Charging strategy optimization method and system of power battery

Abstract

The invention relates to the technical field of vehicles, and discloses a charging strategy optimization method and system for a power battery. The charging strategy optimization method of the power battery comprises the following steps: acquiring a plurality of different battery cell working parameters when a target battery cell is charged at a target charging rate corresponding to a target charging condition; determining the maximum charging rate corresponding to each charging duration under the target charging condition according to the plurality of different battery cell working parameters; and setting a preset charging current of the power battery under the target charging condition according to each charging duration under the target charging condition and the maximum charging rate corresponding to each charging duration. By adopting the embodiment of the invention, the charging speed of the power battery can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method and system for optimizing a charging strategy for a power battery. Background Art

[0002] In the related art, when charging power batteries such as lithium batteries, the commonly used charging strategy is to first perform constant current charging and then constant voltage charging. In the constant current charging stage, although the larger the current, the higher the charging efficiency, the excessively high charging current causes the voltage of the power battery to rise rapidly, and the constant voltage charging stage is switched to ensure that sufficient power is charged. However, the constant voltage charging stage takes a long time, which in turn affects the charging efficiency of the power battery. Summary of the invention

[0003] Based on this, it is necessary to provide a method and system for optimizing the charging strategy of a power battery in response to the above technical problems, which can effectively improve the charging speed of the power battery.

[0004] In a first aspect, a method for optimizing a charging strategy of a power battery is provided, comprising:

[0005] Acquire a plurality of different battery cell operating parameters when the target battery cell is charged at a target charging rate corresponding to a target charging condition;

[0006] According to a plurality of different battery cell operating parameters, determine the maximum charging rate corresponding to each charging time under the target charging condition;

[0007] According to each charging time under the target charging condition and the maximum charging rate corresponding to each charging time, a preset charging current of the power battery under the target charging condition is set.

[0008] In some examples, determining the maximum charging rate corresponding to each charging duration under the target charging condition includes:

[0009] After multiple charge and discharge cycles, the structural integrity of each power battery is evaluated;

[0010] The rate current corresponding to the power battery with the best structural integrity is used as the maximum charging rate.

[0011] In some examples, evaluating the structural integrity of each power battery includes:

[0012] Dismantle the power battery;

[0013] Use any one of a scanning electron microscope SEM, a transmission electron microscope TEM, and a focused ion beam / scanning electron microscope FIB / SEM to characterize the microstructure of the pole piece of the disassembled power battery, so as to obtain microstructure characterization data of the pole piece of the power battery;

[0014] The microstructural integrity of the pole piece is evaluated based on the microstructural characterization data of the pole piece of the power battery.

[0015] In some examples, the microstructure characterization of the pole piece of the disassembled power battery is performed by any one of a scanning electron microscope SEM, a transmission electron microscope TEM, and a focused ion beam / scanning electron microscope FIB / SEM to obtain microstructure characterization data of the pole piece of the power battery, including:

[0016] Use the pre-trained recognition model to identify the target state of the pole piece of the power battery photographed by electron microscope SEM, transmission electron microscope TEM or focused ion beam / scanning electron microscope FIB / SEM;

[0017] The target recognition results are compared with the battery charging parameters and the microscopic structure of the battery itself to obtain the microstructural characterization data of the power battery pole piece.

[0018] In some examples, this also includes:

[0019] Detect the expansion force and voltage of each power battery during charging;

[0020] Generate characteristic curves of different rate currents with respect to the expansion force and voltage according to the detected expansion force and voltage, wherein the characteristic curve of the preset charging current with respect to the expansion force and voltage is a first characteristic curve, and characteristic curves of other rate currents with respect to the expansion force and voltage except the preset charging current are respectively a second characteristic curve;

[0021] Based on the charging speed and the expansion force, the first characteristic curve and each second characteristic curve are respectively compared in sections, and a section charging current that meets the target principle is selected in each section;

[0022] The segmented charging currents of all the segments are combined to generate a step charging current for the power battery under the preset conditions.

[0023] In some examples, based on the charging speed and the expansion force, the first characteristic curve and each second characteristic curve are respectively compared in sections, and a sectioned charging current that meets the target principle is selected in each section, including:

[0024] In each comparison, all second characteristic curves are grouped according to whether the curve trend is similar to the first characteristic curve, so as to obtain a first trend curve group with similar trends and a second trend curve group with different trends;

[0025] The current with the highest rate among all rate currents corresponding to the first trend curve group and the preset charging current is selected as the first characteristic current, and the current with the highest rate among all rate currents corresponding to the second trend curve group is selected as the second characteristic current;

[0026] The current with the lowest rate of change of expansion force between the first characteristic current and the second characteristic current is selected as the segmented charging current for the segment.

[0027] In some examples, before segmentally comparing the first characteristic curve and each second characteristic curve based on the charging speed and the expansion force, the method includes:

[0028] The first characteristic curve and the second characteristic curve are segmented according to the principle of equal division or with the intersection point between the first characteristic curve and the second characteristic curve as the segmentation point.

[0029] In some examples, the characteristic curve is a first-order differential dP / dV curve, a first-order differential dP / dSOC curve, or a second-order differential d2P / dV2 curve, wherein dP is the first-order differential of the expansion force of the lithium-ion battery, dV is the first-order differential of the voltage of the lithium-ion battery, and dSOC is the first-order differential of the capacity of the lithium-ion battery.

[0030] In some examples, this also includes:

[0031] Draw the dQ / dV standard curve of power battery;

[0032] Charge the target power battery and draw the dQ / dV real-time curve during the charging process;

[0033] When the first peak appears on the dQ / dV real-time curve, the corresponding voltage is compared with the voltage corresponding to the first peak of the dQ / dV standard curve to obtain the deviation;

[0034] If the deviation is not greater than the sum of the polarization voltage and the ohmic voltage drop of the target power battery during charging, it is determined that the target power battery is currently in a safe charging state;

[0035] Measuring the consistency of the internal resistance of each single cell in the target power battery or calculating the internal resistance of the target power battery, when the internal resistance of each single cell of the target power battery is consistent, or the internal resistance of the target power battery is within a predetermined range, entering the fast charging stage;

[0036] During the fast charging stage, the charging areas within the adjacent peak and valley ranges are charged using a step-by-step current change method until the end of the fast charging stage.

[0037] In a second aspect, a charging strategy optimization system for a power battery is provided, comprising:

[0038] An acquisition module, used for acquiring a plurality of different battery cell operating parameters when the target battery cell is charged at a target charging rate corresponding to a target charging condition;

[0039] A maximum charging rate calculation module is used to determine the maximum charging rate corresponding to each charging time under target charging conditions according to a plurality of different battery cell operating parameters;

[0040] The optimization module is used to set a preset charging current of the power battery under the target charging condition according to each charging time under the target charging condition and the maximum charging rate corresponding to each charging time.

[0041] According to the embodiment of the present application, firstly, a plurality of different battery cell operating parameters are obtained when the target battery cell is charged at a target charging rate corresponding to the target charging condition under the target charging condition. Then, the maximum charging rate corresponding to each charging time under the target charging condition is determined according to the plurality of different battery cell operating parameters, and the charging strategy under the target charging condition is set according to each charging time under the target charging condition and the maximum charging rate corresponding to each charging time. The method for determining the maximum charging rate includes evaluating the structural integrity of different lithium-ion batteries after multiple charging and discharging; and selecting the rate current corresponding to the lithium-ion battery with the best structural integrity as the preset charging current of the lithium-ion battery under the preset conditions, thereby improving the charging speed of the power battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0043] Figure 1 A flowchart of a method for optimizing a charging strategy for a power battery provided in an embodiment of the present application;

[0044] Figure 2 Schematic diagram of a convolutional neural network.

[0045] Figure 3 Another flow chart of the method for optimizing the charging strategy of a power battery provided in an embodiment of the present application;

[0046] Figure 4 A schematic diagram of a dP / dV curve and a dP / dSOC curve in a charging strategy optimization method for a power battery provided in an embodiment of the present application;

[0047] Figure 5 It is a schematic diagram for comparing the optimization of battery life and expansion force using the method of an embodiment of the present application;

[0048] Figure 6A structural block diagram of a charging strategy optimization system for a power battery provided in an embodiment of the present application;

[0049] Figure 7 A structural block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The present application is further described in detail below in conjunction with the embodiments and drawings. It is to be understood that the specific embodiments described herein are only used to explain the relevant application, rather than to limit the application. It is also necessary to explain that, for ease of description, only the parts related to the application are shown in the drawings.

[0051] It should be noted that, in the absence of conflict, the embodiments of the present application, that is, the features of the embodiments, can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0052] The following describes in detail a method and system for optimizing a charging strategy for a power battery according to an embodiment of the present application in conjunction with the accompanying drawings.

[0053] Figure 1 FIG. 1 is a flow chart of a method for optimizing a charging strategy for a power battery according to an embodiment of the present application. Figure 1 As shown, a method for optimizing a charging strategy of a power battery according to an embodiment of the present application includes the following steps:

[0054] S101: Acquire a plurality of different battery cell operating parameters when a target battery cell is charged at a target charging rate corresponding to a target charging condition.

[0055] S102: Determine a maximum charging rate corresponding to each charging time under a target charging condition according to a plurality of different battery cell operating parameters.

[0056] In one embodiment of the present application, the maximum charging rate corresponding to each charging time under the target charging conditions is determined, including: after multiple charging and discharging, evaluating the structural integrity of each power battery; and taking the rate current corresponding to the power battery with the best structural integrity as the maximum charging rate.

[0057] In this example, evaluating the structural integrity of each power battery includes: disassembling the power battery; performing microstructural characterization on the pole piece of the disassembled power battery using any one of a scanning electron microscope SEM, a transmission electron microscope TEM, and a focused ion beam / scanning electron microscope FIB / SEM to obtain microstructural characterization data of the pole piece of the power battery; and evaluating the microstructural integrity of the pole piece based on the microstructural characterization data of the pole piece of the power battery.

[0058] Among them, any one of a scanning electron microscope SEM, a transmission electron microscope TEM and a focused ion beam / scanning electron microscope FIB / SEM is used to perform microstructural characterization on the pole piece of the disassembled power battery to obtain microstructural characterization data of the pole piece of the power battery, including: using a pre-trained recognition model to perform target recognition on the pole piece state of the power battery photographed by an electron microscope SEM, a transmission electron microscope TEM or a focused ion beam / scanning electron microscope FIB / SEM; comparing the target recognition result with the battery charging parameters and the microscopic structure of the battery itself, to obtain microstructural characterization data of the pole piece of the power battery.

[0059] The convolutional neural network method is used to perform image target recognition on the electrode state of lithium-ion batteries photographed by electron microscope SEM, transmission electron microscope TEM and focused ion beam scanning electron microscope FIB / SEM. By comparing the battery charging parameters with the microscopic structure of the battery itself, it is determined which places are expanded and damaged in the microstructure, so as to further determine the current and voltage based on which a better non-expanded and non-damaged battery microstate can be obtained. The convolutional neural network is pre-trained to train more than 1,000 groups of microstructure differences in the same type of batteries, and the images with good distinguishing features of expansion damage and microstructure are marked to obtain an optimized neural network model (i.e., recognition model), wherein the neural network model (i.e., recognition model) is as follows Figure 2 As shown. Includes:

[0060] Input Layer

[0061] Function: Accepts a raw input image, such as a 32x32 grayscale image.

[0062] Processing: The image is first normalized and the grayscale value is normalized to the interval [0,1].

[0063] The first convolutional layer

[0064] Number of convolution kernels: Use 6 convolution kernels.

[0065] Kernel size: Each convolution kernel size is usually 5x5.

[0066] Output: The output of this layer is 6 28x28 feature maps, each of which corresponds to a feature extracted by a convolution kernel.

[0067] The first pooling layer (Subsampling)

[0068] Pooling method: Use maximum pooling.

[0069] Pooling Window: The window size is usually 2x2.

[0070] Function: This layer reduces the dimension of the data by downsampling, and the output dimension is 6 14x14 feature maps.

[0071] The second convolutional layer

[0072] Number of convolution kernels: 16 convolution kernels are used.

[0073] Kernel size: Each convolution kernel size is usually 5x5.

[0074] Output: The output is 16 10x10 feature maps.

[0075] The second pooling layer

[0076] Pooling method: Maximum pooling is also used.

[0077] Pooling window: The window size is 2x2.

[0078] Function: Further downsample, the output dimension is 16 5x5 feature maps.

[0079] Fully connected layer

[0080] Function: The fully connected layer converts the two-dimensional feature map output by the previous layer into a one-dimensional vector. This step is to integrate local features into global features, which usually involves a large number of parameters.

[0081] Output: There are usually several fully connected layers, and the number of neurons in each layer gradually decreases, preparing for the final classification.

[0082] Output Layer

[0083] Classification task: The design of the output layer depends on the requirements of the classification task. For lithium ion microscopic images, it can usually be a fully connected layer with 10 neurons, each neuron corresponding to a microscopic state and degree of damage. Different degrees of damage can be marked with 0-9, for example, 0 indicates no damage, 9 indicates complete damage, and 4 indicates tolerable damage or recoverable damage.

[0084] Activation function: The output layer usually uses a softmax activation function, which converts the output of the neuron into a predicted probability.

[0085] S103: According to each charging time under the target charging condition and the maximum charging rate corresponding to each charging time, setting a preset charging current of the power battery under the target charging condition.

[0086] According to the embodiment of the present application, firstly, a plurality of different battery cell operating parameters are obtained when the target battery cell is charged at a target charging rate corresponding to the target charging condition under the target charging condition. Then, the maximum charging rate corresponding to each charging time under the target charging condition is determined according to the plurality of different battery cell operating parameters, and the charging strategy under the target charging condition is set according to each charging time under the target charging condition and the maximum charging rate corresponding to each charging time. The method for determining the maximum charging rate includes evaluating the structural integrity of different lithium-ion batteries after multiple charging and discharging; and selecting the rate current corresponding to the lithium-ion battery with the best structural integrity as the preset charging current of the lithium-ion battery under the preset conditions, thereby improving the charging speed of the power battery.

[0087] In one embodiment of the present application, the charging strategy optimization method for a power battery also includes: detecting the expansion force and voltage of each power battery during the charging process; generating characteristic curves of different rate currents with respect to the expansion force and voltage based on the detected expansion force and voltage, wherein the characteristic curve of the preset charging current with respect to the expansion force and voltage is a first characteristic curve, and the characteristic curves of the other rate currents with respect to the expansion force and voltage other than the preset charging current are respectively a second characteristic curve; based on the charging speed and the expansion force, respectively performing segmented comparison on the first characteristic curve and each second characteristic curve, and selecting a segmented charging current in each segment that meets the target principle; combining the segmented charging currents of all segments to generate a step charging current for the power battery under the preset conditions.

[0088] In this example, based on the charging speed and the expansion force, the first characteristic curve and each second characteristic curve are respectively compared in sections, and a sectioned charging current that meets the target principle is selected in each section, including: in each section comparison, all the second characteristic curves are grouped according to whether the curve trend is similar to the first characteristic curve, so as to obtain a first trend curve group with similar trends and a second trend curve group with different trends; the current with the highest rate among all the rate currents corresponding to the first trend curve group and the preset charging current is selected as the first characteristic current, and the current with the highest rate among all the rate currents corresponding to the second trend curve group is selected as the second characteristic current; the current with a low rate of change of expansion force in the first characteristic current and the second characteristic current is selected as the sectioned charging current of the section.

[0089] Before segmentally comparing the first characteristic curve and each second characteristic curve based on the charging speed and the expansion force, the method includes segmenting the first characteristic curve and the second characteristic curve according to the principle of equal division or using the intersection point between the first characteristic curve and the second characteristic curve as the segmentation point.

[0090] In a specific example, the characteristic curve is a first-order differential dP / dV curve, a first-order differential dP / dSOC curve, or a second-order differential d2P / dV2 curve, wherein dP is the first-order differential of the expansion force of the lithium-ion battery, dV is the first-order differential of the voltage of the lithium-ion battery, and dSOC is the first-order differential of the capacity of the lithium-ion battery.

[0091] Taking lithium-ion batteries as an example, Figure 3 As shown, the expansion force and voltage of different lithium-ion batteries during the charging process are detected in real time; according to the detected expansion force and voltage, characteristic curves of different rate currents with respect to the expansion force and voltage are generated, the preset charging current corresponds to a first characteristic curve, and other rate currents except the preset charging current respectively correspond to a second characteristic curve; the first characteristic curve and the second characteristic curve are divided and proportioned according to the principle of taking into account both the charging speed and the expansion force, and the segmented charging current that meets the principle in each segment is selected, and the segmented charging currents of all the segments are combined to generate a step charging current for the lithium-ion battery under the preset conditions. Optionally, the first characteristic curve and the second characteristic curve are compared in sections according to the principle of balancing charging speed and expansion force, and a sectioned charging current that meets the principle is selected in each section, including: in each section comparison, all the second characteristic curves are divided into two groups according to whether the curve trend is similar to the first characteristic curve, namely a first trend curve group with similar trends and a second trend curve group with different trends, wherein the similar trend means that the slope difference between the two compared characteristic curves is within a preset range; among all the rate currents corresponding to the first trend curve group and the preset charging current, the current with the highest rate is selected as the first characteristic current; among all the rate currents corresponding to the second trend curve group, the current with the highest rate is selected as the second characteristic current; and among the first characteristic current and the second characteristic current, the current with a lower rate of change of expansion force is selected as the section charging current of the section.

[0092] When the target battery cell is charged with the charging strategy rate curve set as above under the target charging condition, the target battery cell will be charged with the maximum charging rate corresponding to the charging time for each charging time under the target charging condition. That is to say, at each charging time corresponding to the target charging condition, the target battery cell is charged with the maximum charging rate under the current that satisfies the lower rate of change of the expansion force of the first characteristic current and the second characteristic current of the target battery cell. Optionally, the first characteristic curve and the second characteristic curve are segmented and compared according to the principle of taking both the charging speed and the expansion force into consideration, including: segmenting the first characteristic curve and the second characteristic curve according to the principle of equal division or taking the intersection point between the first characteristic curve and the second characteristic curve as the segmentation point. Optionally, the characteristic curve is a first-order differential dP / dV curve, a first-order differential dP / dSOC curve or a second-order differential d2P / dV2 curve, dP is the first-order differential of the expansion force of the lithium-ion battery, dV is the first-order differential of the voltage of the lithium-ion battery, and dSOC is the first-order differential of the capacity of the lithium-ion battery. Figure 4 As shown, a dP / dV curve and a dP / dSOC curve are shown.

[0093] In one embodiment of the present application, the charging strategy optimization method of the power battery also includes: drawing a dQ / dV standard curve of the power battery; charging the target power battery and drawing a dQ / dV real-time curve during the charging process; when the first peak appears on the dQ / dV real-time curve, comparing the corresponding voltage with the voltage corresponding to the first peak of the dQ / dV standard curve to obtain a deviation; if the deviation is not greater than the sum of the polarization voltage and the ohmic voltage drop of the target power battery, it is judged that the target power battery is currently in a safe charging state; measuring the consistency of the internal resistance of each single cell in the target power battery or calculating the internal resistance of the target power battery, when the internal resistance of each single cell of the target power battery is consistent, or the internal resistance of the target power battery is within a predetermined range, entering a fast charging stage; in the fast charging stage, the charging area within the adjacent peak and valley ranges is charged using a step-by-step current change method until the end of the fast charging stage.

[0094] Specifically, the charging process is monitored through the dQ / dV curve, and the status of each single cell can be monitored to ensure the consistency of each single cell, avoid a single cell being in a state of overcharge or over-discharge for a long time, and make the entire charging process proceed in a safe and fast direction. It includes the following steps: S1, draw the dQ / dV standard curve of a certain model of lithium battery; S2, normally charge the target lithium battery of the same model as step S1, draw the dQ / dV real-time curve and record the dQ / dV extreme value; S3, when the first peak of dQ / dV in step S2 appears, compare the corresponding voltage with the voltage corresponding to the first peak of the standard curve dQ / dV in step S1. When the deviation between the two is not greater than the sum of the polarization voltage and the ohmic voltage drop of the current lithium battery charging, it is judged that the target lithium battery is currently in a safe charging state; S4, measure the consistency of the internal resistance of each single cell in the target lithium battery Or calculate the internal resistance of the battery pack. When the internal resistance of each single cell of the target lithium battery remains consistent, or the internal resistance value of the current target lithium battery is between 0.8 and 1.5 times the normal internal resistance value, enter the fast charging stage and charge the target battery cell at the maximum charging rate; S5. In the fast charging stage, the charging area within the adjacent peak and valley ranges is charged using a step-by-step current change method; S6. After the fast charging stage is over, the method recommended by the electric vehicle manufacturer is used until the charging is completed, or the target lithium battery is continued to be charged with a constant current of 1.0C until the charging termination voltage, and then switched to constant voltage charging until the charging current is 0.05C rate to complete the charging.

[0095] By using the condition that the battery voltage reaches the full charge cut-off voltage and lasts for a certain period of time as the end condition for charging using the maximum output current, the authenticity of the battery voltage reaching the full charge cut-off voltage can be ensured. In one embodiment of the present application, the multiple constant voltage charging according to the preset full charge cut-off voltage includes: determining the floating range of the battery voltage according to the full charge cut-off voltage; charging with the maximum output current of the charging device as the initial charging current of the constant voltage charging, and adjusting the charging current to gradually decrease according to the first battery state to maintain the battery voltage within the floating range to complete a constant voltage charge. Each constant voltage charge (CV) is performed by adaptively adjusting the large charging current to continuously decrease to maintain the battery voltage floating near the full charge cut-off voltage to charge a certain amount of electricity. After multiple constant voltage charging adjustments, the battery is charged with enough electricity. Compared with the charging strategy of the prior art that causes the battery to be not fully charged, the battery charging capacity can be effectively increased, the battery service life can be increased, and the battery expansion force can be optimized. Figure 5 As shown, it can be clearly seen that by adopting the method of the embodiment of the present application, the life and expansion force of the power battery are better optimized.

[0096] The dQ / dV curve is distinguished by convolutional neural network features. The dQ / dV is measured and multiple curves are drawn through multiple tests with different voltages, currents, charging times, frequencies, etc. The image features of the polarization voltage and ohmic voltage drop of the entire battery pack are recognized through convolutional neural network pre-training and annotation. The recognition process is the same as the above-mentioned method and steps for identifying microscopic images. Finally, it is applied to the process of optimizing rechargeable batteries to protect and identify battery charging risks and control charging indicators.

[0097] According to the charging strategy optimization method of the power battery of the embodiment of the present application, firstly, a plurality of different battery cell operating parameters are obtained when the target battery cell is charged at the target charging rate corresponding to the target charging condition under the target charging condition. Then, according to the plurality of different battery cell operating parameters, the maximum charging rate corresponding to each charging time under the target charging condition is determined, and the charging strategy under the target charging condition is set according to each charging time under the target charging condition and the maximum charging rate corresponding to each charging time. The method for determining the maximum charging rate includes evaluating the structural integrity of different lithium-ion batteries after multiple charging and discharging; the rate current corresponding to the lithium-ion battery with the best structural integrity is selected as the preset charging current of the lithium-ion battery under the preset conditions, thereby improving the charging speed of the power battery.

[0098] Figure 6 is a structural block diagram of a charging strategy optimization system for a power battery according to an embodiment of the present application. Figure 6 As shown, a charging strategy optimization system for a power battery according to an embodiment of the present application includes: an acquisition module 710, a maximum charging rate calculation module 720 and an optimization module 730, wherein:

[0099] An acquisition module 710 is used to acquire a plurality of different battery cell operating parameters when the target battery cell is charged at a target charging rate corresponding to a target charging condition;

[0100] The maximum charging rate calculation module 720 is used to determine the maximum charging rate corresponding to each charging time under the target charging condition according to a plurality of different battery cell operating parameters;

[0101] The optimization module 730 is used to set a preset charging current of the power battery under the target charging condition according to each charging time under the target charging condition and the maximum charging rate corresponding to each charging time.

[0102] According to the charging strategy optimization system of the power battery of the embodiment of the present application, firstly, a plurality of different battery cell operating parameters are obtained when the target battery cell is charged at the target charging rate corresponding to the target charging condition under the target charging condition. Then, according to the plurality of different battery cell operating parameters, the maximum charging rate corresponding to each charging time under the target charging condition is determined, and the charging strategy under the target charging condition is set according to each charging time under the target charging condition and the maximum charging rate corresponding to each charging time. The method for determining the maximum charging rate includes evaluating the structural integrity of different lithium-ion batteries after multiple charging and discharging; and selecting the rate current corresponding to the lithium-ion battery with the best structural integrity as the preset charging current of the lithium-ion battery under the preset conditions, thereby improving the charging speed of the power battery.

[0103] For the specific definition of the charging strategy optimization system for power batteries, please refer to the definition of the charging strategy optimization method for power batteries mentioned above, which will not be repeated here. The various modules of the above-mentioned charging strategy optimization system for power batteries can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0104] Reference below Figure 7 , Figure 7 A schematic diagram of the structure of a computer device suitable for implementing the embodiments of the present application is shown.

[0105] like Figure 7 As shown, computer system 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage part 1008 into a random access memory (RAM) 1003. Various programs and data required for the operation instructions of the system are also stored in RAM 1003. CPU 1001, ROM 1002 and RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to bus 1004.

[0106] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage section 1008 as needed.

[0107] In particular, according to an embodiment of the present application, the above reference flow chart Figure 1 The described process may be implemented as a computer-readable storage medium. For example, an embodiment of the present application includes a computer-readable storage medium, which includes a computer program, and the computer program includes a program code for executing the method shown in the flowchart.

[0108] In particular, according to an embodiment of the present application, the above reference flow chart Figure 1 The described process can be implemented as a computer software program. For example, a computer program product is included in an embodiment of the present application, which includes a computer program carried on a computer readable medium, and the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the central processing unit (CPU) 1001, the above-mentioned functions defined in the system of the present application are executed.

[0109] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0110] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operating instructions of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the aforementioned module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, the boxes represented by two connections can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operating instruction, or can be implemented with a combination of dedicated hardware and computer instructions.

[0111] The units or modules involved in the embodiments described in the present application may be implemented by software or hardware. The units or modules described may also be arranged in a processor. The names of these units or modules do not, in some cases, constitute limitations on the units or modules themselves.

[0112] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A method for optimizing a charging strategy of a power battery, characterized in that: include: Acquire a plurality of different battery cell operating parameters when the target battery cell is charged at a target charging rate corresponding to a target charging condition; According to a plurality of different battery cell operating parameters, determine the maximum charging rate corresponding to each charging time under the target charging condition; According to each charging time under the target charging condition and the maximum charging rate corresponding to each charging time, a preset charging current of the power battery under the target charging condition is set.

2. The method for optimizing the charging strategy of a power battery according to claim 1, characterized in that: The determining of the maximum charging rate corresponding to each charging duration under the target charging condition includes: After multiple charge and discharge cycles, the structural integrity of each power battery is evaluated; The rate current corresponding to the power battery with the best structural integrity is used as the maximum charging rate.

3. The method for optimizing the charging strategy of a power battery according to claim 2, characterized in that: The assessment of the structural integrity of each power battery includes: Dismantle the power battery; Use any one of a scanning electron microscope SEM, a transmission electron microscope TEM, and a focused ion beam / scanning electron microscope FIB / SEM to characterize the microstructure of the pole piece of the disassembled power battery, so as to obtain microstructure characterization data of the pole piece of the power battery; The microstructural integrity of the pole piece is evaluated based on the microstructural characterization data of the pole piece of the power battery.

4. The method for optimizing the charging strategy of a power battery according to claim 3, characterized in that: The method of using any one of a scanning electron microscope SEM, a transmission electron microscope TEM and a focused ion beam / scanning electron microscope FIB / SEM to characterize the microstructure of the pole piece of the disassembled power battery to obtain microstructure characterization data of the pole piece of the power battery includes: Use the pre-trained recognition model to identify the target state of the pole piece of the power battery photographed by electron microscope SEM, transmission electron microscope TEM or focused ion beam / scanning electron microscope FIB / SEM; The target recognition results are compared with the battery charging parameters and the microscopic structure of the battery itself to obtain the microstructural characterization data of the power battery pole piece.

5. The method for optimizing the charging strategy of a power battery according to any one of claims 1 to 4, characterized in that: Also includes: Detect the expansion force and voltage of each power battery during charging; Generate characteristic curves of different rate currents with respect to the expansion force and voltage according to the detected expansion force and voltage, wherein the characteristic curve of the preset charging current with respect to the expansion force and voltage is a first characteristic curve, and characteristic curves of other rate currents with respect to the expansion force and voltage except the preset charging current are respectively a second characteristic curve; Based on the charging speed and the expansion force, the first characteristic curve and each second characteristic curve are respectively compared in sections, and a section charging current that meets the target principle is selected in each section; The segmented charging currents of all the segments are combined to generate a step charging current for the power battery under the preset conditions.

6. The method for optimizing the charging strategy of a power battery according to claim 5, characterized in that: The first characteristic curve and each second characteristic curve are respectively compared in sections based on the charging speed and the expansion force, and a sectioned charging current that meets the target principle in each section is selected, including: In each comparison, all second characteristic curves are grouped according to whether the curve trend is similar to the first characteristic curve, so as to obtain a first trend curve group with similar trends and a second trend curve group with dissimilar trends; The current with the highest rate among all rate currents corresponding to the first trend curve group and the preset charging current is selected as the first characteristic current, and the current with the highest rate among all rate currents corresponding to the second trend curve group is selected as the second characteristic current; The current with the lowest rate of change of expansion force between the first characteristic current and the second characteristic current is selected as the segmented charging current for the segment.

7. The method for optimizing the charging strategy of a power battery according to claim 6, characterized in that: Before segmentally comparing the first characteristic curve and each second characteristic curve based on the charging speed and the expansion force, the method includes: The first characteristic curve and the second characteristic curve are segmented according to the principle of equal division or with the intersection point between the first characteristic curve and the second characteristic curve as the segmentation point.

8. The method for optimizing the charging strategy of a power battery according to claim 5, characterized in that: The characteristic curve is a first-order differential dP / dV curve, a first-order differential dP / dSOC curve or a second-order differential d2P / dV2 curve, wherein dP is the first-order differential of the expansion force of the lithium-ion battery, dV is the first-order differential of the voltage of the lithium-ion battery, and dSOC is the first-order differential of the capacity of the lithium-ion battery.

9. The method for optimizing the charging strategy of a power battery according to claim 1, characterized in that: Also includes: Draw the dQ / dV standard curve of power battery; Charge the target power battery and draw the dQ / dV real-time curve during the charging process; When the first peak appears on the dQ / dV real-time curve, the corresponding voltage is compared with the voltage corresponding to the first peak of the dQ / dV standard curve to obtain the deviation; If the deviation is not greater than the sum of the polarization voltage and the ohmic voltage drop of the target power battery during charging, it is determined that the target power battery is currently in a safe charging state; Measuring the consistency of the internal resistance of each single cell in the target power battery or calculating the internal resistance of the target power battery, when the internal resistance of each single cell of the target power battery is consistent, or the internal resistance of the target power battery is within a predetermined range, entering the fast charging stage; During the fast charging stage, the charging areas within the adjacent peak and valley ranges are charged using a step-by-step current change method until the end of the fast charging stage.

10. A charging strategy optimization system for a power battery, characterized in that: include: An acquisition module, used for acquiring a plurality of different battery cell operating parameters when the target battery cell is charged at a target charging rate corresponding to a target charging condition; A maximum charging rate calculation module is used to determine the maximum charging rate corresponding to each charging time under target charging conditions according to a plurality of different battery cell operating parameters; The optimization module is used to set a preset charging current of the power battery under the target charging condition according to each charging time under the target charging condition and the maximum charging rate corresponding to each charging time.

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