Battery management method and system for battery replacement electric vehicle team

Abstract

According to the battery management method and system for the battery-changing electric vehicle team, when the battery distribution scheme is determined for the vehicle, the influence of factors such as the vehicle load data, the actual use data of the battery and the driving habit of a driver on the endurance mileage of the battery is considered; a battery dispatching scheme can be more accurately designed for a battery-replaceable electric vehicle team, and the distribution cost and the overall energy efficiency optimization are considered.

Description

Technical Field

[0001] The present application relates to the technical field of logistics transportation and vehicle dispatch management, and specifically to a battery management method and system for a battery-swap electric vehicle fleet. Background Art

[0002] Battery swapping and organized charging are new technologies and models currently being promoted in the electric vehicle sector, helping to elevate vehicle electrification. Battery swapping involves quickly removing a low-capacity battery from a removable electric vehicle (EV) and replacing it with a high-capacity one in 3-5 minutes, thereby increasing the EV's range. Demand for battery swapping is already emerging within the industry. For example, logistics vehicles, after adopting battery-swap-enabled electric fleets, are seeking better battery swap planning to reduce delivery costs and improve energy efficiency.

[0003] Chinese patent publication number CN111325409B discloses a method for battery swap station site selection and mixed fleet route planning. The method includes: obtaining candidate data; constructing an integer programming model for joint decision-making on battery swap station site selection and mixed fleet distribution of electric and fuel vehicles; reconstructing the integer programming model into a main problem model and subproblem models; and designing an exact branch-and-price algorithm with an adaptive selection mechanism for this model. By designing seven accelerated solver operators, the algorithm can adaptively call these operators for different enterprise distribution scenarios to accelerate the solution speed. Furthermore, the algorithm uses precise labels to expand the complete search solution space to obtain the optimal solution.

[0004] The above method does not consider the impact of load on the mileage of electric vehicles, nor does it combine the attenuation caused by normal use of electric vehicles and the different driving habits of drivers to estimate the mileage of electric vehicles. It is difficult to accurately design a battery scheduling plan for the battery-swappable electric vehicle fleet and take into account the distribution cost and overall energy efficiency optimization. Summary of the Invention

[0005] At least one embodiment of the present application provides a battery management method and system for a battery-swap electric vehicle fleet, which can accurately provide a battery allocation plan for a battery-swap electric vehicle fleet while taking into account both distribution costs and overall energy efficiency optimization.

[0006] According to a first aspect of the present application, at least one embodiment provides a battery management method for a battery-swap electric vehicle fleet, comprising:

[0007] Obtaining a delivery plan generated by a vehicle dispatch center and a first temperature range corresponding to the delivery plan, wherein the delivery plan includes information about the driver and delivery route of each vehicle, and the delivery route information includes starting and ending stops, intermediate stops, and end stops, and the corresponding load range and mileage for each segment of the delivery route, where a delivery route between adjacent stops is considered a segment.

[0008] Determining, based on pre-generated carrying capacity information for each battery, a range that can be driven by the available battery at the starting station under the first temperature range, the baseline load range, and the current battery charge level, wherein the carrying capacity information includes the range that can be driven by the battery under different temperature ranges, load ranges, and battery charges;

[0009] converting the mileage corresponding to each segment of the delivery route into a first required mileage within the first temperature range and the reference load range based on the first temperature range and the load range corresponding to each segment of the delivery route; calculating a sum of the first required mileage for each segment of the delivery route; and converting the sum of the first required mileage into a second required mileage for the delivery route based on the energy consumption optimization capability information of the driver corresponding to the delivery route;

[0010] The drivable mileage of the available batteries at the starting site is matched with the second required mileage of the delivery route of each vehicle to determine a battery allocation plan for each vehicle.

[0011] Optionally, historical transport data of each battery is obtained based on historical order data of the fleet delivery and the collected historical battery data and historical environmental data, wherein the historical transport data includes the mileage and power consumption of the battery under different temperature ranges and load ranges;

[0012] Using the historical transport data of each battery, a transport capacity estimation model is trained for each battery, wherein the input of the transport capacity estimation model is the temperature range, load range, and battery power, and the output is the range that the battery can travel under the input temperature range, load range, and battery power;

[0013] By using the carrying capacity estimation model corresponding to each battery, the mileage of each battery under different temperature ranges, load ranges and battery power levels is generated to obtain the carrying capacity information of each battery.

[0014] Optionally, also include:

[0015] Obtain at least one of the driver's historical average battery energy recovery ratio, historical average driving speed, historical average number of starts and stops, and cumulative mileage during the trip based on historical order data delivered by the fleet and collected historical driving operation data and historical battery data of the driver;

[0016] Based on the at least one data, energy consumption optimization capability information of the driver is generated.

[0017] Optionally, the battery data includes at least one of the following: battery identification, vehicle identification corresponding to the battery, battery power, battery energy recovery ratio of the battery during the trip, charging time, charging power, and geographic location;

[0018] The driving operation data includes at least one of the following: driving speed, number of starts and stops, and cumulative mileage;

[0019] The environmental data includes at least one of the following: temperature, the temperature range to which the temperature belongs, geographical location, and road slope;

[0020] The order data includes at least one of the following: the vehicle corresponding to the order, the driver, the departure station and destination station of each segment of the journey, the load of each segment of the journey, the load range to which the load of each segment of the journey belongs, and the latest arrival time at the destination station of each journey.

[0021] Optionally, matching the drivable range of the available batteries at the starting station with the second required range of the delivery routes of the respective vehicles to determine a battery allocation plan for the respective vehicles includes:

[0022] sorting the available batteries in descending order of the drivable mileage; and sorting the vehicles in descending order of the second required mileage of the delivery routes of the vehicles;

[0023] Filter out vehicles whose second required mileage is not greater than the maximum drivable mileage to obtain first-category vehicles;

[0024] Allocate the battery with the largest mileage among the remaining batteries that is not less than the second required mileage of the current vehicle to each vehicle in the first category in descending order of the second required mileage, and delete the allocated battery from the remaining batteries after each vehicle is allocated, until all vehicles in the first category have been allocated or the mileage of the remaining batteries is less than the second required mileage of the current vehicle;

[0025] For the second type of vehicles that have not yet been allocated a battery, the battery with the largest mileage among the remaining batteries is allocated to each vehicle in the second type in descending order of the second required mileage.

[0026] Optionally, also include:

[0027] The battery allocation plan for each vehicle is sent to the starting site, so that the starting site configures corresponding batteries for each vehicle according to the battery allocation plan.

[0028] Optionally, also include:

[0029] During the process of the vehicle executing the delivery route, obtaining the remaining mileage and remaining battery power of the vehicle, and evaluating whether the remaining battery power meets the remaining mileage requirement;

[0030] If the requirements are not met, the vehicle is directed to a battery swap substation, and the mileage of the available batteries at the battery swap substation is determined based on the pre-generated carrying capacity information of each battery under the first temperature range, the reference load range, and the current battery charge level;

[0031] converting the mileage corresponding to each remaining segment of the delivery route into a third required mileage within the first temperature range and the reference load range based on the first temperature range and the load range corresponding to the remaining segments of the delivery route; calculating the sum of the third required mileage for each remaining segment of the delivery route; and converting the sum of the third required mileage into a fourth required mileage based on the energy consumption optimization capability information of the driver corresponding to the delivery route;

[0032] Match the drivable mileage of the available batteries at the battery swap station with the fourth required mileage of the vehicle, screen out candidate batteries whose drivable mileage is not less than the fourth required mileage, and use the battery with the smallest mileage among the candidate batteries as the battery swap for the vehicle.

[0033] Optionally, also include:

[0034] Based on the fleet's historical delivery order data and collected historical battery data, the average driving time and average energy consumption of each route within each temperature range of each trip are calculated. The route with the lowest average energy consumption is selected as the recommended route for that temperature range, and the route with the lowest average driving time is selected as the backup route for that temperature range to create an energy consumption map.

[0035] Based on the first temperature range and the energy consumption map, a recommended route and an alternative route for each trip in the delivery route of the vehicle are determined, and based on the remaining battery power of the vehicle and / or the remaining delivery time of the delivery route, the recommended route or the alternative route for each trip in the delivery route are sent to the vehicle.

[0036] According to a second aspect of the present application, at least one embodiment provides a battery management system for a battery-swap electric vehicle fleet, comprising:

[0037] a first acquisition module configured to acquire a delivery plan generated by a vehicle dispatch center and a first temperature range corresponding to the delivery plan, wherein the delivery plan includes information about the driver and delivery route of each vehicle, and the delivery route information includes information about the starting station, intermediate stations, and end stations, and the corresponding load range and mileage for each segment of the delivery route, wherein a delivery route between adjacent stations is considered a segment;

[0038] a first determining module, configured to determine, based on pre-generated carrying capacity information of each battery, a drivable range of the available battery at the starting station under the first temperature range, the reference load range, and the current battery charge level, wherein the carrying capacity information includes the drivable range of the battery under different temperature ranges, load ranges, and battery charges;

[0039] a second determination module configured to convert the mileage corresponding to each segment of the delivery route into a first required mileage within the first temperature range and the reference load range based on the first temperature range and the load range corresponding to each segment of the delivery route; calculate a sum of the first required mileage for each segment of the delivery route; and convert the sum of the first required mileage into a second required mileage for the delivery route based on the energy consumption optimization capability information of the driver corresponding to the delivery route;

[0040] The first allocation module is used to match the drivable mileage of the available batteries at the starting site with the second required mileage of the delivery route of each vehicle to determine the battery allocation plan for each vehicle.

[0041] According to the third aspect of the present application, at least one embodiment provides a battery management system for a battery-swap electric vehicle fleet, comprising a processor, a memory, and a program or instruction stored on the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in any one of the first aspects.

[0042] According to the fourth aspect of the present application, at least one embodiment provides a computer-readable storage medium having a program stored thereon, and when the program is executed by a processor, the steps of any method of the first aspect are implemented.

[0043] According to a fifth aspect of the present application, at least one embodiment provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the method described in any one of the first aspects.

[0044] Compared with the existing technology, the battery management method and system for the battery-swap electric vehicle fleet provided in the embodiments of the present application take into account the impact of factors such as vehicle load data, actual battery usage data, and driver's driving habits on battery life when determining the battery allocation plan for the vehicle. It can more accurately design battery scheduling plans for battery-swap electric vehicle fleets, and take into account both distribution costs and overall energy efficiency optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0046] Figure 1 A flow chart of a battery management method for a battery-swap electric vehicle fleet according to an embodiment of the present application;

[0047] Figure 2 This is an example diagram of a battery management system for a battery-swap electric vehicle fleet according to an embodiment of the present application;

[0048] Figure 3 This is an example diagram of a vehicle dispatch center for a battery-swap electric vehicle fleet according to an embodiment of the present application;

[0049] Figure 4 This is an example flow chart of a battery management method for a battery-swap electric vehicle fleet according to an embodiment of the present application;

[0050] Figure 5 A schematic structural diagram of a battery management system for a battery-swap electric vehicle fleet according to an embodiment of the present application;

[0051] Figure 6 This is another structural schematic diagram of the battery management system of the battery-swap electric vehicle fleet according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following describes exemplary embodiments of the present application in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0053] It should be understood that references throughout this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic associated with the embodiment is included in at least one embodiment of the present application. Therefore, the appearance of "in one embodiment" or "in an embodiment" throughout this specification does not necessarily refer to the same embodiment. Furthermore, these particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. The terms "first," "second," and so on, used in the specification and claims of this application are used to distinguish similar items and are not necessarily used to describe a particular order or sequential sequence. It should be understood that such usage is interchangeable where appropriate, such that the embodiments of the present application described herein can, for example, be implemented in an order other than that illustrated or described herein. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, product, or apparatus. The term "and / or" used in the specification and claims refers to at least one of the connected items.

[0054] In the various embodiments of the present application, it should be understood that the size of the serial numbers of the following processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0055] The following description provides examples and does not limit the scope, applicability, or configuration set forth in the claims. Changes may be made to the function and arrangement of the elements discussed without departing from the spirit and scope of this disclosure. The various examples may appropriately omit, substitute, or add various procedures or components. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

[0056] In this article, the vehicles mentioned are all electric vehicles that support battery replacement. In addition, this article also involves a variety of intervals, such as temperature intervals and load intervals, and these intervals are all pre-divided. For example, the temperature range is divided into multiple continuous and non-overlapping intervals to obtain multiple temperature intervals, and the load range is divided into multiple continuous and non-overlapping intervals to obtain multiple load intervals. Specifically, the upper and lower limits of each interval and the number of intervals can be set as needed or based on experience in electric vehicle operation, and this application does not make specific restrictions on this.

[0057] Please refer to Figure 1 The battery management method for a battery-swap electric vehicle fleet provided in an embodiment of the present application includes the following steps:

[0058] Step 11: Obtain the delivery plan generated by the vehicle dispatch center and the first temperature range corresponding to the delivery plan. The delivery plan includes: the driver and delivery route information of each vehicle. The delivery route information includes: the starting station, intermediate stations, end stations, and the load range and mileage corresponding to each segment of the delivery route. The delivery route between adjacent stations is a segment.

[0059] Here, the vehicle dispatch center generates a delivery plan based on the current delivery task. Furthermore, the first temperature range corresponding to the delivery plan is determined based on the temperature range to which the temperature at the time the delivery plan is executed. When a vehicle executes a delivery route, it sequentially passes through each station along the route to pick up and / or deliver goods. Upon leaving each station, the vehicle's load data can be pre-determined based on the pre-determined delivery task. Therefore, the load range for each trip can be determined, and the mileage for each trip can also be pre-determined based on the path planning algorithm. A trip refers to the route between adjacent stations along the delivery route.

[0060] Step 12: Determine the mileage that the available battery at the starting site can travel in the first temperature range, the baseline load range, and the current battery charge based on the pre-generated carrying capacity information of each battery, wherein the carrying capacity information refers to the mileage that the battery can travel in different temperature ranges, load ranges, and battery charges.

[0061] Here, the reference load interval is a pre-selected load interval. For example, the load interval to which the vehicle's load belongs when it is empty, or the load interval to which the vehicle belongs when it is fully loaded, or the load interval to which the vehicle belongs when its load is 1 / 2 of the full load weight can be used as the reference load interval.

[0062] The embodiment of the present application pre-generates the carrying capacity information for each battery. Specifically, prior to step 11 above, the embodiment of the present application can obtain historical carrying data for each battery based on historical order data from the fleet's delivery, as well as collected historical battery data and historical environmental data. The historical carrying data includes the battery's mileage and power consumption under different temperature and load ranges. Then, using each battery's historical carrying data, a carrying capacity estimation model is trained for each battery. The inputs to the carrying capacity estimation model are the temperature range, load range, and battery power level, and the output is the battery's mileage under the input temperature range, load range, and battery power level. The carrying capacity estimation model can specifically be based on machine learning or deep learning algorithms. Then, using the carrying capacity estimation model corresponding to each battery, the mileage of each battery under different temperature ranges, load ranges, and battery power levels is generated to obtain the carrying capacity information for each battery. For example, the carrying capacity information for each battery can be stored in a table format to facilitate table lookup and determination of relevant data.

[0063] Specifically, the battery data includes at least one of the following: battery identification, vehicle identification corresponding to the battery, battery power (usually expressed as a percentage of the power level, in English, SoC: State of Charge), battery energy recovery ratio during the journey, charging time, charging power, and geographic location. The environmental data includes at least one of the following: temperature, the temperature range to which the temperature belongs, geographic location, and road slope. The order data includes at least one of the following: the vehicle corresponding to the order, the driver, the departure and destination stations of each segment of the journey, the load of each segment of the journey, the load range to which the load of each segment of the journey belongs, and the latest arrival time at the destination station of each journey.

[0064] Step 13: Convert the mileage corresponding to each segment of the delivery route into a first required mileage under the first temperature range and the reference load range based on the first temperature range and the load range corresponding to each segment of the delivery route; calculate the sum of the first required mileage for each segment of the delivery route; and convert the sum of the first required mileage into a second required mileage for the delivery route based on the energy consumption optimization capability information of the driver corresponding to the delivery route.

[0065] Here, the driver's energy consumption optimization capability information is a weight parameter related to the driver's energy consumption optimization capability. Usually, the weight parameter is a positive value and is negatively correlated with the driver's energy consumption optimization capability, that is, the higher the driver's energy consumption optimization capability, the smaller the weight parameter; the lower the driver's energy consumption optimization capability, the larger the weight parameter. When converting the sum of the first required mileage into the second required mileage of the delivery route based on the driver's energy consumption optimization capability information corresponding to the delivery route, the driver's energy consumption optimization capability information can be multiplied by the sum of the first required mileage to obtain the second required mileage. The above step 13 can introduce the impact of the driver's driving habits on the battery life into the method through the driver's energy consumption optimization capability information, thereby improving the accuracy of the solution.

[0066] In embodiments of the present application, historical driving operation data of a driver can be pre-collected. This driving operation data includes at least one of the following: driving speed, number of starts and stops, and accumulated mileage. Here, the number of starts and stops includes the number of times the vehicle started from a decelerated or stationary state and the number of times the vehicle brake pedal was depressed. Then, based on the fleet's historical delivery order data and the collected driver's historical driving operation data and historical battery data, at least one of the driver's historical average battery energy recovery ratio, historical average driving speed, historical average number of starts and stops, and accumulated mileage during the trip is obtained. Based on this at least one data set, information on the driver's energy consumption optimization capability is generated.

[0067] In the embodiment of the present application, the driver's energy efficiency optimization capability information reflects the driver's driving habits' ability to optimize vehicle energy efficiency. For example, a driver's driving habit score can be determined based on the at least one data item, and then different energy efficiency optimization capability information can be set for the driver based on the score.

[0068] For example, the energy consumption optimization capability information is a weight parameter greater than or equal to 1, and the weight parameter is negatively correlated with the driver's historical average battery energy recovery ratio, that is, the higher the historical average battery energy recovery ratio, the smaller the weight parameter; the lower the historical average battery energy recovery ratio, the larger the weight parameter.

[0069] For another example, the weight parameter is positively correlated with a first absolute value, i.e., the larger the first absolute value, the larger the weight parameter; and the smaller the first absolute value, the smaller the weight parameter. The first absolute value is the absolute value of the difference between the driver's historical average driving speed and a preset reference speed. Here, the preset reference speed can be a pre-set reference speed, which can be set based on the vehicle's uniform driving speed with minimal energy consumption, for example, 60 kilometers per hour.

[0070] For another example, the weight parameter is positively correlated with the historical average number of starts and stops, or negatively correlated with the accumulated mileage, and so on.

[0071] When two or more types of data are used to generate the driver's energy consumption optimization capability information, the driver's energy consumption optimization capability information can be generated by weighting the two or more types of data and then generating the driver's energy consumption optimization capability information based on the data obtained after the weighting process.

[0072] Step 14: Match the drivable mileage of the available batteries at the starting site with the second required mileage of the delivery route of each vehicle to determine a battery allocation plan for each vehicle.

[0073] Through the above steps, when determining the battery allocation plan for the vehicle, the embodiment of the present application takes into account the impact of factors such as vehicle load data, actual battery usage data, and the driver's driving habits on battery life. It can more accurately design battery scheduling plans for swappable electric vehicle fleets, and take into account both distribution costs and overall energy efficiency optimization.

[0074] In step 14 above, the drivable range of the available batteries at the starting station is matched with the second required range of the delivery route of each vehicle to determine the battery allocation plan for each vehicle. There are multiple implementation methods. An optional implementation method is provided below, which is as follows:

[0075] (1) sorting the available batteries in descending order of the drivable mileage; and sorting the vehicles in descending order of the second required mileage of the delivery routes of the vehicles;

[0076] (2) filtering out vehicles whose second required mileage is not greater than the maximum drivable mileage to obtain first-category vehicles;

[0077] (3) allocating a battery with the largest mileage among the remaining batteries that is not less than the second required mileage of the current vehicle to each vehicle in the first category in descending order of the second required mileage, and deleting the allocated battery from the remaining batteries after each vehicle is allocated, until all vehicles in the first category are allocated or the mileage of the remaining batteries is less than the second required mileage of the current vehicle;

[0078] (4) For the second type of vehicles that have not yet been allocated a battery, the battery with the largest mileage among the remaining batteries is allocated to each vehicle in the second type in descending order of the second required mileage.

[0079] After the above step 14, the embodiment of the present application can send the battery allocation plan of each vehicle to the starting site, so that the starting site configures the corresponding battery for each vehicle according to the battery allocation plan.

[0080] During the actual driving process of the vehicle, traffic jams and other situations may occur. The above situation may cause the originally planned battery allocation plan to be unable to meet the energy consumption requirements of the original journey. Therefore, the embodiment of the present application can also be processed according to the following steps:

[0081] (1) When the vehicle is executing the delivery route, the remaining mileage and the remaining battery charge of the vehicle are obtained, and whether the remaining battery charge meets the remaining mileage requirement is evaluated. The specific evaluation method can refer to the similar method in steps 12-14 above, determine the mileage that can be driven by the remaining battery charge in the first temperature range and the reference load range, and calculate the required mileage for the remaining mileage and compare them.

[0082] (2) When the remaining battery charge cannot meet the remaining mileage requirement, guide the vehicle to a battery swap substation, and determine the mileage that the available batteries of the battery swap substation can travel in the first temperature range, the reference load range, and the current battery charge based on the pre-generated carrying capacity information of each battery. Here, the vehicle can be guided to the nearest battery swap substation, or, based on the mileage that the vehicle's remaining battery charge can travel, guide the vehicle to a battery swap substation that has the least impact on the original delivery route and is accessible. The least impact can be the least added mileage on the basis of the original delivery route, or the least added driving time.

[0083] (3) Based on the first temperature range and the load range corresponding to the remaining journey in the delivery route, the mileage corresponding to each remaining journey is converted into a third required mileage under the first temperature range and the reference load range; the sum of the third required mileage for each remaining journey in the delivery route is calculated; and based on the energy consumption optimization capability information of the driver corresponding to the delivery route, the sum of the third required mileage is converted into a fourth required mileage. Here, the remaining journey in the delivery route is the journey starting from the battery swap station and passing through each remaining station in sequence.

[0084] (4) Match the mileage of the available batteries at the battery swap station with the fourth required mileage of the vehicle, screen out candidate batteries whose mileage is not less than the fourth required mileage, and use the battery with the smallest mileage among the candidate batteries as the battery swap for the vehicle.

[0085] Through the above method, the embodiment of the present application can generate a new battery matching solution for the vehicle in real time, ensuring the normal driving of the vehicle and the smooth execution of the delivery task.

[0086] Of course, if the temperature range changes during the delivery process, the mileage and battery allocation can also be calculated based on the collected current temperature range.

[0087] In a fleet delivery scenario, the fleet often performs fixed delivery tasks for some fixed customers. The embodiment of the present application can generate an energy consumption map by collecting relevant data to further reduce vehicle delivery costs and / or reduce delivery time.

[0088] Specifically, the embodiment of the present application can, based on the historical order data of the fleet's distribution and the collected historical battery data, respectively count the average driving time and average energy consumption of each driving route of the trip in each temperature range for each trip, and establish an energy consumption map by using the driving route with the lowest average energy consumption as the recommended route for the trip in the temperature range, and the driving route with the lowest average driving time as the backup route for the trip in the temperature range. That is, the energy consumption map includes the recommended route with the lowest average energy consumption and the backup route with the lowest average driving time for a specific trip (such as a trip between two stations) in various temperature ranges. Then, based on the first temperature range and the energy consumption map, the recommended route and backup route for each trip in the vehicle's delivery route are determined, and based on the remaining battery power of the vehicle and / or the remaining delivery time of the delivery route, the recommended route or backup route for each trip in the delivery route is sent to the vehicle. For example, when the remaining battery power of a vehicle is lower than the expected remaining power, the recommended routes for each trip in the delivery route are sent to the vehicle to reduce battery energy consumption; when the remaining delivery time of the delivery route is less than the expected remaining time, the alternative routes for each trip in the delivery route are sent to the vehicle to reduce delivery time.

[0089] The above method will be further described below with reference to the accompanying drawings through more specific embodiments.

[0090] Please refer to Figure 2 , a schematic diagram of a battery management system for a battery swapping electric vehicle fleet provided in an embodiment of the present application includes a vehicle dispatching center, a charging and swapping station (main station), a battery swapping station (branch station) and a fleet, etc. The fleet includes at least one electric vehicle (EV) that supports battery swapping. Figure 3 It provides multiple modules including the vehicle dispatch center, such as data acquisition module, battery carrying capacity evaluation module, energy consumption optimization module, battery matching module, etc.

[0091] The data collection in the embodiments of the present application is described below.

[0092] The entities involved in data collection include: EV, charging and swapping stations (main stations), swapping stations (branch stations), and vehicle dispatch centers.

[0093] The collected data is used to track energy consumption, determining the energy consumption for each segment of each delivery route, given a specific load. For example, the energy consumption (e.g., 2 kWh) per unit distance (e.g., 10 km) corresponding to a unit load (e.g., 100 kg) can be calculated. Furthermore, energy consumption maps are mapped to temperature ranges, requiring updates when temperature ranges change.

[0094] In this embodiment, coordinate data can be collected to reflect the geographic location of the route corresponding to the trip. Vehicle speed can also be recorded to reflect the average speed level of the route, i.e., the energy consumption changes caused by different road conditions (e.g., different start-stop frequencies due to congestion). Furthermore, the proportion of energy recovery in the current trip can be recorded to reflect the energy consumption changes caused by driving operations (e.g., slow acceleration to reduce the energy consumption increase caused by frequent acceleration / braking, slow braking to enhance energy recovery, etc.).

[0095] Through data collection, the entire battery usage process can be visualized and fully transparent, enabling full tracking of individual battery usage to accurately assess battery carrying capacity. Furthermore, it can record driving habits that are relatively energy-intensive and routes that are energy-efficient.

[0096] The data collected during the battery's different stages are used for different analytical purposes. For example, the battery data collected during the discharge phase can represent the distance traveled per unit of power consumed, which is used to evaluate carrying capacity. The battery data collected during the charging phase can be used to evaluate battery health. The battery data collected during the battery swap phase can be used to record the battery swap location and the amount of newly added power.

[0097] The data collected from the different locations of the battery can be used for the following purposes: for example, when the battery is located at a battery swap or charging station, the battery can be charged, and this application can use slow charging; when the battery is located at a battery swap station (which only provides battery swap and battery allocation), the battery can be allocated to the vehicle to which the battery belongs (battery swap); when the battery is located in an EV, it means that the battery is driving the electric vehicle, realizing logistics distribution and other services.

[0098] The collection of energy data mainly includes the collection of battery data, driving operation data and environmental data. Among them, battery data includes: vehicle identification (ID), battery ID, SoC potential level, energy recovery ratio, etc. collected during vehicle driving; battery ID, SoC potential level, charging time, charging power, etc. collected during the charging process when the vehicle is stationary; battery ID after replacement, SoC potential level after replacement and battery position, etc. collected during the vehicle battery replacement process. Driving operation data can include vehicle speed, brake pedal depth, number of starts and stops per hour, cumulative mileage, etc. Environmental data can include temperature, location, road slope, etc.

[0099] Data collection can be performed at a preset frequency, for example, every 10 seconds (for vehicles in the public sector). Data reporting can be sent from the vehicle, the charging and swapping station hub, and each swapping station to the vehicle dispatch center after daily delivery.

[0100] Fleet delivery order data typically includes: 1) vehicle IDs for matching delivery tasks; 2) driver IDs for different driving styles; and 3) the departure and destination locations, and cargo weights, for each leg of the journey. Additionally, for delivery tasks, the latest arrival time at each destination can also be included.

[0101] Regarding the estimation of battery carrying capacity, the input data of the embodiment of the present application includes battery data, environmental data, and order data. Then, through data sorting and preparation, the data of each battery is screened out. Then, a carrying capacity estimation model is established through machine learning, deep learning, etc., and the estimation model is obtained through model training. In addition, the embodiment of the present application can also regularly update the training data and retrain the model based on the updated training data. The update frequency can be every preset kilometer added to the cumulative mileage of each battery, such as 30,000 kilometers, or updated according to the time frequency, which can be about every 8 months.

[0102] The following combination Figure 4 , the above method is further illustrated by a specific example.

[0103] Assume that the charging and swapping terminal is located in the suburbs and uses green energy such as photovoltaics for charging and energy storage to recharge the fleet's batteries. Vehicles depart from the charging and swapping terminal daily, typically with a charge level close to 100%. The batteries are standard batteries manufactured by the same supplier. On flat, moderately sized urban roads (approximately 20°C), at a constant speed of 60 km / h (with minimal stopping and starting), a brand new battery can travel 250 km. The dispatch center builds a battery capacity prediction model based on historical battery data, environmental data, and order data. The temperature that day was 0-8°C, and the dispatch center generated a delivery plan for each vehicle based on the delivery task.

[0104] For example, on a certain day, vehicle 1 (vid=1) received a delivery task from the dispatch center. It was required to deliver to seven destinations (a total of nine legs: the first leg was from the charging station to the warehouse; the second leg was from the warehouse to the first delivery station; and the last leg was from the seventh destination back to the charging station). The total mileage was approximately 200 km. Driver 11 was assigned to complete the delivery that day.

[0105] Vehicle 3 (vid=3) needs to deliver to 8 destinations (a total of 10 trips), with a total mileage of approximately 180 km. Driver 13 is assigned.

[0106] The battery matching module of the dispatching center converts the required mileage for the day of the delivery task based on the cargo weight of a single trip, and then adds the driver's driving energy consumption optimization evaluation level (Driver A No. 11, Driver B No. 13) to finally convert it into a second mileage value.

[0107] For vehicle No. 1, the second mileage requirement is 230km after conversion, a change of 30km (the base cargo weight is 1000kg for example, and the light truck is fully loaded with 1.5 tons).

[0108] For car No. 3, the second mileage requirement is 200km after conversion, a change of 20km.

[0109] The dispatch center uses the battery carrying capacity assessment module to obtain the predicted carrying capacity of all batteries (the mileage of each battery at different cargo weight ranges). Assuming the conversion to the cargo base weight, the carrying capacity of all batteries is ranked from high to low:

[0110] For example, No. 2 battery - 200km, No. 1 battery - 195km, No. 5 battery - 190km... (Due to battery aging, traffic congestion, and low temperatures, the average carrying capacity of all batteries is 190km, which is lower than the 250km carrying capacity of new batteries at a uniform speed and temperature of 20 degrees).

[0111] The battery allocation module calculates energy consumption, generates the first battery matching plan for the day, and sends it to the charging and swapping station:

[0112] (1) Sort the second mileage requirements of all vehicles from high to low: for example, vehicle 1 - 230 km, vehicle 3 - 200 km, vehicle 4 - 180 km, etc.

[0113] (2) Select vehicles whose second mileage is less than or equal to the maximum battery carrying capacity: vehicle No. 3 - 200km, vehicle No. 4 180km, etc.

[0114] (3) First, match batteries for the selected vehicles: Vehicle No. 3 - Battery No. 2, Vehicle No. 4 - Battery No. 1, ...

[0115] (4) Then, a battery is allocated to the vehicle whose second mileage exceeds the maximum battery carrying capacity, and a preliminary battery replacement trip is planned: for example, vehicle No. 1 - battery No. 10 (180 km), travel to the sixth destination to replace the battery and complete the next 50 km.

[0116] The charging and swapping center receives the battery allocation plan from the dispatching center and replaces the batteries with corresponding serial numbers for all vehicles.

[0117] In addition, the dispatch center can push energy-saving (optimal) routes to vehicles based on the energy consumption optimization module.

[0118] Assume that during the delivery process, the actual battery energy consumption of a day will vary significantly, often exceeding the historical average, depending on the degree of road congestion, the temperature, and the additional energy consumption of the vehicle (such as prolonged use of the air conditioner to increase or decrease the cabin temperature). This situation may occur in the fleet: the energy consumption of some vehicles exceeds the predicted level, while the energy consumption of some vehicles remains in line with the forecast. For example:

[0119] Car No. 3 - No. 2 battery, 0 battery replacements to complete the same-day delivery;

[0120] Car No. 4 - Battery No. 1, energy consumption increased due to congestion and needed to be replaced, and there was still 50km of travel to be completed.

[0121] Car No. 1 - Battery No. 10. Due to the low temperature, the driver's use of air conditioning and other equipment increased energy consumption, so the battery needed to be replaced earlier than the expected battery replacement before the sixth trip, and there were still 100km to complete.

[0122] At this point, the dispatch center uses the battery carrying capacity assessment module to obtain the corresponding carrying capacity of all batteries at each battery swap station; based on the vehicle's remaining delivery tasks, it converts the remaining second mileage required and directs the vehicle to the appropriate battery swap station. The second battery matching plan for the day is generated, specifically:

[0123] Car No. 4 and Car No. 1 both went to Battery Swap Station No. 1. There were two batteries in the station. Battery No. 11 had a SoC of 100% and a carrying capacity of 190km. The other one was replaced by another vehicle, Battery No. 8. After low-power charging, the current SoC was 40%, with a carrying capacity of 76km.

[0124] Car No. 4 - Battery No. 1, the remaining range is converted to a second mileage of 60km.

[0125] Car No. 1 - Battery No. 10, the remaining range is converted to a second mileage of 120km.

[0126] The dispatch center generates a battery allocation plan, swapping out the battery with the lowest capacity for the vehicle meeting the second mileage requirement and notifying the battery swap station. For example, if vehicle #4 arrives first, the battery swap station assigns it battery #8; if vehicle #1 arrives later, the battery swap station assigns it battery #11.

[0127] In addition, the dispatch center can also use the energy consumption optimization module to recommend energy-saving (optimal) routes suitable for the remaining journey to vehicles 4 and 1. The vehicles depart.

[0128] Finally, all vehicles completed their delivery tasks and returned to the charging and swapping station.

[0129] Please refer to Figure 5 , a structure of a battery management system for a battery swapping electric vehicle fleet provided in an embodiment of the present application includes:

[0130] A first acquisition module 51 is configured to acquire a delivery plan generated by a vehicle dispatch center and a first temperature range corresponding to the delivery plan. The delivery plan includes information about the driver and delivery route of each vehicle. The delivery route information includes starting and ending stops, intermediate stops, and end stops, and the corresponding load range and mileage for each segment of the delivery route. A delivery route between adjacent stops is considered a segment.

[0131] a first determining module 52 for determining, based on pre-generated carrying capacity information of each battery, a drivable range of the available battery at the starting station in the first temperature range, the reference load range, and the current battery charge level, wherein the carrying capacity information includes the drivable range of the battery in different temperature ranges, load ranges, and battery charges;

[0132] A second determining module 53 is configured to convert the mileage corresponding to each segment of the delivery route into a first required mileage within the first temperature range and the reference load range based on the first temperature range and the load range corresponding to each segment of the delivery route; calculate the sum of the first required mileage for each segment of the delivery route; and convert the sum of the first required mileage into a second required mileage for the delivery route based on the energy consumption optimization capability information of the driver corresponding to the delivery route;

[0133] The first allocation module 54 is configured to match the drivable mileage of the available batteries at the starting site with the second required mileage of the delivery route of each vehicle to determine a battery allocation plan for each vehicle.

[0134] Through the above modules, the embodiments of the present application can more accurately design battery scheduling plans for battery-swappable electric vehicle fleets, while taking into account both distribution costs and overall energy efficiency optimization.

[0135] Optionally, the above system further includes:

[0136] A second acquisition module is configured to acquire historical transport data of each battery based on historical order data delivered by the fleet and the collected historical battery data and historical environmental data, wherein the historical transport data includes the mileage and power consumption of the battery under different temperature ranges and load ranges;

[0137] a training module for training a carrying capacity estimation model for each battery using the historical carrying data of each battery, wherein the carrying capacity estimation model takes as input a temperature range, a load range, and a battery charge, and outputs the range that the battery can travel under the input temperature range, load range, and battery charge;

[0138] The first generation module is used to use the carrying capacity estimation model corresponding to each battery to generate the drivable mileage of each battery under different temperature ranges, load ranges and battery power levels, and obtain the carrying capacity information of each battery.

[0139] Optionally, the above system further includes:

[0140] a third acquisition module, configured to acquire at least one of a historical average battery energy recovery ratio, a historical average driving speed, a historical average number of starts and stops, and a cumulative mileage of the driver during the trip based on the historical order data delivered by the fleet and the collected historical driving operation data and historical battery data of the driver;

[0141] The second generating module is used to generate the energy consumption optimization capability information of the driver based on the at least one data.

[0142] Optionally, the battery data includes at least one of the following: battery identification, vehicle identification corresponding to the battery, battery power, battery energy recovery ratio of the battery during the trip, charging time, charging power, and geographic location;

[0143] The driving operation data includes at least one of the following: driving speed, number of starts and stops, and cumulative mileage;

[0144] The environmental data includes at least one of the following: temperature, the temperature range to which the temperature belongs, geographical location, and road slope;

[0145] The order data includes at least one of the following: the vehicle corresponding to the order, the driver, the departure station and destination station of each segment of the journey, the load of each segment of the journey, the load range to which the load of each segment of the journey belongs, and the latest arrival time at the destination station of each journey.

[0146] Optionally, the first allocation module is further used to sort the available batteries in descending order of the drivable mileage; and sort the vehicles in descending order of the second required mileage of the vehicle's delivery route; screen out vehicles whose second required mileage is not greater than the maximum drivable mileage to obtain first-category vehicles; allocate the battery with the largest drivable mileage among the remaining batteries and not less than the second required mileage of the current vehicle to each vehicle in the first category in descending order of the second required mileage, and delete the allocated battery from the remaining batteries after each battery is allocated to a vehicle, until all vehicles in the first category are allocated or the drivable mileage of the remaining batteries is less than the second required mileage of the current vehicle; for second-category vehicles that have not yet been allocated a battery, allocate the battery with the largest drivable mileage among the remaining batteries to each vehicle in the second category in descending order of the second required mileage.

[0147] Optionally, the above system further includes:

[0148] The sending module is used to send the battery allocation plan of each vehicle to the starting site, so that the starting site configures the corresponding battery for each vehicle according to the battery allocation plan.

[0149] Optionally, the above system further includes:

[0150] Redistribution module, used to:

[0151] During the process of the vehicle executing the delivery route, obtaining the remaining mileage and remaining battery power of the vehicle, and evaluating whether the remaining battery power meets the remaining mileage requirement;

[0152] If the requirements are not met, the vehicle is directed to a battery swap substation, and the mileage of the available batteries at the battery swap substation is determined based on the pre-generated carrying capacity information of each battery under the first temperature range, the reference load range, and the current battery charge level;

[0153] converting the mileage corresponding to each remaining segment of the delivery route into a third required mileage within the first temperature range and the reference load range based on the first temperature range and the load range corresponding to the remaining segments of the delivery route; calculating the sum of the third required mileage for each remaining segment of the delivery route; and converting the sum of the third required mileage into a fourth required mileage based on the energy consumption optimization capability information of the driver corresponding to the delivery route;

[0154] Match the drivable mileage of the available batteries at the battery swap station with the fourth required mileage of the vehicle, screen out candidate batteries whose drivable mileage is not less than the fourth required mileage, and use the battery with the smallest mileage among the candidate batteries as the battery swap for the vehicle.

[0155] Optionally, the above system further includes:

[0156] The first establishment module is used to calculate the average driving time and average energy consumption of each driving route in each temperature range of each trip based on the historical order data of the fleet delivery and the collected historical battery data. The driving route with the lowest average energy consumption is used as the recommended route for the trip in the temperature range, and the driving route with the lowest average driving time is used as the backup route for the trip in the temperature range to establish an energy consumption map.

[0157] a route recommendation module, configured to determine, based on the first temperature range and the energy consumption map, a recommended route and an alternative route for each trip in the delivery route of the vehicle, and to send the recommended route or alternative route for each trip in the delivery route to the vehicle based on the remaining battery power of the vehicle and / or the remaining delivery time of the delivery route.

[0158] It should be noted that the various systems provided in the above embodiments are systems corresponding to the battery management method for the battery swapping electric vehicle fleet. The implementation methods in the above embodiments are all applicable to the embodiments of the device and can achieve the same technical effects. The above device provided in the embodiment of the present application can implement all the method steps implemented in the above method embodiment and can achieve the same technical effects. The parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0159] Please refer to Figure 6 , a structural diagram of another battery management system for a battery-swap electric vehicle fleet provided in an embodiment of the present application, the device includes: a processor 601, a transceiver 602, a memory 603, a user interface 604 and a bus interface.

[0160] In the embodiment of the present application, the device further includes: a program stored in the memory 603 and executable on the processor 601 .

[0161] The transceiver 602 is configured to send and receive data under the control of the processor;

[0162] The processor 601 is configured to read the computer program in the memory and perform the following operations:

[0163] Obtaining a delivery plan generated by a vehicle dispatch center and a first temperature range corresponding to the delivery plan, wherein the delivery plan includes information about the driver and delivery route of each vehicle, and the delivery route information includes starting and ending stops, intermediate stops, and end stops, and the corresponding load range and mileage for each segment of the delivery route, where a delivery route between adjacent stops is considered a segment.

[0164] Determining, based on pre-generated carrying capacity information for each battery, a range that can be driven by the available battery at the starting station under the first temperature range, the baseline load range, and the current battery charge level, wherein the carrying capacity information includes the range that can be driven by the battery under different temperature ranges, load ranges, and battery charges;

[0165] converting the mileage corresponding to each segment of the delivery route into a first required mileage within the first temperature range and the reference load range based on the first temperature range and the load range corresponding to each segment of the delivery route; calculating a sum of the first required mileage for each segment of the delivery route; and converting the sum of the first required mileage into a second required mileage for the delivery route based on the energy consumption optimization capability information of the driver corresponding to the delivery route;

[0166] The drivable mileage of the available batteries at the starting site is matched with the second required mileage of the delivery route of each vehicle to determine a battery allocation plan for each vehicle.

[0167] It can be understood that in the embodiment of the present application, when the computer program is executed by the processor 601, each process of the above-mentioned battery management method embodiment for the battery swapping electric vehicle fleet can be implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0168] exist Figure 6 In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 601 and memory represented by memory 603. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 602 may be a plurality of components, i.e., a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium. For different user devices, the user interface 604 may also be an interface capable of connecting external or internal devices as required, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, etc.

[0169] The processor 601 is responsible for managing the bus architecture and general processing, and the memory 603 can store data used by the processor 601 when performing operations.

[0170] It should be noted that the device in this embodiment is a system corresponding to the battery management method for the above-mentioned battery-swap electric vehicle fleet, and the implementation methods in the above-mentioned embodiments are all applicable to the embodiments of the device, and can also achieve the same technical effects. In the device, the transceiver 602 and the memory 603, as well as the transceiver 602 and the processor 601 can be connected through the bus interface communication, the function of the processor 601 can also be implemented by the transceiver 602, and the function of the transceiver 602 can also be implemented by the processor 601. It should be noted here that the above-mentioned device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effects, and the parts and beneficial effects that are the same as those in the method embodiment in this embodiment will not be specifically described here.

[0171] In some embodiments of the present application, a computer-readable storage medium is further provided, on which a program is stored. When the program is executed by a processor, the following steps are implemented:

[0172] Obtaining a delivery plan generated by a vehicle dispatch center and a first temperature range corresponding to the delivery plan, wherein the delivery plan includes information about the driver and delivery route of each vehicle, and the delivery route information includes starting and ending stops, intermediate stops, and end stops, and the corresponding load range and mileage for each segment of the delivery route, where a delivery route between adjacent stops is considered a segment.

[0173] Determining, based on pre-generated carrying capacity information for each battery, a range that can be driven by the available battery at the starting station under the first temperature range, the baseline load range, and the current battery charge level, wherein the carrying capacity information includes the range that can be driven by the battery under different temperature ranges, load ranges, and battery charges;

[0174] converting the mileage corresponding to each segment of the delivery route into a first required mileage within the first temperature range and the reference load range based on the first temperature range and the load range corresponding to each segment of the delivery route; calculating a sum of the first required mileage for each segment of the delivery route; and converting the sum of the first required mileage into a second required mileage for the delivery route based on the energy consumption optimization capability information of the driver corresponding to the delivery route;

[0175] The drivable mileage of the available batteries at the starting site is matched with the second required mileage of the delivery route of each vehicle to determine a battery allocation plan for each vehicle.

[0176] When this program is executed by the processor, it can implement all the implementation methods of the battery management method of the above-mentioned battery-swap electric vehicle fleet and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0177] An embodiment of the present application also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the various processes of the above-mentioned battery management method embodiment for a battery-swap electric vehicle fleet are implemented, and the same technical effect can be achieved. To avoid repetition, they will not be repeated here.

[0178] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0179] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0180] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as 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 interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0181] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0182] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0183] If the functions are implemented in the form of software functional units and sold or used as independent products, they 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 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0184] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A battery management method for a battery swapping electric vehicle fleet, characterized in that: include: Obtaining a delivery plan generated by a vehicle dispatch center and a first temperature range corresponding to the delivery plan, wherein the delivery plan includes information about the driver and delivery route of each vehicle, and the delivery route information includes starting and ending stops, intermediate stops, and end stops, and the corresponding load range and mileage for each segment of the delivery route, where a delivery route between adjacent stops is considered a segment. Determining, based on pre-generated carrying capacity information for each battery, a range that can be driven by the available battery at the starting station under the first temperature range, the baseline load range, and the current battery charge level, wherein the carrying capacity information includes the range that can be driven by the battery under different temperature ranges, load ranges, and battery charges; converting the mileage corresponding to each segment of the delivery route into a first required mileage within the first temperature range and the reference load range based on the first temperature range and the load range corresponding to each segment of the delivery route; calculating a sum of the first required mileage for each segment of the delivery route; and converting the sum of the first required mileage into a second required mileage for the delivery route based on the energy consumption optimization capability information of the driver corresponding to the delivery route; The drivable mileage of the available batteries at the starting site is matched with the second required mileage of the delivery route of each vehicle to determine a battery allocation plan for each vehicle.

2. The method according to claim 1, wherein Also includes: Obtaining historical transport data for each battery based on historical order data delivered by the fleet and collected historical battery data and historical environmental data, wherein the historical transport data includes the mileage and power consumption of the battery under different temperature ranges and load ranges; Using the historical transport data of each battery, a transport capacity estimation model is trained for each battery, wherein the input of the transport capacity estimation model is the temperature range, load range, and battery power, and the output is the range that the battery can travel under the input temperature range, load range, and battery power; By using the carrying capacity estimation model corresponding to each battery, the mileage of each battery under different temperature ranges, load ranges and battery power levels is generated to obtain the carrying capacity information of each battery.

3. The method according to claim 2, wherein Also includes: Obtain at least one of the driver's historical average battery energy recovery ratio, historical average driving speed, historical average number of starts and stops, and cumulative mileage during the trip based on historical order data delivered by the fleet and collected historical driving operation data and historical battery data of the driver; Based on the at least one data, energy consumption optimization capability information of the driver is generated.

4. The method according to claim 3, wherein The battery data includes at least one of the following: battery identification, vehicle identification corresponding to the battery, battery power, battery energy recovery ratio during the trip, charging time, charging power, and geographic location; The driving operation data includes at least one of the following: driving speed, number of starts and stops, and cumulative mileage; The environmental data includes at least one of the following: temperature, the temperature range to which the temperature belongs, geographical location, and road slope; The order data includes at least one of the following: the vehicle corresponding to the order, the driver, the departure station and destination station of each segment of the journey, the load of each segment of the journey, the load range to which the load of each segment of the journey belongs, and the latest arrival time at the destination station of each journey.

5. The method according to claim 1, wherein Matching the drivable mileage of the available batteries at the starting station with the second required mileage of the delivery routes of the respective vehicles to determine a battery allocation plan for the respective vehicles, including: sorting the available batteries in descending order of the drivable mileage; and sorting the vehicles in descending order of the second required mileage of the delivery routes of the vehicles; Filter out vehicles whose second required mileage is not greater than the maximum drivable mileage to obtain first-category vehicles; Allocate the battery with the largest mileage among the remaining batteries that is not less than the second required mileage of the current vehicle to each vehicle in the first category in descending order of the second required mileage, and delete the allocated battery from the remaining batteries after each vehicle is allocated, until all vehicles in the first category have been allocated or the mileage of the remaining batteries is less than the second required mileage of the current vehicle; For the second type of vehicles that have not yet been allocated a battery, the battery with the largest mileage among the remaining batteries is allocated to each vehicle in the second type in descending order of the second required mileage.

6. The method according to claim 5, wherein Also includes: The battery allocation plan for each vehicle is sent to the starting site, so that the starting site configures corresponding batteries for each vehicle according to the battery allocation plan.

7. The method according to claim 5, wherein Also includes: During the process of the vehicle executing the delivery route, obtaining the remaining mileage and remaining battery power of the vehicle, and evaluating whether the remaining battery power meets the remaining mileage requirement; If the requirements are not met, the vehicle is directed to a battery swap substation, and the mileage of the available batteries at the battery swap substation is determined based on the pre-generated carrying capacity information of each battery under the first temperature range, the reference load range, and the current battery charge level; converting the mileage corresponding to each remaining segment of the delivery route into a third required mileage under the first temperature range and the reference load range based on the first temperature range and the load range corresponding to the remaining segments of the delivery route; and calculating a sum of the third required mileage for each remaining segment of the delivery route; converting the sum of the third required mileage into a fourth required mileage based on the energy consumption optimization capability information of the driver corresponding to the delivery route; Match the drivable mileage of the available batteries at the battery swap station with the fourth required mileage of the vehicle, screen out candidate batteries whose drivable mileage is not less than the fourth required mileage, and use the battery with the smallest mileage among the candidate batteries as the battery swap for the vehicle.

8. The method according to claim 1, wherein Also includes: Based on the fleet's historical delivery order data and collected historical battery data, the average driving time and average energy consumption of each route within each temperature range of each trip are calculated. The route with the lowest average energy consumption is selected as the recommended route for that temperature range, and the route with the lowest average driving time is selected as the backup route for that temperature range to create an energy consumption map. Based on the first temperature range and the energy consumption map, a recommended route and an alternative route for each trip in the delivery route of the vehicle are determined, and based on the remaining battery power of the vehicle and / or the remaining delivery time of the delivery route, the recommended route or the alternative route for each trip in the delivery route are sent to the vehicle.

9. A battery management system for a battery swapping electric vehicle fleet, characterized in that: include: a first acquisition module configured to acquire a delivery plan generated by a vehicle dispatch center and a first temperature range corresponding to the delivery plan, wherein the delivery plan includes information about the driver and delivery route of each vehicle, and the delivery route information includes information about the starting station, intermediate stations, and end stations, and the corresponding load range and mileage for each segment of the delivery route, wherein a delivery route between adjacent stations is considered a segment; a first determining module, configured to determine, based on pre-generated carrying capacity information of each battery, a drivable range of the available battery at the starting station under the first temperature range, the reference load range, and the current battery charge level, wherein the carrying capacity information includes the drivable range of the battery under different temperature ranges, load ranges, and battery charges; a second determination module configured to convert the mileage corresponding to each segment of the delivery route into a first required mileage within the first temperature range and the reference load range based on the first temperature range and the load range corresponding to each segment of the delivery route; calculate a sum of the first required mileage for each segment of the delivery route; and convert the sum of the first required mileage into a second required mileage for the delivery route based on the energy consumption optimization capability information of the driver corresponding to the delivery route; The first allocation module is used to match the drivable mileage of the available batteries at the starting site with the second required mileage of the delivery route of each vehicle to determine the battery allocation plan for each vehicle.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 8.

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