Charging strategy determination method, device, electronic device and storage medium

Abstract

The present application provides a charging strategy determination method, device, electronic device and storage medium, the method comprising: obtaining the state of charge and temperature parameters of the starting point of the charging path of the battery, and the state of charge of the end point of the charging path; starting from i=1, looping the following steps until the target state of charge of the i-th intermediate charging node is the same as the state of charge of the end point; determining multiple candidate state nodes according to the target state of charge, target temperature parameters and preset selection algorithm of the i-1th intermediate charging node; determining the i-th intermediate charging node according to the target state of charge and target temperature parameters of the i-1th intermediate charging node, and the target state of charge and target temperature parameters of each candidate state node; determining the charging strategy according to the charging current sequence corresponding to the battery charging from the i-1th intermediate charging node to the i-th intermediate charging node in the charging path. The above charging strategy determination method can improve the safety of the charging process.

Description

Technical Field

[0001] The present application relates to the technical field of battery charging, and in particular to a charging strategy determination method, device, electronic device and storage medium. Background Art

[0002] During the charging process of the battery, in order to make the battery reach a specific state of charge in a short time, it is necessary to increase the charging current as much as possible and speed up the charging speed without exceeding the maximum charging current supported by the battery.

[0003] The current charging strategy is mainly constant voltage charging or constant current charging. However, in actual application, charging the battery by constant voltage charging or constant current charging will cause damage to the battery and capacity loss, thereby reducing the battery life. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a charging strategy determination method, device, electronic device and storage medium, which are used to determine a charging strategy for quickly charging a battery to a specific state of charge, and adopting this charging strategy can improve the safety of the charging process and effectively protect the service life and capacity health of the battery.

[0005] In a first aspect, the present application provides a charging strategy determination method, comprising: obtaining the state of charge and temperature parameters of the starting point of the charging path of the battery, and the state of charge of the end point of the charging path; starting from i being 1, looping through the following steps until the target state of charge of the i-th intermediate charging node is the same as the state of charge of the end point; determining multiple candidate state nodes corresponding to the i-1th intermediate charging node based on the target state of charge, target temperature parameters and a preset selection algorithm of the i-1th intermediate charging node; wherein, when i is 1, the i-1th intermediate charging node is the starting point; determining the i-th intermediate charging node and the charging current sequence corresponding to the battery charging from the i-1th intermediate charging node to the i-th intermediate charging node based on the target state of charge and target temperature parameters of the i-1th intermediate charging node, and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node; determining the charging strategy based on the charging current sequence corresponding to the battery charging from the i-1th intermediate charging node in the charging path to the i-th intermediate charging node.

[0006] In the embodiment of the present application, in the process of determining the charging path, the state of charge and temperature parameters of the determined intermediate state node and its corresponding candidate state node are used as state quantities to construct a prediction model, and the prediction model is solved by rolling optimization through the model prediction control algorithm to determine the charging path with the shortest charging time and the charging current of each intermediate charging node in the charging path, thereby determining the charging strategy. The charging path determined in the above manner takes into account the impact of the accumulated heat during the charging process on the charging path planning, so that the battery can be charged with this charging path, and can be quickly charged to the specified state of charge (the state of charge corresponding to the end point of the charging path), and can improve the safety of the charging process, and effectively protect the battery's service life and capacity health.

[0007] In an optional embodiment, the multiple candidate state nodes corresponding to the i-1th intermediate charging node are determined based on the target state of charge, target temperature parameters and a preset selection algorithm of the i-1th intermediate charging node, including: when i is 1, n points in the discrete state space that meet a preset distance from the starting point are determined as the candidate state nodes; wherein the dimension of the discrete state space corresponds to the state of charge and temperature parameters, and the starting point and the candidate state nodes are points in the discrete state space.

[0008] In an optional embodiment, the method of determining multiple candidate state nodes corresponding to the i-1th intermediate charging node based on the target state of charge, target temperature parameters and a preset selection algorithm of the i-1th intermediate charging node also includes: when i is greater than 1, determining the candidate state node from the discrete state space based on the target distance; wherein the target distance is determined based on the maximum charging current of the i-1th intermediate charging node and the charging time from the starting point to the i-1th intermediate charging node, and the maximum charging current of the i-1th intermediate charging node represents the maximum charging current supported by the battery when the state is at the i-1th intermediate charging node.

[0009] In the embodiment of the present application, when determining the charging path, if the target state of charge and target temperature parameters of two adjacent intermediate charging nodes differ greatly, the solution time is long and the error is large. Therefore, by using a preset selection algorithm to determine a node closer to the i-1th intermediate charging node from multiple state nodes as a candidate state node for the i-1th intermediate charging node, the solution time can be shortened and the error can be reduced.

[0010] In an optional embodiment, the method of determining the i-th intermediate charging node based on the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node includes: obtaining a charging current sequence corresponding to charging the battery from the i-1th intermediate charging node to each candidate state node according to the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node; determining a charging time corresponding to charging the battery from the i-1th intermediate charging node to each candidate state node according to the charging current sequence; and determining the i-th intermediate charging node according to the charging time corresponding to charging the battery from the i-1th intermediate charging node to each candidate state node.

[0011] In an optional embodiment, the method obtains a charging current sequence corresponding to the battery charging from the i-1th intermediate charging node to each candidate state node according to the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node, including: for each candidate state node corresponding to the i-1th intermediate charging node, determining a prediction model according to the target state of charge and target temperature parameters of the i-1th intermediate charging node and a state transfer equation; determining an optimization relationship and constraints according to the prediction model and the target state of charge and target temperature parameters of the candidate state node; and determining a charging current sequence by solving the optimization relationship and constraints; wherein the charging current sequence includes multiple charging currents, and the process of charging the battery from the i-1th intermediate charging node to the candidate state node includes multiple charging states, and each of the charging currents represents the charging current used by the battery in the process of charging from one charging state to the next charging state.

[0012] In an optional embodiment, the state transfer equation characterizes the linear change relationship satisfied by the battery when charging from the i-1th intermediate charging node to the candidate state node under the stimulation of the charging current and under the conditions of the target state of charge and target temperature parameters of the i-1th intermediate charging node.

[0013] In the embodiment of the present application, a linear model is used to simulate the changes in the state of charge and temperature parameters of the battery when it is charged from the i-1th intermediate charging node to the candidate state node under the stimulation of the charging current, thereby improving the accuracy of determining the charging current.

[0014] In an optional embodiment, the prediction model represents the linear change relationship satisfied by the battery when it is charged from the i-1th intermediate charging node to the charging state corresponding to the prediction time domain under the stimulation of the charging current, and the prediction time domain represents the time step of the predicted charging current.

[0015] In an optional embodiment, the optimization relationship represents the difference between the target state of charge and target temperature parameters of the charging state corresponding to the predicted time domain and the target state of charge and target temperature parameters of the candidate state node while satisfying the constraint conditions.

[0016] In an optional embodiment, the constraints include: each charging current in the charging current sequence is less than the maximum charging current supported by the battery at the i-1 intermediate charging node; the rate of change of two adjacent charging currents in the charging current sequence is less than a preset threshold; the initial state of the optimization relationship is the target state of charge and target temperature parameters of the i-1th intermediate charging node, and the final state of the optimization relationship is the target state of charge and target temperature parameters of the candidate state node; the charging state corresponding to the predicted time domain is within the charging state range to which the state transfer equation is applicable.

[0017] In an optional embodiment, the method of determining the i-th intermediate charging node based on the charging time corresponding to charging from the i-1th intermediate charging node to each of the candidate state nodes includes: for each of the candidate state nodes, determining a first charging time from the starting point to the candidate state node based on the charging time corresponding to charging from the i-1th intermediate charging node to the candidate state node; determining a second charging time from the candidate state node to the end point based on a maximum charging current corresponding to the candidate state node; wherein the maximum charging current corresponding to the candidate state node represents the maximum charging current supported when the battery is in the candidate state node; and determining the i-th intermediate charging node from each of the candidate state nodes based on the first charging time and the second charging time.

[0018] In an embodiment of the present application, the i-th intermediate charging node is determined by jointly using a first charging time for the battery to charge from a starting point to a candidate state node and a second charging time required for the battery to charge from the candidate state node to a preset end point with a maximum charging current supported by the battery at the candidate state node, so that the determined charging strategy can shorten the charging time required for the battery to charge from the starting point to the end point as much as possible.

[0019] In an optional embodiment, determining the first charging time from the starting point to the candidate state node based on the charging time corresponding to the charging time from the i-1th intermediate charging node to the candidate state node includes: taking the sum of the charging time from the starting point to the i-1th intermediate charging node and the charging time from the i-1th intermediate charging node to the candidate state node as the first charging time.

[0020] In an optional embodiment, determining the second charging time from the candidate state node to the end point based on the maximum charging current corresponding to the candidate state node includes: taking the ratio of the charge state difference to the maximum charging current corresponding to the candidate state node as the second charging time; wherein the charge state difference is the difference between the charge state of the end point and the target charge state of the candidate state node.

[0021] In an optional embodiment, the method of determining the i-th intermediate charging node from each of the candidate status nodes based on the first charging time and the second charging time includes: determining a time coefficient corresponding to the candidate status node based on the first charging time and the second charging time; and taking a candidate status node whose time coefficient meets a preset condition among each of the candidate status nodes as the i-th intermediate charging node.

[0022] In an optional implementation, determining the time coefficient corresponding to the candidate state node according to the first charging time and the second charging time includes: taking the sum of the first charging time and the second charging time as the time coefficient.

[0023] In an optional embodiment, the type of the starting temperature parameter and the type of the target temperature parameter both include a first battery cell temperature, a second battery cell temperature, a first water cooling temperature, and a second water cooling temperature.

[0024] In the embodiment of the present application, the type of the starting temperature parameter and the type of the target temperature parameter both include the first temperature of the battery cell, the second temperature of the battery cell, the first water cooling temperature and the second water cooling temperature. The above four temperature types are used to describe the charging status of each intermediate charging node and simulate the internal core temperature of the battery. When determining the charging strategy, the impact of the accumulated heat of the battery on the charging performance is considered, thereby maximizing the safety of the charging process and effectively protecting the battery's service life and capacity health.

[0025] In a second aspect, the present application provides a charging strategy determination device, comprising:

[0026] An acquisition module, used to acquire the state of charge and temperature parameters of the starting point of the charging path of the battery, and the state of charge of the end point of the charging path; a charging path determination module, used to start from i being 1, and cyclically execute the following steps until the target state of charge of the i-th intermediate charging node is the same as the state of charge of the end point; determine multiple candidate state nodes corresponding to the i-1th intermediate charging node according to the target state of charge, target temperature parameters and a preset selection algorithm of the i-1th intermediate charging node; wherein, when i is 1, the i-1th intermediate charging node is the starting point; determine the i-th intermediate charging node and the charging current sequence corresponding to the battery charging from the i-1th intermediate charging node to the i-th intermediate charging node according to the target state of charge and target temperature parameters of the i-1th intermediate charging node, and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node; a charging strategy determination module, used to determine the charging strategy according to the charging current sequence corresponding to the battery charging from the i-1th intermediate charging node to the i-th intermediate charging node in the charging path.

[0027] In an optional embodiment, the charging path determination module is specifically used to determine n points in the discrete state space that meet a preset distance from the starting point as the candidate state nodes when i is 1; wherein the dimension of the discrete state space corresponds to the charge state and temperature parameters, and the starting point and the candidate state nodes are points in the discrete state space.

[0028] In an optional embodiment, the charging path determination module is specifically used to determine the candidate state node from the discrete state space according to the target distance when i is greater than 1; wherein the target distance is determined based on the maximum charging current of the i-1th intermediate charging node and the charging time taken from the starting point to the i-1th intermediate charging node, and the maximum charging current of the i-1th intermediate charging node represents the maximum charging current supported when the battery state is at the i-1th intermediate charging node.

[0029] In an optional embodiment, the charging path determination module is specifically used to obtain a charging current sequence corresponding to the battery charging from the i-1th intermediate charging node to each candidate state node based on the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node; determine the charging time corresponding to the battery charging from the i-1th intermediate charging node to each candidate state node based on the charging current sequence; and determine the i-th intermediate charging node based on the charging time corresponding to the battery charging from the i-1th intermediate charging node to each candidate state node.

[0030] In an optional embodiment, the charging path determination module is specifically used to determine a prediction model for each candidate state node corresponding to the i-1th intermediate charging node according to the target state of charge and target temperature parameters and the state transfer equation of the i-1th intermediate charging node; determine an optimization relationship and constraints according to the prediction model, the target state of charge and target temperature parameters of the candidate state node; and determine a charging current sequence by solving the optimization relationship and constraints; wherein the charging current sequence includes multiple charging currents, and the battery includes multiple charging states in the process of charging from the i-1th intermediate charging node to the candidate state node, and each of the charging currents represents the charging current used by the battery in the process of charging from one charging state to the next charging state.

[0031] In an optional embodiment, the state transfer equation characterizes the linear change relationship satisfied by the battery when charging from the i-1th intermediate charging node to the candidate state node under the stimulation of the charging current and under the conditions of the target state of charge and target temperature parameters of the i-1th intermediate charging node.

[0032] In an optional embodiment, the prediction model represents the linear change relationship satisfied by the battery when it is charged from the i-1th intermediate charging node to the charging state corresponding to the prediction time domain under the stimulation of the charging current, and the prediction time domain represents the time step of the predicted charging current.

[0033] In an optional embodiment, the optimization relationship represents the difference between the target state of charge and target temperature parameters of the charging state corresponding to the predicted time domain and the target state of charge and target temperature parameters of the candidate state node while satisfying the constraint conditions.

[0034] In an optional embodiment, the constraints include: each charging current in the charging current sequence is less than the maximum charging current supported by the battery at the i-1 intermediate charging node; the rate of change of two adjacent charging currents in the charging current sequence is less than a preset threshold; the initial state of the optimization relationship is the target state of charge and target temperature parameters of the i-1th intermediate charging node, and the final state of the optimization relationship is the target state of charge and target temperature parameters of the candidate state node; the charging state corresponding to the predicted time domain is within the charging state range to which the state transfer equation is applicable.

[0035] In an optional embodiment, the charging path determination module is specifically used to determine, for each of the candidate state nodes, a first charging time from the starting point to the candidate state node based on the charging time corresponding to the i-1th intermediate charging node to the candidate state node; and a second charging time from the candidate state node to the end point based on the maximum charging current corresponding to the candidate state node; wherein the maximum charging current corresponding to the candidate state node represents the maximum charging current supported when the battery is in the candidate state node; and the i-th intermediate charging node is determined from each of the candidate state nodes based on the first charging time and the second charging time.

[0036] In an optional implementation, the charging path determination module is specifically configured to take the sum of the charging time from the starting point to the i-1th intermediate charging node and the charging time from the i-1th intermediate charging node to the candidate state node as the first charging time.

[0037] In an optional embodiment, the charging path determination module is specifically used to take the ratio of the state of charge difference to the maximum charging current corresponding to the candidate state node as the second charging time; wherein the state of charge difference is the difference between the state of charge of the endpoint and the target state of charge of the candidate state node.

[0038] In an optional embodiment, the charging path determination module is specifically used to determine the time coefficient corresponding to the candidate state node according to the first charging time and the second charging time; and take the candidate state node whose time coefficient meets the preset conditions among the candidate state nodes as the i-th intermediate charging node.

[0039] In an optional implementation, the charging path determination module is specifically configured to use the sum of the first charging time and the second charging time as the time coefficient.

[0040] In an optional embodiment, the type of the starting temperature parameter and the type of the target temperature parameter both include a first battery cell temperature, a second battery cell temperature, a first water cooling temperature, and a second water cooling temperature.

[0041] In a third aspect, the present application provides an electronic device comprising: a processor, a memory and a bus; the processor and the memory communicate with each other through the bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a method corresponding to any implementation method in the first aspect.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are read and executed by a computer, a method corresponding to any one of the implementations in the first aspect is executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application, and other related drawings can also be obtained based on these drawings.

[0044] Figure 1 A flowchart of a charging strategy determination method provided in an embodiment of the present application;

[0045] Figure 2 A schematic diagram of a discrete state space provided in an embodiment of the present application;

[0046] Figure 3 A flowchart for determining the i-th intermediate charging node provided in an embodiment of the present application;

[0047] Figure 4 A structural block diagram of a charging strategy determination device provided in an embodiment of the present application;

[0048] Figure 5 A schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0050] It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it is not necessary to further define and explain it in the subsequent drawings. At the same time, in the description of this application, relational terms such as "first", "second", etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements that are not clearly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements.

[0051] Batteries are widely used in electric vehicle power systems. The charging speed of electric vehicles affects the user experience and is also a factor that affects the further promotion of electric vehicles. Increasing the charging current is the most direct and effective way to speed up the charging speed, but it can also easily lead to problems such as battery overheating and rapid decline in life. In severe cases, it can even cause catastrophic accidents such as thermal runaway, fire, and explosion.

[0052] During the charging process of the battery, in order to make the battery reach a specific state of charge in a short time, it is necessary to increase the charging current as much as possible and speed up the charging speed without exceeding the maximum charging current supported by the battery.

[0053] The current charging strategies are mainly constant voltage charging or constant current charging. Constant voltage charging refers to charging the battery at a fixed voltage, and constant current charging refers to charging the battery at a fixed current. In order to reduce the probability of safety accidents, the fixed voltage and fixed current set during constant voltage charging or constant current charging will be less than the maximum charging voltage or maximum charging current supported by the battery, which will cause constant voltage charging or constant current charging to fail to fully utilize the effective range of charging current. In addition, the battery will accumulate heat during the charging process, which will affect the charging performance. However, the impact of the accumulated heat of the battery on the charging performance is not considered during constant voltage charging or constant current charging, which will cause damage to the battery and capacity loss.

[0054] Model Predictive Control (MPC) is a method for dealing with systems with hard control constraints. It predicts future system behavior based on the mathematical model of the system and determines the current control input through an optimization algorithm to achieve the best control effect. In the process of determining the battery charging strategy, the state of charge and temperature parameters of the battery at the beginning of charging are first obtained. The state of charge and temperature parameters of each intermediate charging node in the charging process are used as state quantities to build a prediction model. The prediction model is then optimized and solved by MPC to determine the charging path with the shortest charging time and the charging current of each intermediate charging node in the charging path, thereby determining the charging strategy.

[0055] The charging strategy determination method disclosed in the embodiment of the present application can be used, but is not limited to, to plan the charging strategy of the battery in the electrical device such as a vehicle, a ship or an aircraft. The charging strategy determination method disclosed in the present application is used to plan the charging strategy of the battery in the electrical device, so that the battery can be quickly charged to a specific state of charge, and the safety of the charging process is improved, effectively protecting the battery's service life and capacity health.

[0056] See also Figure 1 , Figure 1 A flowchart of a charging strategy determination method provided in an embodiment of the present application, the charging strategy determination method may include the following steps:

[0057] S1: Acquire the state of charge and temperature parameters of the starting point of the charging path of the battery and the state of charge at the end point of the charging path.

[0058] S2: Starting from i being 1, the following steps are executed in a loop until the target state of charge of the i-th intermediate charging node is the same as the state of charge of the end point; multiple candidate state nodes corresponding to the i-1th intermediate charging node are determined according to the target state of charge, target temperature parameters and a preset selection algorithm of the i-1th intermediate charging node; wherein, when i is 1, the i-1th intermediate charging node is the starting point; according to the target state of charge and target temperature parameters of the i-1th intermediate charging node, and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node, the i-th intermediate charging node and the charging current sequence corresponding to the battery charging from the i-1th intermediate charging node to the i-th intermediate charging node are determined.

[0059] S3: Determine a charging strategy according to a charging current sequence corresponding to charging the battery from the i-1th intermediate charging node to the i-th intermediate charging node in the charging path.

[0060] In the embodiment of the present application, the state parameter of the starting point of the charging path of the battery is the state parameter of the battery when it needs to be charged. The state parameter of the starting point includes the state of charge and the starting temperature parameter. The state of charge at the starting point represents the state of charge (SOC) of the battery at the starting point, and the temperature parameter at the starting point represents the temperature of the battery at the starting point. The temperature parameter can be expressed in many forms, for example: the temperature parameter can be one or more combinations of the core temperature of the battery, the first temperature of the battery cell, the second temperature of the battery cell, the first water cooling temperature, and the second water cooling temperature.

[0061] Since it is difficult to directly set the temperature sensor at the core position of the battery, in order to more accurately determine the core temperature of the battery, multiple temperature sensors are set around the battery. The first temperature of the battery cell is the highest temperature among the multiple temperatures measured by the multiple temperature sensors; the second temperature of the battery cell is the lowest temperature among the multiple temperatures measured by the multiple temperature sensors.

[0062] The temperature of the coolant in the battery water cooling system varies at different locations, and temperature sensors are set at different locations of the water cooling system. The first water cooling temperature is the highest temperature among multiple temperatures measured by multiple temperature sensors, and the second water cooling temperature is the highest temperature among multiple temperatures measured by multiple temperature sensors.

[0063] The charging path can be determined by S2. The charging path includes a starting point, M intermediate charging nodes and an end point. The process of determining the charging path is the process of cyclically determining the M intermediate charging nodes in the charging path, and each cyclic process determines an intermediate charging node.

[0064] During the cycle, firstly, multiple candidate state nodes corresponding to the i-1th intermediate charging node are determined based on the target state of charge, target temperature parameters and preset selection algorithm of the i-1th intermediate charging node. Each candidate state node includes a target state of charge and a target temperature parameter. The target state of charge represents the SOC of the battery when the battery is charged to the candidate state node; the target temperature parameter represents the temperature of the battery when the battery is charged to the candidate state node. Then, the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node are used to determine the charging current sequence corresponding to the i-1th intermediate charging node and the battery charging from the i-1th intermediate charging node to the i-th intermediate charging node.

[0065] The above steps are repeated until the target state of charge of the last intermediate charging node is the same as the state of charge of the end point of the charging path.

[0066] In the above S3, through the above cycle process, each intermediate charging node in the charging path and the charging current sequence from one intermediate charging node to the next intermediate charging node can be determined. The charging current sequences are combined to form a charging strategy.

[0067] In the above process, since the battery will accumulate heat during the charging process, the changes in the battery's state of charge and temperature parameters are taken into consideration in determining the charging path. The determined charging path takes into account the impact of the accumulated heat during the charging process on the charging path planning, so that the battery can be charged along this charging path. It can be quickly charged to the specified SOC (the SOC corresponding to the end point of the charging path), and can improve the safety of the charging process, effectively protecting the battery's service life and capacity health.

[0068] When determining the charging path, if the target state of charge and target temperature parameters of two adjacent intermediate charging nodes differ greatly, the solution time is long and the error is large. Therefore, a node closer to the i-1th intermediate charging node is determined from multiple state nodes through a preset selection algorithm as a candidate state node for the i-1th intermediate charging node. Each state node includes a target state of charge and a target temperature parameter. The target state of charge represents the SOC of the battery when the battery is charged to the state node; the target temperature parameter represents the temperature of the battery when the battery is charged to the state node.

[0069] There are many ways to implement the preset selection algorithm, for example: set a distance threshold, calculate the distance between the i-1th intermediate charging node and multiple state nodes, and use the state nodes that are less than the distance threshold as candidate state nodes. Another example: set a quantity threshold T, calculate the distance between the i-1th intermediate charging node and multiple state nodes, and select T state nodes as candidate state nodes according to the distance from small to large.

[0070] As an optional implementation manner, determining the multiple candidate status nodes corresponding to the i-1th intermediate charging node in step S2 may include:

[0071] When i is 1, n points in the discrete state space that meet a preset distance from the starting point are determined as candidate state nodes; wherein the dimension of the discrete state space corresponds to the state of charge and temperature parameters, and the starting point and the candidate state nodes are points in the discrete state space.

[0072] In this embodiment, when determining a candidate state node of an intermediate charging node from multiple state nodes, it is necessary to determine the distance between the intermediate charging node and the state node. A discrete state space is established, and the intermediate charging node and the candidate state node correspond to points in the discrete state space respectively.

[0073] The discrete state space can be generated in the following way. First, establish a multidimensional rectangular coordinate system, and the dimensions of the multidimensional rectangular coordinate system correspond to the state of charge and temperature parameters. For example, the state of charge corresponds to a dimension of the multidimensional rectangular coordinate system. If the target temperature parameters include 4 temperature types: battery cell first temperature, battery cell second temperature, water cooling first temperature and water cooling second temperature, the battery cell first temperature corresponds to a dimension in the multidimensional rectangular coordinate system, the battery cell second temperature corresponds to a dimension in the multidimensional rectangular coordinate system, the water cooling first temperature corresponds to a dimension in the multidimensional rectangular coordinate system, and the water cooling second temperature corresponds to a dimension in the multidimensional rectangular coordinate system. The multidimensional rectangular coordinate system is a five-dimensional rectangular coordinate system. Discretize the multidimensional rectangular coordinate system by 1% and 1°C to obtain a discrete state space, such as Figure 2 shown.

[0074] When i is 1, the i-1th intermediate charging node is the starting point of the charging path, and the n points closest to the starting point are determined from the discrete state space as candidate state nodes. The number of n can be 4, 5, 6, etc., and the specific value of n is not limited in this application.

[0075] In some embodiments, since the discrete state space includes five dimensions, the state nodes closest to the starting point in the discrete state space are selected in order of charge state, battery cell first temperature, battery cell second temperature, water cooling first temperature and water cooling second temperature as candidate state nodes.

[0076] For example, the five state nodes closest to the starting point are: the points whose Euler distance from the starting point in the discrete state space is 1. If there are more than five candidate state nodes, these five points are all candidate state nodes. If there are less than five candidate state nodes, the candidate state nodes are selected in the order of state of charge, first battery cell temperature, second battery cell temperature, first water cooling temperature, and second water cooling temperature. If there are more than five candidate state nodes, first take the five points whose Euler distance from the starting point is 1 as candidate state nodes, and then select the five state nodes (with the Euler distance from the starting point) that are the second closest to the starting point in the order of state of charge, first battery cell temperature, second battery cell temperature, first water cooling temperature, and second water cooling temperature. Select the candidate state node from the five points.

[0077] As an optional implementation manner, determining a plurality of candidate status nodes corresponding to the (i-1)th intermediate charging node in step S2 may also include:

[0078] When i is 2 to M, the candidate state node is determined from the discrete state space according to the target distance; wherein the target distance is determined according to the maximum charging current of the i-1th intermediate charging node and the charging time from the starting point to the i-1th intermediate charging node.

[0079] In this embodiment, when i is 2 to M, the maximum charging current of the i-1th intermediate charging node is first determined according to the maximum charging current table. The maximum charging current table represents the correspondence between the state parameter and the maximum charging current, that is, the maximum charging current that the battery can support under a certain state of charge and temperature parameter. The maximum charging current of the i-1th intermediate charging node is multiplied by the charging time from the starting point to the i-1th intermediate charging node to obtain the target distance.

[0080] In some embodiments, a point in the discrete state space that is a target distance away from the starting point is used as a candidate state node for the (i-1)th intermediate charging node.

[0081] In other embodiments, a quantity threshold is set, and if the number of points in the discrete state space that are at a target distance from the starting point is less than the quantity threshold, all points in the discrete state space that are at a target distance from the starting point are used as candidate state nodes for the i-1th intermediate charging node. If the number of points in the discrete state space that are at a target distance from the starting point is greater than the quantity threshold R, R points are selected from the points in the discrete state space that are at a target distance from the starting point in the order of state of charge, first battery cell temperature, second battery cell temperature, first water cooling temperature, and second water cooling temperature as candidate state nodes for the i-1th intermediate charging node.

[0082] As an optional implementation, Figure 3 As shown, determining the i-th intermediate charging node in step S2 may include:

[0083] A1: According to the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node, a charging current sequence corresponding to charging the battery from the i-1th intermediate charging node to each candidate state node is obtained.

[0084] A2: Determine the charging time corresponding to charging the battery from the i-1th intermediate charging node to each candidate state node according to the charging current sequence.

[0085] A3: Determine the i-th intermediate charging node according to the charging time corresponding to charging from the i-1-th intermediate charging node to each candidate state node.

[0086] In this embodiment, the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of the candidate state nodes corresponding to each i-1th intermediate charging node are used as state quantities and input into the MPC controller, and the rolling optimization solution is performed by the MPC controller to determine the charging current sequence corresponding to the candidate state nodes corresponding to each i-1th intermediate charging node when the battery is charged from the i-1th intermediate charging node to each i-1th intermediate charging node. After determining the charging current sequence, the charging time required for the battery to charge from the i-1th intermediate charging node to each i-1th intermediate charging node can be calculated based on the SOC corresponding to the i-1th intermediate charging node and the SOC corresponding to the candidate state node, and then the i-th intermediate charging node is determined based on the charging time corresponding to the i-1th intermediate charging node to each candidate state node.

[0087] As an optional implementation, the above step A2 may include:

[0088] For each candidate state node corresponding to the i-1th intermediate charging node, a prediction model is determined according to the target state of charge and target temperature parameters and the state transfer equation of the i-1th intermediate charging node;

[0089] Determine an optimization relationship and constraint conditions according to the prediction model, the target state of charge of the candidate state node, and the target temperature parameter;

[0090] The charging current sequence is determined by solving the optimization relationship and the constraint conditions; wherein the charging current sequence includes multiple charging currents, and the battery includes multiple charging states in the process of charging from the i-1th intermediate charging node to the candidate state node, and each charging current represents the charging current used in the process of charging the battery from one charging state to the next charging state.

[0091] In this implementation, in the process of using the MPC controller to solve the charging current sequence, it is necessary to determine the state transfer equation and the prediction model. The state transfer equation and the prediction model are described below.

[0092] The state transfer equation characterizes the linear change relationship satisfied by the battery when charging from the i-1th intermediate charging node to the candidate state node under the stimulation of the charging current and under the conditions of the target state of charge and target temperature parameters of the i-1th intermediate charging node.

[0093] The state transfer equation is the linear relationship between two state nodes in the discrete state space. The state transfer equation is:

[0094] .

[0095] in, represents the target state of charge and target temperature parameters of the i-1th intermediate charging node, represents the target state of charge and target temperature parameters of the candidate node corresponding to the i-th intermediate charging node, is the charging current. , , is the coefficient in the state transfer equation.

[0096] The relationship between any two state nodes in the discrete state space can be determined by the above state transfer equation. The target state of charge and target temperature parameters of each state node in the discrete state space are processed by the system identification method to determine the state transfer equation, that is, to determine the coefficients in the state transfer equation. , , .

[0097] The prediction model represents the linear change relationship satisfied by the battery when it is charged from the i-1th intermediate charging node to the charging state corresponding to the prediction time domain under the stimulation of the charging current. The prediction time domain represents the time step of the predicted charging current.

[0098] When determining the charging current corresponding to the candidate state node corresponding to each i-1th intermediate charging node when the battery is charged from the i-1th intermediate charging node, according to the state transfer equation corresponding to the candidate state node when the i-1th intermediate charging node is charged , , Determining Linear Simulation Model Parameters , , , thus building a prediction model.

[0099] The target state of charge and target temperature parameters of the i-1th intermediate charging node are defined as the state quantities of the system, expressed as: , where k represents the kth moment, which is the moment when the battery is charged from the i-1th intermediate charging node. is the first temperature of the battery cell at the i-1th intermediate charging node, is the second temperature of the battery cell at the i-1th intermediate charging node, is the first water cooling temperature of the i-1th intermediate charging node, is the water cooling second temperature of the i-1th intermediate charging node, is the target state of charge of the i-1th intermediate charging node. The charging current of the battery from the i-1th intermediate charging node to the i-th intermediate charging node For control input , According to the state transfer equation, the system is constructed and recursively extended to the maximum prediction time domain at time k+1 The prediction model of , and the prediction model is:

[0100] .

[0101] in, Characterize the target state of charge and target temperature parameters of the charging state corresponding to the prediction time domain, , , The coefficient that characterizes the linear change relationship satisfied during the process of charging the battery from the i-1th intermediate charging node to the charging state corresponding to the predicted time domain. In the above formula, , , , and They are all matrices, and the specific forms are as follows:

[0102]

[0103] in, is the identity matrix, The prediction time domain represents the time step set in the process of MPC model predicting charging current. Control domain,The control domain represents the time range in which the control algorithm in the MPC model operates.

[0104] After the prediction model is determined, the prediction model, the target state of charge of the candidate state node, and the target temperature parameters determine the optimization relationship and constraint conditions.

[0105] The optimization relationship represents the difference between the target state of charge and target temperature parameters of the charging state corresponding to the predicted time domain and the target state of charge and target temperature parameters of the candidate state node under the condition that the constraint conditions are met.

[0106] The constraints include: each charging current in the charging current sequence is less than the maximum charging current supported by the battery at the i-1 intermediate charging nodes; the rate of change of two adjacent charging currents in the charging current sequence is less than a preset threshold; the initial state of the optimization relationship is the target state of charge and target temperature parameters of the i-1th intermediate charging node, and the final state of the optimization relationship is the target state of charge and target temperature parameters of the candidate state node; the charging state corresponding to the prediction time domain is within the charging state range applicable to the state transfer equation.

[0107] In some embodiments, the specific forms of the optimization relationship and constraint conditions are as follows:

[0108] .

[0109] The constraints are:

[0110] .

[0111] in, represents the predicted state quantity, Represents the prediction input.

[0112] Represents the target state, which is determined based on the target state of charge and target temperature parameters of the candidate state node.

[0113] In some embodiments,

[0114] , where, in the target state R, The number and prediction time domain same.

[0115] is the prediction weight, represents the weight of the predicted state, The weight representing the final state, Represents the weight of the input. represents the maximum charging current supported by the battery at the i-1th intermediate charging node, Indicates the maximum charging current change rate supported by the battery at the i-1th intermediate charging node, represents the initial and terminal constraints of the state quantity, Indicates the upper and lower boundary values ​​of the charge state range to which the state transfer equation is applicable.

[0116] The charging current sequence includes multiple charging currents. The process of charging the battery from the i-1th intermediate charging node to the candidate state node includes multiple charging states. At each sampling time k, the optimization relationship is solved by rolling optimization in the prediction time domain Np to obtain the optimal control sequence. The first item of the optimal control sequence is used as the charging current used in the process of charging the battery from one charging state to the next charging state. The first item of the obtained optimal control sequence is used as the input of the next sampling time k+1, and the optimization relationship is solved again by rolling optimization in the prediction time domain Np until the target state of charge and target temperature parameters of the candidate state node are reached in the linear region, thereby obtaining the charging current sequence.

[0117] In the above step A2, after determining the charging current sequence, according to the formula:

[0118]

[0119] Determine the charging time for the battery to charge from the i-1th intermediate charging node to the candidate state node. represents the SOC corresponding to the jth state of charge, represents the charging current used to charge from the jth charging state to the j+1th charging state, and N represents the number of charging states of the battery during the process of charging from the i-1th intermediate charging node to the candidate state node.

[0120] In the above step A3, the charging time of the battery from the i-1th intermediate charging node to each candidate state node is determined through step A2, and the i-th intermediate charging node is determined from the candidate state nodes according to the charging time.

[0121] In some implementations, the charging time of the battery from the i-1th intermediate charging node to each candidate state node is compared, and the candidate state node with the shortest charging time is used as the i-th intermediate charging node.

[0122] In another embodiment, the above step A3 may include:

[0123] For each candidate state node, a first charging time from a starting point to the candidate state node is determined according to a charging time from an i-1th intermediate charging node to the candidate state node; a second charging time from the candidate state node to an end point is determined according to a maximum charging current corresponding to the candidate state node; wherein the maximum charging current corresponding to the candidate state node represents a maximum charging current supported by the battery when the battery is in the candidate state node; and an i-th intermediate charging node is determined from each candidate state node according to the first charging time and the second charging time.

[0124] In this embodiment, in order to reduce the charging time of the battery, the charging time of the battery from the starting point to the candidate state node of the i-1th intermediate charging node is used as the first charging time. Since the subsequent charging intermediate charging node of the battery has not yet been determined, it is assumed that the battery is subsequently charged with the maximum charging current supported by the candidate state node of the i-1th intermediate charging node, thereby determining the second charging time. The i-th intermediate charging node is determined from each candidate state node in combination with the first charging time and the second charging time.

[0125] In some implementations, the sum of the charging time from the starting point to the (i-1)th intermediate charging node and the charging time from the (i-1)th intermediate charging node to the candidate state node is taken as the first charging time.

[0126] In some embodiments, the ratio of the state of charge difference to the maximum charging current corresponding to the candidate state node is taken as the second charging time; wherein the state of charge difference is the difference between the state of charge at the endpoint and the target state of charge of the candidate state node.

[0127] The second charging current can be determined by the following formula:

[0128]

[0129] in, is the charge state at the middle endpoint, is the target state of charge of the candidate state node, is the maximum charging current corresponding to the candidate state node. The maximum charging current supported by the candidate state node can be determined by the maximum charging current table.

[0130] The maximum charging current table represents the maximum charging current supported by each state node in the discrete state space. The maximum charging current table can be determined in the following way.

[0131] According to the thermal performance simulation model of the lithium battery, the battery charging lithium deposition window and thermal balance window are obtained, and the initial charging current I, the first battery cell temperature Tmax, the second battery cell temperature Tmin, the first water cooling temperature TLmax, the second water cooling temperature TLmin and the initial SOC are set, and the maximum charging current table is obtained by substituting them into the simulation model.

[0132] In some implementations, determining the i-th intermediate charging node from each candidate state node according to the first charging time and the second charging time includes:

[0133] The time coefficient corresponding to the candidate state node is determined according to the first charging time and the second charging time; and the candidate state node whose time coefficient meets the preset condition among the candidate state nodes is taken as the i-th intermediate charging node.

[0134] In this embodiment, there are multiple ways to determine the time coefficient.

[0135] In some embodiments, the sum of the first charging time and the second charging time is used as the time coefficient. The time coefficient can be expressed as:

[0136] .

[0137] in, is the time coefficient of the nth candidate state node, For the first charging time, It is the second charging time.

[0138] In some other embodiments, the sum of the product of the first charging time and the first weight and the product of the second charging time and the first weight is used as the time coefficient.

[0139] The time coefficient can be expressed as:

[0140] .

[0141] in, is the time coefficient of the nth candidate state node, For the first charging time, is the first weight, For the second charging time, is the second weight.

[0142] The candidate state node that meets the preset condition is the candidate state node with the smallest time coefficient among the time coefficients of the candidate state nodes.

[0143] As an optional implementation manner, the type of the starting temperature parameter and the type of the target temperature parameter both include a first battery cell temperature, a second battery cell temperature, a first water cooling temperature, and a second water cooling temperature.

[0144] In this embodiment, when determining the charging strategy, it is necessary to consider the impact of the accumulated heat of the battery on the charging performance. Therefore, it is necessary to input the temperature of the battery at each state node in the model predictive control algorithm. During the actual operation of the battery, the internal core temperature of the battery is difficult to measure directly. Therefore, multiple temperature sensors are set around the battery to measure the temperature of different positions of the battery cell, and the highest temperature is used as the first temperature of the battery cell, and the lowest temperature is used as the second temperature of the battery cell. Multiple temperature sensors are also set in the battery water cooling system to measure the temperature of different positions of the water cooling system, and the highest temperature is used as the first water cooling temperature, and the lowest temperature is used as the second water cooling temperature. The above 4 temperature types are used as the input of the model predictive control algorithm to simulate the internal core temperature of the battery. The battery is charged according to the charging path determined by the model predictive control algorithm, which can improve the safety of the charging process and effectively protect the service life and capacity health of the battery.

[0145] The present application also provides a charging strategy determination device. Figure 4 , Figure 4 A structural block diagram of a charging strategy determination device provided in an embodiment of the present application, the charging strategy determination device 200 includes: an acquisition module 201, used to obtain the charge state and temperature parameters of the starting point of the charging path of the battery, and the charge state of the end point of the charging path; a charging path determination module 202, used to start from i=1, and cyclically execute the following steps until the target charge state of the i-th intermediate charging node is the same as the charge state of the end point; determine multiple candidate state nodes corresponding to the i-1th intermediate charging node according to the target charge state, target temperature parameters and a preset selection algorithm of the i-1th intermediate charging node; In the example, when i is 1, the i-1th intermediate charging node is the starting point; according to the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node, the i-th intermediate charging node and the charging current sequence corresponding to the battery charging from the i-1th intermediate charging node to the i-th intermediate charging node are determined; a charging strategy determination module 203 is used to determine the charging strategy according to the charging current sequence corresponding to the battery charging from the i-1th intermediate charging node in the charging path to the i-th intermediate charging node.

[0146] In an optional embodiment, the charging path determination module 202 is specifically used to determine n points in the discrete state space that meet a preset distance from the starting point as the candidate state nodes when i is 1; wherein the dimension of the discrete state space corresponds to the charge state and temperature parameters, and the starting point and the candidate state nodes are points in the discrete state space.

[0147] In an optional embodiment, the charging path determination module 202 is specifically used to determine the candidate state node from the discrete state space according to the target distance when i is greater than 1; wherein the target distance is determined according to the maximum charging current of the i-1th intermediate charging node and the charging time taken from the starting point to the i-1th intermediate charging node, and the maximum charging current of the i-1th intermediate charging node represents the maximum charging current supported when the battery state is at the i-1th intermediate charging node.

[0148] In an optional embodiment, the charging path determination module 202 is specifically used to obtain a charging current sequence corresponding to the battery charging from the i-1th intermediate charging node to each candidate state node according to the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node; determine the charging time corresponding to the battery charging from the i-1th intermediate charging node to each candidate state node according to the charging current sequence; and determine the i-th intermediate charging node according to the charging time corresponding to the battery charging from the i-1th intermediate charging node to each candidate state node.

[0149] In an optional embodiment, the charging path determination module 202 is specifically used to determine a prediction model for each candidate state node corresponding to the i-1th intermediate charging node according to the target state of charge and target temperature parameters of the i-1th intermediate charging node and the state transfer equation; determine an optimization relationship and constraints according to the prediction model, the target state of charge and target temperature parameters of the candidate state node; and determine a charging current sequence by solving the optimization relationship and constraints; wherein the charging current sequence includes multiple charging currents, and the battery includes multiple charging states in the process of charging from the i-1th intermediate charging node to the candidate state node, and each of the charging currents represents the charging current used by the battery in the process of charging from one charging state to the next charging state.

[0150] In an optional embodiment, the state transfer equation characterizes the linear change relationship satisfied by the battery when charging from the i-1th intermediate charging node to the candidate state node under the stimulation of the charging current and under the conditions of the target state of charge and target temperature parameters of the i-1th intermediate charging node.

[0151] In an optional embodiment, the prediction model represents the linear change relationship satisfied by the battery when it is charged from the i-1th intermediate charging node to the charging state corresponding to the prediction time domain under the stimulation of the charging current, and the prediction time domain represents the time step of the predicted charging current.

[0152] In an optional embodiment, the optimization relationship represents the difference between the target state of charge and target temperature parameters of the charging state corresponding to the predicted time domain and the target state of charge and target temperature parameters of the candidate state node while satisfying the constraint conditions.

[0153] In an optional embodiment, the constraints include: each charging current in the charging current sequence is less than the maximum charging current supported by the battery at the i-1 intermediate charging node; the rate of change of two adjacent charging currents in the charging current sequence is less than a preset threshold; the initial state of the optimization relationship is the target state of charge and target temperature parameters of the i-1th intermediate charging node, and the final state of the optimization relationship is the target state of charge and target temperature parameters of the candidate state node; the charging state corresponding to the predicted time domain is within the charging state range to which the state transfer equation is applicable.

[0154] In an optional embodiment, the charging path determination module 202 is specifically used to determine, for each of the candidate state nodes, a first charging time from the starting point to the candidate state node based on the charging time corresponding to the i-1th intermediate charging node to the candidate state node; and to determine a second charging time from the candidate state node to the end point based on the maximum charging current corresponding to the candidate state node; wherein the maximum charging current corresponding to the candidate state node represents the maximum charging current supported when the battery is in the candidate state node; and to determine the i-th intermediate charging node from each of the candidate state nodes based on the first charging time and the second charging time.

[0155] In an optional implementation, the charging path determination module 202 is specifically configured to take the sum of the charging time from the starting point to the i-1th intermediate charging node and the charging time from the i-1th intermediate charging node to the candidate state node as the first charging time.

[0156] In an optional embodiment, the charging path determination module 202 is specifically used to convert the ratio of the state of charge difference to the maximum charging current corresponding to the candidate state node into the second charging time; wherein the state of charge difference is the difference between the state of charge of the endpoint and the target state of charge of the candidate state node.

[0157] In an optional embodiment, the charging path determination module 202 is specifically used to determine the time coefficient corresponding to the candidate state node according to the first charging time and the second charging time; and take the candidate state node whose time coefficient meets the preset conditions among the candidate state nodes as the i-th intermediate charging node.

[0158] In an optional implementation manner, the charging path determination module 202 is specifically configured to use the sum of the first charging time and the second charging time as the time coefficient.

[0159] In an optional embodiment, the type of the starting temperature parameter and the type of the target temperature parameter both include a first battery cell temperature, a second battery cell temperature, a first water cooling temperature, and a second water cooling temperature.

[0160] See also Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device of an embodiment of the present application. The electronic device 300 includes: at least one processor 301, at least one communication interface 302, at least one memory 303 and at least one bus 304. Among them, the bus 304 is used to realize the direct connection and communication of these components, the communication interface 302 is used to communicate signaling or data with other node devices, and the memory 303 stores machine-readable instructions executable by the processor 301. When the electronic device 300 is running, the processor 301 communicates with the memory 303 through the bus 304, and the machine-readable instructions are called by the processor 301 to execute the charging strategy determination method as described above.

[0161] The processor 301 may be an integrated circuit chip with signal processing capabilities. The processor 301 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. It may implement or execute various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0162] The memory 303 may include but is not limited to random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), etc.

[0163] Understandably, Figure 5 The structure shown is for illustration only. The electronic device 300 may also include Figure 5 More or fewer components as shown, or with Figure 5 Different configurations are shown. Figure 5 Each component shown in can be implemented by hardware, software or a combination thereof. In the embodiment of the present application, the electronic device 300 can be a device for controlling the voltage output by the DC energy storage valve, which can be a physical device such as a desktop computer, a laptop computer, a smart phone, a smart wearable device, a vehicle-mounted device, or a virtual device such as a virtual machine. In addition, the electronic device 300 is not necessarily a single device, but can also be a combination of multiple devices, such as a server cluster, etc.

[0164] In addition, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a computer, the steps of the charging strategy determination method in the above embodiment are executed.

[0165] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0166] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0168] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0169] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A charging strategy determination method, characterized in that: include: Acquire the state of charge and temperature parameters of the starting point of the charging path of the battery and the state of charge at the end point of the charging path; Starting from i being 1, the following steps are executed in a loop until the target state of charge of the i-th intermediate charging node is the same as the state of charge of the endpoint; Determine a plurality of candidate state nodes corresponding to the i-1th intermediate charging node according to the target state of charge, the target temperature parameter and the preset selection algorithm of the i-1th intermediate charging node; wherein when i is 1, the i-1th intermediate charging node is the starting point; Determine the i-th intermediate charging node and the charging current sequence corresponding to charging the battery from the i-th intermediate charging node to the i-th intermediate charging node according to the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node; Determine a charging strategy according to a charging current sequence corresponding to charging the battery from the i-1th intermediate charging node in the charging path to the i-th intermediate charging node; The step of determining the i-th intermediate charging node according to the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node includes: For each candidate state node corresponding to the i-1th intermediate charging node, determine a prediction model according to a target state of charge and a target temperature parameter and a state transfer equation of the i-1th intermediate charging node; Determine an optimization relationship and constraint conditions according to the prediction model, the target state of charge of the candidate state node, and the target temperature parameter; Determine a charging current sequence by solving the optimization relationship and constraint conditions; wherein the charging current sequence includes multiple charging currents, the battery includes multiple charging states in the process of charging from the i-1th intermediate charging node to the candidate state node, and each charging current represents the charging current used by the battery in the process of charging from one charging state to the next charging state; The i-th intermediate charging node is determined according to a charging current sequence corresponding to charging the battery from the i-1-th intermediate charging node to each of the candidate state nodes.

2. The charging strategy determination method according to claim 1, characterized in that: The determining, according to the target state of charge, target temperature parameter and preset selection algorithm of the i-1th intermediate charging node, a plurality of candidate state nodes corresponding to the i-1th intermediate charging node comprises: When i is 1, n points in the discrete state space that meet a preset distance from the starting point are determined as the candidate state nodes; wherein the dimension of the discrete state space corresponds to the state of charge and temperature parameters, and the starting point and the candidate state nodes are points in the discrete state space.

3. The charging strategy determination method according to claim 1, characterized in that: The method of determining a plurality of candidate state nodes corresponding to the i-1th intermediate charging node according to the target state of charge, the target temperature parameter and the preset selection algorithm of the i-1th intermediate charging node further includes: When i is greater than 1, the candidate state node is determined from the discrete state space according to the target distance; wherein the target distance is determined according to the maximum charging current of the i-1th intermediate charging node and the charging time taken from the starting point to the i-1th intermediate charging node, and the maximum charging current of the i-1th intermediate charging node represents the maximum charging current supported when the battery state is at the i-1th intermediate charging node.

4. The charging strategy determination method according to claim 1, characterized in that: The determining the i-th intermediate charging node according to the charging current sequence corresponding to the battery being charged from the i-1-th intermediate charging node to each of the candidate state nodes comprises: Determine, according to the charging current sequence, the charging time corresponding to charging the battery from the (i-1)th intermediate charging node to each of the candidate state nodes; The i-th intermediate charging node is determined according to the charging time corresponding to charging from the i-1-th intermediate charging node to each of the candidate state nodes.

5. The charging strategy determination method according to claim 1, characterized in that: The state transfer equation characterizes the linear change relationship satisfied by the battery when charging from the i-1th intermediate charging node to the candidate state node under the stimulation of the charging current and under the conditions of the target state of charge and target temperature parameters of the i-1th intermediate charging node.

6. The charging strategy determination method according to claim 1, characterized in that: The prediction model represents the linear variation relationship satisfied by the battery when it is charged from the i-1th intermediate charging node to the charging state corresponding to the prediction time domain under the stimulation of the charging current. The prediction time domain represents the time step of the predicted charging current.

7. The charging strategy determination method according to claim 1, characterized in that: The optimization relational expression represents the difference between the target state of charge and target temperature parameters of the charging state corresponding to the prediction time domain and the target state of charge and target temperature parameters of the candidate state node when the constraint condition is satisfied.

8. The charging strategy determination method according to claim 7, characterized in that: The constraints include: Each charging current in the charging current sequence is less than a maximum charging current supported by the battery at the i-1 intermediate charging nodes; The change rate of two adjacent charging currents in the charging current sequence is less than a preset threshold; The initial state of the optimization relation is the target state of charge and target temperature parameters of the i-1th intermediate charging node, and the final state of the optimization relation is the target state of charge and target temperature parameters of the candidate state node; The charging state corresponding to the prediction time domain is within the charging state range to which the state transfer equation is applicable.

9. The charging strategy determination method according to claim 4, characterized in that: The determining the i-th intermediate charging node according to the charging time corresponding to charging from the i-1th intermediate charging node to each of the candidate state nodes includes: For each of the candidate state nodes, determine a first charging time from the starting point to the candidate state node according to the charging time corresponding to the i-1th intermediate charging node to the candidate state node; Determine a second charging time from the candidate state node to the end point according to the maximum charging current corresponding to the candidate state node; wherein the maximum charging current corresponding to the candidate state node represents the maximum charging current supported when the battery is in the candidate state node; The i-th intermediate charging node is determined from each of the candidate status nodes according to the first charging time and the second charging time.

10. The charging strategy determination method according to claim 9, characterized in that: The determining a first charging time from the starting point to the candidate state node according to the charging time corresponding to the i-1th intermediate charging node to the candidate state node includes: The sum of the charging time from the starting point to the (i-1)th intermediate charging node and the charging time from the (i-1)th intermediate charging node to the candidate state node is taken as the first charging time.

11. The charging strategy determination method according to claim 9, characterized in that: The determining, according to the maximum charging current corresponding to the candidate state node, a second charging time for charging from the candidate state node to the end point includes: The ratio of the state of charge difference to the maximum charging current corresponding to the candidate state node is used as the second charging time; wherein the state of charge difference is the difference between the state of charge of the endpoint and the target state of charge of the candidate state node.

12. The charging strategy determination method according to claim 9, characterized in that: The determining the i-th intermediate charging node from each of the candidate state nodes according to the first charging time and the second charging time includes: Determine a time coefficient corresponding to the candidate state node according to the first charging time and the second charging time; The candidate state nodes whose time coefficients satisfy the preset conditions among the candidate state nodes are taken as the i-th intermediate charging nodes.

13. The charging strategy determination method according to claim 12, characterized in that: The determining the time coefficient corresponding to the candidate state node according to the first charging time and the second charging time includes: The sum of the first charging time and the second charging time is used as the time coefficient.

14. The charging strategy determination method according to any one of claims 1 to 13, characterized in that: The type of the temperature parameter of the starting point and the type of the target temperature parameter both include a first battery cell temperature, a second battery cell temperature, a first water cooling temperature, and a second water cooling temperature.

15. A charging strategy determination device, characterized in that: include: An acquisition module, used to acquire the state of charge and temperature parameters of the starting point of the charging path of the battery and the state of charge at the end point of the charging path; A charging path determination module, configured to start from i being 1 and cyclically execute the following steps until the target state of charge of the i-th intermediate charging node is the same as the state of charge of the end point; Determine a plurality of candidate state nodes corresponding to the i-1th intermediate charging node according to the target state of charge, the target temperature parameter and the preset selection algorithm of the i-1th intermediate charging node; wherein when i is 1, the i-1th intermediate charging node is the starting point; Determine the i-th intermediate charging node and the charging current sequence corresponding to charging the battery from the i-th intermediate charging node to the i-th intermediate charging node according to the target state of charge and target temperature parameters of the i-1th intermediate charging node and the target state of charge and target temperature parameters of each candidate state node corresponding to the i-1th intermediate charging node; A charging strategy determination module, used to determine a charging strategy according to a charging current sequence corresponding to charging the battery from the i-1th intermediate charging node in the charging path to the i-th intermediate charging node; The charging path determination module is specifically used to determine a prediction model for each candidate state node corresponding to the i-1th intermediate charging node according to the target state of charge and target temperature parameters of the i-1th intermediate charging node and the state transfer equation; determine an optimization relationship and constraints according to the prediction model, the target state of charge and the target temperature parameters of the candidate state node; and determine a charging current sequence by solving the optimization relationship and the constraints; wherein the charging current sequence includes multiple charging currents, and the battery includes multiple charging states during the process of charging from the i-1th intermediate charging node to the candidate state node, and each of the charging currents represents the charging current used by the battery during the process of charging from one charging state to the next charging state; the i-th intermediate charging node is determined according to the charging current sequence corresponding to the battery charging from the i-1th intermediate charging node to each of the candidate state nodes.

16. An electronic device, characterized in that: include: processor, memory, and bus; The processor and the memory communicate with each other via the bus; The memory stores program instructions executable by the processor, and the processor can execute the method according to any one of claims 1 to 14 by calling the program instructions.

17. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are read and executed by a computer, the method according to any one of claims 1 to 14 is executed.

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