New energy automobile rental order-charging collaborative energy consumption optimization system and method

Abstract

The invention discloses a new energy automobile rental order-charging collaborative energy consumption optimization system and method, and aims to solve the problems of high operation cost, poor user experience and difficult power grid adaptation in the new energy automobile rental industry. The system adopts a layered architecture of a hardware layer, a data layer, a business logic layer and an application layer, the hardware layer collects data of vehicles, charging piles and the like, the business logic layer realizes collaborative management and control through order management, charging scheduling, energy consumption prediction and a strategy optimization module, and the application layer serves enterprises, users and a power grid. According to the method, through data acquisition, order processing, energy consumption prediction, charging plan optimization and dynamic adjustment, peak-valley charging cost reduction, efficient charging pile utilization and power grid load balance are realized. According to the scheme, the enterprise operation cost can be reduced, the user satisfaction degree is improved, the power grid stability is guaranteed, the industry intelligent development is promoted, and remarkable economic and social benefits are achieved.

Description

Technical Field

[0001] This invention relates to the field of car rental order-charging coordination, specifically to a new energy vehicle rental order-charging coordination energy consumption optimization system and method. Background Technology

[0002] With the global energy transition and increased environmental awareness, the new energy vehicle rental industry is developing rapidly. However, existing pain points in the industry are hindering its high-quality development. At the operational level, rental companies face high costs: charging is often arranged based on experience, frequently resulting in increased energy expenditures due to peak-hour charging; companies with 30 vehicles experience an average annual additional loss of over 60,000 yuan. Disorganized charging pile scheduling leads to equipment overload during peak hours and idleness during off-peak hours, shortening lifespan by 20%-30% and increasing maintenance costs. Order review and charging plan formulation rely on manual labor, which is inefficient and prone to errors, with labor costs accounting for over 30% of operating costs.

[0003] In terms of user experience, the traditional model has many inconveniences: order review takes 5-10 minutes and progress is difficult to track; after returning the car, it is impossible to know the charging status of the vehicle, and occasional charging pile failures may affect subsequent use; the fee details are vague, which can easily lead to disputes, and user satisfaction is less than 60%.

[0004] In terms of grid adaptability, the concentrated charging of a large number of vehicles can easily cause fluctuations in grid load. During peak hours, the combined effect of residential and industrial electricity consumption increases the probability of exceeding the grid's capacity limit. Furthermore, it is difficult to adapt to the demand for the consumption of clean energy sources such as photovoltaic and wind power, thus hindering the optimization of the energy structure. These pain points highlight the industry's urgent need for collaborative optimization technologies for "orders-charging-energy consumption". Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the above-mentioned technologies and provide a new energy vehicle rental order-charging collaborative energy consumption optimization system and method.

[0006] To solve the above-mentioned technical problems, the technical solution provided by the present invention is a new energy vehicle rental order-charging collaborative energy consumption optimization system and method: New energy vehicle rental order-charging collaborative energy consumption optimization system;

[0007] It includes a hardware layer, a data layer, a business logic layer, and an application layer. Each layer achieves data interaction and functional collaboration through standardized interfaces.

[0008] The hardware layer includes new energy vehicles, charging piles, smart sensors, and communication equipment. The new energy vehicles are equipped with on-board terminals to collect battery status and energy consumption data. The charging piles are equipped with smart monitoring modules to monitor the charging process and upload data. The smart sensors are deployed in parking lots and road areas to collect environmental and road condition information. The communication equipment uses 5G or IoT technology to achieve high-speed data transmission between the terminal devices and the system platform.

[0009] The data layer adopts a distributed database architecture to store order data, vehicle data, charging pile data, energy data, and environmental data, and establishes a data security mechanism to ensure data confidentiality, integrity, and availability.

[0010] The business logic layer includes an order management module, a charging scheduling module, an energy consumption prediction module, and a strategy optimization module;

[0011] The application layer includes a leasing company management platform, a user mobile APP, and a power grid dispatch interface, which provide corresponding functional services to leasing companies, users, and the power grid dispatch center, respectively.

[0012] As an improvement, the order management module has the following functions:

[0013] It supports multiple order entry methods, both online and offline, and automatically verifies order information, including user qualification review and vehicle availability check. Once the review is passed, a formal order is generated.

[0014] Track order execution status in real time, including vehicle rental, waiting to be charged, and returned, and send order progress reminders to users and enterprises via APP push or SMS;

[0015] We conduct multi-dimensional statistical analysis of order data, including order volume, rental duration, and user preferences, to provide data support for vehicle dispatching and marketing strategy development.

[0016] As an improvement, the charging scheduling module has the following functions:

[0017] Real-time collection of charging pile operating status, including idle, charging, and fault status; when faulty equipment is detected, an alarm notification is issued and maintenance scheduling is triggered.

[0018] Based on the vehicle pick-up and drop-off times of the rental order, the remaining battery power of the vehicle, and the location and status of the charging station, the system matches the vehicle with the optimal charging station and generates a charging reservation plan.

[0019] When orders change or unexpected situations occur, such as charging pile malfunctions or sudden increases in grid load, the charging plan is automatically adjusted and charging pile resources are reallocated.

[0020] As an improvement, the workflow of the energy consumption prediction module includes:

[0021] Collect multi-dimensional data such as vehicle historical energy consumption, driving trajectory, ambient temperature, and road conditions, and perform data cleaning, deduplication, and standardization preprocessing.

[0022] An energy consumption prediction model is constructed using random forest or neural network algorithms. The model input includes vehicle parameters, driving conditions, and environmental factors, and the output is the predicted energy consumption demand of the vehicle within a specific time period.

[0023] Regularly train and optimize the prediction model using new historical data to improve prediction accuracy.

[0024] As an improvement, the strategy optimization module has the following functions:

[0025] With the optimization objectives of minimizing energy consumption costs, balancing grid load, and maximizing user satisfaction, a multi-objective optimization algorithm is designed. The algorithm constraints include charging pile capacity, vehicle charging time window, and grid load limit.

[0026] Based on real-time electricity price information, grid load data and energy consumption forecast results, a dynamic charging strategy is formulated: charging is prioritized when electricity prices are low and grid load is low, and charging power is adjusted or charging is delayed when electricity prices are high and grid load is high.

[0027] The optimized charging strategy is distributed to charging piles and vehicle terminals, and the execution effect of the strategy is monitored in real time and adjusted according to the actual situation.

[0028] As an improvement, in the application layer, the leasing company management platform supports order management, charging pile monitoring, energy consumption statistics, and report generation functions; the user's mobile APP supports order inquiry, charging reservation, charging navigation, and payment functions; and the power grid dispatch interface enables data connection with the power grid dispatch center to obtain power grid load information.

[0029] The method for optimizing energy consumption through charging collaboration in new energy vehicle rental orders includes the following steps:

[0030] S1: Hardware layer terminal equipment collects data on new energy vehicles, charging piles, environment, and road conditions, and transmits it to the data layer for storage via communication equipment;

[0031] S2: The business logic layer order management module processes rental orders, completing order entry, review, tracking, and statistical analysis;

[0032] S3: The energy consumption prediction module builds and optimizes the energy consumption prediction model based on the data layer data, and outputs the predicted value of vehicle energy consumption demand.

[0033] S4: The charging scheduling module combines order information, charging pile status, and energy consumption prediction to generate an initial charging plan;

[0034] S5: The strategy optimization module combines real-time electricity price and grid load data to optimize the initial charging plan and formulate a dynamic charging strategy;

[0035] S6: Send the dynamic charging strategy to the hardware layer for execution, and monitor the execution status of the strategy. If an emergency occurs, return to step S4 to readjust the charging plan.

[0036] As an improvement, in step S5, the strategy optimization module adopts a multi-objective optimization algorithm to optimize the charging strategy under the constraints of charging pile capacity, vehicle charging time window, and grid load limit, with the goals of minimizing energy consumption cost, balancing grid load, and maximizing user satisfaction.

[0037] As an improvement, a pre-allocation step for charging plans based on order forecasts is also included:

[0038] Analyze historical rental order data to uncover patterns in the temporal and spatial distribution of orders and predict order demand in the future.

[0039] Based on order forecasts, charging pile resources are pre-allocated, and charging tasks are assigned to different time periods and different charging piles to avoid charging pile congestion during peak hours.

[0040] As an improvement, a dynamic energy consumption adjustment step based on real-time data is also included:

[0041] Real-time data collection of vehicle driving status, battery status, and environmental conditions; combined with energy consumption prediction models to adjust vehicle energy consumption control strategies.

[0042] For vehicles in motion, the route is optimized based on road condition information to avoid congested areas; for vehicles charging, the charging power is adjusted in real time based on changes in grid load and electricity prices to balance charging progress and energy consumption costs.

[0043] The advantages of this invention compared to existing technologies are as follows: This technical solution brings significant value to multiple stakeholders through the synergy of "order-charging-energy consumption". For leasing companies, it can significantly reduce operating costs: On the energy side, charging can be automatically selected during off-peak hours. Taking a fleet of 30 vehicles as an example, this can save nearly 70,000 yuan in charging costs annually and reduce waste from refueling mid-journey; on the equipment side, charging pile tasks are evenly distributed, extending their lifespan by 2-3 years and reducing maintenance frequency by 30%; on the human resource side, automatic order review and automatic generation of charging plans reduce manpower input by more than 50% and increase profitability.

[0044] For users, the rental experience has been comprehensively optimized: order approval is completed within 30 seconds, and there are real-time reminders throughout the pick-up and drop-off process; after returning the vehicle, users can check the vehicle's charging progress through the APP, and the system will adjust the plan in time in case of malfunction to meet personalized charging needs; the cost details are clear and transparent, and users can also earn points discounts, enhancing trust and stickiness.

[0045] For the power grid, it can ensure stable operation and energy optimization: delay non-emergency charging during peak hours and reduce charging power to avoid load superposition; prioritize charging during clean energy generation periods to help achieve the "dual carbon" goal.

[0046] For the industry, promote standardization and intelligentization: establish full-process standards, unify data formats and statistical standards; integrate big data, AI and other technologies to provide replicable intelligent solutions and accelerate the industry's transformation from traditional to intelligent services. Attached Figure Description

[0047] Figure 1 This invention relates to a new energy vehicle rental order-charging collaborative energy consumption optimization system and method.

[0048] Figure 2 This invention relates to a new energy vehicle rental order-charging collaborative energy consumption optimization system and method, specifically a new energy vehicle rental order-charging collaborative energy consumption optimization method. Detailed Implementation

[0049] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0051] It is understood that spatial relation terms such as "below," "under," "below," "below," "above," "over," etc., can be used here to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, the element or feature described as "below" or "under" or "below" of the other element or feature will be oriented "over" the other element or feature. Therefore, the exemplary terms "below" and "under" can include both upper and lower orientations. Furthermore, the device may also include other orientations, such as being rotated 90 degrees or other orientations, and the spatial descriptive terms used herein will be interpreted accordingly.

[0052] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. In the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have the transmission of electrical signals or data between them.

[0053] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” or “having,” etc., specify the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0054] Referring to the attached figures, a system and method for optimizing energy consumption through collaborative charging for new energy vehicle rental orders are presented; the system for optimizing energy consumption through collaborative charging for new energy vehicle rental orders is described.

[0055] It includes a hardware layer, a data layer, a business logic layer, and an application layer. Each layer achieves data interaction and functional collaboration through standardized interfaces.

[0056] The hardware layer includes new energy vehicles, charging piles, smart sensors, and communication equipment. The new energy vehicles are equipped with on-board terminals to collect battery status and energy consumption data. The charging piles are equipped with smart monitoring modules to monitor the charging process and upload data. The smart sensors are deployed in parking lots and road areas to collect environmental and road condition information. The communication equipment uses 5G or IoT technology to achieve high-speed data transmission between the terminal devices and the system platform.

[0057] The data layer adopts a distributed database architecture to store order data, vehicle data, charging pile data, energy data, and environmental data, and establishes a data security mechanism to ensure data confidentiality, integrity, and availability.

[0058] The business logic layer includes an order management module, a charging scheduling module, an energy consumption prediction module, and a strategy optimization module;

[0059] The application layer includes a leasing company management platform, a user mobile APP, and a power grid dispatch interface, which provide corresponding functional services to leasing companies, users, and the power grid dispatch center, respectively.

[0060] The order management module has the following functions:

[0061] It supports multiple order entry methods, both online and offline, and automatically verifies order information, including user qualification review and vehicle availability check. Once the review is passed, a formal order is generated.

[0062] Track order execution status in real time, including vehicle rental, waiting to be charged, and returned, and send order progress reminders to users and enterprises via APP push or SMS;

[0063] We conduct multi-dimensional statistical analysis of order data, including order volume, rental duration, and user preferences, to provide data support for vehicle dispatching and marketing strategy development.

[0064] The charging scheduling module has the following functions:

[0065] Real-time collection of charging pile operating status, including idle, charging, and fault status; when faulty equipment is detected, an alarm notification is issued and maintenance scheduling is triggered.

[0066] Based on the vehicle pick-up and drop-off times of the rental order, the remaining battery power of the vehicle, and the location and status of the charging station, the system matches the vehicle with the optimal charging station and generates a charging reservation plan.

[0067] When orders change or unexpected situations occur, such as charging pile malfunctions or sudden increases in grid load, the charging plan is automatically adjusted and charging pile resources are reallocated.

[0068] The workflow of the energy consumption prediction module includes:

[0069] Collect multi-dimensional data such as vehicle historical energy consumption, driving trajectory, ambient temperature, and road conditions, and perform data cleaning, deduplication, and standardization preprocessing.

[0070] An energy consumption prediction model is constructed using random forest or neural network algorithms. The model input includes vehicle parameters, driving conditions, and environmental factors, and the output is the predicted energy consumption demand of the vehicle within a specific time period.

[0071] Regularly train and optimize the prediction model using new historical data to improve prediction accuracy.

[0072] The strategy optimization module has the following functions:

[0073] With the optimization objectives of minimizing energy consumption costs, balancing grid load, and maximizing user satisfaction, a multi-objective optimization algorithm is designed. The algorithm constraints include charging pile capacity, vehicle charging time window, and grid load limit.

[0074] Based on real-time electricity price information, grid load data and energy consumption forecast results, a dynamic charging strategy is formulated: charging is prioritized when electricity prices are low and grid load is low, and charging power is adjusted or charging is delayed when electricity prices are high and grid load is high.

[0075] The optimized charging strategy is distributed to charging piles and vehicle terminals, and the execution effect of the strategy is monitored in real time and adjusted according to the actual situation.

[0076] In the application layer, the leasing company management platform supports order management, charging pile monitoring, energy consumption statistics, and report generation; the user mobile APP supports order inquiry, charging reservation, charging navigation, and payment functions; and the power grid dispatch interface enables data connection with the power grid dispatch center to obtain power grid load information.

[0077] The method for optimizing energy consumption through charging collaboration in new energy vehicle rental orders includes the following steps:

[0078] S1: Hardware layer terminal equipment collects data on new energy vehicles, charging piles, environment, and road conditions, and transmits it to the data layer for storage via communication equipment;

[0079] S2: The business logic layer order management module processes rental orders, completing order entry, review, tracking, and statistical analysis;

[0080] S3: The energy consumption prediction module builds and optimizes the energy consumption prediction model based on the data layer data, and outputs the predicted value of vehicle energy consumption demand.

[0081] S4: The charging scheduling module combines order information, charging pile status, and energy consumption prediction to generate an initial charging plan;

[0082] S5: The strategy optimization module combines real-time electricity price and grid load data to optimize the initial charging plan and formulate a dynamic charging strategy;

[0083] S6: Send the dynamic charging strategy to the hardware layer for execution, and monitor the execution status of the strategy. If an emergency occurs, return to step S4 to readjust the charging plan.

[0084] In step S5, the strategy optimization module adopts a multi-objective optimization algorithm to optimize the charging strategy under the constraints of charging pile capacity, vehicle charging time window, and grid load limit, with the goals of minimizing energy consumption cost, balancing grid load, and maximizing user satisfaction.

[0085] It also includes a pre-allocation step for charging plans based on order forecasts:

[0086] Analyze historical rental order data to uncover patterns in the temporal and spatial distribution of orders and predict order demand in the future.

[0087] Based on order forecasts, charging pile resources are pre-allocated, and charging tasks are assigned to different time periods and different charging piles to avoid charging pile congestion during peak hours.

[0088] It also includes dynamic energy consumption adjustment steps based on real-time data:

[0089] Real-time data collection of vehicle driving status, battery status, and environmental conditions; combined with energy consumption prediction models to adjust vehicle energy consumption control strategies.

[0090] For vehicles in motion, the route is optimized based on road condition information to avoid congested areas; for vehicles charging, the charging power is adjusted in real time based on changes in grid load and electricity prices to balance charging progress and energy consumption costs.

[0091] The following example, using the daily operations of a small rental company with 3 service outlets, 30 new energy rental vehicles, and 15 charging stations, illustrates the specific implementation process of this system in detail and in simple terms. When key data calculations are involved, simple and formal mathematical formulas are introduced, clearly explaining the meaning of each letter and symbol in the formula and its role in practical applications. The overall approach is primarily textual, ensuring the content is clear and easy to understand.

[0092] I. Basic System Configuration:

[0093] (I) Simplified Hardware Deployment:

[0094] In terms of hardware configuration, we deploy from three core parts: vehicles, charging piles, and auxiliary equipment to ensure that each device can stably collect and transmit data.

[0095] Vehicle-side equipment: Each rental car is equipped with a small in-vehicle terminal. This terminal collects key vehicle data every minute and uploads the data to the system in real time. Two of the most crucial data points are: First, the remaining battery charge, represented by the symbol SOC (State of Charge), which is a percentage (%) ranging from 0 to 100. For example, an SOC of 30% means the vehicle's battery has 30% charge remaining. This data is primarily used to determine if the vehicle needs timely charging to avoid insufficient power during subsequent rentals. The other key data point is the vehicle's real-time energy consumption, represented by the symbol E, measured in kilowatt-hours (kWh / 100km). For example, an E value of 16 kWh / 100km means the car consumes approximately 16 kWh per 100 kilometers. This data is mainly used to predict the vehicle's energy consumption over specific distances, providing a reference for subsequent charging plans.

[0096] Charging station terminal equipment: Each charging station is equipped with an intelligent monitoring module, which collects the charging station's operational status data every 30 seconds. The core data collected includes the charging station's current status and charging power. The charging station status is represented by the symbol S, which has three values: 0 represents that the charging station is idle and can be assigned new charging tasks; 1 represents that the charging station is charging a vehicle; and 2 represents that the charging station is malfunctioning and cannot be used normally. Through this status data, staff can quickly understand the availability of each charging station and promptly handle faulty equipment. The charging power is represented by the symbol P_c, and the unit is kilowatts (kW). For example, when P_c is 50kW, it means that this charging station can charge a vehicle with a maximum of 50 kWh per hour. This data is mainly used to calculate the time required to fully charge a vehicle, facilitating the development of reasonable charging plans.

[0097] Auxiliary Equipment: To ensure more stable data transmission, we use 4G networks as the primary communication method, guaranteeing that data collected by the vehicle-mounted terminal and charging piles can be transmitted quickly and accurately to the system backend. Simultaneously, temperature sensors and road condition sensors are installed at each service point. The temperature sensor collects ambient temperature, denoted by the symbol T, with the unit being degrees Celsius (°C); the road condition sensor determines the traffic conditions of the roads surrounding the service point, denoted by the symbol R, where 1 represents smooth traffic and 2 represents congested traffic. These two sensors collect data every 10 minutes. Their function is to assist the system in more accurately predicting vehicle energy consumption. For example, vehicle energy consumption increases in low temperatures, and congested traffic also leads to increased energy consumption. This data allows the energy consumption prediction results to be more accurate.

[0098] (II) Software and Data Preparation:

[0099] On the software side, the entire system is developed using the Java programming language combined with the Spring Boot framework. This combination ensures stable system operation and convenient maintenance. Data storage uses a MySQL database, and the data is categorized for easy querying and use. The key data falls into two main categories: order data and energy data. Order data includes the user's pick-up time (t_p), return time (t_r), pick-up location (A_p), and return location (A_r). This data forms the basis for the system to process orders, schedule vehicles, and manage charging tasks. Energy data primarily consists of electricity prices at different times of day. The day is divided into peak and off-peak hours. Peak hours are from 8:00 AM to 10:00 PM (8:00 AM to 10:00 PM), with a corresponding price of 0.8 yuan / kWh (C_e_peak). Off-peak hours are from 10:00 PM to 8:00 AM the following day (22:00 PM to 8:00 AM the next day), with a corresponding price of 0.3 yuan / kWh (C_e_valley). Electricity price data is primarily used to optimize charging strategies and help businesses reduce charging costs.

[0100] II. Simplified Implementation of Core Modules:

[0101] (I) Implementation of the Order Management Module:

[0102] The order management module is the core of the system for processing user rental needs, and mainly includes two key steps: order review and fee calculation.

[0103] Order Review Process: When submitting a rental order via the mobile app, users need to select a pick-up location, a drop-off location, and specific pick-up and drop-off times. For example, a user might select location 1 (A_p = location 1) as the pick-up location and location 2 (A_r = location 2) as the drop-off location, with a pick-up time of 10:00 AM on November 16, 2025 (t_p = 2025-11-16 10:00) and a drop-off time of 3:00 PM on the same day (t_r = 3:00 PM). After the user submits the order, the system will automatically review it. First, it checks for any outstanding rental fees or other negative records to confirm the user's eligibility. Then, it checks if there are any available vehicles at the pick-up location (location 1) at the user's specified pick-up time (10:00 AM). If both checks pass, the system will generate a unique order number, such as "rent-20251116-001," and mark the corresponding vehicle status as "reserved" to prevent the same vehicle from being booked repeatedly.

[0104] Fee Calculation Method: After the user completes the rental and returns the car, the system will calculate the rental fee based on the user's actual pick-up and return times. For example, if the user's actual pick-up time is 10:10 (t_p:10) and the actual return time is 14:50 (t_r:50), the rental duration needs to be calculated first, using a simple formula:

[0105] T r =t r′ -t p′

[0106] In the formula, T_r represents the rental duration in hours (h); t_r is the actual return time; and t_p is the actual pick-up time. This formula calculates the actual vehicle usage time by subtracting the actual pick-up time from the actual return time, providing a basis for calculating the rental fee. Substituting the actual time into the formula, subtracting 10:10 from 14:50 gives a rental duration T_r of approximately 4.67 hours, or about 4 hours and 40 minutes.

[0107] Next, we calculate the car rental cost. The company's basic rental fee is 45 yuan per hour. The formula for calculating the cost is as follows:

[0108] F = 45 × T r

[0109] In the formula, F represents the total rental cost in yuan; 45 is the base hourly rate; and T_r is the previously calculated rental duration. This formula multiplies the hourly rate by the rental duration to obtain the total rental cost. Substituting T_r = 4.67 into the formula, multiplying 45 by 4.67 gives a rental cost F of approximately 210.15 yuan. The system will display this detailed cost to the user, who can then pay directly through the app.

[0110] (II) Implementation of the charging scheduling module:

[0111] After the user returns the car, the system will automatically arrange a charging task for the vehicle based on the remaining battery power. The whole process mainly includes two steps: calculating the amount of charge required and matching the charging station and calculating the charging time.

[0112] Required Charge Calculation: When a user returns the vehicle, the onboard terminal uploads the vehicle's current State of Charge (SOC) to the system. Assuming the SOC is 25%, and the total battery capacity of this model is fixed (denoted by C), where C = 45 kWh (45 kilowatt-hours), to ensure the vehicle can be rented out normally next time, we typically charge the battery to 80%. Therefore, we need to calculate the required charge, denoted by E_need, in kilowatt-hours (kWh). The formula for calculating the required charge is as follows:

[0113] E n eed = C × (80% - SOC)

[0114] In the formula, C is the total battery capacity, 80% is the target charging percentage, and SOC is the percentage of remaining battery charge when returning the vehicle. This formula calculates the specific amount of charge the vehicle needs by multiplying the total battery capacity by the difference between the target charge and the remaining charge, allowing staff to clearly understand how much charge is required. Substituting C = 45 and SOC = 25% into the formula, we first calculate 80% minus 25% (which equals 55%, or 0.55). Then, we multiply 45 by 0.55 to get E_need = 24.75 kWh, meaning the vehicle needs 24.75 kWh of additional charge.

[0115] Charging Pile Matching and Charging Time Calculation: After determining the amount of electricity the vehicle needs to replenish, the system automatically queries the return location (here, location 2) for currently available charging piles (S=0). Assuming a charging pile with a charging power of P_c=50kW is found at location 2, this charging pile will be assigned to the vehicle. During actual charging, there will be some energy loss; we typically set the loss factor to 0.9 (i.e., 10% loss), denoted by η. Next, we need to calculate the time required for the vehicle to fully charge, denoted by T_c, in hours (h). The formula for calculating the charging time is as follows:

[0116]

[0117] In the formula, E_need is the amount of electricity needed, P_c is the charging power of the charging station, and η is the charging loss coefficient. This formula divides the amount of electricity needed by the effective charging power that the charging station can actually provide (charging power multiplied by the loss coefficient) to obtain the time required to fully charge the vehicle, facilitating subsequent vehicle scheduling by staff. Substituting E_need = 24.75, P_c = 50, and η = 0.9 into the formula, we first calculate that the denominator 50 multiplied by 0.9 equals 45, then divide 24.75 by 45 to obtain T_c = 0.55h, which is approximately 33 minutes. Based on this, the system will generate a charging plan, scheduling the vehicle to start charging at 15:00 and end charging at 15:33.

[0118] (III) Implementation of Energy Consumption Prediction and Strategy Optimization Module:

[0119] Energy consumption prediction and strategy optimization are the core elements of achieving energy consumption optimization. By accurately predicting energy consumption and reasonably adjusting charging strategies, we can ensure the normal use of vehicles and reduce the operating costs of enterprises.

[0120] Energy Consumption Prediction Process: Before the user picks up the vehicle, the system predicts the energy consumption of the vehicle during the journey from the pick-up point to the return point, determining whether the vehicle needs to recharge along the way. For example, if the user travels from point 1 to point 2, the system first determines the distance between the two points, denoted by D, where D = 20km. Simultaneously, the system incorporates the ambient temperature T and road conditions R to aid in the prediction. Assuming the ambient temperature T = 26℃ (suitable temperature with minimal impact on energy consumption) and road condition R = 1 (unobstructed roads, no congestion), the system further analyzes the average energy consumption data for each vehicle model, denoted by E_avg, which is 16kWh / 100km. Based on this data, the formula for calculating the vehicle's driving energy consumption is as follows:

[0121]

[0122] In the formula, E_pred is the predicted energy consumption for driving, measured in kilowatt-hours (kWh); E_avg is the vehicle's average energy consumption; and D is the driving distance. This formula works by multiplying the average energy consumption by the driving distance and then dividing by 100 to obtain the predicted energy consumption for that distance, helping to determine if the vehicle's current battery level is sufficient to complete the journey. Substituting E_avg = 16 and D = 20 into the formula, 16 multiplied by 20 equals 320, which, divided by 100, gives E_pred = 3.2 kWh. This means the vehicle will consume approximately 3.2 kWh to travel this distance. Combined with the remaining battery power when returning the vehicle, it can be determined that the vehicle does not require recharging during the journey and can complete the trip directly.

[0123] Charging strategy optimization: After determining the amount of electricity the vehicle needs to replenish, the system also considers electricity prices at different times to select the most economical charging time and reduce charging costs. Suppose the system finds that the next pick-up time for the vehicle is 18:00 on the same day (denoted as t_p_next), while the current time is 15:00, which is during peak hours when electricity prices are higher (C_e peak = 0.8 yuan / kWh). To compare the cost difference between peak and off-peak charging, we will calculate the charging costs for both periods separately.

[0124] First, calculate the peak charging cost, denoted by F_c1, in yuan. The calculation formula is as follows:

[0125]

[0126] In the formula, E_need is the amount of electricity needed, and C_e_peak is the peak electricity price. This formula multiplies the amount of electricity needed by the peak electricity price to obtain the cost of charging during peak hours. Substituting E_need = 24.75 and C_e_peak = 0.8 into the formula, multiplying 24.75 by 0.8 gives F_c1 = 19.8 yuan.

[0127] Next, calculate the off-peak charging cost, denoted by F_c2, in yuan, using the following formula:

[0128] èF c 2 = E n eed×C e è。 。 。

[0129] In the formula, E_need is the amount of electricity needed, and C_e_valley is the off-peak electricity price. This formula multiplies the amount of electricity needed by the off-peak electricity price to obtain the cost of charging during off-peak hours. Substituting E_need = 24.75 and C_e_valley = 0.3 into the formula, multiplying 24.75 by 0.3 gives F_c2 = 7.425 yuan.

[0130] A comparison reveals that charging during off-peak hours saves 12.375 yuan (19.8 - 7.425) compared to charging during peak hours. Furthermore, choosing off-peak charging avoids the load pressure on the power grid caused by concentrated charging during peak hours, achieving a win-win situation of reduced enterprise costs and stable grid operation. Therefore, the system will ultimately adjust the charging time to off-peak hours (after 22:00) and update the charging plan.

[0131] III. Simple handling of emergencies:

[0132] In actual operation, some unexpected situations may inevitably occur, such as the assigned charging pile suddenly malfunctioning. In this case, the system will respond quickly and adjust the plan to ensure that the charging task is completed smoothly.

[0133] Suppose that at 14:55, 5 minutes before the scheduled charging time (starting at 15:00), the system detects that the charging station previously assigned to the vehicle has become faulty (S=2) and cannot charge the vehicle normally. At this point, the system will immediately initiate an emergency response procedure. The first step is to check the charging station status of nearby service points. It quickly finds that a charging station with a charging power of P_c=45kW at service point 3 is idle (S=0). Next, the system calculates the distance between service point 3 (where the idle charging station is located) and the vehicle's current location at service point 2, which is approximately 2 kilometers. Based on the average vehicle speed, it estimates the time required for the vehicle to move from service point 2 to service point 3, denoted by T_trans, where T_trans=8 minutes.

[0134] Then, the system will recalculate the charging time based on the charging power of the new charging station. The charging power of the new charging station is P_c = 45kW, the loss coefficient is still η = 0.9, and the amount of electricity to be replenished is still E_need = 24.75kWh. The formula for recalculating the charging time is the same as before, only the new charging power value is used. The calculated new charging time is T_c / (45×0.9) = 0.61h, which is approximately 36.6 minutes.

[0135] Finally, the system will generate a new charging plan, scheduling the vehicle to start charging at 15:08 (the time the vehicle is transferred to point 3) and finish charging at 15:45 (15:08 plus 36.6 minutes). Simultaneously, the system will send notifications via the app to both the staff responsible for vehicle dispatch and the user, informing them of the charging station malfunction and the new charging plan. This ensures both parties are informed promptly and that the entire charging process is unaffected by unforeseen problems and completed smoothly.

[0136] This technical solution, through a deep collaborative design of "order-charging-energy consumption," breaks down the disconnect between order management, charging scheduling, and energy consumption control in the traditional new energy vehicle rental industry. It brings significant and comprehensive benefits from multiple dimensions, including rental company operations, user experience, grid stability, and sustainable industry development. These benefits can be elaborated in detail from the following aspects:

[0137] Significantly reduce operating costs for leasing companies and improve their profit margins: For new energy vehicle leasing companies, controlling operating costs is directly related to their profitability. This system addresses core cost items such as energy costs, equipment costs, and labor costs, providing practical solutions for companies to reduce costs and increase efficiency.

[0138] Regarding energy costs, in traditional rental models, companies often lack scientific planning for charging times, mostly arranging charging immediately after vehicles are returned. If this happens to coincide with peak electricity prices, charging costs will remain high. This system, however, uses a strategy optimization module to automatically select off-peak hours with lower electricity prices by combining real-time electricity price data with the vehicle's next rental date. Taking a company with 30 rental cars as an example, each car requires an average of 25 kWh of electricity per charge. With peak electricity prices at 0.8 yuan / kWh and off-peak prices at 0.3 yuan / kWh, a single charge can save (0.8-0.3)×25 = 12.5 yuan. Assuming each car is charged 15 times per month, 30 cars can save 12.5×15×30 = 5625 yuan per month in charging costs, resulting in nearly 70,000 yuan in savings per year. Simultaneously, the system's energy consumption prediction module accurately predicts vehicle energy consumption during driving, avoiding mid-trip charging due to insufficient battery estimation, reducing unnecessary energy waste, and further lowering the company's energy expenditure.

[0139] Regarding equipment costs, in the traditional model, due to a lack of reasonable scheduling of charging pile usage, some charging piles are often over-occupied during peak hours, leading to accelerated equipment wear and tear, while others remain idle for extended periods during off-peak hours, resulting in low equipment utilization. This not only increases the frequency of charging pile maintenance and replacement but also makes it difficult for companies to efficiently utilize their investment in charging pile procurement. This system's charging scheduling module can monitor the operating status of all charging piles in real time and evenly distribute charging tasks to each charging pile based on order distribution and vehicle charging demand, avoiding long-term high-load operation of any single charging pile. Data shows that through reasonable scheduling, the average lifespan of charging piles can be extended by 2-3 years, and the maintenance frequency can be reduced by more than 30%, significantly reducing companies' costs for charging pile maintenance and upgrades. Simultaneously, the system's real-time alarms and rapid response to charging pile faults ensure that faulty equipment is repaired in the shortest possible time, reducing the waste of charging resources caused by equipment failure and further improving the effective utilization rate of charging piles.

[0140] In terms of labor costs, under the traditional model, tasks such as order review, charging plan formulation, and charging pile fault diagnosis are mostly done manually, which is not only inefficient but also prone to human error. For example, when manually reviewing orders, it is necessary to verify the user's qualifications and vehicle inventory one by one, with each order taking an average of 5-10 minutes to review; when manually arranging charging plans, it is necessary to manually check the charging pile status and vehicle battery level before formulating a charging plan, which is time-consuming and prone to unreasonable scheduling. This system, through its order management module, realizes automatic order review and tracking, improving the review speed to within 30 seconds per order, with an accuracy rate of 100%; the charging scheduling module can automatically match charging piles and generate charging plans without manual intervention; at the same time, the system's automatic alarm and maintenance scheduling for charging pile faults also reduce the workload of manual inspection. According to statistics, after the system is put into use, enterprises can reduce their manpower input in order processing and charging management by more than 50%, significantly reducing labor costs and allowing staff to devote more energy to core businesses such as user service and vehicle maintenance.

[0141] Comprehensive optimization of the user rental experience to enhance user stickiness and brand recognition:

[0142] Users are the core service recipients of rental companies, and a good rental experience is key to attracting and retaining users. This system starts from the entire user car rental process and solves many pain points faced by users in the traditional rental model through intelligent and convenient design, greatly improving the user experience.

[0143] In the traditional order processing model, users have to wait a long time for the review results after submitting an order, and they cannot keep track of the order progress in real time, which can easily cause anxiety. However, this system's order management module enables real-time order review. After a user submits an order, the system can complete the qualification and inventory verification within 30 seconds and provide feedback on the review results to the user. Simultaneously, the system will send real-time reminders to the user via app push notifications and SMS, including vehicle pickup reminders, vehicle location reminders, and return time reminders, keeping the user fully informed of the order progress. For example, if a user books a pickup service for 10:00 AM the next day, the system will send a pickup reminder at 8:00 PM the night before, informing the user of the vehicle's exact location and charging status; a second reminder will be sent at 9:30 AM on the day of pickup to ensure the user does not miss the pickup time. This real-time and personalized reminder service makes the car rental process more relaxed and organized.

[0144] Regarding the charging experience, in the traditional model, users often worry about insufficient battery power affecting subsequent use after returning the vehicle, or about charging station malfunctions preventing normal charging. This system, through the collaborative work of the charging scheduling module and energy consumption prediction module, completely eliminates these concerns. When the user returns the vehicle, the system automatically detects the vehicle's battery level and generates a detailed charging plan, simultaneously informing the user of the charging progress and estimated completion time via the app. If a charging station malfunctions, the system immediately adjusts the charging plan and promptly informs the user of the new plan, allowing them to clearly understand the vehicle's charging status. Furthermore, for users with specific charging needs, such as wanting the vehicle to have over 90% battery power upon next pickup, the system can adjust the charging strategy accordingly to ensure personalized needs are met. This transparent and controllable charging service greatly enhances users' trust in the rental service.

[0145] Regarding cost transparency, in traditional models, users are often unclear about the specific breakdown of car rental fees, easily leading to disputes. This system, however, generates a detailed breakdown during the fee calculation process, including rental duration, hourly rental price, total rental price, and charging costs (if the user chooses additional charging services), clearly displayed on the app for easy understanding. Simultaneously, the system automatically provides users with benefits such as points rewards and discount coupons based on the number of rentals and total spending, further enhancing the user experience. For example, after a user accumulates 10 rentals, the system automatically issues a 10% discount coupon and notifies the user via the app, encouraging repeat purchases. This transparent fee system and personalized discount services effectively reduce fee disputes between users and businesses, enhancing user loyalty and brand recognition.

[0146] Ensuring stable grid operation and contributing to energy structure optimization: Large-scale charging of new energy vehicles, if not properly scheduled, can easily place enormous load pressure on the grid during peak hours, even causing grid fluctuations and affecting the safety of residential and industrial electricity use. This system, through coordinated operation with the grid dispatch center, achieves precise control of charging load, providing strong support for stable grid operation and contributing to the optimization of the energy structure.

[0147] In terms of grid load regulation, under the traditional model, a large number of rental vehicles are charged during the period after users return the vehicles (such as 3-6 PM), which is often the peak period for residential and industrial electricity consumption. The combined effect of charging load and grid load causes a sharp increase in grid load, exceeding the grid's carrying capacity and leading to voltage instability, power outages, and other problems. This system's strategy optimization module can obtain grid load data transmitted from the grid dispatch center in real time. When the grid load is detected to be approaching its upper limit, it automatically adjusts the charging strategy: on the one hand, it delays the execution of non-emergency charging tasks, rescheduling them to off-peak hours when grid load is lower; on the other hand, it appropriately reduces the charging power of ongoing charging tasks to reduce the impact of charging load on the grid. For example, when the grid load reaches 90% of its carrying capacity limit, the system will delay the charging of 10 vehicles originally scheduled to start at 4 PM to 10 PM (off-peak hour); simultaneously, it will reduce the charging power of the 5 vehicles currently charging from 60kW to 40kW to ensure that the grid load is controlled within a safe range. This dynamic adjustment can effectively prevent the charging load from overlapping with residential and industrial electricity loads, alleviate the pressure on the power grid during peak hours, and ensure the stable operation of the power grid.

[0148] Regarding the integration of clean energy, as the proportion of clean energy sources such as photovoltaic and wind power in the power grid continues to increase, improving the integration rate of clean energy has become a crucial issue in the energy sector. This system, through deep collaboration with the power grid dispatch center, can prioritize charging during clean energy generation periods. For example, when the power grid dispatch center indicates that photovoltaic power generation is high and the proportion of clean energy exceeds 60% during a certain period, the system will proactively increase charging tasks for that period, encouraging rental vehicles to charge during this time, thereby increasing the integration of clean energy and reducing reliance on traditional energy sources such as thermal power. In the long term, this collaborative model can promote the transformation of the power grid towards "cleaner and lower carbon" power, helping the country achieve its "dual carbon" goals, and has significant social and environmental benefits.

[0149] Promote the standardized and intelligent development of the new energy vehicle rental industry:

[0150] Currently, the new energy vehicle rental industry is in a phase of rapid development, but problems such as unregulated management, low technical levels, and inconsistent service quality still exist, hindering further development. This system, through advanced technology and a scientific management model, sets a benchmark for standardized and intelligent development in the industry, promoting the overall improvement of the industry's level.

[0151] Regarding industry standardization, traditional leasing models lack unified standards in areas such as order management, charging scheduling, and energy consumption statistics, leading to chaotic industry data and difficulty in measuring service quality. This system establishes a complete standardized process, from order entry, review, and tracking to charging pile status monitoring, charging plan development, and energy consumption data statistics, with clear operational standards and data specifications for each step. For example, in order data management, the system standardizes the format and storage methods for user information, rental time, and vehicle information; in energy consumption statistics, the system adopts unified calculation standards to ensure the comparability of energy consumption data from different companies and vehicle models. This standardized management model not only facilitates data statistics and analysis within companies but also provides clear and accurate industry data for regulatory authorities, helping them formulate scientific industry policies and promote the industry's standardization.

[0152] In terms of industry intelligence, this system integrates advanced technologies such as big data, artificial intelligence, and the Internet of Things, realizing intelligent management of the entire leasing business process and providing a replicable and scalable solution for the industry's intelligent upgrade. For example, the system's energy consumption prediction module uses machine learning algorithms to continuously optimize the prediction model based on historical energy consumption data, environmental factors, and road condition information, improving the accuracy of energy consumption prediction; the charging scheduling module achieves optimal allocation of charging pile resources through real-time data collection and dynamic scheduling algorithms. The application of these intelligent technologies not only improves the operational efficiency of individual enterprises but also provides a reference for the technological upgrade of the entire industry. As more and more enterprises introduce similar intelligent systems, the overall technical level of the industry will be significantly improved, driving the transformation of the new energy vehicle leasing industry from a "traditional service model" to an "intelligent service model," and injecting strong momentum into the long-term sustainable development of the industry.

[0153] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A new energy vehicle rental order-charging collaborative energy consumption optimization system, characterized by: It includes a hardware layer, a data layer, a business logic layer, and an application layer. Each layer achieves data interaction and functional collaboration through standardized interfaces. The hardware layer includes new energy vehicles, charging piles, smart sensors, and communication equipment. The new energy vehicles are equipped with on-board terminals to collect battery status and energy consumption data. The charging piles are equipped with smart monitoring modules to monitor the charging process and upload data. The smart sensors are deployed in parking lots and road areas to collect environmental and road condition information. The communication equipment uses 5G or IoT technology to achieve high-speed data transmission between the terminal devices and the system platform. The data layer adopts a distributed database architecture to store order data, vehicle data, charging pile data, energy data, and environmental data, and establishes a data security mechanism to ensure data confidentiality, integrity, and availability. The business logic layer includes an order management module, a charging scheduling module, an energy consumption prediction module, and a strategy optimization module; The application layer includes a leasing company management platform, a user mobile APP, and a power grid dispatch interface, which provide corresponding functional services to leasing companies, users, and the power grid dispatch center, respectively.

2. The new energy vehicle rental order-charging collaborative energy consumption optimization system according to claim 1, characterized in that: The order management module has the following functions: It supports multiple order entry methods, both online and offline, and automatically verifies order information, including user qualification review and vehicle availability check. Once the review is passed, a formal order is generated. Track order execution status in real time, including vehicle rental, waiting to be charged, and returned, and send order progress reminders to users and enterprises via APP push or SMS; We conduct multi-dimensional statistical analysis of order data, including order volume, rental duration, and user preferences, to provide data support for vehicle dispatching and marketing strategy development.

3. The new energy vehicle rental order-charging collaborative energy consumption optimization system according to claim 1, characterized in that: The charging scheduling module has the following functions: Real-time collection of charging pile operating status, including idle, charging, and fault status; when faulty equipment is detected, an alarm notification is issued and maintenance scheduling is triggered. Based on the vehicle pick-up and drop-off times of the rental order, the remaining battery power of the vehicle, and the location and status of the charging station, the system matches the vehicle with the optimal charging station and generates a charging reservation plan. When orders change or unexpected situations occur, such as charging pile malfunctions or sudden increases in grid load, the charging plan is automatically adjusted and charging pile resources are reallocated.

4. The new energy vehicle rental order-charging collaborative energy consumption optimization system according to claim 1, characterized in that: The workflow of the energy consumption prediction module includes: Collect multi-dimensional data such as vehicle historical energy consumption, driving trajectory, ambient temperature, and road conditions, and perform data cleaning, deduplication, and standardization preprocessing. An energy consumption prediction model is constructed using random forest or neural network algorithms. The model input includes vehicle parameters, driving conditions, and environmental factors, and the output is the predicted energy consumption demand of the vehicle within a specific time period. Regularly train and optimize the prediction model using new historical data to improve prediction accuracy.

5. The new energy vehicle rental order-charging collaborative energy consumption optimization system according to claim 1, characterized in that: The strategy optimization module has the following functions: With the optimization objectives of minimizing energy consumption costs, balancing grid load, and maximizing user satisfaction, a multi-objective optimization algorithm is designed. The algorithm constraints include charging pile capacity, vehicle charging time window, and grid load limit. Based on real-time electricity price information, grid load data and energy consumption forecast results, a dynamic charging strategy is formulated: charging is prioritized when electricity prices are low and grid load is low, and charging power is adjusted or charging is delayed when electricity prices are high and grid load is high. The optimized charging strategy is distributed to charging piles and vehicle terminals, and the execution effect of the strategy is monitored in real time and adjusted according to the actual situation.

6. The new energy vehicle rental order-charging collaborative energy consumption optimization system according to claim 1, characterized in that: In the application layer, the leasing company management platform supports order management, charging pile monitoring, energy consumption statistics, and report generation; the user mobile APP supports order inquiry, charging reservation, charging navigation, and payment functions; and the power grid dispatch interface enables data connection with the power grid dispatch center to obtain power grid load information.

7. A method for optimizing energy consumption in collaboration between new energy vehicle rental orders and charging based on the system described in any one of claims 1-6, characterized in that: Includes the following steps: S1: Hardware layer terminal equipment collects data on new energy vehicles, charging piles, environment, and road conditions, and transmits it to the data layer for storage via communication equipment; S2: The business logic layer order management module processes rental orders, completing order entry, review, tracking, and statistical analysis; S3: The energy consumption prediction module builds and optimizes the energy consumption prediction model based on the data layer data, and outputs the predicted value of vehicle energy consumption demand. S4: The charging scheduling module combines order information, charging pile status, and energy consumption prediction to generate an initial charging plan; S5: The strategy optimization module combines real-time electricity price and grid load data to optimize the initial charging plan and formulate a dynamic charging strategy; S6: Send the dynamic charging strategy to the hardware layer for execution, and monitor the execution status of the strategy. If an emergency occurs, return to step S4 to readjust the charging plan.

8. The new energy vehicle rental order-charging collaborative energy consumption optimization method according to claim 7, characterized in that: In step S5, the strategy optimization module adopts a multi-objective optimization algorithm to optimize the charging strategy under the constraints of charging pile capacity, vehicle charging time window, and grid load limit, with the goals of minimizing energy consumption cost, balancing grid load, and maximizing user satisfaction.

9. The new energy vehicle rental order-charging collaborative energy consumption optimization method according to claim 7, characterized in that: It also includes a pre-allocation step for charging plans based on order forecasts: Analyze historical rental order data to uncover patterns in the temporal and spatial distribution of orders and predict order demand in the future. Based on order forecasts, charging pile resources are pre-allocated, and charging tasks are assigned to different time periods and different charging piles to avoid charging pile congestion during peak hours.

10. The new energy vehicle rental order-charging collaborative energy consumption optimization method according to claim 7, characterized in that: It also includes dynamic energy consumption adjustment steps based on real-time data: Real-time data collection of vehicle driving status, battery status, and environmental conditions; combined with energy consumption prediction models to adjust vehicle energy consumption control strategies. For vehicles in motion, optimize the driving route based on road condition information to avoid congested sections; For vehicles charging, the charging power is adjusted in real time according to changes in grid load and electricity price to balance charging progress and energy consumption costs.

Images