New energy heavy truck cargo matching system and method based on battery health degree

Abstract

The invention discloses a new energy heavy truck cargo matching system and method based on the battery health degree, and relates to the technical field of truck cargo matching, and the method comprises the steps: constructing a dynamic transport capacity portrait based on the current battery data of a vehicle, and predicting the maximum cumulative operation mileage of the day; according to the current SOC, the accumulated operation mileage on the current day and the to-be-matched cargo weight, determining equivalent cruising ability by referring to a cargo weight sensitive factor corresponding to the energy consumption level of the to-be-matched line, and determining an available mileage upper limit by combining the residual available limit; extracting a round-trip energy consumption characteristic value of a to-be-matched line, calculating an estimated energy consumption mileage, and carrying out available mileage upper limit, portrait and line energy consumption grade matching and accumulated discharge depth corresponding threshold pre-screening to bring the line meeting the condition into a feasible line set; and screening the total order control times, and pushing the order according to the portrait adaptation degree and the historical line matching success rate.

Description

Technical Field

[0001] This invention relates to the field of vehicle-cargo matching technology, specifically a new energy heavy-duty truck cargo matching system and method based on battery health. Background Technology

[0002] In the context of large-scale operation of new energy heavy-duty trucks, vehicle-cargo matching is a core link connecting transportation capacity and cargo demand, and its rationality directly determines operational efficiency, battery degradation, and overall operating costs. Current vehicle-cargo matching technologies have significant shortcomings, often limited to simple matching of cargo and vehicle basic transportation capacity. They fail to fully consider the unique characteristics of new energy heavy-duty trucks, which rely on batteries as their core power source, and in particular, do not incorporate battery health status into the core considerations of the matching logic, resulting in matching solutions lacking specificity and scientific rigor.

[0003] Traditional technologies fail to construct a dynamic capacity profile deeply coupled with battery health. Mileage calculation relies on a single parameter, neglecting the synergistic impact of historical operational data and route energy consumption characteristics. The determination of equivalent range is not linked to the compatibility between cargo weight and route energy consumption levels, and a full-process parameter feedback and update mechanism is lacking. This makes the matching process prone to problems such as insufficient vehicle range, excessive battery discharge, and invalid order matching. It fails to achieve accurate matching of capacity resources and cargo demand, reducing operational efficiency, while accelerating battery wear, shortening battery life, and increasing operating costs. It struggles to balance operational economics and battery health protection, failing to meet the actual needs of large-scale, refined operation of new energy heavy-duty trucks. Summary of the Invention

[0004] The purpose of this invention is to provide a new energy heavy-duty truck cargo matching system and method based on battery health, so as to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for matching freight to new energy heavy-duty trucks based on battery health, the method comprising:

[0006] S10. Based on the vehicle’s current battery health status (SOH), SOH decay rate, battery rated total capacity, historical full-load mileage utilization rate and cumulative discharge depth, construct a dynamic capacity profile and predict the basic maximum cumulative operating mileage for the day based on the battery status parameters and mileage constraint parameters in the dynamic capacity profile. S20. Combining the historical full-load mileage utilization rate with the energy consumption fluctuation coefficient of the vehicle's historical operating route that matches the vehicle's historical driving route type and road conditions in the line energy consumption feature database, the daily maximum cumulative operating mileage is corrected in two dimensions to obtain the daily maximum cumulative operating mileage that eliminates the influence of energy consumption fluctuation and load deviation. S30. Based on the current State of Charge (SOC), the cumulative operating mileage of the day, and the weight of the cargo to be matched, the weight of the cargo to be matched is associated with the energy consumption level of the line. The equivalent range of the combined battery remaining power and load conditions is determined by referring to the cargo weight sensitivity factor corresponding to the energy consumption level of the line to be matched, which is used to quantify the amplifying effect of cargo weight on range reduction. The equivalent range is used to characterize the maximum mileage that the vehicle can currently drive safely. Combined with the remaining available credit, the upper limit of available mileage is determined, which is simultaneously constrained by battery status, operating mileage, and cargo weight. S40. Extract the round-trip energy consumption feature values ​​of the route to be matched, including round-trip mileage, road condition energy consumption, and gradient loss, and calculate the estimated energy consumption mileage that reflects the actual energy consumption of the route. The available mileage limit constrained by the vehicle safe driving threshold, the profile of the vehicle performance matching the route transportation demand and the route energy consumption level matching, and the threshold corresponding to the cumulative discharge depth to prevent battery over-discharge are pre-screened. Only when all three conditions are met can it be identified as a suitable route, and those that meet the conditions are included in the feasible route set. S50. Determine the remaining number of cycles for the day based on the mapping relationship between the cumulative depth of discharge and the battery charge-discharge cycle life. Use the remaining number of cycles as the upper limit of the maximum number of transportation trips for the day. Filter orders to control the total number of trips. Prioritize pushing orders with high matching degree and high success rate. Sort and push orders according to profile adaptation degree and historical matching success rate of the route. S60 records actual operating data including actual mileage, actual energy consumption, actual discharge depth, and actual matching results; updates relevant parameters related to vehicle operation and battery status; corrects the correspondence between line energy consumption level and cargo weight sensitivity factor used for subsequent equivalent range calculation and line selection; and improves the historical line matching success rate used to optimize order push strategy.

[0007] According to the above scheme, step S10 includes: S11. Obtain the current battery health (SOH) value of the vehicle, the SOH decay rate within the preset historical period and the rated total battery capacity, and retrieve the historical operating data corresponding to the unique identifier of the vehicle from the historical database to obtain the historical full-load mileage utilization rate and cumulative discharge depth of the vehicle. S12. Based on the current battery health status (SOH) value and the rated total battery capacity, the theoretical maximum driving range of the vehicle is obtained through the conversion relationship between capacity and range. S13. Determine the degradation trend coefficient of vehicle battery performance reflecting the continuous decline trend of battery performance based on the SOH degradation rate, and make a double correction to the degradation trend coefficient by combining the historical full-load mileage utilization rate and cumulative discharge depth. Use the corrected degradation trend coefficient to reduce the theoretical maximum driving range and obtain the basic maximum cumulative operating mileage of the vehicle on the current day that conforms to the current actual health status of the vehicle. S14. The current battery health (SOH) value, the SOH decay rate, the rated total battery capacity, the historical full-load mileage utilization rate, the cumulative depth of discharge, and the actual available driving range are combined to form a dynamic capacity profile of the vehicle.

[0008] According to the above scheme, step S20 includes: S21. Obtain the historical full-load mileage utilization rate of the vehicle within a preset historical period. The historical full-load mileage utilization rate refers to the proportion of the mileage driven by the vehicle under full load to the total operating mileage during the same period, which is used to reflect the historical load usage intensity of the vehicle. S22. Extract the energy consumption fluctuation coefficient related to the vehicle's historical operating route from the route energy consumption feature library. The energy consumption fluctuation coefficient represents the energy consumption dispersion of the vehicle's past routes and is used to reflect the fluctuation impact of different routes on battery energy consumption. S23. Obtain the corresponding mileage correction coefficient based on the historical full-load mileage utilization rate and the energy consumption fluctuation coefficient; obtain the maximum cumulative operating mileage of the day based on the daily maximum cumulative operating mileage and the mileage correction coefficient.

[0009] According to the above scheme, step S30 includes: S31. Obtain the vehicle's current State of Charge (SOC) value and the cumulative operating mileage for the day, and at the same time obtain the weight of the cargo that uniquely corresponds to the order to be matched. S32. Based on the energy consumption level of the route to be matched, extract the corresponding cargo weight sensitive factor from the route energy consumption feature library. The cargo weight sensitive factor is used to characterize the amplification degree of the impact of cargo weight on the range under the route level. Different energy consumption levels correspond to different values ​​of cargo weight sensitive factor. According to the correspondence between cargo weight and range influence coefficient, and combined with the cargo weight sensitive factor, determine the influence coefficient of cargo weight on vehicle range. Use the influence coefficient to convert the theoretical range to obtain the equivalent range capability of the vehicle adapted to the current cargo weight and route level. S33. Based on the maximum cumulative operating mileage and the cumulative operating mileage of the day, obtain the remaining available quota; the remaining available quota represents the maximum theoretical remaining driving mileage of the vehicle in the day; compare the equivalent range capability with the remaining available quota, and select the smaller value as the upper limit of the vehicle's available mileage that takes into account both battery safety and operational constraints.

[0010] According to the above scheme, step S40 includes: S41. Extract the round-trip energy consumption feature value corresponding to the route to be matched. The round-trip energy consumption feature value is determined by the route mileage, road conditions, gradient and average speed. Calculate the estimated energy consumption mileage required for the vehicle to complete the route by converting the route energy consumption into equivalent driving mileage based on the round-trip energy consumption feature value. S42. Determine whether the estimated energy consumption mileage is less than or equal to the available mileage limit, and at the same time determine whether the battery performance parameters and capacity parameters in the dynamic capacity profile match the energy consumption level of the route. If they match, it means that the vehicle can safely complete the transportation on the route. S43. Obtain the threshold of the remaining cycle count for protecting the battery from over-discharge corresponding to the current cumulative discharge depth of the vehicle. If the estimated discharge depth corresponding to the estimated energy consumption mileage of the route may cause the threshold of the remaining cycle count to be exceeded, the route is deemed to exceed the battery's safe carrying capacity and the route is directly excluded. S44. Under the condition that the estimated energy consumption mileage meets the upper limit requirement, the dynamic capacity profile matches the energy consumption level of the line, and does not exceed the threshold of the remaining number of cycles, the line is identified as a safe, feasible, and suitable transportation line, and the line is included in the set of feasible lines.

[0011] According to the above scheme, step S50 includes: S51. Obtain the cumulative discharge depth of the vehicle, wherein the cumulative discharge depth characterizes the cumulative discharge degree of the vehicle battery within a preset historical period and is adapted to the vehicle battery health status. S52. Based on the cumulative discharge depth, determine the remaining number of cycles for the vehicle on that day according to the correspondence between the cumulative discharge depth and the remaining number of cycles. The remaining number of cycles refers to the upper limit of the number of charge-discharge cycles that the vehicle battery can continue to complete on that day in the current state. S53. Select orders to be matched from the set of feasible routes, so that the total number of trips of the selected orders does not exceed the remaining number of cycles; S54. Sort the filtered orders according to the matching degree score from high to low, and push the sorted orders to the corresponding vehicles. The matching degree score is obtained from the matching degree of the vehicle's dynamic capacity profile, the energy consumption level of the route, and the historical matching success rate of the route.

[0012] A new energy heavy truck freight matching system based on battery health includes: a profile building module, a mileage correction module, an available mileage module, a route filtering module, and a push update module; The profile building module is used to build a dynamic capacity profile and predict the vehicle’s basic maximum cumulative operating mileage for the day based on the vehicle’s current battery health status (SOH), SOH decay rate, battery rated total capacity, historical full-load mileage utilization rate and cumulative discharge depth. The mileage correction module is used to combine the historical full-load mileage utilization rate with the energy consumption fluctuation coefficient of the vehicle's historical operating route extracted from the line energy consumption feature database to correct the basic maximum cumulative operating mileage for the day, so as to obtain the maximum cumulative operating mileage for the day. The available mileage module is used to determine the equivalent range based on the vehicle's current state of charge (SOC), the accumulated operating mileage of the day, the weight of the cargo to be matched, and the cargo weight sensitivity factor corresponding to the energy consumption level of the route to be matched. Combined with the remaining available quota of the maximum accumulated operating mileage of the day, the upper limit of the vehicle's available mileage is determined. The route screening module is used to extract the round-trip energy consumption feature value of the route to be matched and calculate the estimated energy consumption mileage. After pre-screening based on the available mileage limit, dynamic capacity profile and route energy consumption level matching, and the threshold of the remaining cycle times corresponding to the current cumulative discharge depth of the vehicle, the routes that meet the conditions are included in the feasible route set. The push update module is used to determine the remaining number of cycles for the day based on the vehicle's cumulative discharge depth, filter orders from the feasible route set to ensure that the total number of trips does not exceed the remaining number of cycles, and push orders according to the compatibility between the dynamic capacity profile and the route energy consumption level and the historical matching success rate of the route. It also records the actual operation data for this time, updates the historical full load mileage utilization rate, route energy consumption characteristic value, and cumulative discharge depth, and corrects the correspondence between the route energy consumption level and the cargo weight sensitive factor and the historical matching success rate of the route.

[0013] According to the above scheme, the profile construction module includes a parameter acquisition unit, a theoretical mileage unit, and an attenuation correction unit; The parameter acquisition unit is used to acquire the current battery health (SOH) value of the vehicle, the SOH decay rate within a preset historical period, the rated total capacity of the battery, and to acquire the historical full-load mileage utilization rate and cumulative discharge depth of the vehicle from the historical database. The theoretical mileage unit is used to obtain the vehicle's current theoretical maximum driving range based on the current battery health (SOH) value and the battery's rated total capacity. The attenuation correction unit is used to determine the attenuation trend coefficient of the vehicle battery performance based on the SOH attenuation rate, and to dynamically correct the attenuation trend coefficient by combining the historical full-load mileage utilization rate and cumulative discharge depth. The theoretical maximum driving range is reduced by the corrected attenuation trend coefficient to obtain the vehicle's basic maximum cumulative operating mileage for the day. According to the above scheme, the mileage correction module includes a utilization rate acquisition unit, a fluctuation coefficient unit, a correction coefficient unit, and a mileage correction unit; The utilization rate acquisition unit is used to acquire the historical full-load mileage utilization rate of the vehicle within a preset historical period. The historical full-load mileage utilization rate refers to the proportion of the mileage driven by the vehicle in a fully loaded state to the total operating mileage in the same period. The fluctuation coefficient unit is used to extract the energy consumption fluctuation coefficient related to the vehicle's historical operating route from the route energy consumption feature library. The energy consumption fluctuation coefficient characterizes the degree of energy consumption dispersion of the route previously traveled by the vehicle. The correction coefficient unit is used to obtain the corresponding mileage correction coefficient based on the historical full-load mileage utilization rate and the energy consumption fluctuation coefficient. The mileage correction unit is used to obtain the maximum cumulative operating mileage of the day based on the maximum cumulative operating mileage of the day and the mileage correction coefficient.

[0014] According to the above scheme, the push update module includes a loop count unit, an order filtering unit, a sorting push unit, and a data update unit; The cycle count unit is used to obtain the cumulative discharge depth of the vehicle, and determine the remaining cycle count of the vehicle for the day according to the correspondence between the cumulative discharge depth and the remaining cycle count. The order filtering unit is used to filter orders to be matched from the set of feasible routes, so that the total number of trips of the selected orders does not exceed the remaining number of cycles. The sorting and push unit is used to sort and push the filtered orders according to the matching degree score from high to low. The matching degree score is obtained by the matching degree of the vehicle dynamic capacity profile, the energy consumption level of the route, and the historical matching success rate of the route. The data update unit is used to record the actual operation data, update the vehicle's historical full-load mileage utilization rate, the energy consumption characteristic value of the line, and the cumulative discharge depth, and correct the correspondence between the line energy consumption level and the cargo weight sensitivity factor and the line's historical matching success rate based on the matching results.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs a dynamic capacity profile by integrating multi-dimensional parameters such as battery health status (SOH), SOH decay rate, and historical full-load mileage utilization rate. It accurately calculates available mileage by combining route energy consumption characteristics and cargo weight sensitive factors, effectively reducing empty running rate and invalid orders, and improving overall operational efficiency and resource utilization. 2. This invention constrains the total number of trips for an order by cumulative discharge depth and dynamically corrects the operating mileage by combining the SOH decay rate, thereby avoiding over-discharge of the battery. It also optimizes the matching strategy through full-process data feedback, reducing battery wear and extending battery life. 3. This invention uses the energy consumption fluctuation coefficient and cargo weight sensitivity factor in the line energy consumption feature library to enable the matching process to adaptively adjust cargo weight constraints and mileage limits according to the energy consumption characteristics of different lines, effectively avoiding the risk of breakdowns or excessive battery discharge during transportation caused by deviations in line energy consumption prediction. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of a new energy heavy-duty truck freight matching system and method based on battery health according to the present invention; Figure 2 This is a schematic diagram of the structure of a new energy heavy-duty truck cargo matching system and method based on battery health according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example: Figures 1-2 As shown, the present invention provides a technical solution: a method for matching freight to new energy heavy-duty trucks based on battery health, the method comprising: S10. Based on the vehicle’s current battery health (SOH), SOH decay rate, battery rated total capacity, historical full-load mileage utilization rate and cumulative discharge depth, construct a dynamic capacity profile and predict the basic maximum cumulative operating mileage for the day based on the battery state parameters and mileage constraint parameters in the dynamic capacity profile. S20. Combining the historical full-load mileage utilization rate with the energy consumption fluctuation coefficient of the vehicle's historical operating route that matches the vehicle's historical driving route type and road conditions in the line energy consumption feature database, the daily maximum cumulative operating mileage is corrected in two dimensions to obtain the daily maximum cumulative operating mileage that eliminates the influence of energy consumption fluctuation and load deviation. S30. Based on the current State of Charge (SOC), the cumulative operating mileage of the day, and the cargo weight to be matched, the cargo weight to be matched is associated with the line energy consumption level. The equivalent range of the combined battery remaining power and load conditions is determined by referring to the cargo weight sensitivity factor corresponding to the energy consumption level of the line to be matched, which is used to quantify the amplifying effect of cargo weight on range reduction. The equivalent range is used to characterize the maximum mileage that the vehicle can currently drive safely. Combined with the remaining available credit, the upper limit of available mileage is determined, which is simultaneously constrained by battery status, operating mileage, and cargo weight. S40. Extract the round-trip energy consumption feature values ​​of the route to be matched, including round-trip mileage, road condition energy consumption, and gradient loss, and calculate the estimated energy consumption mileage that reflects the actual energy consumption of the route. The available mileage limit constrained by the vehicle safe driving threshold, the profile of the vehicle performance matching the route transportation demand and the route energy consumption level matching, and the threshold corresponding to the cumulative discharge depth to prevent battery over-discharge are pre-screened. Only when all three conditions are met can it be identified as a suitable route, and those that meet the conditions are included in the feasible route set. S50. Determine the remaining number of cycles for the day based on the mapping relationship between the cumulative depth of discharge and the battery charge-discharge cycle life. Use the remaining number of cycles as the upper limit of the maximum number of transportation trips for the day. Filter orders to control the total number of trips. Prioritize pushing orders with high matching degree and high success rate. Sort and push orders according to profile adaptation degree and historical matching success rate of the route. S60 records actual operating data including actual mileage, actual energy consumption, actual discharge depth, and actual matching results; updates relevant parameters related to vehicle operation and battery status; corrects the correspondence between line energy consumption level and cargo weight sensitivity factor used for subsequent equivalent range calculation and line selection; and improves the historical line matching success rate used to optimize order push strategy.

[0019] Specifically, step S10 includes: S11. Obtain the current battery health (SOH) value of the vehicle, the SOH decay rate within the preset historical period and the rated total battery capacity, and retrieve the historical operating data corresponding to the unique identifier of the vehicle from the historical database to obtain the historical full-load mileage utilization rate and cumulative discharge depth of the vehicle. Specifically, the current battery health status (SOH) value of a certain new energy heavy truck is obtained as 85% (0.85) through the vehicle battery status acquisition module. The preset historical period is a statistical time interval pre-set by the system, which is set to 30 days in this embodiment. The SOH decay rate is obtained by linear fitting of the SOH value within the preset historical period, which is 0.002 / day. The rated total battery capacity is the nominal total battery capacity at the time of vehicle delivery, obtained from the vehicle's factory parameters. In this embodiment, the rated total battery capacity is 350kWh. The historical operating data corresponding to the unique identifier of the vehicle is retrieved from the historical database. The historical full-load mileage within 30 days is 12,000km, and the total operating mileage during the same period is 15,000km. Therefore, the historical full-load mileage utilization rate is 12,000 / 15,000 = 0.8, or 80%. The cumulative discharge depth within 30 days is 180kWh. The cumulative discharge depth is the sum of the discharge depths of each time within the preset historical period, and the unit is consistent with the battery capacity. This is only an example and is not a limitation. S12. Based on the current battery health status (SOH) value and the battery's rated total capacity, the vehicle's current theoretical maximum driving range is obtained through the conversion relationship between capacity and range. Specifically, based on the current battery health status (SOH) value and the battery's rated total capacity, the theoretical maximum driving range is obtained through the conversion relationship between capacity and range. This embodiment uses a linear conversion algorithm, with the formula: Lt=C×SOH×k; where Lt represents the theoretical maximum driving range, C represents the battery's rated total capacity, SOH represents the battery health status, and k represents the range coefficient per unit capacity, which is the driving range per unit battery capacity obtained through real vehicle road testing calibration. In this embodiment, the value is 0.8, and the theoretical maximum driving range Lt=350×0.85×0.8=238km; S13. Determine the degradation trend coefficient of vehicle battery performance based on the SOH degradation rate, which reflects the continuous decline in battery performance. Combine the historical full-load mileage utilization rate and cumulative discharge depth to double correct the degradation trend coefficient. Use the corrected degradation trend coefficient to reduce the theoretical maximum driving range and obtain the vehicle's basic maximum cumulative operating mileage for the day that conforms to the current actual health status of the vehicle. Specifically, the degradation trend coefficient of battery performance is determined based on the SOH degradation rate. This embodiment uses an exponential degradation algorithm, with the formula: kd=e (-r×t) Where kd represents the decay trend coefficient, r represents the SOH decay rate, and t represents the preset historical period days; the decay trend coefficient kd = e (-0.002×30) ≈0.9418; The method of double correction of the attenuation trend coefficient is as follows: the historical full-load mileage utilization rate and the ratio of cumulative discharge depth to rated capacity are weighted to obtain a comprehensive correction factor, which is then multiplied by the attenuation trend coefficient to obtain the corrected attenuation trend coefficient; the corrected attenuation trend coefficient is multiplied by the theoretical maximum driving range to obtain the basic maximum cumulative operating mileage Lb=153.7km for the day. S14. The current battery health status (SOH) value, SOH decay rate, total rated battery capacity, historical full-load mileage utilization rate, cumulative depth of discharge, and actual available driving range are combined to form a dynamic capacity profile of the vehicle.

[0020] Specifically, step S20 includes: S21. Obtain the historical full-load mileage utilization rate of the vehicle within a preset historical period. The historical full-load mileage utilization rate refers to the proportion of the mileage driven by the vehicle under full load to the total operating mileage during the same period, which is used to reflect the historical load usage intensity of the vehicle. Specifically, the historical full-load mileage utilization rate of the vehicle within the preset historical period is 0.8. This historical full-load mileage utilization rate is used to reflect the historical load intensity of the vehicle. The higher the value, the closer the historical load of the vehicle is to the full load state. S22. Extract the energy consumption fluctuation coefficient related to the vehicle's historical operating route from the route energy consumption feature library. The energy consumption fluctuation coefficient represents the degree of energy consumption dispersion of the vehicle's past routes and is used to reflect the fluctuation impact of different routes on battery energy consumption. Specifically, the route energy consumption feature library is a pre-established database that stores energy consumption fluctuation coefficients corresponding to different route types, road conditions, mileage, and transportation scenarios. Energy consumption fluctuation coefficients consistent with the road conditions, mileage, and transportation scenarios of the vehicle's historical operating routes are extracted from the route energy consumption feature library. After querying the route energy consumption feature library, the energy consumption fluctuation coefficient of this type of route is 1.05. This coefficient represents the degree of energy consumption dispersion of the routes previously traveled by the vehicle. A value greater than 1 indicates that the energy consumption of this type of route is slightly higher than the average level, and a value less than 1 indicates that the energy consumption is lower than the average level. S23. Obtain the corresponding mileage correction coefficient based on the historical full-load mileage utilization rate and energy consumption fluctuation coefficient; obtain the maximum cumulative operating mileage of the day based on the basic maximum cumulative operating mileage and the mileage correction coefficient. Specifically, based on the historical full-load mileage utilization rate and energy consumption fluctuation coefficient, this embodiment uses a linear mapping algorithm to obtain the corresponding mileage correction coefficient, with the formula: km=0.9+0.1×η-0.05×(kb-1); where km represents the mileage correction coefficient, η represents the historical full-load mileage utilization rate, and kb represents the energy consumption fluctuation coefficient. The obtained mileage correction coefficient km=0.9+0.1×0.8-0.05×(1.05-1)=0.978. Based on the daily maximum cumulative operating mileage and the mileage correction coefficient, the daily maximum cumulative operating mileage is obtained with the formula: Lmax=Lb×km; where Lmax represents the daily maximum cumulative operating mileage, and Lb represents the daily basic maximum cumulative operating mileage. The obtained daily maximum cumulative operating mileage Lmax=153.7×0.978≈150.3km is only for illustrative purposes and is not a limitation.

[0021] Specifically, step S30 includes: S31. Obtain the vehicle's current State of Charge (SOC) value and the cumulative operating mileage for the day, and at the same time obtain the weight of the cargo that uniquely corresponds to the order to be matched. Specifically, the current State of Charge (SOC) value is 70% obtained through the vehicle battery status acquisition module. The SOC value is the percentage of remaining power that the battery management system collects and reports in real time. The cumulative operating mileage for the day is 40km obtained through the vehicle operation data acquisition module. At the same time, the weight of the cargo to be matched, which is uniquely associated with the order to be matched, is 30t. The cargo weight data is directly obtained from the order information. S32. Based on the energy consumption level of the route to be matched, extract the corresponding cargo weight sensitive factor from the route energy consumption feature library. The cargo weight sensitive factor is used to characterize the amplification degree of the impact of cargo weight on the range under the route level. Different energy consumption levels correspond to different values ​​of cargo weight sensitive factor. According to the correspondence between cargo weight and range influence coefficient, and combined with the cargo weight sensitive factor, determine the influence coefficient of cargo weight on vehicle range. Use the influence coefficient to convert the theoretical range to obtain the equivalent range capability of the vehicle adapted to the current cargo weight and route level. Specifically, the energy consumption level of the route is a pre-defined level for all routes to be matched, categorized by energy consumption, including Level 1, Level 2, and Level 3. Based on the energy consumption level of the route to be matched, and after querying the route information, the energy consumption level of this route is determined to be Level 2. The cargo weight sensitivity factor corresponding to Level 2 energy consumption is extracted from the route energy consumption feature library and is set to 1.2. This cargo weight sensitivity factor characterizes the amplification of the impact of cargo weight on range on Level 2 routes. Different energy consumption levels correspond to different values ​​of the cargo weight sensitivity factor, which are pre-calibrated and stored in the route energy consumption feature library based on extensive real-vehicle test data by those skilled in the art. In this embodiment, the cargo weight sensitivity factor is 1.0 for Level 1 routes, 1.2 for Level 2 routes, and 1.5 for Level 3 routes. The cargo weight sensitivity factor is determined according to the correspondence between cargo weight and range impact coefficient, combined with the cargo weight sensitivity factor. The influence coefficient of cargo weight on vehicle range is calculated using an influence coefficient algorithm in this embodiment. The formula is kw=1+ks×(W-W0) / W0; where kw represents the influence coefficient of cargo weight on range, ks represents the cargo weight sensitivity factor, W represents the cargo weight to be matched, and W0 represents the baseline cargo weight, which is the standard load value preset by the system. The cargo weight influence coefficient is obtained as kw=1+1.2×(30-20) / 20=1.6. The equivalent range formula in this embodiment is: Leq=(C×SOH×SOC×k) / kw; where Leq represents the equivalent range, C represents the rated total capacity of the battery, and k represents the range coefficient per unit capacity. The equivalent range is obtained as Leq=(350×0.85×0.7×0.8) / 1.6≈83.3km. This is only an example and is not a limitation. S33. Based on the maximum cumulative operating mileage and the cumulative operating mileage of the day, obtain the remaining available quota; the remaining available quota represents the maximum theoretical mileage that the vehicle can still drive on the day; compare the equivalent range and the remaining available quota, and select the smaller value as the upper limit of the vehicle's available mileage that takes into account both battery safety and operational constraints. Specifically, the maximum cumulative operating mileage of the day minus the cumulative operating mileage of the day yields a remaining available quota of 111.7km. Comparing the equivalent range of 81.8km with the remaining available quota of 111.7km, the smaller value is selected as the upper limit of the available mileage of the vehicle that balances battery safety and operational constraints, i.e., the upper limit of available mileage is 81.8km.

[0022] Specifically, step S40 includes: S41. Extract the round-trip energy consumption feature value corresponding to the route to be matched. The round-trip energy consumption feature value is determined by the route mileage, road conditions, gradient and average speed. Calculate the estimated energy consumption mileage required for the vehicle to complete the route by converting the route energy consumption into an equivalent driving mileage based on the round-trip energy consumption feature value. Specifically, the round-trip energy consumption characteristic value is the total electricity consumed to complete the round-trip transportation on the route, which is obtained through statistical analysis of historical operation data of similar routes in the route energy consumption characteristic database. The round-trip energy consumption characteristic value corresponding to the route to be matched is extracted. This round-trip energy consumption characteristic value is determined by the route mileage, road conditions, gradient, and average speed. According to the route energy consumption characteristic database, the round-trip energy consumption characteristic value of this route is 90 kWh. Based on the round-trip energy consumption characteristic value, the estimated energy consumption mileage is calculated by converting the route energy consumption into the equivalent driving mileage. In this embodiment, the energy consumption-mileage conversion algorithm is used, and the formula is: Lp=(E×k) / (SOH×SOC); Lp represents the estimated energy consumption mileage, and E represents the round-trip energy consumption characteristic value. The estimated energy consumption mileage is obtained as Lp=(90×0.8) / (0.85×0.7)≈121km. S42. Determine whether the estimated energy consumption mileage is less than or equal to the upper limit of the available mileage. At the same time, determine whether the battery performance parameters and capacity parameters in the dynamic capacity profile match the energy consumption level of the route. If they match, it means that the vehicle can safely complete the transportation on the route. Specifically, the process involves determining whether the estimated energy consumption mileage is less than or equal to the upper limit of the available mileage. A comparison shows 121.0km > 81.8km, which does not meet the condition. Simultaneously, the process checks whether the battery performance parameters and capacity parameters in the dynamic capacity profile match the energy consumption level of the route. The dynamic capacity profile matches the energy consumption level of the secondary route, but because the estimated energy consumption mileage does not meet the upper limit of the available mileage, it is not included in the feasible route set. Another route to be matched is selected, and its round-trip energy consumption characteristic value is extracted as 60kWh. The above calculation is repeated, and the estimated energy consumption mileage is Lp = (60 × 0.8) / (0.85 × 0.7) ≈ 80.7km. The process then checks whether the estimated energy consumption mileage is less than or equal to the upper limit of the available mileage, which meets the condition. Simultaneously, the dynamic capacity profile matches the energy consumption level of the route, which also meets the condition. This is only an example and does not impose any restrictions. S43. Obtain the threshold of the remaining cycle count for protecting the battery from over-discharge corresponding to the current cumulative discharge depth of the vehicle. If the estimated discharge depth corresponding to the estimated energy consumption mileage of the route may cause the threshold of the remaining cycle count to be exceeded, the route is deemed to exceed the battery's safe carrying capacity and the route is directly excluded. Specifically, the remaining cycle life threshold corresponding to the current cumulative discharge depth of the vehicle, used to protect the battery from over-discharge, is obtained. In this embodiment, a linear mapping algorithm is used to determine the threshold, with the formula: N=Na×(1-Dl / Da); where N represents the remaining cycle life threshold, Na represents the total charge-discharge cycle life of the battery, which is the battery's nominal life parameter at the factory, preset to 2000 times in this embodiment, Dl represents the cumulative discharge depth within a preset historical period, and Da represents the maximum allowable cumulative discharge depth within the historical period, which is a threshold preset according to the battery safety usage specifications, preset to 300kWh in this embodiment. The remaining cycle life threshold is obtained as N=2000×(1-180 / 300)=800 times. Based on the estimated energy of the circuit to be matched... The estimated discharge depth is calculated based on the mileage consumed, using a linear conversion algorithm: Dp = (Lp × C) / (Lt); where Dp represents the estimated discharge depth; Lp represents the estimated energy consumption mileage; C represents the rated total capacity of the battery; and Lt represents the theoretical maximum driving range. The estimated discharge depth is obtained as Dp = (80.7 × 350) / 238 ≈ 118.6 kWh. The judgment method is to compare the estimated discharge depth with the maximum allowable single discharge depth corresponding to the remaining cycle count threshold. If it is less than the threshold, the threshold is not exceeded. After analysis, the estimated discharge depth corresponding to a single trip on this route will not cause the remaining cycle count of the battery to fall below the remaining cycle count threshold, that is, it does not exceed the remaining cycle count threshold, and meets the battery safety constraints. Therefore, this route does not need to be excluded. S44. Under the condition that the estimated energy consumption mileage meets the mileage upper limit requirement, the dynamic capacity profile matches the line energy consumption level and does not exceed the threshold of the remaining number of cycles, the line is identified as a safe, feasible and suitable transportation line and is included in the feasible line set. Specifically, the estimated energy consumption mileage of the route to be matched is 80.7km ≤ the upper limit of available mileage is 81.8km, which meets the upper limit of mileage requirement; the dynamic capacity profile matches the energy consumption level of the route, which meets the profile matching requirement; the cycle consumption corresponding to the estimated discharge depth has not exceeded the threshold of 800 remaining cycles, which meets the battery safety constraint requirement. All three conditions are met simultaneously, so the route is identified as a safe, feasible and suitable transportation route and included in the feasible route set. In this embodiment, the feasible route set includes this route and other routes that meet the conditions after screening. This is only used as an example for illustration and is not a limitation. Specifically, step S50 includes: S51. Obtain the cumulative discharge depth of the vehicle. The cumulative discharge depth represents the cumulative discharge level of the vehicle battery within a preset historical period and is adapted to the vehicle battery health status. Specifically, through the vehicle battery status acquisition module and historical database, the cumulative discharge depth of the vehicle within a preset historical period of 30 days is obtained as 180kWh. This cumulative discharge depth is negatively correlated with the battery health status (SOH). The larger the cumulative discharge depth, the faster the battery health status deteriorates. The two are mutually compatible and serve as the core parameters for battery status assessment. S52. Based on the cumulative depth of discharge, determine the remaining number of cycles for the vehicle on the current day according to the correspondence between the cumulative depth of discharge and the remaining number of cycles. The remaining number of cycles refers to the upper limit of the number of charge-discharge cycles that the vehicle battery can continue to complete on the current day under the current state. Specifically, the remaining number of cycles per day is calculated using an algorithm that combines the remaining number of cycles threshold with a preset historical period. The formula is: Nday = N / T; where Nday represents the remaining number of cycles per day; N represents the remaining number of cycles threshold; and T represents the preset historical period. Substituting the data, we can calculate: Nday = 800 / 30 ≈ 26 times, meaning that the vehicle has 26 remaining cycles per day. This serves as the maximum number of trips allowed per day, ensuring that the battery does not degrade rapidly due to overcharging and discharging. S53. Select orders to be matched from the set of feasible routes, so that the total number of trips of the selected orders does not exceed the remaining number of cycles; Specifically, the system iterates through all orders to be matched in the feasible route set, counts the number of transport trips for each order, filters out orders and order combinations with a total number of trips ≤ 26, and removes orders and order combinations with a total number of trips exceeding 26. This is to avoid the number of transport trips in a day exceeding the battery's safe cycle range, prevent excessive battery discharge, and ensure the battery's health. In this embodiment, each order in the feasible route set is a single transport. After filtering, orders with 26 or fewer are selected for inclusion in the push range. This is only an example and is not a limitation. S54. Sort the filtered orders according to the matching degree score from high to low, and push the sorted orders to the corresponding vehicles. The matching degree score is obtained from the matching degree of the vehicle's dynamic capacity profile, the energy consumption level of the route, and the historical matching success rate of the route. Specifically, a weighted scoring algorithm is used to calculate the suitability score for each order to be pushed, using the formula: S = ω1 × M + ω2 × R; where S represents the suitability score; ω1 represents the weight of the matching degree between the dynamic capacity profile and the route energy consumption level, which is 0.6 in this embodiment; M represents the matching degree between the dynamic capacity profile and the route energy consumption level, which is M = 0.9 in this embodiment; ω2 represents the weight of the historical matching success rate of the route, which is 0.4 in this embodiment; and R represents the historical matching success rate of the route. The ratio of successful matches to total matches for this route is obtained from the historical database. A query of the historical database shows that the historical matching success rate for this route is R=0.85. The suitability score is calculated as S=0.6×0.9+0.4×0.85=0.54+0.34=0.88. Following the above method, the suitability score of all orders to be pushed after filtering is calculated. Orders are sorted in descending order of score, and the sorted orders are pushed to the vehicle sequentially to complete the order push operation. For orders with the same score, random sorting is acceptable; no restrictions are placed here. Specifically, after a vehicle completes the transportation task for a given order, the actual operational data is recorded, including actual mileage, actual energy consumption, actual depth of discharge, and actual matching results. This operational data is then written to the historical database and associated with the vehicle's unique identifier. Based on the recorded operational data, parameters related to vehicle operation and battery status are updated: historical full-load mileage and total operational mileage are updated, and the historical full-load mileage utilization rate is recalculated; the cumulative depth of discharge is updated; and the SOH decay rate is updated. Combining the actual depth of discharge with changes in battery health, the SOH decay rate is corrected by refitting data from a preset historical period. Simultaneously, based on the actual energy consumption, actual cargo weight, and line... The correspondence between route energy consumption levels and cargo weight sensitivity factors is corrected to make the cargo weight sensitivity factors more consistent with actual operating conditions. Based on the current order matching results, the historical matching success rate of the route is updated using the formula: Rnew=(Rold×n+Sresult) / n+1; where Rnew represents the updated historical matching success rate, Rold represents the previous historical matching success rate, n represents the number of matches before the update, and Sresult represents the current matching result, with 1 for a successful match and 0 for a failed match. This provides more accurate data support for subsequent vehicle-cargo matching equivalent range calculation, route selection, and order sorting and pushing, forming a closed-loop optimization mechanism.

[0023] This invention provides another technical solution: a new energy heavy truck freight matching system based on battery health, which includes: a profile construction module, a mileage correction module, an available mileage module, a route screening module, and a push update module; The profile building module is used to build a dynamic capacity profile and predict the vehicle’s basic maximum cumulative operating mileage for the day based on the vehicle’s current battery health status (SOH), SOH decay rate, battery rated total capacity, historical full-load mileage utilization rate, and cumulative discharge depth. The mileage correction module is used to combine the historical full-load mileage utilization rate with the energy consumption fluctuation coefficient of the vehicle's historical operating route extracted from the line energy consumption feature database to correct the basic maximum cumulative operating mileage for the day, thus obtaining the maximum cumulative operating mileage for the day. The available mileage module is used to determine the equivalent range based on the vehicle's current state of charge (SOC), the cumulative operating mileage of the day, the weight of the cargo to be matched, and the cargo weight sensitivity factor corresponding to the energy consumption level of the route to be matched. Combined with the remaining available quota of the maximum cumulative operating mileage of the day, the upper limit of the vehicle's available mileage is determined. The route screening module is used to extract the round-trip energy consumption feature values ​​of the routes to be matched and calculate the estimated energy consumption mileage. After pre-screening based on the upper limit of available mileage, dynamic capacity profile and route energy consumption level matching, and the threshold of the remaining number of cycles corresponding to the current cumulative discharge depth of the vehicle, the routes that meet the conditions are included in the feasible route set. The push update module is used to determine the remaining number of cycles for the day based on the vehicle's cumulative discharge depth, filter orders from feasible routes to ensure that the total number of trips does not exceed the remaining number of cycles, and push orders according to the compatibility between the dynamic capacity profile and the route energy consumption level and the historical matching success rate of the route. It also records the actual operation data for this time, updates the historical full load mileage utilization rate, route energy consumption characteristic value, and cumulative discharge depth, and corrects the correspondence between the route energy consumption level and the cargo weight sensitive factor and the historical matching success rate of the route.

[0024] Specifically, the profile construction module includes a parameter acquisition unit, a theoretical mileage unit, and a degradation correction unit. The parameter acquisition unit is used to acquire the vehicle's current battery health (SOH) value, the SOH degradation rate within a preset historical period, and the rated total battery capacity, and to obtain the vehicle's historical full-load mileage utilization rate and cumulative discharge depth from the historical database. The theoretical mileage unit is used to obtain the vehicle's current theoretical maximum driving range based on the current battery health (SOH) value and the rated total battery capacity. The degradation correction unit is used to determine the degradation trend coefficient of the vehicle's battery performance based on the SOH degradation rate, and to dynamically correct the degradation trend coefficient in combination with the historical full-load mileage utilization rate and cumulative discharge depth. The theoretical maximum driving range is then reduced using the corrected degradation trend coefficient to obtain the vehicle's basic maximum cumulative operating mileage for the day. Specifically, the mileage correction module includes a utilization rate acquisition unit, a fluctuation coefficient unit, a correction coefficient unit, and a mileage correction unit. The utilization rate acquisition unit is used to obtain the historical full-load mileage utilization rate of the vehicle within a preset historical period. The historical full-load mileage utilization rate refers to the proportion of the mileage traveled by the vehicle under full load to the total operating mileage during the same period. The fluctuation coefficient unit is used to extract the energy consumption fluctuation coefficient related to the vehicle's historical operating routes from the route energy consumption feature library. The energy consumption fluctuation coefficient characterizes the degree of energy consumption dispersion of the routes previously traveled by the vehicle. The correction coefficient unit is used to obtain the corresponding mileage correction coefficient based on the historical full-load mileage utilization rate and the energy consumption fluctuation coefficient. The mileage correction unit is used to obtain the maximum cumulative operating mileage of the day based on the maximum cumulative operating mileage of the day and the mileage correction coefficient.

[0025] Specifically, the push update module includes a cycle count unit, an order filtering unit, a sorting push unit, and a data update unit. The cycle count unit is used to obtain the vehicle's cumulative discharge depth and determine the vehicle's remaining cycle count for the day based on the correspondence between the cumulative discharge depth and the remaining cycle count. The order filtering unit is used to filter orders to be matched from the set of feasible routes, ensuring that the total number of trips for the selected orders does not exceed the remaining cycle count. The sorting push unit is used to sort and push the filtered orders according to their suitability score from high to low. The suitability score is obtained by weighting the vehicle's dynamic capacity profile, the matching degree of the route's energy consumption level, and the historical matching success rate of the route. The data update unit is used to record the actual operation data for this operation, update the vehicle's historical full-load mileage utilization rate, the route's energy consumption characteristic value, and the cumulative discharge depth, and correct the correspondence between the route's energy consumption level and the cargo weight sensitivity factor, as well as the route's historical matching success rate, based on the current matching results.

[0026] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for matching freight to new energy heavy-duty trucks based on battery health, characterized in that: The method includes: S10. Based on the vehicle's current battery health status (SOH), SOH decay rate, battery rated total capacity, historical full-load mileage utilization rate, and cumulative discharge depth, construct a dynamic capacity profile and predict the basic maximum cumulative operating mileage for the day. S20. Combining the historical full-load mileage utilization rate with the energy consumption fluctuation coefficient of the vehicle's historical operating route in the line energy consumption characteristic database, the basic maximum cumulative operating mileage for the day is corrected to obtain the maximum cumulative operating mileage for the day. S30. Based on the current State of Charge (SOC), the cumulative operating mileage of the day, and the cargo weight to be matched, the equivalent range is determined by referring to the cargo weight sensitivity factor corresponding to the energy consumption level of the line to be matched, and the upper limit of the available mileage is determined by combining the remaining available credit. S40. Extract the round-trip energy consumption feature values ​​of the routes to be matched and calculate the estimated energy consumption mileage. After pre-screening based on the upper limit of available mileage, dynamic capacity profile and route energy consumption level matching, and the threshold corresponding to the cumulative discharge depth, those that meet the conditions are included in the feasible route set. S50: Determine the remaining number of cycles for the day based on the cumulative discharge depth, filter orders to control the total number of trips, and push orders in order of dynamic capacity profile adaptability and historical route matching success rate. S60. Record actual operating data, update relevant parameters, correct the correspondence between line energy consumption level and freight weight sensitivity factor, and improve the historical matching success rate of the line.

2. The new energy heavy truck freight matching method based on battery health according to claim 1, characterized in that: Step S10 includes: S11. Obtain the current battery health (SOH) value of the vehicle, the SOH decay rate within the preset historical period, and the rated total battery capacity, and obtain the historical full-load mileage utilization rate and cumulative discharge depth of the vehicle from the historical database. S12. Based on the current battery health status (SOH) value and the rated total battery capacity, obtain the vehicle's current theoretical maximum driving range; S13. Determine the degradation trend coefficient of the vehicle battery performance based on the SOH degradation rate, and dynamically correct the degradation trend coefficient by combining the historical full-load mileage utilization rate and cumulative discharge depth. Use the corrected degradation trend coefficient to reduce the theoretical maximum driving range to obtain the vehicle's basic maximum cumulative operating mileage for the day. S14. The current battery health (SOH) value, the SOH decay rate, the rated total battery capacity, the historical full-load mileage utilization rate, the cumulative depth of discharge, and the actual available driving range are combined to form a dynamic capacity profile of the vehicle.

3. The new energy heavy truck freight matching method based on battery health according to claim 1, characterized in that: Step S20 includes: S21. Obtain the historical full-load mileage utilization rate of the vehicle within a preset historical period. The historical full-load mileage utilization rate refers to the proportion of the mileage driven by the vehicle under full load to the total operating mileage during the same period. S22. Extract energy consumption fluctuation coefficients related to the vehicle's historical operating routes from the route energy consumption feature database. The energy consumption fluctuation coefficients characterize the degree of energy consumption dispersion of the routes previously traveled by the vehicle. S23. Obtain the corresponding mileage correction coefficient based on the historical full-load mileage utilization rate and the energy consumption fluctuation coefficient; obtain the maximum cumulative operating mileage of the day based on the daily maximum cumulative operating mileage and the mileage correction coefficient.

4. The new energy heavy truck freight matching method based on battery health according to claim 1, characterized in that: Step S30 includes: S31. Obtain the vehicle's current State of Charge (SOC) value and the cumulative operating mileage for the day, and also obtain the weight of the cargo to be matched. S32. Based on the energy consumption level of the route to be matched, extract the corresponding cargo weight sensitive factor from the route energy consumption feature library. The cargo weight sensitive factor is used to characterize the amplification degree of the impact of cargo weight on the range under the route level. According to the correspondence between cargo weight and range impact coefficient, and combined with the cargo weight sensitive factor, determine the impact coefficient of cargo weight on vehicle range. Use the impact coefficient to obtain the vehicle's equivalent range capability. S33. Based on the maximum cumulative operating mileage of the day and the cumulative operating mileage of the day, obtain the remaining available quota; compare the equivalent range with the remaining available quota, and select the smaller value as the upper limit of the vehicle's available mileage.

5. The new energy heavy truck freight matching method based on battery health according to claim 1, characterized in that: Step S40 includes: S41. Extract the round-trip energy consumption feature value corresponding to the route to be matched, and calculate the estimated energy consumption mileage required for the vehicle to complete the route to be matched based on the round-trip energy consumption feature value. S42. Determine whether the estimated energy consumption mileage is less than or equal to the available mileage limit, and at the same time determine whether it matches the energy consumption level of the line based on the dynamic capacity profile. S43. Obtain the threshold of the remaining number of cycles corresponding to the current cumulative discharge depth of the vehicle. If the estimated discharge depth corresponding to the estimated energy consumption mileage of the route may cause the threshold of the remaining number of cycles to be exceeded, then the route will be directly excluded. S44. Under the condition that the estimated energy consumption mileage meets the upper limit requirement, the dynamic capacity profile matches the energy consumption level of the line, and does not exceed the threshold of the remaining number of cycles, the line is included in the feasible line set.

6. The new energy heavy truck freight matching method based on battery health according to claim 1, characterized in that: Step S50 includes: S51. Obtain the cumulative discharge depth of the vehicle, wherein the cumulative discharge depth characterizes the cumulative discharge degree of the vehicle battery within a preset historical period and is adapted to the vehicle battery health status. S52. Based on the cumulative discharge depth, determine the remaining number of cycles for the vehicle on that day according to the correspondence between the cumulative discharge depth and the remaining number of cycles. The remaining number of cycles refers to the upper limit of the number of charge-discharge cycles that the vehicle battery can continue to complete on that day in the current state. S53. Select orders to be matched from the set of feasible routes, so that the total number of trips of the selected orders does not exceed the remaining number of cycles; S54. Sort the filtered orders according to the matching degree score from high to low, and push the sorted orders to the corresponding vehicles. The matching degree score is obtained based on the matching degree of the vehicle's dynamic capacity profile, the energy consumption level of the route, and the historical matching success rate of the route.

7. A new energy heavy-duty truck freight matching system based on battery health, characterized in that: The system includes: a profile building module, a mileage correction module, an available mileage module, a route filtering module, and a push update module; The profile building module is used to build a dynamic capacity profile and predict the vehicle’s basic maximum cumulative operating mileage for the day based on the vehicle’s current battery health status (SOH), SOH decay rate, battery rated total capacity, historical full-load mileage utilization rate and cumulative discharge depth. The mileage correction module is used to combine the historical full-load mileage utilization rate with the energy consumption fluctuation coefficient of the vehicle's historical operating route extracted from the line energy consumption feature database to correct the basic maximum cumulative operating mileage for the day, so as to obtain the maximum cumulative operating mileage for the day. The available mileage module is used to determine the equivalent range based on the vehicle's current state of charge (SOC), the accumulated operating mileage of the day, the weight of the cargo to be matched, and the cargo weight sensitivity factor corresponding to the energy consumption level of the route to be matched. Combined with the remaining available quota of the maximum accumulated operating mileage of the day, the upper limit of the vehicle's available mileage is determined. The route screening module is used to extract the round-trip energy consumption feature value of the route to be matched and calculate the estimated energy consumption mileage. After pre-screening based on the available mileage limit, dynamic capacity profile and route energy consumption level matching, and the threshold of the remaining cycle times corresponding to the current cumulative discharge depth of the vehicle, the routes that meet the conditions are included in the feasible route set. The push update module is used to determine the remaining number of cycles for the day based on the vehicle's cumulative discharge depth, filter orders from the feasible route set to ensure that the total number of trips does not exceed the remaining number of cycles, and push orders according to the compatibility between the dynamic capacity profile and the route energy consumption level and the historical matching success rate of the route. It also records the actual operation data for this time, updates the historical full load mileage utilization rate, route energy consumption characteristic value, and cumulative discharge depth, and corrects the correspondence between the route energy consumption level and the cargo weight sensitive factor and the historical matching success rate of the route.

8. A new energy heavy-duty truck freight matching system based on battery health according to claim 7, characterized in that: The profile construction module includes a parameter acquisition unit, a theoretical mileage unit, and an attenuation correction unit; The parameter acquisition unit is used to acquire the current battery health (SOH) value of the vehicle, the SOH decay rate within a preset historical period, the rated total capacity of the battery, and to acquire the historical full-load mileage utilization rate and cumulative discharge depth of the vehicle from the historical database. The theoretical mileage unit is used to obtain the vehicle's current theoretical maximum driving range based on the current battery health (SOH) value and the battery's rated total capacity. The attenuation correction unit is used to determine the attenuation trend coefficient of the vehicle battery performance based on the SOH attenuation rate, and to dynamically correct the attenuation trend coefficient by combining the historical full-load mileage utilization rate and cumulative discharge depth. The theoretical maximum driving range is reduced by the corrected attenuation trend coefficient to obtain the vehicle's basic maximum cumulative operating mileage for the day.

9. A new energy heavy-duty truck freight matching system based on battery health according to claim 7, characterized in that: The mileage correction module includes a utilization rate acquisition unit, a fluctuation coefficient unit, a correction coefficient unit, and a mileage correction unit; The utilization rate acquisition unit is used to acquire the historical full-load mileage utilization rate of the vehicle within a preset historical period. The historical full-load mileage utilization rate refers to the proportion of the mileage driven by the vehicle in a fully loaded state to the total operating mileage in the same period. The fluctuation coefficient unit is used to extract the energy consumption fluctuation coefficient related to the vehicle's historical operating route from the route energy consumption feature library. The energy consumption fluctuation coefficient characterizes the degree of energy consumption dispersion of the route previously traveled by the vehicle. The correction coefficient unit is used to obtain the corresponding mileage correction coefficient based on the historical full-load mileage utilization rate and the energy consumption fluctuation coefficient. The mileage correction unit is used to obtain the maximum cumulative operating mileage of the day based on the maximum cumulative operating mileage of the day and the mileage correction coefficient.

10. A new energy heavy-duty truck freight matching system based on battery health according to claim 7, characterized in that: The push update module includes a loop count unit, an order filtering unit, a sorting push unit, and a data update unit; The cycle count unit is used to obtain the cumulative discharge depth of the vehicle, and determine the remaining cycle count of the vehicle for the day according to the correspondence between the cumulative discharge depth and the remaining cycle count. The order filtering unit is used to filter orders to be matched from the set of feasible routes, so that the total number of trips of the selected orders does not exceed the remaining number of cycles. The sorting and push unit is used to sort and push the filtered orders according to the matching degree score from high to low. The matching degree score is obtained by the matching degree of the vehicle dynamic capacity profile, the energy consumption level of the route, and the historical matching success rate of the route. The data update unit is used to record the actual operation data, update the vehicle's historical full-load mileage utilization rate, the energy consumption characteristic value of the line, and the cumulative discharge depth, and correct the correspondence between the line energy consumption level and the cargo weight sensitivity factor and the line's historical matching success rate based on the matching results.

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