset value X.
4. A protected BMS battery management system according to claim 3, characterized by: the process that the data processing module sends the regulation and control instruction to the state regulation and control module is as follows:
the first step is as follows: the data processing module acquires the real-time temperature of the battery pack, the real-time humidity of the environment where the battery pack is located and the real-time current value of the battery pack through the data acquisition module;
the second step is that: inputting real-time temperature, real-time humidity and real-time current values to obtain predicted temperature, predicted humidity and predicted current values through an LSTM neural network prediction model;
the third step: and comparing the predicted temperature, the predicted humidity and the predicted current value with the preset temperature T, the preset humidity R and the preset current A respectively, and when at least one of the predicted temperature, the predicted humidity and the predicted current value is greater than the preset value, sending an instruction to the state regulation and control module through the data processing module.
5. A protected BMS battery management system according to claim 4, characterized in that: the state regulation and control module regulates and controls the state of the battery pack as follows:
the first step is as follows: the state regulation and control module does not receive the instruction to regulate and control the state of the battery pack, and normal heat dissipation and dehumidification operations are carried out on the battery pack through the heat dissipation device and the dehumidification device;
the second step is that: the state regulation and control module receives the instruction to regulate and control the state of the battery pack, starts a powerful regulation and control mode to increase the output power of the heat dissipation device and the dehumidification device, and enhances the heat dissipation and dehumidification operations of the battery pack.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116031542A (en) * | 2023-03-28 | 2023-04-28 | 安徽慧鹏新能源科技有限公司 | Temperature control management system of middle-mounted battery box of commercial vehicle |
| CN117096504A (en) * | 2023-10-17 | 2023-11-21 | 厦门海辰储能科技股份有限公司 | Temperature control method and device, equipment and storage medium |
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- 2022-12-14 CN CN202211608621.9A patent/CN115810814A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116031542A (en) * | 2023-03-28 | 2023-04-28 | 安徽慧鹏新能源科技有限公司 | Temperature control management system of middle-mounted battery box of commercial vehicle |
| CN117096504A (en) * | 2023-10-17 | 2023-11-21 | 厦门海辰储能科技股份有限公司 | Temperature control method and device, equipment and storage medium |
| CN117096504B (en) * | 2023-10-17 | 2024-01-26 | 厦门海辰储能科技股份有限公司 | Temperature control method and device, equipment and storage medium |
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