Method, apparatus, cloud data platform, and vehicle for prolonging battery life

Abstract

The application discloses a method and device for prolonging battery life, a cloud data platform and a vehicle, and relates to the technical field of electric vehicles. The method comprises the following steps: acquiring the actual health state and actual aging rate of a battery, and the health state threshold and aging rate threshold corresponding to the cumulative mileage data of the vehicle and the cumulative charge-discharge capacity of the battery; determining the life-prolonging grade of the battery according to the actual aging rate, actual health state, health state threshold and aging rate threshold of the battery; acquiring a target fast-charging optimization map according to the life-prolonging grade and the available charging duration determined based on user habits; wherein the predicted aging track corresponding to the target fast-charging optimization map satisfies a preset condition; and sending a first message to the vehicle according to the target fast-charging optimization map, wherein the first message is used to instruct the vehicle to charge the battery according to the target fast-charging optimization map. The scheme of the application reduces the aging rate of the battery by adjusting the charging map.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicles, in particular to a method and device for prolonging battery life, a cloud data platform and a vehicle. BACKGROUND

[0002] In the use process of lithium-ion power batteries, some irreversible damage often occurs, which causes the lithium-ion power batteries to age to varying degrees. The direct impact of lithium-ion power battery aging is the capacity attenuation and power reduction of the battery, and even the risk of thermal runaway, which directly relates to the endurance, power and safety of electric vehicles. Therefore, in order to ensure the long-term safe and effective operation of lithium-ion power batteries and electric vehicles, improve the economic benefits of lithium-ion power batteries, and slow down the aging rate of lithium-ion power batteries, it is particularly important to prolong the battery life. SUMMARY

[0003] The present application aims to provide a method and device for prolonging battery life, a cloud data platform and a vehicle, thereby solving the problem of fast battery aging rate in the prior art.

[0004] In order to achieve the above-mentioned purpose, the present application provides a method for prolonging battery life, applied to a cloud data platform, comprising:

[0005] obtaining the actual health state, the actual aging rate of the battery, the cumulative mileage data of the vehicle, and the health state threshold and the aging rate threshold corresponding to the cumulative charge and discharge capacity of the battery;

[0006] determining the life prolonging grade of the battery according to the actual aging rate, the actual health state, the health state threshold and the aging rate threshold of the battery;

[0007] obtaining a target fast charging optimization map according to the life prolonging grade and the available charging duration determined based on user habits, wherein the predicted aging trajectory corresponding to the target fast charging optimization map meets a preset condition;

[0008] sending a first message to the vehicle according to the target fast charging optimization map, the first message being used to instruct the vehicle to charge the battery according to the target fast charging optimization map.

[0009] Optionally, determining the life prolonging grade of the battery according to the actual aging rate, the actual health state, the health state threshold and the aging rate threshold of the battery comprises:

[0010] calculating a first difference value between the actual health state and the health state threshold;

[0011] calculating a second difference value between the actual aging rate and the aging rate threshold;

[0012] determine the life extension level according to the first difference and / or the second difference.

[0013] Optionally, determining the life extension level according to the first difference and / or the second difference comprises at least one of the following:

[0014] when the first difference is greater than or equal to a first threshold value and the second difference is greater than or equal to a second threshold value, determining the life extension level as level 0;

[0015] when the first difference is less than the first threshold value and greater than or equal to a third threshold value, or the second difference is less than the second threshold value and greater than or equal to a fourth threshold value, determining the life extension level as level 1;

[0016] when the first difference is less than the third threshold value and greater than or equal to a fifth threshold value, or the second difference is less than the fourth threshold value and greater than or equal to a sixth threshold value, determining the life extension level as level 2;

[0017] when the first difference is less than the fifth threshold value and greater than or equal to a seventh threshold value, or the second difference is less than the sixth threshold value and greater than or equal to an eighth threshold value, determining the life extension level as level 3;

[0018] when the first difference is less than the seventh threshold value and greater than or equal to a ninth threshold value, or the second difference is less than the eighth threshold value and greater than or equal to a ninth threshold value, determining the life extension level as level 4;

[0019] wherein the life extension range of the battery increases with the increase of the life extension level.

[0020] Optionally, determining the life extension level according to the first difference and / or the second difference comprises:

[0021] when two life extension levels are determined according to the first difference and the second difference, determining the one with higher level as the life extension level.

[0022] Optionally, according to the life extension level and the available charging duration determined based on the user habit, obtaining a target fast charging optimization map comprises:

[0023] inputting the life extension level and the available charging duration into a battery life extension model to output an initial fast charging optimization map;

[0024] determining a predicted aging track corresponding to the initial fast charging optimization map;

[0025] determining the initial fast-charging optimization map as the target fast-charging optimization map when the predicted aging trajectory corresponding to the initial fast-charging optimization map meets the preset condition;

[0026] updating the initial fast-charging optimization map iteratively when the predicted aging trajectory corresponding to the initial fast-charging optimization map does not meet the preset condition, wherein the aging rate deviation amount is a deviation amount determined according to the predicted aging trajectory and a nominal aging trajectory under the cumulative mileage data and the cumulative charging and discharging capacity.

[0027] Optionally, determining the predicted aging trajectory corresponding to the initial fast-charging optimization map comprises:

[0028] obtaining a vehicle operating condition;

[0029] generating a charging and discharging driving condition of the vehicle according to the initial fast-charging optimization map and the vehicle operating condition;

[0030] inputting the charging and discharging driving condition into a battery life prediction simulation model to output the predicted aging trajectory corresponding to the initial fast-charging optimization map.

[0031] Optionally, the preset condition is that the life extension level of the battery is reduced by at least one level within a preset mileage or within a preset charging and discharging capacity.

[0032] Optionally, the first message further comprises a trigger scenario.

[0033] In a second aspect, in order to achieve the above object, the embodiments of the present application further provide a method for prolonging battery life, applied to a vehicle, comprising:

[0034] receiving a first message sent by a cloud data platform, wherein the first message comprises a target fast-charging optimization map;

[0035] charging the battery according to the target fast-charging optimization map.

[0036] Optionally, the first message further comprises a trigger scenario.

[0037] Optionally, charging the battery according to the target fast-charging optimization map comprises:

[0038] charging the battery according to the target fast-charging optimization map in a case where a scenario in which the vehicle is located is the trigger scenario.

[0039] In a third aspect, in order to achieve the above object, the embodiments of the present application further provide a device for prolonging battery life, applied to a cloud data platform, comprising:

[0040] a first obtaining module, configured to obtain an actual health state, an actual aging rate of a battery, cumulative mileage data of a vehicle, and a health state threshold and an aging rate threshold corresponding to cumulative charge-discharge capacity of the battery;

[0041] a determining module, configured to determine a service life extension level of the battery according to the actual aging rate, the actual health state of the battery, the health state threshold, and the aging rate threshold;

[0042] a second obtaining module, configured to obtain a target fast-charging optimization map according to the service life extension level and an available charging duration determined based on user habits, wherein a predicted aging track corresponding to the target fast-charging optimization map meets a preset condition;

[0043] a sending module, configured to send a first message to the vehicle according to the target fast-charging optimization map, the first message being used to instruct the vehicle to charge the battery according to the target fast-charging optimization map.

[0044] In a fourth aspect, to achieve the above object, the embodiments of the present application further provide a device for prolonging a service life of a battery, applied to a vehicle, comprising:

[0045] a receiving module, configured to receive a first message sent by a cloud data platform, the first message comprising a target fast-charging optimization map;

[0046] a charging module, configured to charge the battery according to the target fast-charging optimization map.

[0047] In a fifth aspect, to achieve the above object, the embodiments of the present application further provide a cloud data platform, comprising a transceiver, a processor, a memory, and a program stored in the memory and executable on the processor; the processor implements the method for prolonging a service life of a battery according to the first aspect when executing the program.

[0048] In a sixth aspect, to achieve the above object, the embodiments of the present application further provide a vehicle, comprising a transceiver, a processor, a memory, and a program stored in the memory and executable on the processor; the processor implements the method for prolonging a service life of a battery according to the second aspect when executing the program.

[0049] In a seventh aspect, to achieve the above object, the embodiments of the present application further provide a readable storage medium, the readable storage medium storing a program, the program being executable on a processor to implement the method for prolonging a service life of a battery according to the first aspect, or to implement the method for prolonging a service life of a battery according to the second aspect.

[0050] The above technical solutions of the present application have at least the following beneficial effects:

[0051] The method for prolonging the battery life of the embodiment of the application first acquires, by a cloud data platform, an actual health state, an actual aging rate, cumulative mileage data of a vehicle, and a health state threshold and an aging rate threshold corresponding to cumulative charge and discharge capacity of the battery; second, determines a life extension grade of the battery according to the actual aging rate, the actual health state, the health state threshold, and the aging rate threshold of the battery; third, acquires a target fast charging optimization map according to the life extension grade and an available charging duration determined based on user habits; wherein a predicted aging trajectory corresponding to the target fast charging optimization map meets a preset condition; in this way, a fast charging optimization map that matches the state of the vehicle and the user habits is generated; finally, sends a first message to the vehicle according to the target fast charging optimization map, the first message being used to instruct the vehicle to charge the battery according to the target fast charging optimization map. In this way, the vehicle uses the target fast charging optimization map for fast charging, which can slow down the aging rate of the battery and prolong the life of the battery. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 One of the flowcharts of the method for prolonging the battery life of the embodiment of the application;

[0053] Figure 2 The second flowchart of the method for prolonging the battery life of the embodiment of the application;

[0054] Figure 3 The third flowchart of the method for prolonging the battery life of the embodiment of the application;

[0055] Figure 4 One of the structural diagrams of the device for prolonging the battery life of the embodiment of the application;

[0056] Figure 5 The second structural diagram of the device for prolonging the battery life of the embodiment of the application;

[0057] Figure 6 The structural diagram of the cloud server of the embodiment of the application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are some but not all of the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the application.

[0059] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein. In addition, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / ", generally indicates that the front and rear associated objects are in a "or" relationship.

[0060] The method, device, cloud data platform and vehicle for prolonging battery life provided by the embodiments of the present application will be described in detail below in conjunction with the drawings, specific embodiments and application scenarios.

[0061] As shown in Figure 1 is one of the flowcharts of the method for prolonging battery life of the embodiments of the present application, the method is applied to a cloud data platform, and the method comprises:

[0062] Step 101, obtaining the actual health state, actual aging rate of the battery, cumulative mileage data of the vehicle, health state threshold and aging rate threshold corresponding to the cumulative charge and discharge capacity of the battery;

[0063] In this step, the actual health state of the battery can be obtained based on the real vehicle running data of the vehicle; the actual aging rate can be obtained based on the average change rate of the battery health state in the last three months of vehicle operation; and the health state threshold and the aging rate threshold can be obtained based on the standard battery aging curve table.

[0064] Step 102, determining the life extension grade of the battery according to the actual aging rate, actual health state, health state threshold and aging rate threshold of the battery;

[0065] In this step, the life extension grade of the battery can also be referred to as the scheme grade of the battery life extension. This grade is related to the nominal condition corresponding to the actual condition of the battery and the current running data of the vehicle. The greater the difference between the two, the higher the life extension grade of the battery.

[0066] Step 103, obtaining a target fast charging optimization map according to the life extension grade and the available charging time determined based on the user habit; wherein the predicted aging trajectory corresponding to the target fast charging optimization map satisfies a preset condition;

[0067] In this step, the available charging time can be determined based on the user's historical charging habits, for example, the available charging time can be predicted according to the date (weekdays, weekends), time period (8:00~9:00, 15:00~17:00), vehicle location (home, company, shopping mall), etc.

[0068] At step 104, a first message is sent to the vehicle according to the target fast charging optimization map, and the first message is used to instruct the vehicle to charge the battery according to the target fast charging optimization map.

[0069] The method for prolonging the battery life of the embodiment of the present application first acquires, by the cloud data platform, the actual health state of the battery, the actual aging rate of the battery, the cumulative mileage data of the vehicle, and the health state threshold and the aging rate threshold corresponding to the cumulative charging and discharging capacity of the battery; secondly, the life-prolonging grade of the battery is determined according to the actual aging rate of the battery, the actual health state of the battery, the health state threshold, and the aging rate threshold; thirdly, the target fast charging optimization map is acquired according to the life-prolonging grade and the available charging duration determined based on the user habit; wherein the predicted aging track corresponding to the target fast charging optimization map satisfies a preset condition; in this way, the fast charging optimization map that matches the state of the vehicle and the user habit is generated; finally, a first message is sent to the vehicle according to the target fast charging optimization map, and the first message is used to instruct the vehicle to charge the battery according to the target fast charging optimization map. In this way, the vehicle uses the target fast charging optimization map for fast charging, which can slow down the aging rate of the battery and prolong the life of the battery.

[0070] As an optional implementation, at step 102, the life-prolonging grade of the battery is determined according to the actual aging rate of the battery, the actual health state of the battery, the health state threshold, and the aging rate threshold, including:

[0071] calculating a first difference value of the actual health state and the health state threshold;

[0072] calculating a second difference value of the actual aging rate and the aging rate threshold;

[0073] determining the life-prolonging grade according to the first difference value and / or the second difference value.

[0074] That is, the life-prolonging grade of the battery is related to the deviation of the actual health state of the battery and the nominal health state (the health state threshold), and the deviation of the actual aging rate of the battery and the nominal aging rate (the aging rate threshold). For example, the greater the deviation, the higher the life-prolonging grade, that is, the greater the deviation, the worse the health condition (the health state and / or the aging rate) of the battery, and the more the battery needs to be treated for life prolonging.

[0075] As a specific implementation, the life-prolonging grade is determined according to the first difference value and / or the second difference value, including at least one of the following:

[0076] when the first difference value is greater than or equal to a first threshold value and the second difference value is greater than or equal to a second threshold value, the life-prolonging grade is determined to be 0 grade; wherein when the life-prolonging grade is 0 grade, it indicates that the current state of the battery is good and does not need to be treated for life prolonging.

[0077] In a case that the first difference is less than the first threshold value and greater than or equal to a third threshold value, or the second difference is less than the second threshold value and greater than or equal to a fourth threshold value, the life extension level is determined to be level 1; at this time, the corresponding life extension amplitude is, for example, a first amplitude.

[0078] In a case that the first difference is less than the third threshold value and greater than or equal to a fifth threshold value, or the second difference is less than the fourth threshold value and greater than or equal to a sixth threshold value, the life extension level is determined to be level 2; at this time, the corresponding life extension amplitude is, for example, a second amplitude; specifically, the second amplitude is greater than the first amplitude.

[0079] In a case that the first difference is less than the fifth threshold value and greater than or equal to a seventh threshold value, or the second difference is less than the sixth threshold value and greater than or equal to an eighth threshold value, the life extension level is determined to be level 3; at this time, the corresponding life extension amplitude is, for example, a third amplitude; specifically, the third amplitude is greater than the second amplitude.

[0080] In a case that the first difference is less than the seventh threshold value and greater than or equal to a ninth threshold value, or the second difference is less than the eighth threshold value and greater than or equal to a ninth threshold value, the life extension level is determined to be level 4; at this time, the corresponding life extension amplitude is, for example, a fourth amplitude; specifically, the fourth amplitude is greater than the third amplitude.

[0081] Wherein, with the increase of the life extension level, the life extension amplitude of the battery increases.

[0082] In the specific implementation, the life extension level is determined according to the state of health and the aging rate of the battery, and the corresponding life extension amplitude is determined based on the life extension level, so that the corresponding life extension amplitude is determined according to the specific condition of the battery, and the targeted life extension of the battery is completed, thereby improving the service life of the battery.

[0083] In addition, as a specific implementation, the life extension level is determined according to the first difference and / or the second difference, including:

[0084] In a case that two life extension levels are determined according to the first difference and the second difference, the one with a higher level among the two life extension levels is determined as the life extension level.

[0085] For example, if the first difference is less than the third threshold value and greater than or equal to the fifth threshold value, the life extension level determined according to the first difference is level 2; if the second difference is less than the sixth threshold value and greater than or equal to the eighth threshold value, the life extension level determined according to the second difference is level 3, then the final life extension level of the battery is determined to be level 3.

[0086] Of course, in a case that two life extension levels are determined according to the first difference and the second difference, the final life extension level of the battery can also be determined according to the actual situation, and in the foregoing example, the final life extension level can be determined to be level 2, etc.

[0087] As an optional implementation, in step 103, the target fast charging optimization map is obtained according to the life extension level and the available charging duration determined based on the user habit, including:

[0088] The life extension level and the available charging duration are input into the battery life extension model, and an initial fast charging optimization map is output.

[0089] In this step, the battery life extension model is a pre-trained model, for example, a pre-trained neural network model. Specifically, the battery life extension model is a fusion model of an electrochemical model, a thermal model, a lithium precipitation model, and a mechanical damage model. The life extension principle is to control the charging current at different temperatures and voltages within a specified time, control the battery temperature within a suitable range, and reduce the lithium precipitation and mechanical damage of the battery as much as possible.

[0090] The predicted aging trajectory corresponding to the initial fast charging optimization map is determined.

[0091] In this step, the predicted aging trajectory corresponding to the initial fast charging optimization map can be determined by using a prediction model, or the corresponding relationship between the map and the aging trajectory can be obtained based on the pre-stored map.

[0092] When the predicted aging trajectory corresponding to the initial fast charging optimization map meets the preset condition, the initial fast charging optimization map is determined as the target fast charging optimization map.

[0093] In this step, the preset condition is a pre-configured judgment condition, for example, the deviation between the predicted aging trajectory and the nominal aging trajectory is within a preset deviation range.

[0094] When the predicted aging trajectory corresponding to the initial fast charging optimization map does not meet the preset condition, the aging rate deviation amount and the available charging duration are input into the battery life extension model, and the initial fast charging optimization map is iteratively updated. The aging rate deviation amount is a deviation amount determined according to the predicted aging trajectory and the nominal aging trajectory under the cumulative mileage data and the cumulative charging and discharging capacity.

[0095] The cumulative mileage data in this step is determined based on the real vehicle running data, as shown in Figure 3 The cumulative charging and discharging capacity is determined based on the real vehicle running data, as shown in Figure 3

[0096] In this optional implementation, the initial fast charging optimization map is iteratively updated, so that the predicted aging trajectory corresponding to the target fast charging optimization map finally obtained meets the preset condition, thereby improving the life of the battery.

[0097] ​As a specific implementation, the preset condition is that the service life level of the battery is reduced by at least one level within the preset mileage or within the preset charging and discharging capacity. Wherein, the service life level of the battery is reduced by at least one level means that the service life level of the battery is reduced by one level from the currently determined service life level, for example, the currently determined service life level is 4 levels, and the preset condition is that the service life level of the battery is reduced to 3 levels within the preset mileage or within the preset charging and discharging capacity.

[0098] As a specific implementation, determining the predicted aging trajectory corresponding to the initial fast charging optimization map includes:

[0099] Obtaining the vehicle operating condition; in this step, the vehicle operating condition is as shown in Figure 3 The vehicle operating condition obtained based on the real vehicle operating data, for example, the vehicle operating condition is directly obtained based on the real vehicle operating data, or the real vehicle operating data is processed to obtain an equivalent vehicle operating condition according to the processing result.

[0100] According to the initial fast charging optimization map and the vehicle operating condition, the charging and discharging driving condition of the vehicle is generated; this step can be specifically that the actual charging map of the vehicle is replaced by the initial fast charging optimization map.

[0101] The charging and discharging driving condition is input into the battery life prediction simulation model, and the predicted aging trajectory corresponding to the initial fast charging optimization map is output. In this step, the battery life prediction simulation model is a pre-trained model. Specifically, the battery life prediction simulation model is a life prediction simulation model combined with a machine learning model, which specifically judges the future battery attenuation trend based on the driving condition of the vehicle and the historical decline trajectory of the battery capacity.

[0102] Further, as an optional implementation, the first message further includes a trigger scenario. That is, the first message sent by the cloud data platform to the vehicle includes a trigger scenario and a target fast charging optimization map. In this way, the vehicle charges the battery according to the target fast charging optimization map to prolong the battery life when the scenario of the vehicle is the trigger scenario in the first message.

[0103] As shown in Figure 2 The application embodiment also provides a method for prolonging the battery life, applied to a vehicle, including:

[0104] Step 201, receiving the first message sent by the cloud data platform, the first message including a target fast charging optimization map;

[0105] Step 202, charging the battery according to the target fast charging optimization map.

[0106] In the method for extending battery life according to the embodiments of this application, after the vehicle receives the first message sent by the cloud data platform, it charges the battery according to the target fast charging optimization map carried by the first message, so that the battery ages as much as possible according to the nominal aging trajectory, thus extending the battery life to a certain extent.

[0107] Furthermore, the first message also includes the triggering scenario.

[0108] As an optional implementation, step 202 involves charging the battery according to the target fast-charging optimization map, including:

[0109] When the vehicle is in a scenario that triggers the charging event, the battery is charged according to the target fast-charging optimization map. That is, when the vehicle is charging, if the current scenario belongs to the triggering scenario, the battery is charged according to the target fast-charging optimization map.

[0110] Below, in conjunction with Figure 3 The implementation process of the method for extending battery life according to the embodiments of this application will be described as follows:

[0111] First, based on real-vehicle operating data, we obtain ODO mileage, cumulative charging Ah, battery health status, battery aging rate, available charging time and scenarios, and equivalent vehicle operating conditions. Specifically, the vehicle's ODO mileage, cumulative charging and discharging Ah, and battery health status data can be directly obtained from vehicle operating data; the battery aging rate is selected from the average rate of change of battery health status over the past three months of vehicle operation; and the available charging time based on user habits and the equivalent vehicle operating condition data are obtained through big data analysis of user real-vehicle data.

[0112] Secondly, by inputting the ODO mileage and cumulative charge / discharge Ah into the standard battery aging curve, the safety thresholds for the corresponding health status and battery aging rate of the vehicle are obtained.

[0113] Next, the actual battery health status and aging rate of the vehicle are compared with the corresponding safety thresholds for the vehicle's health status and aging rate to determine the battery's aging level (and thus, the level of the battery life extension program). The principles for determining the level of the battery life extension program are as follows:

[0114] 1) If the actual battery health status of the vehicle minus the safety threshold of the battery health status is ≥ k s0 And the aging rate safety threshold - the actual battery aging rate of the vehicle ≥ k a0 If the battery is in a state of aging, then the current battery aging status is at level 0, and no life extension optimization will be performed at the current aging level.

[0115] 2) If k s0The actual battery health state of the vehicle - the safety threshold of the battery health state ≥ k s1 , or k a0 The actual battery aging rate of the vehicle - the safety threshold of the battery aging rate ≥ k a1 If the current battery aging state is in level 1, the life extension optimization is started under the current aging level, and the life extension range is k b1 .

[0116] 3) If k s1 The actual battery health state of the vehicle - the safety threshold of the battery health state ≥ k s2 , or k a1 The actual battery aging rate of the vehicle - the safety threshold of the battery aging rate ≥ k a2 If the current battery aging state is in level 2, the life extension range is k b2 under the current aging level, and the life extension range of the aging level 2 is greater than that of the aging level 1.

[0117] 4) If k s2 The actual battery health state of the vehicle - the safety threshold of the battery health state ≥ k s3 , or k a2 The actual battery aging rate of the vehicle - the safety threshold of the battery aging rate ≥ k a3 If the current battery aging state is in level 3, the life extension range is k b3 under the current aging level, and the life extension range of the aging level 3 is greater than that of the aging level 2.

[0118] 5) If k s3 The actual battery health state of the vehicle - the safety threshold of the battery health state ≥ k s4 , or k a3 The actual battery aging rate of the vehicle - the safety threshold of the battery aging rate ≥ k a4 If the current battery aging state is in level 4, the life extension range is k b4 under the current aging level, and the life extension range of the aging level 4 is greater than that of the aging level 3.

[0119] 6) If the above trigger conditions 1) - 5) occur simultaneously, the larger life extension scheme is selected as the battery life extension scheme level.

[0120] Then, the current aging level of the battery and the available charging time data are input as constraint conditions into the battery life extension model to generate a fast charging optimization map.

[0121] Then, the generated fast charging optimization map is combined with the vehicle operation equivalent working condition to generate a new charging and discharging driving equivalent working condition, and the working condition is input into a battery life prediction simulation model to calculate the aging condition of the battery under the charging and discharging equivalent working condition, and a future aging trajectory of the battery is obtained based on the aging condition.

[0122] Finally, whether the generated fast charging optimization map meets the expected requirement is determined according to the predicted future aging trajectory of the battery, if the requirement is met, the fast charging optimization map and the triggering scene data are sent to the specified vehicle through the vehicle-cloud interconnection channel, and the vehicle stores the fast charging optimization map after receiving the fast charging optimization map, and when the vehicle encounters a triggering scene, the fast charging optimization map is executed during fast charging, and if the triggering scene is not met, a standard fast charging map is executed.

[0123] If the requirement is not met, the aging rate deviation amount needs to be returned to the battery life extension model, the fast charging optimization map is regenerated, and the fast charging optimization map is retransferred to the life simulation model for re-simulation, and whether the generated fast charging optimization map meets the requirement is determined, and the iteration is continuously performed until the fast charging optimization map that meets the expected requirement is generated.

[0124] In the method, the battery life prediction simulation model is used to simulate the new charging and discharging driving equivalent working condition generated by combining the fast charging optimization map with the vehicle operation equivalent working condition, and if the simulation result shows that the aging level of the battery is reduced from K level to K-1 level within k odo miles or K Ah , it is considered that the generated fast charging optimization map meets the expected requirement.

[0125] The method for extending the battery life according to the embodiments of the present application formulates a life extension scheme matched with the vehicle state and user habit according to the vehicle battery health state, aging rate, user habit, and standard battery aging trajectory, and generates a fast charging optimization map. The generated map is combined with the equivalent working condition of the user to simulate the future aging trajectory of the vehicle until the fast charging map meets the expected requirement, and the fast charging map is sent to the vehicle end through the vehicle-cloud interconnection channel for execution. In this way, the long-term safe and effective operation of the (lithium ion power) battery and the electric vehicle can be ensured, the economic benefit of the lithium ion power battery is improved, the aging rate of the lithium ion power battery is slowed down, and the battery life is extended, which is particularly important.

[0126] As shown in Figure 4 , the embodiments of the present application also provide a device for extending the battery life, which is applied to a cloud data platform and includes:

[0127] A first acquisition module 401 is configured to acquire an actual health state, an actual aging rate of a battery, a cumulative mileage data of a vehicle, and a health state threshold and an aging rate threshold corresponding to a cumulative charging and discharging capacity of the battery;

[0128] The determining module 402 is configured to determine a life extension level of the battery according to an actual aging rate, an actual state of health, a state of health threshold, and an aging rate threshold of the battery.

[0129] The second obtaining module 403 is configured to obtain a target fast charging optimization map according to the life extension level and an available charging duration determined based on a user habit, wherein a predicted aging track corresponding to the target fast charging optimization map satisfies a preset condition.

[0130] The sending module 404 is configured to send a first message to the vehicle according to the target fast charging optimization map, wherein the first message is used to instruct the vehicle to charge the battery according to the target fast charging optimization map.

[0131] Optionally, the determining module 402 comprises:

[0132] The first calculating sub-module is configured to calculate a first difference between the actual state of health and the state of health threshold.

[0133] The second calculating sub-module is configured to calculate a second difference between the actual aging rate and the aging rate threshold.

[0134] The first determining sub-module is configured to determine the life extension level according to the first difference and / or the second difference.

[0135] Optionally, the first determining sub-module is specifically configured to perform at least one of the following:

[0136] When the first difference is greater than or equal to a first threshold and the second difference is greater than or equal to a second threshold, the life extension level is determined to be level 0.

[0137] When the first difference is less than the first threshold and greater than or equal to a third threshold, or the second difference is less than the second threshold and greater than or equal to a fourth threshold, the life extension level is determined to be level 1.

[0138] When the first difference is less than the third threshold and greater than or equal to a fifth threshold, or the second difference is less than the fourth threshold and greater than or equal to a sixth threshold, the life extension level is determined to be level 2.

[0139] When the first difference is less than the fifth threshold and greater than or equal to a seventh threshold, or the second difference is less than the sixth threshold and greater than or equal to an eighth threshold, the life extension level is determined to be level 3.

[0140] When the first difference is less than the seventh threshold and greater than or equal to a ninth threshold, or the second difference is less than the eighth threshold and greater than or equal to a ninth threshold, the life extension level is determined to be level 4.

[0141] wherein, as the life extension level increases, the life extension range of the battery increases.

[0142] Optionally, the first determining sub-module is further configured to:

[0143] In a case where two life extension levels are determined according to the first difference value and the second difference value, one of the two life extension levels with a higher level is determined as the life extension level.

[0144] Optionally, the second obtaining module 403 comprises:

[0145] a first processing sub-module, configured to input the life extension level and the available charging duration into a battery life extension model, and output an initial fast-charging optimization map;

[0146] a second determining sub-module, configured to determine a predicted aging trajectory corresponding to the initial fast-charging optimization map;

[0147] a third determining sub-module, configured to determine the initial fast-charging optimization map as the target fast-charging optimization map when the predicted aging trajectory corresponding to the initial fast-charging optimization map meets the preset condition.

[0148] an updating sub-module, configured to input an aging rate deviation amount and the available charging duration into the battery life extension model, and iteratively update the initial fast-charging optimization map when the predicted aging trajectory corresponding to the initial fast-charging optimization map does not meet the preset condition, wherein the aging rate deviation amount is a deviation amount determined according to the predicted aging trajectory and a nominal aging trajectory under the cumulative mileage data and the cumulative charging and discharging capacity.

[0149] Optionally, the second determining sub-module comprises:

[0150] an obtaining unit, configured to obtain a vehicle operating condition;

[0151] a generating unit, configured to generate a charging and discharging driving condition of the vehicle according to the initial fast-charging optimization map and the vehicle operating condition;

[0152] a processing unit, configured to input the charging and discharging driving condition into a battery life prediction simulation model, and output a predicted aging trajectory corresponding to the initial fast-charging optimization map.

[0153] Optionally, the preset condition is that the life extension level of the battery is reduced by at least one level within a preset mileage or within a preset charging and discharging capacity.

[0154] Optionally, the first message further comprises a triggering scenario.

[0155] It should be noted that the above-mentioned device for prolonging battery life provided by the embodiments of the present application can realize all the method steps achieved by the above-mentioned method embodiments for prolonging battery life applied to a cloud data platform, and can achieve the same technical effects. Therefore, the same parts and beneficial effects of the method embodiments in the embodiments will not be described in detail.

[0156] As shown in Figure 5 The embodiments of the present application also provide a device for prolonging battery life, applied to a vehicle, comprising:

[0157] The receiving module 501 is configured to receive a first message sent by the cloud data platform, wherein the first message comprises a target fast charging optimization map.

[0158] The charging module 502 is configured to charge the battery according to the target fast charging optimization map.

[0159] Optionally, the first message further comprises a trigger scenario.

[0160] Optionally, the charging module 502 is specifically configured to:

[0161] In a case where the scenario in which the vehicle is located is the trigger scenario, the battery is charged according to the target fast charging optimization map.

[0162] It should be noted that the above-mentioned device for prolonging battery life provided by the embodiments of the present application can realize all the method steps achieved by the above-mentioned method embodiments for prolonging battery life applied to a vehicle, and can achieve the same technical effects. Therefore, the same parts and beneficial effects of the method embodiments in the embodiments will not be described in detail.

[0163] As shown in Figure 6 The embodiments of the present application also provide a cloud data platform, which comprises a transceiver 610, a processor 600, a memory 620, and a program stored in the memory 620 and executable on the processor 600. When the processor 600 executes the program, the method for prolonging battery life is realized.

[0164] The transceiver 610 is configured to receive and send data under the control of the processor 600.

[0165] In a case where the scenario in which the vehicle is located is the trigger scenario, the battery is charged according to the target fast charging optimization map. Figure 6In particular embodiments, the bus architecture can include any number of interconnecting buses and bridges, depending on the specific application of the processor 600. The bus architecture can link various circuits of the various circuitries represented by the processor 600 and the memory 620, which is represented by one or more processors and memories, respectively. The bus architecture can also link various other circuitries, such as peripheral devices, voltage regulators, and power management circuitries, which are well known in the art and thus, not further described herein. The bus interface provides an interface. The transceiver 610 can be a plurality of elements, i.e., including a transmitter and a receiver, which provides a means for communicating with various other apparatuses over a transmission medium. The processor 600 is responsible for managing the bus architecture and general processing, while the memory 620 can store data used by the processor 600 in executing its operations.

[0166] The embodiments of the present application further provide a vehicle, comprising a transceiver, a processor, a memory, and a program stored in the memory and capable of running on the processor; the processor implements the method for prolonging battery life applied to the vehicle as described above when executing the program, and achieves the same technical effects. To avoid repetition, details are not described here.

[0167] The embodiments of the present application further provide a readable storage medium, which stores a program. The program is executed by a processor to implement each process of the method for prolonging battery life applied to the cloud data platform or the vehicle as described above, and achieves the same technical effects. To avoid repetition, details are not described here. The readable storage medium is, for example, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, etc.

[0168] Finally, it needs to be noted that, in this document, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or terminal device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0169] The above are preferred embodiments of the present application, it should be pointed out that, for those skilled in the art, without departing from the principles described in the present application, can make several improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method of extending battery life, characterized by, The application is applied to a cloud data platform, comprising: obtaining the actual health state, the actual aging rate of the battery, the health state threshold and the aging rate threshold corresponding to the cumulative mileage data of the vehicle and the cumulative charge-discharge capacity of the battery; determining the service life extension level of the battery according to the actual aging rate, the actual health state, the health state threshold and the aging rate threshold of the battery; obtaining the target fast charging optimization map according to the service life extension level and the available charging time length determined based on user habits, wherein the predicted aging trajectory corresponding to the target fast charging optimization map meets the preset condition; sending a first message to the vehicle according to the target fast charging optimization map, wherein the first message is used to instruct the vehicle to charge the battery according to the target fast charging optimization map; wherein, according to the service life extension level and the available charging time length determined based on user habits, the target fast charging optimization map is obtained, comprising: inputting the service life extension level and the available charging time length into the battery service life extension model to output an initial fast charging optimization map; determining the predicted aging trajectory corresponding to the initial fast charging optimization map; when the predicted aging trajectory corresponding to the initial fast charging optimization map meets the preset condition, determining the initial fast charging optimization map as the target fast charging optimization map; when the predicted aging trajectory corresponding to the initial fast charging optimization map does not meet the preset condition, inputting the aging rate deviation amount and the available charging time length into the battery service life extension model to iteratively update the initial fast charging optimization map; wherein the aging rate deviation amount is the deviation amount determined according to the predicted aging trajectory and the nominal aging trajectory under the cumulative mileage data and the cumulative charge-discharge capacity.

2. The method of claim 1, wherein, determining the service life extension level of the battery according to the actual aging rate, the actual health state, the health state threshold and the aging rate threshold of the battery, comprising: calculating the first difference value between the actual health state and the health state threshold; calculating the second difference value between the actual aging rate and the aging rate threshold; determining the service life extension level according to the first difference value and / or the second difference value.

3. The method of claim 2, wherein, determining the service life extension level according to the first difference value and / or the second difference value, comprising at least one of the following: when the first difference value is greater than or equal to a first threshold value and the second difference value is greater than or equal to a second threshold value, determining the service life extension level as 0 level; when the first difference value is less than the first threshold value and greater than or equal to a third threshold value, or the second difference value is less than the second threshold value and greater than or equal to a fourth threshold value, determining the service life extension level as 1 level; when the first difference value is less than the third threshold value and greater than or equal to a fifth threshold value, or the second difference value is less than the fourth threshold value and greater than or equal to a sixth threshold value, determining the service life extension level as 2 level; when the first difference value is less than the fifth threshold value and greater than or equal to a seventh threshold value, or the second difference value is less than the sixth threshold value and greater than or equal to an eighth threshold value, determining the service life extension level as 3 level; determining that the life extension level is level 4 when the first difference is less than the seventh threshold value and greater than or equal to a ninth threshold value, or the second difference is less than the eighth threshold value and greater than or equal to the ninth threshold value; wherein the life extension range of the battery increases with the increase of the life extension level.

4. The method of claim 3, wherein, determining the life extension level according to the first difference and / or the second difference, comprising: in the case of determining two life extension levels according to the first difference and the second difference, determining the life extension level as the one with a higher level between the two life extension levels.

5. The method of claim 1, wherein, determining the predicted aging trajectory corresponding to the initial fast-charging optimization map, comprising: obtaining a vehicle operating condition; generating a charging and discharging driving condition of the vehicle according to the initial fast-charging optimization map and the vehicle operating condition; inputting the charging and discharging driving condition into a battery life prediction simulation model to output the predicted aging trajectory corresponding to the initial fast-charging optimization map.

6. The method according to claim 1 or 4, characterized in that, The preset condition is that the life extension level of the battery is reduced by at least one level within a preset mileage or within a preset charging and discharging capacity.

7. The method of claim 1, wherein, The first message further comprises a trigger scenario.

8. An apparatus for extending battery life, the apparatus comprising: application to a cloud data platform, comprising: a first obtaining module configured to obtain an actual health status of a battery, an actual aging rate, a health status threshold value and an aging rate threshold value corresponding to cumulative mileage data of a vehicle and cumulative charging and discharging capacity of the battery; a determining module configured to determine a life extension level of the battery according to the actual aging rate, the actual health status, the health status threshold value and the aging rate threshold value; a second obtaining module configured to obtain a target fast-charging optimization map according to the life extension level and an available charging duration determined based on user habits, wherein a predicted aging trajectory corresponding to the target fast-charging optimization map satisfies a preset condition; a sending module configured to send a first message to the vehicle according to the target fast-charging optimization map, the first message being used to instruct the vehicle to charge the battery according to the target fast-charging optimization map; wherein the second obtaining module comprises: a first processing submodule configured to input the life extension level and the available charging duration into a battery life extension model to output an initial fast-charging optimization map; a second determining submodule configured to determine a predicted aging trajectory corresponding to the initial fast-charging optimization map; a third determining submodule configured to determine the initial fast-charging optimization map as the target fast-charging optimization map when the predicted aging trajectory corresponding to the initial fast-charging optimization map satisfies the preset condition; an updating submodule configured to input an aging rate deviation amount and the available charging duration into the battery life extension model to iteratively update the initial fast-charging optimization map when the predicted aging trajectory corresponding to the initial fast-charging optimization map does not satisfy the preset condition, wherein the aging rate deviation amount is a deviation amount determined according to the predicted aging trajectory and a nominal aging trajectory under the cumulative mileage data and the cumulative charging and discharging capacity.

9. A cloud data platform comprising a transceiver, a processor, a memory, and a program stored on the memory and executable on the processor; wherein, The processor implements the method for prolonging the life of the battery when executing the program. The processor implements the method for prolonging the life of the battery when executing the program.

10. A readable storage medium, characterized by, The readable storage medium has a program stored thereon, and the program is executed by the processor to implement the method for prolonging battery life according to any one of claims 1 to 7.

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