Multi-protocol compatible extreme charge safety state-aware early warning system

Abstract

The application provides a multi-protocol compatible super-charging safety state sensing early warning system, and relates to the technical field of super-charging safety state sensing. A data acquisition module acquires super-charging system component data and environmental data; a super-charging strategy generation module outputs a super-charging strategy; a super-charging temperature rise prediction module outputs a temperature prediction curve of the super-charging system component; a liquid cooling temperature drop calculation module outputs a temperature drop curve of the super-charging system component; a theoretical temperature acquisition module acquires a theoretical super-charging system component temperature; and a safety state sensing module judges whether the super-charging process is safe. The application breaks through the technical barrier through multi-protocol compatibility, reduces the construction and operation cost of the super-charging network, solves the user's concern about the super-charging safety through precise temperature control and active early warning, improves the acceptance of the consumer end, clears the obstacles for the popularization of the high-power super-charging technology, helps the electric two-wheeled vehicle industry to break through the charging efficiency bottleneck, speeds up the progress towards the goal of faster and safer charging, and has significant technical value and industrial significance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of extreme charging safety state perception, and particularly relates to a multi-protocol compatible extreme charging safety state perception early warning system. BACKGROUND

[0002] With the rapid development of electric two-wheeled vehicle industry, high-power extreme charging technology has become a key direction to solve the charging efficiency pain points of users. However, the characteristics of high voltage and large current in the extreme charging process make the charging system components (such as batteries, charging pile modules, cables, etc.) face severe safety challenges, such as abnormal temperature rise, which can easily lead to thermal runaway, equipment damage and even safety accidents.

[0003] At the same time, there are many charging protocols in the current market, and different protocols have different definitions of safety boundaries and communication logic. In the multi-protocol compatible scenario, the traditional safety monitoring method is difficult to adapt to the dynamic requirements of different protocols, and safety early warning lag or misjudgment may occur.

[0004] In addition, the thermal management complexity of the extreme charging system is significantly improved, the components heat up quickly, the heat is unevenly distributed, and the dynamic response of the cooling means such as liquid cooling is often difficult to accurately match the actual heat dissipation demand. Simply relying on real-time temperature collection monitoring mode cannot predict the temperature change trend in advance, and it is difficult to meet the high requirements of timeliness and accuracy of safety early warning in the extreme charging process. SUMMARY

[0005] The present application provides a multi-protocol compatible extreme charging safety state perception early warning system to solve the defects in the prior art.

[0006] The present application provides a multi-protocol compatible extreme charging safety state perception early warning system, comprising:

[0007] A data acquisition module is configured to acquire extreme charging system component data and environmental data.

[0008] An extreme charging strategy generation module is configured to obtain extreme charging protocol content, and combine the extreme charging system component data to compare the extreme charging system component data with the protocol safety boundary to construct a target function and solve it, and output an extreme charging strategy.

[0009] An extreme charging temperature rise prediction module is configured to obtain characteristic materials and characteristic parameters of the extreme charging system components, and combine the extreme charging strategy to establish a three-dimensional thermal network model, and output a temperature prediction curve of the extreme charging system components changing with time.

[0010] A liquid cooling calculation module is configured to dynamically generate a real-time flow rate of the cooling liquid according to the extreme charging strategy and the extreme charging protocol content, and output a temperature drop curve of the extreme charging system components according to the heat exchange efficiency.

[0011] A theoretical temperature acquisition module is configured to obtain a theoretical extreme charging system component temperature according to a temperature prediction curve, a temperature drop curve, and heat loss.

[0012] A safety state perception module is configured to set a difference threshold of the theoretical extreme charging system component temperature and an actual extreme charging system component temperature, and to determine whether the extreme charging process is safe according to a difference between the theoretical extreme charging system component temperature and the actual extreme charging system component temperature.

[0013] According to the application, the extreme charging safety state perception and early warning system compatible with multiple protocols, the extreme charging system component data includes extreme charging pile data, extreme charging gun line data and extreme charging battery data. The extreme charging pile data includes output current, liquid cooling system inlet temperature and outlet temperature, and cooling liquid flow rate. The extreme charging gun line data includes conductor core line temperature, surface temperature and connector contact resistance. The extreme charging battery data includes single cell voltage difference, maximum cell temperature and battery state of charge. The environmental data includes environmental temperature and environmental wind speed.

[0014] According to the application, the extreme charging safety state perception and early warning system compatible with multiple protocols, the extreme charging protocol content includes safety boundary parameters and cooling constraint parameters. The safety boundary parameters include maximum charging current, maximum temperature and maximum temperature rise rate. The cooling constraint parameters include maximum cooling liquid flow rate and minimum heat exchange efficiency.

[0015] According to the application, the extreme charging safety state perception and early warning system compatible with multiple protocols, the process of outputting the extreme charging strategy includes:

[0016] According to the extreme charging protocol content, the safety boundary parameters are extracted by using semantic analysis technology.

[0017] According to the extreme charging system component data, the input vector after alignment is obtained by using time series alignment technology.

[0018] According to the safety boundary parameters and the input vector, the target function is established and solved by using the rolling horizon optimization algorithm with constraint conditions.

[0019] According to the optimization solving result, the control sequence decoding is performed to obtain the extreme charging strategy, and the extreme charging strategy includes a dynamic current curve.

[0020] According to the application, the extreme charging safety state perception and early warning system compatible with multiple protocols, the extreme charging system component includes an extreme charging pile, an extreme charging gun line and an extreme charging battery. The characteristic materials include a pile body copper bar, a gun line conductor and a battery tab. The characteristic parameters include geometric structure, thermal resistance, resistance and heat capacity.

[0021] According to the application, the extreme charging safety state perception and early warning system compatible with multiple protocols, the process of establishing a three-dimensional thermal network model includes:

[0022] According to the geometry of the polar charging system component, a non-structured grid division technology is adopted to obtain a spatial grid model of the characteristic material.

[0023] A parameter mapping is adopted to assign a thermal resistance and a thermal capacity to each grid node in the spatial grid model.

[0024] According to the basic law of heat conduction, an energy conservation principle is adopted to establish a grid node heat balance equation to obtain a three-dimensional heat network model.

[0025] According to the multi-protocol compatible polar charging safety state sensing and early warning system provided by the application, the process of outputting the temperature prediction curve changing with time includes:

[0026] According to the dynamic current curve, Joule heat calculation is performed on the copper bar of the pile body and the gun line channel to obtain the heating power of the polar charging pile and the polar charging gun line.

[0027] According to the polar charging battery data, a polarization voltage model is adopted to calculate the battery heating power.

[0028] The heating power of the polar charging pile and the polar charging gun line and the battery heating power are loaded to the corresponding grid nodes by adopting a heat source mapping.

[0029] According to the heat network model, an explicit Euler numerical integration is adopted to solve the evolution of temperature with time.

[0030] The solving result is subjected to spatial interpolation processing to output the prediction curve of the temperature changing with time at any position on the polar charging system component.

[0031] According to the multi-protocol compatible polar charging safety state sensing and early warning system provided by the application, the process of dynamically generating the real-time flow rate of the cooling liquid includes:

[0032] According to the maximum temperature and the real-time prediction temperature, a temperature deviation control algorithm is adopted to calculate the basic flow rate requirement.

[0033] According to the current change rate, a differential regulator is adopted to generate a flow rate compensation amount.

[0034] In combination with the safety boundary of the protocol constraint, a saturation function is adopted to output the real-time flow rate.

[0035] According to the multi-protocol compatible polar charging safety state sensing and early warning system provided by the application, the process of outputting the temperature drop curve of the polar charging system component according to the heat exchange efficiency includes:

[0036] According to the real-time flow rate, a turbulent flow correction model is adopted to calculate a transient convective heat transfer coefficient.

[0037] In combination with the grid node heat balance equation, an energy conservation equation is adopted to calculate a local temperature drop.

[0038] An implicit Euler method is adopted for numerical solution to output the time-varying temperature drop curve.

[0039] The multi-protocol compatible extreme charging safety state sensing and warning system provided by the application, the process of obtaining the theoretical extreme charging system component temperature comprises:

[0040] According to the space coordinate mapping, the equivalent temperature drop of the extreme charging system component is calculated by using a weighted synthesis algorithm.

[0041] Combined with the environmental wind speed, the air convection compensation is calculated.

[0042] The theoretical extreme charging system component temperature is obtained by comprehensively considering the real-time predicted temperature, the equivalent temperature drop of the extreme charging system component and the air convection compensation.

[0043] The multi-protocol compatible extreme charging safety state sensing and warning system provided by the application, through the extreme charging strategy generation module, the safety boundaries of different protocols are dynamically analyzed, and the target function is constructed combined with the real-time collected system component data, so that the extreme charging strategy always adapts to the safety threshold required by the protocol in the switching of national standards, European standards, private protocols of vehicle enterprises and other scenes. Avoid the conflict of charging parameters caused by incompatible protocols, reduce the safety blind area in the protocol adaptation process, significantly reduce the risk of equipment damage during cross-protocol charging, and provide core safety protection for multi-brand and multi-model shared extreme charging network.

[0044] A three-dimensional thermal network model is constructed through the extreme charging temperature rise prediction module, combined with the component material characteristics and the extreme charging strategy, the temperature change curve is output in advance, and the cooling liquid flow rate is dynamically generated by using the liquid cooling calculation module, so that the heat dissipation demand and the cooling capacity are accurately matched. The prediction and control linkage mechanism solves the problem of passive response lag in traditional thermal management, which can not only avoid the energy waste caused by excessive cooling, but also prevent local overheating caused by insufficient cooling, so that the extreme charging system always maintains in the safe temperature interval during high-power operation.

[0045] By setting the difference threshold of the theoretical temperature and the actual temperature, the theoretical temperature is comprehensively calculated based on the temperature rise prediction, the cooling effect and the heat loss, and reflects the normal state of the system; on the other hand, the actual temperature is obtained in real time through the data acquisition module, and the difference between the two can quickly identify sensor failure, heat runaway precursor and other abnormalities. Compared with the traditional monitoring mode which simply depends on real-time temperature, potential risks can be captured in advance, and safety warning is upgraded from post-alarm to pre-intervention, which greatly improves the safety redundancy of the extreme charging process.

[0046] The multi-protocol compatibility breaks the technical barriers and reduces the construction and operation cost of the extreme charging network; through precise temperature control and active warning, the user's concern about the safety of extreme charging is solved, and the acceptance of the consumer end is improved. It clears the obstacles for the popularization of high-power extreme charging technology, helps the electric two-wheeled vehicle industry to break through the bottleneck of charging efficiency, and accelerates the progress towards faster and safer charging, which has significant technical value and industrial significance. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the present application or the prior art, the drawings required to be used in the following embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0048] Figure 1 is a structural schematic diagram of a multi-protocol compatible super-charging safety state sensing early warning system provided by an embodiment of the present application.

[0049] Figure 2 is a flowchart of establishing a three-dimensional thermal network model in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely in the following with reference to the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0051] The multi-protocol compatible super-charging safety state sensing early warning system of the present application will be described below. Figures 1-2

[0052] Figure 1 is a structural schematic diagram of a multi-protocol compatible super-charging safety state sensing early warning system provided by an embodiment of the present application.

[0053] As shown in Figure 1 , the multi-protocol compatible super-charging safety state sensing early warning system provided by an embodiment of the present application includes a data acquisition module, a super-charging strategy generation module, a super-charging temperature rise prediction module, a liquid cooling temperature calculation module, a theoretical temperature acquisition module and a safety state sensing module.

[0054] The data acquisition module is used to acquire super-charging system component data and environmental data.

[0055] The super-charging system component data includes super-charging pile data, super-charging gun line data and super-charging battery data. The super-charging pile data includes output current, liquid cooling system inlet temperature and outlet temperature, cooling liquid flow rate. The super-charging gun line data includes conductor core line temperature, surface temperature and connector contact resistance. The super-charging battery data includes single cell voltage difference, maximum cell temperature and battery state of charge. The environmental data includes environmental temperature and environmental wind speed.

[0056] ​The extreme charging strategy generation module is configured to obtain extreme charging protocol content, combine the extreme charging system component data, compare the extreme charging system component data with the protocol security boundary, construct a target function and solve the target function, and output the extreme charging strategy.

[0057] The extreme charging protocol content includes security boundary parameters and cooling constraint parameters. The security boundary parameters include maximum charging current, maximum temperature, and maximum temperature rise rate. The cooling constraint parameters include maximum flow rate of cooling liquid and minimum heat exchange efficiency.

[0058] The process of outputting the extreme charging strategy includes:

[0059] According to the extreme charging protocol content, the security boundary parameters are extracted using semantic parsing technology.

[0060] According to the extreme charging system component data, the input vector after alignment is obtained using time series alignment technology .

[0061] According to the security boundary parameters and the input vector, a rolling horizon optimization algorithm with constraint conditions is used to construct a target function and solve the target function, and the formula of the target function is represented as:

[0062]

[0063] In the formula, the formula of the constraint condition is represented as:

[0064]

[0065] In the formula, represents the prediction step, represents the current tracking weight coefficient, represents the temperature safety weight coefficient, represents the k-step current prediction value, represents the maximum allowed current, represents the k-step temperature prediction value, represents the k-1-step temperature prediction value, represents the reference current curve, represents the time step, represents the maximum temperature rise rate, represents the maximum allowed temperature.

[0066] According to the optimization solution, a control sequence is decoded to obtain the extreme charging strategy, and the extreme charging strategy includes a dynamic current curve.

[0067] The extreme charging temperature rise prediction module is configured to obtain the characteristic materials and characteristic parameters of the extreme charging system components, and combine the extreme charging strategy to construct a three-dimensional thermal network model, and output a temperature prediction curve of the extreme charging system components changing over time.

[0068] The polar charging system components include polar charging posts, polar charging gun wires and polar charging batteries. The characteristic materials include post body copper bars, gun wire conductors and battery tabs. The characteristic parameters include geometric structures, thermal resistances, electrical resistances and thermal capacities.

[0069] Figure 2 is a flowchart of establishing a three-dimensional thermal network model in an embodiment of the present application.

[0070] As shown in Figure 2 , the process of establishing a three-dimensional thermal network model includes:

[0071] According to the geometric structure of the polar charging system components, an unstructured grid division technique is used to obtain a spatial grid model of the characteristic materials.

[0072] A parameter mapping is used to assign thermal resistances and thermal capacities to each grid node in the spatial grid model.

[0073] According to the basic law of heat conduction, an energy conservation principle is used to establish a grid node heat balance equation to obtain a three-dimensional thermal network model, and the grid node heat balance equation is expressed as:

[0074]

[0075] In the formula, represents the thermal capacity of node i, represents the temperature change rate of node i, represents a set of adjacent nodes of node i, represents the temperature of adjacent node j, represents the current temperature of node i, represents the thermal resistance between node i and adjacent node j, represents the heat generation power of node i.

[0076] The process of outputting a temperature prediction curve changing over time includes:

[0077] According to the dynamic current curve, a joule heat calculation is performed on the post body copper bar and the gun wire to obtain the heat generation power of the polar charging post and the polar charging gun wire, and the formula is expressed as:

[0078]

[0079]

[0080] In the formula, represents a time-dependent AC resistance considering the skin effect, represents the resistance of the conductor material, and d represents the skin depth coefficient, represents the vacuum permeability, and f represents the current working frequency.

[0081] According to the polar battery data, the battery heating power is calculated by using the polarization voltage model, and the formula is expressed as:

[0082]

[0083] In the formula, represents the current at time t, represents the open circuit voltage, represents the battery terminal voltage at time t.

[0084] The heat source mapping is used to load the heating power of the polar charging pile and the polar charging gun line into the corresponding grid node.

[0085] According to the thermal network model, the temperature evolution over time is solved by using the explicit Euler numerical integration, and the formula is expressed as:

[0086]

[0087] In the formula, represents the temperature of node i at the nth time step, represents the time step, represents the heat capacity of node i, represents the temperature of node j at the nth time step, represents the thermal resistance between node i and adjacent node j, represents the heating power of node i.

[0088] The spatial interpolation processing is performed on the solution result, and the prediction curve of the temperature change over time at any position on the polar charging system component is output.

[0089] The liquid cooling calculation module is used to dynamically generate the real-time flow rate of the cooling liquid according to the polar charging strategy and the content of the polar charging protocol, and output the temperature drop curve of the polar charging system component according to the heat exchange efficiency.

[0090] The process of dynamically generating the real-time flow rate of the cooling liquid includes:

[0091] According to the highest temperature and the real-time predicted temperature, the temperature deviation control algorithm is used to calculate the basic flow rate requirement, and the formula is expressed as:

[0092]

[0093] In the formula, represents the system flow rate gain coefficient, represents the real-time predicted temperature, represents the temperature control starting threshold, represents the allowable maximum temperature, and n represents the nonlinear adjustment index.

[0094] According to the current change rate, the flow rate compensation is generated by using the differential regulator, and the formula is expressed as:

[0095]

[0096] where, denotes the differential gain coefficient, denotes the absolute value of the rate of change of the polar charge strategy current curve.

[0097] The real-time flow rate is output by using a saturation function in combination with the security boundary of the protocol constraint, and the formula is:

[0098]

[0099] where sat(·) denotes a saturation limiting function, denotes the minimum flow rate of the coolant, denotes the maximum flow rate of the coolant.

[0100] The process of outputting the polar charge system component temperature drop curve according to the heat exchange efficiency includes:

[0101] According to the real-time flow rate, the transient convective heat transfer coefficient is calculated by using a turbulent flow correction model, and the formula is:

[0102]

[0103] where, denotes the thermal conductivity of the coolant, and D denotes the characteristic length, denotes the density of the coolant, denotes the dynamic viscosity of the coolant, denotes the real-time flow rate, denotes a Reynolds number correlation parameter, m denotes a Reynolds number index, Pr denotes a Prandtl number, and n denotes a Prandtl number index.

[0104] In combination with the grid node heat balance equation, the local temperature drop is calculated by using an energy conservation equation, and the formula is:

[0105]

[0106] where, denotes the mass flow rate through node i, denotes the constant-pressure specific heat capacity of the coolant, denotes the rate of change of the temperature of node i with time, denotes the convective heat transfer coefficient at position j, denotes the heat transfer area, denotes the solid wall temperature at position j, denotes the local temperature of the coolant, denotes the current heat generation power of node i.

[0107] The implicit Euler method is used for numerical solution, and the time-varying temperature drop curve is output, which is expressed as:

[0108]

[0109] In the formula, represents the real-time predicted temperature, represents the numerical solution temperature.

[0110] The theoretical temperature acquisition module is used to obtain the theoretical extreme charging system component temperature according to the temperature prediction curve, the temperature drop curve, and the heat loss, and the process includes:

[0111] According to the spatial coordinate mapping, the weighted synthesis algorithm is used to calculate the equivalent temperature drop of the extreme charging system component, and the formula is expressed as:

[0112]

[0113] In the formula, represents the position weight factor, represents the position The liquid cooling temperature drop value at time t.

[0114] Combined with the environmental wind speed, the air convection compensation is calculated, and the formula is expressed as:

[0115]

[0116] In the formula, represents the convection compensation coefficient, represents the environmental wind speed, represents the environmental temperature, represents the real-time value of the component surface temperature.

[0117] The theoretical extreme charging system component temperature is obtained by comprehensively considering the real-time predicted temperature, the equivalent temperature drop of the extreme charging system component, and the air convection compensation, and the formula is expressed as:

[0118]

[0119] In the formula, represents the real-time predicted temperature, represents the equivalent temperature drop of the extreme charging system component, represents the air convection compensation.

[0120] The safety state perception module is used to set the difference threshold of the theoretical extreme charging system component temperature and the actual extreme charging system component temperature, and to judge whether the extreme charging process is safe according to the difference between the theoretical extreme charging system component temperature and the actual extreme charging system component temperature.

[0121] In this embodiment, the safety state perception module can be implemented around a three-level response mechanism.

[0122] A basic deviation threshold is set. When the monitoring data shows that the difference between the theoretical temperature and the actual temperature exceeds the basic deviation threshold but does not break through the early warning threshold, the system enters a first level response. At this time, the system will enhance the monitoring measures, increase the temperature data sampling frequency to the millisecond level, and start the high-speed data caching mechanism to record the temperature change trend completely. At the same time, a slope reduction instruction is sent to the extreme charging strategy generation module to reduce the charging current rising slope and slow down the temperature rise rate. A gradient adjustment command is sent to the liquid cooling calculation module to increase the cooling liquid flow rate in stages and improve the heat capacity buffer. After these actions are performed, the response effect is continuously monitored. If the deviation value does not return to the safe zone within a few monitoring periods, it will automatically upgrade to a second level response to eliminate potential risk accumulation.

[0123] If the temperature deviation breaks through the early warning threshold, or the key part temperature difference gradient is abnormally increased, the system immediately enters a second level response. The system sends a power reduction instruction to the charging pile power unit to reduce the output power, and activates the dynamic pressure boosting mode of the liquid cooling system to instantaneously increase the cooling liquid flow rate. In addition, the fault pre-diagnosis engine is started to compare the current temperature distribution with the historical fault feature library to identify potential fault modes. For different situations, the system has corresponding composite protection strategies: if it is a temporary fluctuation, the second level response is maintained for a few seconds and then automatically evaluated whether to return to the first level; if the situation continues to deteriorate, it will be forced to upgrade to a third level response after 5 seconds of countdown; if a local hot spot is detected, a local fuse mechanism is immediately triggered.

[0124] When the emergency fuse threshold is touched, or the spatial hot spot positioning shows that the temperature difference of a certain area exceeds the material safety limit, the system starts the irreversible protection process of the third level response. First, the electrical isolation protection, send a third level power-off unlock instruction to the charging pile power unit, the power module is powered off in order, first disconnect the DC output contactor, then cut off the AC input switch, and activate the backup capacitor discharge loop to ensure that the system is reduced to a safe voltage within 200ms. Then the intensified cooling measures, start the emergency circulation mode of the liquid cooling system to continuously flush the hot area with the maximum flow rate, and start the standby refrigeration unit in parallel to form a double-loop forced cooling, and send a joint refrigeration instruction to the battery management system to activate the cell-level phase change material. Finally, fault positioning and reporting, based on the three-dimensional thermal field reconstruction result, mark the fault core coordinates, generate a holographic fault report containing temperature evolution curve, protection action sequence, environmental influence parameters, and upload it to the cloud diagnosis center through the safety protocol tunnel, and send it to the maintenance personnel mobile terminal at the same time. Maintain the protection state until the manual authorization reset, automatically generate a maintenance work order, mark the key detection components, update the historical fault feature library, and optimize the early warning parameters of the same type of equipment, so as to form a safety state closed loop.

[0125] In summary, the embodiment provides a multi-protocol compatible ultra-fast charging safety state sensing early warning system. The safety boundary of different protocols is dynamically analyzed by the ultra-fast charging strategy generation module, and the target function is constructed by combining the real-time collected system component data, to ensure that the ultra-fast charging strategy always adapts to the safety threshold required by the protocol in the switching of multiple scenes such as national standards, European standards, and private protocols of vehicle enterprises. The charging parameter conflict caused by protocol incompatibility is avoided, the safety blind area in the protocol adaptation process is reduced, the device damage risk during cross-protocol charging is significantly reduced, and the core safety guarantee is provided for the shared ultra-fast charging network of multiple brands and multiple vehicle types.

[0126] A three-dimensional thermal network model is constructed by the ultra-fast charging temperature rise prediction module, combined with the component material properties and the ultra-fast charging strategy, to output the temperature change curve in advance. At the same time, the liquid cooling calculation module is used to dynamically generate the cooling liquid flow rate, to realize the precise matching of heat dissipation demand and cooling capacity. The prediction and control linkage mechanism solves the problem of passive response lag in traditional thermal management, which can not only avoid energy waste caused by excessive cooling, but also prevent local overheating caused by insufficient cooling, so that the ultra-fast charging system always maintains in the safe temperature range during high-power operation.

[0127] By setting the difference threshold between the theoretical temperature and the actual temperature, the theoretical temperature is calculated based on the temperature rise prediction, cooling effect and heat loss, reflecting the normal state of the system. On the other hand, the actual temperature is obtained in real time by the data acquisition module, and the difference between the two can quickly identify sensor failures, precursors of thermal runaway and other abnormalities. Compared with the traditional monitoring mode which simply relies on real-time temperature, potential risks can be captured in advance, and safety warning is upgraded from post-alarm to pre-intervention, greatly improving the safety redundancy of the ultra-fast charging process.

[0128] The multi-protocol compatibility breaks through the technical barriers and reduces the construction and operation cost of the ultra-fast charging network. Through precise temperature control and active warning, the user's concern about the safety of ultra-fast charging is solved, and the acceptance of the consumer end is improved. It clears the obstacles for the popularization of high-power ultra-fast charging technology, helps the electric two-wheeled vehicle industry to break through the bottleneck of charging efficiency, and accelerates the progress towards faster and safer charging. It has significant technical value and industrial significance.

[0129] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by means of software plus necessary general hardware platforms, or by hardware. Based on such understanding, the above technical solutions, essentially or in terms of the contribution to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0130] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-protocol compatible, ultra-safe, state-of-the-art, awareness and warning system, characterized in that, The application relates to a polar charging strategy generation method and device. The data acquisition module is used for acquiring polar charging system component data and environment data; the polar charging system component data comprises polar charging pile data, polar charging gun line data and polar charging battery data; the polar charging pile data comprises output current, liquid cooling system inlet temperature and outlet temperature and cooling liquid flow rate; the polar charging gun line data comprises conductor core line temperature, surface temperature and connector contact resistance; the polar charging battery data comprises single cell voltage difference, maximum cell temperature and battery state of charge; and the environment data comprises environment temperature and environment wind speed. The polar charging strategy generation module is used for acquiring polar charging protocol content, combining the polar charging system component data, comparing the polar charging system component data with a protocol safety boundary, constructing a target function and performing solving, and outputting a polar charging strategy; the process comprises the following steps: According to the polar charging protocol content, a semantic analysis technology is adopted to extract safety boundary parameters; According to the polar charging system component data, a time sequence alignment technology is adopted to obtain an aligned input vector; According to the safety boundary parameters and the input vector, a rolling horizon optimization algorithm with a constraint condition is adopted to construct a target function and perform solving; According to the optimization solving result, a control sequence is decoded to obtain a polar charging strategy, and the polar charging strategy comprises a dynamic current curve; The polar charging temperature rise prediction module is used for acquiring characteristic materials and characteristic parameters of polar charging system components, combining the polar charging strategy, establishing a three-dimensional thermal network model and outputting a temperature prediction curve of the polar charging system components changing with time; The polar charging system components comprise a polar charging pile, a polar charging gun line and a polar charging battery; the characteristic materials comprise a pile body copper bar, a gun line conductor and a battery pole lug; and the characteristic parameters comprise geometric structure, thermal resistance, resistance and heat capacity; The process of establishing the three-dimensional thermal network model comprises the following steps: According to the geometric structure of the polar charging system components, an unstructured grid division technology is adopted to obtain a space grid model of the characteristic materials; Parameter mapping is adopted to allocate thermal resistance and heat capacity to each grid node in the space grid model; According to a basic law of heat conduction, an energy conservation principle is adopted to establish a grid node heat balance equation to obtain a three-dimensional thermal network model; The liquid cooling calculation module is used for dynamically generating a cooling liquid real-time flow rate according to the polar charging strategy and the polar charging protocol content, and outputting a polar charging system component temperature drop curve according to heat exchange efficiency; The theoretical temperature acquisition module is used for obtaining a theoretical polar charging system component temperature according to the temperature prediction curve, the temperature drop curve and heat loss; The safety state perception module is used for setting a difference threshold of the theoretical polar charging system component temperature and an actual polar charging system component temperature, and judging whether a polar charging process is safe according to a difference value of the theoretical polar charging system component temperature and the actual polar charging system component temperature. The polar charging protocol content comprises safety boundary parameters and cooling constraint parameters; the safety boundary parameters comprise maximum charging current, maximum temperature and maximum temperature rise rate; and the cooling constraint parameters comprise maximum cooling liquid flow rate and minimum heat exchange efficiency.

2. The multi-protocol compliant, ultra-safe, state-of-the-art, awareness pre-warning system according to claim 1, characterized in that, The process of outputting the temperature prediction curve changing with time comprises the following steps:

3. The multi-protocol compatible, ultra-safe, state-of-the-art, awareness pre-warning system, according to claim 1, wherein, ​ According to the dynamic current curve, Joule heat calculation is performed on the pile copper bar and the gun wire conductor to obtain the heating power of the pole charging pile and the pole charging gun wire; According to the pole charging battery data, a polarization voltage model is used to calculate the battery heating power; A heat source mapping is used to load the heating power of the pole charging pile and the pole charging gun wire and the battery heating power to the corresponding grid nodes; According to the heat network model, an explicit Euler numerical integration is used to solve the evolution of temperature over time; The solving result is subjected to spatial interpolation processing, and a prediction curve of the temperature change over time at any position of the pole charging system component is output.

4. The multi-protocol compliant, ultra-safe, situation-aware warning system of claim 1, wherein, The process of dynamically generating the real-time flow rate of the cooling liquid includes: According to the highest temperature and the real-time predicted temperature, a temperature deviation control algorithm is used to calculate the basic flow rate requirement; According to the current change rate, a differential regulator is used to generate a flow rate compensation amount; In combination with the safety boundary of the protocol constraint, a saturation function is used to output the real-time flow rate.

5. The multi-protocol compatible, ultra-safe, situational awareness, pre- warning system, according to claim 3, wherein, The process of outputting the pole charging system component temperature drop curve according to the heat exchange efficiency includes: According to the real-time flow rate, a turbulent flow correction model is used to calculate the transient convective heat transfer coefficient; In combination with the grid node heat balance equation, an energy conservation equation is used to calculate the local temperature drop; An implicit Euler method is used for numerical solution, and a time-varying temperature drop curve is output.

6. The multi-protocol compliant, ultra-safe, situational awareness alert system of claim 1, wherein, The process of obtaining the theoretical pole charging system component temperature includes: According to the spatial coordinate mapping, a weighted synthesis algorithm is used to calculate the equivalent temperature drop of the pole charging system component; In combination with the environmental wind speed, air convection compensation is calculated; In combination with the real-time predicted temperature, the equivalent temperature drop of the pole charging system component and the air convection compensation, the theoretical pole charging system component temperature is obtained.

Images