CN104881001A - Energy storage battery management system based on deep learning network - Google Patents
Energy storage battery management system based on deep learning network Download PDFInfo
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- CN104881001A CN104881001A CN201410169036.2A CN201410169036A CN104881001A CN 104881001 A CN104881001 A CN 104881001A CN 201410169036 A CN201410169036 A CN 201410169036A CN 104881001 A CN104881001 A CN 104881001A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/08—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers from or to individual record carriers, e.g. punched card, memory card, integrated circuit [IC] card or smart card
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses an energy storage battery management system based on a deep learning network. The energy storage battery management system comprises a battery management unit BMU, a central management unit CMU, an upper computer, a 4V storage battery, a Hall sensor and a DC load, wherein the 24V storage battery is electrically connected with the battery management unit BMU and the central management unit CMU, the central management unit CMU communicates with the battery management unit BMU through a CAN bus, and the upper computer is in communication connection with the central management unit CMU through an RS485. Compared with the prior art, the energy storage battery management system based on the deep learning network is provided for realizing on-line monitoring of an energy storage battery pack, and improves estimation accuracy of battery management system state-of-charge SOC and storage battery SOH through a deep neural network model and a learning algorithm thereof.
Description
Technical field
The present invention relates to energy management field, be specially a kind of energy storage battery management system based on degree of deep learning network.
Background technology
Along with the develop rapidly of Power Electronic Technique and computer technology, distributed power generation, as one of method solving the problems such as energy economy & environment that conventional power generation systems brings, is more and more subject to people's attention.But, the active type distribution that distributed power source and user mix and formed is that electric system brings new challenge, for solving the problem of distributed power source access, coordinate the contradiction of bulk power grid and distributed power source, abundant excavation distributed power generation is the value brought of electrical network and user and benefit, and micro-capacitance sensor technology is arisen at the historic moment.
Electricity storage technology weighs for the basic function realizing micro-capacitance sensor very much, and high performance accumulator system can ensure power supply quality and the continued reliability of electric power system, improves the comprehensive utilization ratio of electric energy.Therefore, perfect energy storage battery management system is the powerful guarantee that micro-capacitance sensor realizes himself function.Lithium ion battery due to its have specific energy large, have extended cycle life, self-discharge rate is little, memory-less effect and the feature such as pollution-free, has become the study hotspot of countries nowadays energy storage field.But, the high energy ratio performance of lithium ion battery is that it brings the hidden danger of secure context, and incorrect use-pattern will have a strong impact on the use safety of battery, therefore must strictly according to battery parameter management battery, prevent it from overcharging, cross and put and excess temperature, thus ensure its security performance.Because the charge and discharge platform of lithium battery is very smooth, simultaneously, the charge and discharge process of lithium battery is an extremely complicated process, charge-discharge magnification and the factor such as environment temperature, humidity all can have influence on decay and the serviceable life of pack total capacities, use conventional method to carry out state estimation to lithium battery to be difficult to obtain estimated result accurately, this patent provides a kind of method based on degree of depth network for improving the accuracy of battery management system state estimation.
Summary of the invention
The invention provides a kind of energy storage battery management system based on degree of deep learning network, for the monitoring and control of micro-capacitance sensor energy-storage battery group particularly ferric phosphate lithium cell.
For achieving the above object, the invention provides following technical scheme:
Based on an energy storage battery management system for degree of deep learning network, comprising: battery management unit BMU, central management unit CMU, host computer, 24V accumulator, Hall element and DC load; Wherein 24V accumulator is electrically connected with battery management unit BMU and central management unit CMU, carries out communication between central management unit CMU and battery management unit BMU by CAN, is connected between host computer and central management unit CMU by RS485 communication.
Described battery management unit BMU comprises: malfunction coefficient module, data acquisition module, balance module, CAN and power module, data acquisition module carries out voltage, temperature acquisition send information to central management unit CMU by CAN, carry out battery cell equilibrium and failure code display, preferably, described malfunction coefficient module is made up of seven segmentation charactrons; Described data acquisition module is made up of voltage monitoring chip LTC6802, NTC thermistor and buffer circuit; Described balance module is made up of matrix switch, flyback DC/DC transducer and balance controller; Described CAN receives and dispatches TJA1050 by high-speed CAN, two-channel digital isolating chip IS07221A is formed; Described power module is made up of voltage stabilizing PWB2405CS and IB00505S.
Described central management unit CMU comprises: data memory module, current detection module, RS485 communication module, CAN and power module, and preferably, described data memory module is made up of Micro SD card and level transferring chip ADG3308; Current detection module is made up of Hall current sensor, operational amplifier; Described RS485 communication module is made up of differential data transponder chip SN65LBC184D; Described CAN is by high-speed CAN transceiver TJA1050, and two-channel digital isolating chip IS07221A is formed; Described power module is made up of voltage stabilizing chip PWA2412MD, K7805, REG1117 and IB00505S.
Described host computer comprises: HMI touch-screen.
The state-of-charge SOC of battery of the present invention and health status (accumulator SOH) estimation are realized by deep neural network and learning algorithm thereof; Described deep neural network construction step is:
1, input layer and output layer neuron is determined: using battery total capacity, voltage, electric current, temperature and the SOC in k-1 moment as the input layer of neural network; Using the output layer neuron of SOC and SOH of battery as neural network.
2, determine activation function, the neural network hidden layer number of plies and hidden layer neuron number, thus set up neural network.
3, adjust network parameter, train the neural network created, estimate SOC and SOH.
Compared with prior art, the invention provides a kind of energy storage battery management system based on degree of deep learning network, for realizing the on-line monitoring to energy-storage battery group, by deep neural network model and learning algorithm thereof, improve the accuracy that battery management system state-of-charge SOC and accumulator SOH estimates.
Accompanying drawing explanation
Fig. 1 is that the present invention realizes the state-of-charge SOC of battery and the deep neural network structural drawing of health status (accumulator SOH) estimating algorithm.
A kind of energy storage battery management system block diagram based on degree of deep learning network that Fig. 2 provides for the embodiment of the present invention.
Fig. 3 is the structured flowchart of a kind of embodiment of a kind of energy storage battery management system based on degree of deep learning network.
Embodiment
The technological means realized to make the present invention, creation characteristic, reaching object and effect is easy to understand, below in conjunction with concrete diagram, setting forth the present invention further.
A kind of energy storage battery management system schematic diagram based on degree of deep learning network, as shown in Figure 2, comprise: battery management unit BMU, central management unit CMU, host computer, 24V accumulator, Hall element and DC load, in a kind of embodiment of a kind of energy storage battery management system based on degree of deep learning network of the present invention, as shown in Figure 3, described DC load is consumer; Wherein 24V accumulator is electrically connected with battery management unit BMU and central management unit CMU, for powering for battery management unit BMU and central management unit CMU, central management unit CMU carries out current detecting by the Hall element be serially connected between energy-storage battery and DC load, carry out communication by CAN between central management unit CMU and battery management unit BMU, between host computer and central management unit CMU, carry out communication by RS485.
Described battery management unit BMU comprises: malfunction coefficient module, data acquisition module, balance module, CAN and power module, data acquisition module carries out voltage, temperature acquisition send information to central management unit CMU by CAN, carries out the balanced and failure code of battery cell and shows.Further, described malfunction coefficient module is made up of seven segmentation charactrons, for completing the display of failure code; Described data acquisition module is made up of voltage monitoring chip LTC6802, NTC thermistor and buffer circuit, for completing monomer voltage and temperature acquisition; Described balance module is made up of, for realizing battery pack balancing matrix switch, flyback DC/DC transducer and balance controller; Described CAN is made up of, for realizing CAN communication high-speed CAN transceiver TJA1050, two-channel digital isolating chip IS07221A; Described power module is made up of voltage stabilizing chip PWB2405CS and IB00505S, for other module for power supply for battery management unit BMU.
Described central management unit CMU comprises: data memory module, current detection module, RS485 communication module, CAN and power module, and function is: carry out current detecting, electric battery carried out to state estimation, carry out data storage and undertaken alternately by RS485 and host computer; Further, described data memory module is made up of Micro SD card and level transferring chip ADG3308, stores for the critical data produced in operational process; Current detection module is made up of Hall current sensor, operational amplifier, for realizing current acquisition; Described RS485 communication module is made up of differential data transponder chip SN65LBC184D, for realizing the communication of central management unit CMU and HMI touch-screen; Described CAN is by high-speed CAN transceiver TJA1050, and two-channel digital isolating chip IS07221A is formed, for realizing CAN communication; Described power module is made up of voltage stabilizing chip PWA2412MD, K7805, REG1117 and IB00505S, for other module for power supply for central management unit CMU.
Described host computer comprises: HMI touch-screen, and function realizes battery parameter configuration, the status information of Real time displaying battery.
After system electrification, first battery management unit BMU carries out self-inspection, if system discovery fault, then failure message is presented at malfunction coefficient module; If system is normal, then battery cell voltage and temperature is gathered, and monitor CAN by CAN, after finding that there is the CAN request message from central management unit CMU, start to send battery cell infomational message to central management unit CMU.
After central management unit CMU powers on, first need to utilize host computer to carry out parameter configuration to system, after having configured, central management unit CMU can send request message by CAN circulation to battery management unit BMU, receive the battery cell infomational message from battery management unit BMU, central management unit CMU is by Hall element measure loop electric current and utilize deep neural network algorithm to carry out battery status estimation simultaneously.Central management unit CMU also can carry out overcharging in the process run, cross put, the fault diagnosis such as excess temperature the status information of battery and failure message are fed back to user by human-computer interaction interface, on the other hand, the critical data of battery is stored in Micro SD card by central management unit CMU, so that maintainer carries out data analysis.
As shown in Figure 1, the state-of-charge SOC of battery of the present invention and health status (accumulator SOH) estimation are realized by deep neural network and learning algorithm thereof; Described deep neural network construction step is:
1, input layer and output layer neuron is determined: using battery total capacity, voltage, electric current, temperature and the SOC in k-1 moment as the input layer of neural network; Using the output layer neuron of SOC and SOH of battery as neural network.
2, determine activation function, the neural network hidden layer number of plies and hidden layer neuron number, thus set up neural network.
3, adjust network parameter, train the neural network created, estimate SOC and SOH.
To those skilled in the art, obviously the invention is not restricted to the details of above-mentioned one exemplary embodiment, and when not deviating from spirit of the present invention or essential characteristic, the present invention can be realized in other specific forms.Therefore, no matter from which point, all should embodiment be regarded as exemplary, and be nonrestrictive, scope of the present invention is limited by claims instead of above-mentioned explanation, and all changes be therefore intended in the implication of the equivalency by dropping on claim and scope are included in the present invention.Any Reference numeral in claim should be considered as the claim involved by limiting.
Claims (2)
1. based on an energy storage battery management system for degree of deep learning network, it is characterized in that, comprising: battery management unit BMU, central management unit CMU, host computer, 24V accumulator, Hall element and DC load; Wherein 24V accumulator is electrically connected with battery management unit BMU and central management unit CMU, carries out communication between central management unit CMU and battery management unit BMU by CAN, is connected between host computer and central management unit CMU by RS485 communication; Described battery management unit BMU comprises: malfunction coefficient module, data acquisition module, balance module, CAN and power module; Described malfunction coefficient module is made up of seven segmentation charactrons; Described data acquisition module is made up of voltage monitoring chip LTC6802, NTC thermistor and buffer circuit; Described balance module is made up of matrix switch, flyback DC/DC transducer and balance controller; Described CAN receives and dispatches TJA1050 by high-speed CAN, two-channel digital isolating chip IS07221A is formed; Described power module is made up of voltage stabilizing PWB2405CS and IB00505S; Described central management unit CMU comprises: data memory module, current detection module, RS485 communication module, CAN and power module, and described data memory module is made up of Micro SD card and level transferring chip ADG3308; Current detection module is made up of Hall current sensor, operational amplifier; Described RS485 communication module is made up of differential data transponder chip SN65LBC184D; Described CAN is by high-speed CAN transceiver TJA1050, and two-channel digital isolating chip IS07221A is formed; Described power module is made up of voltage stabilizing chip PWA2412MD, K7805, REG1117 and IB00505S; Described host computer comprises: HMI touch-screen.
2. a kind of energy storage battery management system based on degree of deep learning network according to claim 1, is characterized in that, the state-of-charge SOC of battery of the present invention and accumulator health status SOH estimates by deep neural network and learning algorithm realization thereof; Described deep neural network construction step is: the first step, determine input layer and output layer neuron: using battery total capacity, voltage, electric current, temperature and the SOC in k-1 moment as the input layer of neural network, using the output layer neuron of SOC and SOH of battery as neural network; Second step, determines activation function, the neural network hidden layer number of plies and hidden layer neuron number, thus sets up neural network; 3rd step, adjustment network parameter, trains the neural network created, and estimates SOC and SOH.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105137242A (en) * | 2015-09-09 | 2015-12-09 | 南京航空航天大学 | Single-phase photovoltaic inverter on-line state monitoring and residual life prediction method |
| CN105634058A (en) * | 2016-01-22 | 2016-06-01 | 广东志成冠军集团有限公司 | Intelligent balancing method and intelligent balancing system for battery pack |
| CN107887658A (en) * | 2016-09-29 | 2018-04-06 | 法乐第(北京)网络科技有限公司 | Accumulator cell assembly, motor vehicle |
| CN109407007A (en) * | 2018-12-24 | 2019-03-01 | 广东省智能制造研究所 | A kind of battery charge state detection method and device |
| CN109784480A (en) * | 2019-01-17 | 2019-05-21 | 武汉大学 | A kind of power system state estimation method based on convolutional neural networks |
| CN110412470A (en) * | 2019-04-22 | 2019-11-05 | 上海博强微电子有限公司 | Electric automobile power battery SOC estimation method |
| CN110531281A (en) * | 2019-09-09 | 2019-12-03 | 合肥工业大学 | The method and system of health status for estimated driving force secondary battery unit |
| CN110658460A (en) * | 2019-09-29 | 2020-01-07 | 东软睿驰汽车技术（沈阳）有限公司 | Battery life prediction method and device for battery pack |
| CN112254815A (en) * | 2020-08-28 | 2021-01-22 | 华电电力科学研究院有限公司 | Electrochemical energy storage station temperature monitoring system based on three-dimensional infrared imaging temperature measurement |
| CN112557908A (en) * | 2020-12-17 | 2021-03-26 | 温州大学 | SOC and SOH joint estimation method for lithium ion power battery |
| US11171498B2 (en) | 2017-11-20 | 2021-11-09 | The Trustees Of Columbia University In The City Of New York | Neural-network state-of-charge estimation |
| CN115377582A (en) * | 2022-08-30 | 2022-11-22 | 华为数字能源技术有限公司 | Battery monomer, battery module, battery package, energy storage system and electric automobile |
| US11637331B2 (en) | 2017-11-20 | 2023-04-25 | The Trustees Of Columbia University In The City Of New York | Neural-network state-of-charge and state of health estimation |
| CN117523940A (en) * | 2024-01-08 | 2024-02-06 | 深圳风向标教育资源股份有限公司 | Power battery management system and practical training teaching platform |
| CN117523940B (en) * | 2024-01-08 | 2024-04-19 | 深圳风向标教育资源股份有限公司 | Power battery management system and practical training teaching platform |
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Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105137242B (en) * | 2015-09-09 | 2018-04-13 | 南京航空航天大学 | Single-phase photovoltaic inverter on-line condition monitoring and method for predicting residual useful life |
| CN105137242A (en) * | 2015-09-09 | 2015-12-09 | 南京航空航天大学 | Single-phase photovoltaic inverter on-line state monitoring and residual life prediction method |
| CN105634058A (en) * | 2016-01-22 | 2016-06-01 | 广东志成冠军集团有限公司 | Intelligent balancing method and intelligent balancing system for battery pack |
| CN105634058B (en) * | 2016-01-22 | 2018-05-25 | 广东志成冠军集团有限公司 | A kind of intelligent equalization method of battery pack and intelligent equalization system |
| CN107887658A (en) * | 2016-09-29 | 2018-04-06 | 法乐第(北京)网络科技有限公司 | Accumulator cell assembly, motor vehicle |
| US11171498B2 (en) | 2017-11-20 | 2021-11-09 | The Trustees Of Columbia University In The City Of New York | Neural-network state-of-charge estimation |
| US11637331B2 (en) | 2017-11-20 | 2023-04-25 | The Trustees Of Columbia University In The City Of New York | Neural-network state-of-charge and state of health estimation |
| CN109407007A (en) * | 2018-12-24 | 2019-03-01 | 广东省智能制造研究所 | A kind of battery charge state detection method and device |
| CN109784480B (en) * | 2019-01-17 | 2022-11-18 | 武汉大学 | Power system state estimation method based on convolutional neural network |
| CN109784480A (en) * | 2019-01-17 | 2019-05-21 | 武汉大学 | A kind of power system state estimation method based on convolutional neural networks |
| CN110412470B (en) * | 2019-04-22 | 2021-09-21 | 上海博强微电子有限公司 | SOC estimation method for power battery of electric vehicle |
| CN110412470A (en) * | 2019-04-22 | 2019-11-05 | 上海博强微电子有限公司 | Electric automobile power battery SOC estimation method |
| CN110531281B (en) * | 2019-09-09 | 2021-08-13 | 合肥工业大学 | Method and system for estimating state of health of power storage battery unit |
| CN110531281A (en) * | 2019-09-09 | 2019-12-03 | 合肥工业大学 | The method and system of health status for estimated driving force secondary battery unit |
| CN110658460A (en) * | 2019-09-29 | 2020-01-07 | 东软睿驰汽车技术（沈阳）有限公司 | Battery life prediction method and device for battery pack |
| CN112254815A (en) * | 2020-08-28 | 2021-01-22 | 华电电力科学研究院有限公司 | Electrochemical energy storage station temperature monitoring system based on three-dimensional infrared imaging temperature measurement |
| CN112557908A (en) * | 2020-12-17 | 2021-03-26 | 温州大学 | SOC and SOH joint estimation method for lithium ion power battery |
| CN115377582A (en) * | 2022-08-30 | 2022-11-22 | 华为数字能源技术有限公司 | Battery monomer, battery module, battery package, energy storage system and electric automobile |
| CN117523940A (en) * | 2024-01-08 | 2024-02-06 | 深圳风向标教育资源股份有限公司 | Power battery management system and practical training teaching platform |
| CN117523940B (en) * | 2024-01-08 | 2024-04-19 | 深圳风向标教育资源股份有限公司 | Power battery management system and practical training teaching platform |
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Application publication date: 20150902 |