A multi-link cooperative wireless communication battery management system

Abstract

The application discloses a kind of multi-link cooperation's wireless communication battery management system, it is related to wireless communication network field.The application includes battery data acquisition module, multi-link communication module, intelligent control module, energy management module and cloud management platform;Battery data acquisition module is electrically connected to battery module.The heterogeneous link scheduling unit of the application pre-stores scene recognition rule base, according to real-time environmental sensor data matching scene type, and then enable corresponding link combination, in indoor close-range scene, enable Bluetooth 5.0 and ZigBee3.0 link, satisfy low power consumption, short distance, high frequency data acquisition demand;In remote monitoring scene, main 4G / 5G cellular link transmission state data, standby Wi-Fi link transmission large capacity data, give full play to the advantage of each link, improve the spectrum resource utilization efficiency.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of wireless communication networks, in particular to a wireless communication battery management system with multi-link cooperation. BACKGROUND

[0002] Traditional battery management systems have many limitations in wireless communication, most of which adopt single-link communication mode. In complex electromagnetic environments such as industrial plants and urban high-rise dense areas, signals are easily disturbed, leading to data transmission interruption or error, and the key parameters of battery voltage, current, state of charge (SOC) and state of health (SOH) cannot be obtained in real time and accurately. When the device is in a mobile state or a long-distance transmission scene, the coverage range and transmission rate of single-link communication are difficult to meet the demand, causing delay in the monitoring and control of the battery management system on the battery, affecting the safe and stable operation of the battery.

[0003] Although the existing multi-link communication battery management system adopts multi-link, there is a lack of intelligent cooperation mechanism between the links, and the link resources cannot be dynamically adjusted according to environmental changes and data transmission requirements, resulting in low spectrum resource utilization efficiency and increased energy consumption. In terms of heterogeneous link cooperation, there is no effective scene adaptation strategy, and the advantages of each link cannot be fully utilized in different scenes, making it difficult to achieve efficient and reliable data transmission and battery management.

[0004] Therefore, we have made improvements and proposed a wireless communication battery management system with multi-link cooperation. SUMMARY

[0005] The application provides the following technical solutions:

[0006] The application is specifically as follows:

[0007] A wireless communication battery management system with multi-link cooperation comprises a battery data acquisition module, a multi-link communication module, an intelligent control module, an energy management module and a cloud management platform.

[0008] The battery data acquisition module is electrically connected to the battery module and is used to acquire the voltage, current, temperature, state of charge (SOC) and state of health (SOH) parameters of the battery module, generate a data packet with a time stamp and send it to the multi-link communication module.

[0009] The multi-link communication module is in communication connection with the battery data acquisition module and the intelligent control module, integrates Bluetooth, Wi-Fi, ZigBee and cellular network communication units, each communication unit is independently configured with a transceiving antenna and a modulation and demodulation module, and is used to establish a wireless transmission link according to the scheduling instruction of the intelligent control module.

[0010] The time division duplex coordinators are arranged between the communication units of the multi-link communication module, and the channel occupation time slots of the links are divided according to the scheduling instructions of the intelligent control module to avoid the same frequency interference.

[0011] The intelligent control module is electrically connected with the multi-link communication module and the energy management module, and includes a link state monitoring unit, an intelligent dynamic link fusion unit, a heterogeneous link scheduling unit and an anti-interference game decision unit. The link state monitoring unit collects channel signal-to-noise ratio (SNR), transmission delay (RTT), packet loss rate (PER) and spectrum occupation degree parameters of each communication unit in real time, generates a link state vector and inputs the link state vector into the intelligent dynamic link fusion unit. The intelligent dynamic link fusion unit is internally provided with a deep learning neural network model. The deep learning neural network model takes the link state vector and the data packet type of the battery data collection module as input, and outputs a multi-link data fusion strategy. The strategy includes a bandwidth allocation ratio of each link and a data packet recombination rule. The heterogeneous link scheduling unit pre-stores a scene recognition rule library. The rule library contains feature parameter thresholds of indoor / outdoor, close distance / long distance, high dynamic / static scene types. According to real-time environment sensor data, the scene type is matched, and a link combination enabling instruction of the corresponding scene is output to the multi-link communication module. The anti-interference game decision unit constructs a strategy space containing link frequency bands, modulation modes and transmission power. Based on game theory, a profit function of each link is established. Through distributed iterative calculation, a Nash equilibrium state is reached, and an anti-interference parameter adjustment instruction is output to the multi-link communication module.

[0012] The deep learning neural network model of the intelligent dynamic link fusion unit realizes data fusion through the following steps:

[0013] SA1, start;

[0014] SA2, time stamp alignment and Kalman filter noise reduction are performed on the same type of battery data transmitted by different links.

[0015] SA3, link transmission weight is allocated according to data packet priority. The priority of the data packet is control instruction>real-time state data>history data.

[0016] SA4, the convolutional neural network is used to extract the data features of each link, and the optimal data fusion sequence is output through the full connection layer.

[0017] SA5, end.

[0018] The scene recognition rule library of the heterogeneous link scheduling unit includes:

[0019] Indoor close distance scene, distance < 10 m, GPS signal strength < -130 dBm: enable Bluetooth 5.0 and ZigBee 3.0 links to collect battery data, and the transmission period is ≤50 ms.

[0020] In remote monitoring scenarios, the distance is >1km and the cellular network signal strength is >-100dBm. The primary 4G / 5G cellular link is used to transmit status data, and the backup Wi-Fi link is used to transmit large-capacity battery image data.

[0021] In high-dynamic scenarios, where vehicle acceleration is greater than 0.5g, both cellular and Bluetooth links are enabled simultaneously. The cellular link transmits the raw data, while the Bluetooth link transmits the verification code, achieving redundant data transmission.

[0022] The energy management module is electrically connected to the battery pack and the intelligent control module. It collects the total voltage, remaining power SoE and charging / discharging current of the battery pack, generates an energy status signal and sends it to the intelligent control module. At the same time, it receives the link power consumption control command from the intelligent control module and adjusts the operating parameters of the power amplifier modules of each communication unit.

[0023] The link power consumption control strategy of the energy management module includes:

[0024] When the remaining battery power SoE is less than 20%, all communication units are forced to enter low power mode, the Bluetooth link transmit power is ≤4dBm, and the ZigBee link transmission rate is reduced to 250kbps.

[0025] When the battery is charging and the charging current is >0.1C, 5% to 10% of the charging power is allocated to the cellular link, and its modulation method is upgraded to higher-order modulation to improve the transmission rate.

[0026] The cloud management platform is connected to the multi-link communication module. It receives battery data through the multi-link communication module, generates a battery status assessment report and link optimization strategy based on big data analysis, and sends them back to the intelligent control module.

[0027] The working steps of the battery data acquisition module are as follows:

[0028] SB1, Begin;

[0029] SB2. The voltage, current, and temperature signals of the battery module are sampled in real time at a set frequency using an analog-to-digital converter, and the sampling time is recorded to generate a timestamp.

[0030] SB3. Based on the ampere-hour integration method combined with the open-circuit voltage correction algorithm, the state of charge (SOC) of the battery is calculated, and the state of health (SOH) of the battery is estimated using the capacity decay model.

[0031] SB4. Pack the collected and calculated voltage, current, temperature, SOC, and SOH data into a package, add a timestamp and checksum, and generate a timestamped data packet.

[0032] SB5, sending the data packet to the multi-link communication module;

[0033] SB6, end.

[0034] The working steps of the multi-link communication module are as follows:

[0035] SC1, start;

[0036] SC2, receiving the scheduling instruction of the intelligent control module, and determining the wireless communication link to be enabled, wherein the wireless communication link includes Bluetooth, Wi-Fi, ZigBee, and cellular network;

[0037] SC3, corresponding to the enabled communication unit, configuring the transceiver antenna and the modem module parameter, and establishing the wireless transmission link;

[0038] SC4, receiving the data packet sent by the battery data acquisition module, and transmitting the data packet through the established wireless link according to the scheduling instruction of the intelligent control module, or receiving the data returned by the cloud management platform through the wireless link and forwarding the data to the intelligent control module;

[0039] SC5, end.

[0040] The working steps of the intelligent control module include the working steps of the link state monitoring unit, the working steps of the intelligent dynamic link fusion unit, the working steps of the heterogeneous link scheduling unit, and the working steps of the anti-interference game decision unit.

[0041] The working steps of the link state monitoring unit are as follows:

[0042] SD1, start;

[0043] SD2, real-time acquisition of channel signal-to-noise ratio, transmission delay, packet loss rate, and spectrum occupation degree parameters of each communication unit;

[0044] SD3, organizing the collected parameters to generate a link state vector;

[0045] SD4, inputting the link state vector into the intelligent dynamic link fusion unit;

[0046] SD5, end.

[0047] The working steps of the intelligent dynamic link fusion unit are as follows:

[0048] SE1, start;

[0049] SE2, receiving the link state vector input by the link state monitoring unit and the data packet type of the battery data acquisition module;

[0050] SE3, time stamp alignment and Kalman filter denoising of the same type of battery data transmitted by different links;

[0051] SE4, assigning link transmission weight according to data packet priority, wherein the priority is: control instruction > real-time state data > historical data;

[0052] SE5, extracting features of each link data through a convolutional neural network, and outputting an optimal data fusion sequence through a fully connected layer, i.e. a multi-link data fusion strategy, including bandwidth allocation ratio of each link and data packet reorganization rule;

[0053] SE6, sending the multi-link data fusion strategy to the multi-link communication module to guide data transmission;

[0054] SE7, end.

[0055] The working steps of the heterogeneous link scheduling unit are as follows:

[0056] SF1, start;

[0057] SF2, obtain real-time environmental sensor data;

[0058] SF3, match the real-time environmental sensor data with the pre-stored scene recognition rule library to determine the current scene type, wherein the scene recognition rule library contains feature parameter thresholds of indoor / outdoor, close distance / long distance, high dynamic / static scene;

[0059] SF4, according to the scene type, retrieve the link combination enabling instruction of the corresponding scene from the rule library and send it to the multi-link communication module;

[0060] SF5, end.

[0061] The working steps of the anti-interference game decision unit are as follows:

[0062] SG1, start;

[0063] SG2, construct a strategy space containing link frequency band, modulation method and transmission power;

[0064] SG3, establish the revenue function of each link based on game theory, the expression is: Ui=α·(1-PERi)+β·(1-γ·fcollision)-δ·Ptx, wherein Ui is the revenue value of the ith link, α, β, δ are weight coefficients, PERi is the link packet loss rate, fcollision is the frequency band collision probability, Ptx is the transmission power, and the optimal strategy combination of each link is solved through gradient descent method iteration;

[0065] SG4, iteratively calculate the revenue function through the gradient descent method to solve the optimal strategy combination of each link;

[0066] SG5, when the convergence condition is reached, send the anti-interference parameter adjustment instruction of each link to the multi-link communication module, wherein the anti-interference parameter adjustment instruction comprises: frequency band, modulation mode, and transmission power adjustment value;

[0067] SG6, end.

[0068] The working steps of the energy management module are as follows:

[0069] SH1, start;

[0070] SH2, collect the total voltage of the battery pack, the remaining electric quantity SoE and the charging and discharging current in real time, and generate an energy state signal;

[0071] SH3, send the energy state signal to the intelligent control module;

[0072] SH4, receive the link power consumption control instruction sent by the intelligent control module;

[0073] SH5, adjust the power amplifier module working parameters of each communication unit according to the link power consumption control instruction;

[0074] SH6, end.

[0075] The working steps of the cloud management platform are as follows:

[0076] SI1, start;

[0077] SI2, receive the battery data uploaded by the battery data acquisition module through the multi-link communication module;

[0078] SI3, process the battery data based on a big data analysis algorithm, and generate a battery state evaluation report and a link optimization strategy;

[0079] SI4, return the battery state evaluation report and the link optimization strategy to the intelligent control module through the multi-link communication module;

[0080] SI5, end.

[0081] Compared with the prior art, the present application has the following advantages:

[0082] 1. The heterogeneous link scheduling unit of the present application pre-stores a scene recognition rule library, matches the scene type according to real-time environmental sensor data, and then enables the corresponding link combination. In an indoor close-range scene, Bluetooth 5.0 and ZigBee 3.0 links are enabled to meet the low-power, short-distance and high-frequency data acquisition requirements. In a remote monitoring scene, the 4G / 5G cellular link is mainly used to transmit state data, and the Wi-Fi link is used as a backup to transmit large-capacity data, which fully utilizes the advantages of each link and improves the spectrum resource utilization efficiency.

[0083] 2、Anti-interference game decision unit is constructed to contain the strategy space of link frequency band, modulation mode and transmission power, the income function is established based on game theory, and the Nash equilibrium state is reached through distributed iteration calculation, so that the system can automatically adjust the link parameters according to the interference condition, effectively avoid the interference frequency band, reduce the packet loss rate of data transmission in the serious interference environment, and ensure the stability of data transmission.

[0084] 3、The energy management module collects the related parameters of the battery pack, adjusts the working parameters of the power amplifier module of each communication unit according to the remaining battery capacity and charging state, forces each communication unit to enter low-power mode when the remaining battery capacity is low, reduces the battery energy consumption, and reasonably allocates the charging power to the cellular link and upgrades the modulation mode when the battery is charging, improves the transmission rate.

[0085] 4、The cloud management platform receives battery data through the multi-link communication module, generates battery state evaluation report and link optimization strategy by using big data analysis, and returns to the intelligent control module. This closed-loop management mode can realize accurate evaluation of battery state and optimization configuration of link, support remote monitoring and fault diagnosis, and improve the intelligent level and operation and maintenance efficiency of battery management system.

[0086] 5、By integrating multiple communication units, a multi-link cooperative architecture is constructed. The intelligent dynamic link fusion unit uses a deep learning neural network model to perform timestamp alignment and Kalman filter noise reduction processing on data transmitted by different links, and allocates link transmission weight according to data packet priority, making data transmission more stable and reliable, reducing the risk of data loss caused by interference, and improving the accuracy of data transmission in complex electromagnetic environment. BRIEF DESCRIPTION OF DRAWINGS

[0087] Figure 1 The system schematic diagram of the present application;

[0088] Figure 2 The working flowchart of the battery data acquisition module of the present application;

[0089] Figure 3 The working flowchart of the multi-link communication module of the present application;

[0090] Figure 4 The working flowchart of the intelligent control module of the present application;

[0091] Figure 5 The working flowchart of the energy management module of the present application;

[0092] Figure 6 The flowchart of the cloud management platform of the present application. DETAILED DESCRIPTION

[0093] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application.

[0094] Therefore, the following detailed description of the embodiments of the present application is not intended to limit the scope of the claimed application, but merely represents some embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0095] It should be noted that the embodiments in the present application and the features and technical solutions in the embodiments can be combined with each other without conflict.

[0096] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0097] In the description of the present application, it should be noted that the terms "upper", "lower", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present application is usually placed, or the orientation or positional relationship commonly understood by those skilled in the art, and such terms are only for the convenience of describing the present application and simplifying the description, and are not intended to indicate or imply that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0098] The present application provides the following technical solutions:

[0099] Please refer to Figures 1-6 A multi-link cooperative wireless communication battery management system, comprising: a battery data acquisition module, a multi-link communication module, an intelligent control module, an energy management module, and a cloud management platform.

[0100] The battery data acquisition module is electrically connected to the battery module, and is used to acquire voltage, current, temperature, state of charge SOC, and state of health SOH parameters of the battery module, generate a data packet with a time stamp, and send the data packet to the multi-link communication module.

[0101] A multi-link communication module is communicatively connected to the battery data acquisition module and the intelligent control module, and integrates a Bluetooth, Wi-Fi, ZigBee and cellular network communication unit, each of which is independently configured with a transceiving antenna and a modulation and demodulation module, and is used for establishing a wireless transmission link according to a scheduling instruction of the intelligent control module;

[0102] The time division duplex coordinator is arranged between the communication units of the multi-link communication module, and a channel occupation time slot of each link is divided according to the scheduling instruction of the intelligent control module, so as to avoid same-frequency interference.

[0103] The intelligent control module is electrically connected to the multi-link communication module and the energy management module, and includes a link state monitoring unit, an intelligent dynamic link fusion unit, a heterogeneous link scheduling unit and an anti-interference game decision unit. The link state monitoring unit acquires channel signal-to-noise ratio (SNR), transmission delay (RTT), packet loss rate (PER) and spectrum occupation degree parameters of each communication unit in real time, generates a link state vector and inputs the link state vector into the intelligent dynamic link fusion unit. The intelligent dynamic link fusion unit is internally provided with a deep learning neural network model, the deep learning neural network model takes the link state vector and a data packet type of the battery data acquisition module as input, outputs a multi-link data fusion strategy, and the strategy includes a bandwidth allocation ratio of each link and a data packet recombination rule. The heterogeneous link scheduling unit pre-stores a scene recognition rule library, the rule library contains feature parameter thresholds of indoor / outdoor, close distance / long distance, high dynamic / static scene types, matches a scene type according to real-time environment sensor data, and outputs a link combination enabling instruction of the corresponding scene to the multi-link communication module. The anti-interference game decision unit constructs a strategy space containing a link frequency band, a modulation mode and a transmission power, establishes a benefit function of each link based on game theory, reaches a Nash equilibrium state through distributed iterative calculation, and outputs an anti-interference parameter adjustment instruction to the multi-link communication module.

[0104] The deep learning neural network model of the intelligent dynamic link fusion unit realizes data fusion through the following steps:

[0105] SA1, start;

[0106] SA2, time stamp alignment and Kalman filter noise reduction are performed on the same type of battery data transmitted by different links;

[0107] SA3, link transmission weights are allocated according to data packet priorities, and the priorities of the data packets are control instructions>real-time state data>historical data;

[0108] SA4, data features of each link are extracted through a convolutional neural network, and an optimal data fusion sequence is output through a fully connected layer;

[0109] SA5, end.

[0110] The scenario recognition rule library of the heterogeneous link scheduling unit comprises:

[0111] Indoor close-range scenario, distance < 10 m, GPS signal strength < -130 dBm: enable Bluetooth 5.0 and ZigBee 3.0 links to collect battery data, and the transmission period is ≤50 ms;

[0112] Remote monitoring scenario, distance > 1 km, cellular network signal strength > -100 dBm, main 4G / 5G cellular link transmits state data, and the standby Wi-Fi link transmits large-capacity battery image data;

[0113] High-dynamic scenario, vehicle acceleration > 0.5g, cellular link and Bluetooth link are enabled at the same time, the cellular link transmits raw data, and the Bluetooth link transmits check code to realize redundant data transmission.

[0114] The energy management module is electrically connected to the battery pack and the intelligent control module, collects the total voltage, remaining capacity SoE and charging and discharging current of the battery pack, generates an energy state signal and sends it to the intelligent control module, and receives a link power consumption control instruction from the intelligent control module to adjust the working parameters of the power amplifier module of each communication unit.

[0115] The link power consumption control strategy of the energy management module comprises:

[0116] When the remaining capacity SoE of the battery is < 20%, each communication unit is forced to enter a low-power consumption mode, the Bluetooth link transmission power is ≤4 dBm, and the ZigBee link transmission rate is reduced to 250 kbps;

[0117] When the battery is in a charging state and the charging current is > 0.1C, 5% to 10% of the charging power is allocated to the cellular link, and the modulation mode is upgraded to a high-order modulation to improve the transmission rate.

[0118] The cloud management platform is in communication connection with the multi-link communication module, receives battery data through the multi-link communication module, generates a battery state evaluation report and a link optimization strategy based on big data analysis, and returns them to the intelligent control module.

[0119] The working steps of the battery data acquisition module are as follows:

[0120] SB1, start;

[0121] SB2, real-time sampling of the voltage, current and temperature signals of the battery module through an analog-to-digital converter at a set frequency, and recording the sampling time to generate a time stamp;

[0122] SB3, based on the ampere-hour integral method combined with the open circuit voltage correction algorithm, the state of charge SOC of the battery is calculated, and the capacity attenuation model is used to estimate the state of health SOH of the battery;

[0123] SB4, the collected and calculated voltage, current, temperature, SOC, SOH data are packaged, and a time stamp and a check code are added, to generate a data packet with a time stamp;

[0124] SB5, the data packet is sent to the multi-link communication module;

[0125] SB6, end.

[0126] The working steps of the multi-link communication module are as follows:

[0127] SC1, start;

[0128] SC2, receive the scheduling instruction of the intelligent control module, and determine the wireless communication link to be enabled, wherein the wireless communication link includes Bluetooth, Wi-Fi, ZigBee and cellular network;

[0129] SC3, corresponding to the enabled communication unit, configure the receiving and transmitting antenna and the modulation and demodulation module parameters, and establish a wireless transmission link;

[0130] SC4, receive the data packet sent by the battery data acquisition module, and according to the scheduling instruction of the intelligent control module, transmit the data packet through the established wireless link, or receive the data returned by the cloud management platform through the wireless link, and forward to the intelligent control module;

[0131] SC5, end.

[0132] The working steps of the intelligent control module include the working steps of the link state monitoring unit, the working steps of the intelligent dynamic link fusion unit, the working steps of the heterogeneous link scheduling unit and the working steps of the anti-interference game decision unit.

[0133] The working steps of the link state monitoring unit are as follows:

[0134] SD1, start;

[0135] SD2, real-time acquisition of channel signal-to-noise ratio, transmission delay, packet loss rate and spectrum occupation degree parameters of each communication unit;

[0136] SD3, the collected parameters are sorted to generate a link state vector;

[0137] SD4, input the link state vector into the intelligent dynamic link fusion unit;

[0138] SD5, end.

[0139] The working steps of the intelligent dynamic link fusion unit are as follows:

[0140] SE1, start;

[0141] SE2, receive the link state vector input by the link state monitoring unit and the data packet type of the battery data acquisition module;

[0142] SE3, timestamp alignment and Kalman filter denoising of the same type of battery data transmitted by different links;

[0143] SE4, assign link transmission weight according to data packet priority, wherein the priority is: control instruction > real-time state data > historical data;

[0144] SE5, extract features of each link data through convolutional neural network, output optimal data fusion sequence through full connection layer, that is, multi-link data fusion strategy, including bandwidth allocation ratio of each link and data packet reorganization rule;

[0145] SE6, send the multi-link data fusion strategy to the multi-link communication module to guide data transmission;

[0146] SE7, end.

[0147] The working steps of the heterogeneous link scheduling unit are as follows:

[0148] SF1, start;

[0149] SF2, obtain real-time environmental sensor data;

[0150] SF3, match the real-time environmental sensor data with the pre-stored scene recognition rule library to determine the current scene type, wherein the scene recognition rule library contains the feature parameter threshold of indoor / outdoor, close distance / long distance, high dynamic / static scene;

[0151] SF4, according to the scene type, call the link combination enabling instruction of the corresponding scene from the rule library and send it to the multi-link communication module;

[0152] SF5, end.

[0153] The working steps of the anti-interference game decision unit are as follows:

[0154] SG1, start;

[0155] SG2, construct a strategy space containing link frequency band, modulation mode and transmission power;

[0156] SG3, establish the benefit function of each link based on game theory, the expression is: Ui=α·(1-PERi)+β·(1-γ·fcollision)-δ·Ptx, wherein, Ui is the benefit value of the ith link, α, β, δ are weight coefficients, PERi is the link packet error rate, fcollision is the frequency band collision probability, Ptx is the transmit power, and the optimal strategy combination of each link is solved through gradient descent method iteration;

[0157] SG4, the benefit function is iteratively calculated through the gradient descent method to solve the optimal strategy combination of each link;

[0158] SG5, when the convergence condition is reached, the anti-interference parameter adjustment instruction of each link is sent to the multi-link communication module, wherein the anti-interference parameter adjustment instruction includes: frequency band, modulation mode, transmit power adjustment value;

[0159] SG6, end.

[0160] The working steps of the energy management module are as follows:

[0161] SH1, start;

[0162] SH2, real-time acquisition of battery pack total voltage, residual capacity SoE and charge-discharge current, generation of energy state signal;

[0163] SH3, the energy state signal is sent to the intelligent control module;

[0164] SH4, receiving the link power consumption control instruction sent by the intelligent control module;

[0165] SH5, adjusting the power amplifier module working parameters of each communication unit according to the link power consumption control instruction;

[0166] SH6, end.

[0167] The working steps of the cloud management platform are as follows:

[0168] SI1, start;

[0169] SI2, receiving the battery data uploaded by the battery data acquisition module through the multi-link communication module;

[0170] SI3, processing the battery data based on big data analysis algorithm, generating battery state evaluation report and link optimization strategy;

[0171] SI4, the battery state evaluation report and link optimization strategy are returned to the intelligent control module through the multi-link communication module;

[0172] SI5, end.

[0173] In order to make the persons skilled in the art better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be clearly and completely described below with reference to the drawings.

[0174] It should be noted that the embodiments in the present application and the features and technical solutions in the embodiments can be combined with each other without conflict.

[0175] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0176] The above embodiments are only used to illustrate the present application and not to limit the technical scheme described in the present application, although the present application has been described in detail with reference to the above-mentioned various embodiments, the present application is not limited to the above-mentioned specific embodiments, therefore any modification or equivalent replacement of the present application; all technical schemes and improvements without departing from the spirit and scope of the present application, which are all covered in the scope of claims of the present application.

Claims

1. A multi-link cooperative wireless communication battery management system, characterized in that, The application relates to a battery data acquisition module, a multi-link communication module, an intelligent control module, an energy management module and a cloud management platform. The battery data acquisition module is electrically connected to a battery module and is used for collecting voltage, current, temperature, state of charge (SOC) and state of health (SOH) parameters of the battery module, generating a data packet with a time stamp and sending the data packet to the multi-link communication module. The multi-link communication module is in communication connection with the battery data acquisition module and the intelligent control module, integrates a Bluetooth, Wi-Fi, ZigBee and cellular network communication unit, each communication unit is independently configured with a transceiving antenna and a modulation and demodulation module, and is used for establishing a wireless transmission link according to a scheduling instruction of the intelligent control module. The intelligent control module is electrically connected to the multi-link communication module and the energy management module, comprises a link state monitoring unit, an intelligent dynamic link fusion unit, a heterogeneous link scheduling unit and an anti-interference game decision unit, wherein the link state monitoring unit collects channel signal-to-noise ratio (SNR), transmission delay (RTT), packet loss rate (PER) and spectrum occupation degree parameters of each communication unit in real time, generates a link state vector and inputs the link state vector into the intelligent dynamic link fusion unit; the intelligent dynamic link fusion unit is internally provided with a deep learning neural network model, the deep learning neural network model takes the link state vector and the data packet type of the battery data acquisition module as input, outputs a multi-link data fusion strategy, the strategy comprises a bandwidth allocation ratio of each link and a data packet recombination rule; the heterogeneous link scheduling unit pre-stores a scene recognition rule library, the rule library contains characteristic parameter thresholds of indoor / outdoor, close distance / long distance, high dynamic / static scenes, matches a scene type according to real-time environment sensor data, outputs a link combination enabling instruction of the corresponding scene to the multi-link communication module; the anti-interference game decision unit constructs a strategy space containing a link frequency band, a modulation mode and a transmission power, establishes a benefit function of each link based on a game theory, reaches a Nash equilibrium state through distributed iterative calculation, and outputs an anti-interference parameter adjustment instruction to the multi-link communication module; The energy management module is electrically connected to the battery module and the intelligent control module, collects total voltage, residual capacity SoE and charging and discharging current of the battery module, generates an energy state signal and sends the energy state signal to the intelligent control module, simultaneously receives a link power consumption control instruction of the intelligent control module, and adjusts working parameters of a power amplifier module of each communication unit; The cloud management platform is in communication connection with the multi-link communication module, receives battery data through the multi-link communication module, generates a battery state evaluation report and a link optimization strategy based on big data analysis, and returns the battery state evaluation report and the link optimization strategy to the intelligent control module. The deep learning neural network model of the intelligent dynamic link fusion unit realizes data fusion through the following steps:

2. The multi-link cooperative wireless communication battery management system of claim 1, wherein, SA1, starting; SA2, time stamp alignment and Kalman filter noise reduction are performed on the same type of battery data transmitted by different links; SA3, link transmission weight is allocated according to data packet priority, and the priority of the data packet is control instruction > real-time state data > historical data. ​ SA4, extracting each link data features through a convolutional neural network, and outputting an optimal data fusion sequence through a fully connected layer; SA5, end.

3. The multi-link cooperative wireless communication battery management system of claim 1, wherein, The scenario recognition rule base of the heterogeneous link scheduling unit comprises: Indoor close-range scenario, distance < 10 m, GPS signal strength < -130 dBm: enable Bluetooth 5.0 and ZigBee 3.0 links to collect battery data, and the transmission period is ≤50 ms; Remote monitoring scenario, distance > 1 km, cellular network signal strength > -100 dBm, main 4G / 5G cellular link transmits state data, and the standby Wi-Fi link transmits large-capacity battery image data; High-dynamic scenario, vehicle acceleration > 0.5g, simultaneously enable cellular link and Bluetooth link, cellular link transmits original data, and Bluetooth link transmits check code to realize redundant data transmission.

4. The multi-link cooperative wireless communication battery management system of claim 1, wherein, The link power consumption control strategy of the energy management module comprises: When the battery remaining capacity SoE < 20%, force each communication unit to enter a low-power mode, the Bluetooth link transmission power is ≤4 dBm, and the ZigBee link transmission rate is reduced to 250 kbps; When the battery is in a charging state and the charging current > 0.1C, allocate 5% to 10% of the charging power to the cellular link, and upgrade the modulation mode to a high-order modulation to improve the transmission rate.

5. The multi-link cooperative wireless communication battery management system of claim 1, wherein, A time division duplex coordinator is arranged between each communication unit of the multi-link communication module, and according to the scheduling instruction of the intelligent control module, the channel occupation time slots of each link are divided to avoid co-frequency interference.

6. The multi-link cooperative wireless communication battery management system of claim 1, wherein, The working steps of the battery data acquisition module are as follows: SB1, start; SB2, through an analog-to-digital converter, sample the voltage, current and temperature signals of the battery module in real time at a set frequency, and record the sampling time to generate a time stamp; SB3, based on the ampere-hour integration method combined with the open-circuit voltage correction algorithm, calculate the state of charge SOC of the battery, and estimate the state of health SOH of the battery by using a capacity attenuation model; SB4, package the voltage, current, temperature, SOC and SOH data obtained by acquisition and calculation, and add a time stamp and a check code to generate a time-stamped data packet; SB5, send the data packet to the multi-link communication module; SB6, end.

7. The multi-link cooperative wireless communication battery management system of claim 1, wherein, The working steps of the multi-link communication module are as follows: SC1, start; SC2, receive the scheduling instruction of the intelligent control module, and determine the wireless communication link to be enabled, wherein the wireless communication link comprises Bluetooth, Wi-Fi, ZigBee and cellular network; SC3, corresponding to the enabled communication unit, configure the parameters of the transceiver antenna and the modulation and demodulation module, and establish a wireless transmission link; SC4, receive the data packet sent by the battery data acquisition module, and according to the scheduling instruction of the intelligent control module, transmit the data packet through the established wireless link, or receive the data returned by the cloud management platform through the wireless link and forward it to the intelligent control module; SC5, end.

8. The multi-link cooperative wireless communication battery management system of claim 1, wherein, The working steps of the intelligent control module include the working steps of the link state monitoring unit, the working steps of the intelligent dynamic link fusion unit, the working steps of the heterogeneous link scheduling unit and the working steps of the anti-interference game decision unit; The working steps of the link state monitoring unit are as follows: SD1, start; SD2, collect the channel signal-to-noise ratio, transmission delay, packet loss rate and spectrum occupation degree parameters of each communication unit in real time; SD3, organize the collected parameters to generate a link state vector; SD4, input the link state vector into the intelligent dynamic link fusion unit; SD5, end; The working steps of the intelligent dynamic link fusion unit are as follows: SE1, start; SE2, receive the link state vector input by the link state monitoring unit and the data packet type of the battery data acquisition module; SE3, perform time stamp alignment and Kalman filter noise reduction on the same type of battery data transmitted by different links; SE4, assign link transmission weights according to data packet priority, wherein the priority is: control instruction > real-time state data > historical data; SE5, extract link data features through a convolutional neural network, and output an optimal data fusion sequence through a fully connected layer, i.e., a multi-link data fusion strategy, including the bandwidth allocation ratio of each link and the data packet reorganization rule; SE6, send the multi-link data fusion strategy to the multi-link communication module to guide data transmission; SE7, end; The working steps of the heterogeneous link scheduling unit are as follows: SF1, start; SF2, obtain real-time environmental sensor data; SF3, match the real-time environmental sensor data with the pre-stored scene recognition rule library to determine the current scene type, wherein the scene recognition rule library contains the characteristic parameter thresholds of indoor / outdoor, close distance / long distance, high dynamic / static scene; SF4, according to the scene type, retrieve the link combination enabling instructions corresponding to the scene from the rule library and send them to the multi-link communication module; SF5, end; The working steps of the anti-interference game decision unit are as follows: SG1, start; SG2, construct a strategy space containing link frequency bands, modulation methods and transmission power; SG3, establish a revenue function for each link based on game theory, the expression is: Ui = α·(1-PERi) + β·(1-γ·fcollision) - δ·Ptx, wherein Ui is the revenue value of the ith link, α, β, δ are weight coefficients, PERi is the link packet loss rate, fcollision is the frequency band collision probability, Ptx is the transmission power, and the optimal strategy combination of each link is solved through gradient descent method iteration; SG4, perform iterative calculation on the revenue function through the gradient descent method to solve the optimal strategy combination of each link; SG5, when the convergence condition is reached, send the anti-interference parameter adjustment instructions of each link to the multi-link communication module, wherein the anti-interference parameter adjustment instructions include frequency band, modulation method and transmission power adjustment value; SG6, end.

9. The multi-link cooperative wireless communication battery management system of claim 1, wherein, The working steps of the energy management module are as follows: SH1, start; SH2, collect the total voltage, remaining SoE and charging and discharging current of the battery module in real time to generate an energy state signal; SH3, send the energy state signal to the intelligent control module; SH4, receive the link power consumption control instructions sent by the intelligent control module; SH5, adjust the power amplifier module working parameters of each communication unit according to the link power consumption control instructions; SH6, end.

10. The multi-link cooperative wireless communication battery management system of claim 1, wherein, The working steps of the cloud management platform are as follows: SI1, start; SI2, receiving the battery data uploaded by the battery data acquisition module through the multi-link communication module; SI3, processing the battery data based on a big data analysis algorithm to generate a battery state evaluation report and a link optimization strategy; SI4, returning the battery state evaluation report and the link optimization strategy to the intelligent control module through the multi-link communication module; SI5, end.

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