Route optimization for cargo box transportation system

Abstract

The present disclosure relates to a cargo box transportation system, comprising: an electric transportation vehicle; a plurality of cargo boxes, each of which can be transported by the electric transportation vehicle and detached, wherein each cargo box comprises: an integrated battery configured to power local functions of the cargo box; a battery management system configured to measure and communicate a state of charge of the integrated battery; a central processing unit configured to: receive the state of charge from the battery management system from each cargo box; based on the state of charge of the cargo boxes, calculate an optimized route for the electric transportation vehicle to place and / or collect the plurality of cargo boxes. The disclosure further relates to a method for calculating a route for an electric transportation vehicle.

Description

[0001] Route optimization for cargo box transportation system

[0002] The present disclosure relates to a cargo box transportation system with improved route handling. The disclosure further relates to method for calculating a route for an electric transportation vehicle of a cargo box transportation system.

[0003] Background

[0004] In modern logistics systems, the adoption of electric transportation vehicles has become increasingly prevalent due to their environmental benefits and cost efficiency. These electric delivery vehicles are often powered wholly or partially by electricity, providing a cleaner alternative to traditional fossil fuel-powered vehicles.

[0005] In contemporary logistics and delivery systems, parcel lockers or smart lockers are used extensively to facilitate the last mile delivery process. These lockers may be secure, automated units that can be deployed in strategic locations such as parking lots, apartment complexes, office buildings, and retail stores. The delivery vehicle, which may be an electrical transportation vehicle, transports detachable cargo boxes filled with parcels and deposits them in these parcel lockers.

[0006] Customers receive a notification, often through a mobile app or SMS, informing them that their parcel is ready for pickup along with a unique code or a QR code to access their locker. This method offers significant advantages in terms of convenience, security, and efficiency.

[0007] Some logistics systems have explored the concept of integrating energy storage systems into cargo boxes. These cargo boxes, which can be transported by the delivery vehicle, are equipped with batteries. In this setup the cargo boxes can use the energy power internal function but also transfer some of their stored energy to the delivery vehicle during transport.

[0008] Despite the potential benefits, integrating energy storage systems into cargo boxes presents several disadvantages. One major issue is the risk of over-dimensioning the batteries within all cargo boxes. To ensure that there is always sufficient energy to charge the delivery vehicle, the batteries in the cargo boxes may be designed with excessive capacity. This over-dimensioning leads to higher costs for all cargo boxes due to the increased size and capacity of the energy storage systems.

[0009] Additionally, cargo boxes are often detached from the transportation vehicle and left at various locations to supply their own functions using the integrated battery. Over time, the battery in each cargo box will deplete. Accurately predicting when and which cargo boxes need to be picked up and brought back to a central location for recharging is challenging and complex. Incorrect estimations can lead to inefficiencies, increased operational costs, and potential disruptions in the logistics chain.

[0010] It is therefore an objective of the present disclosure to provide a cargo box transportation system and a method for calculating a route for an electric transportation vehicle that optimizes the energy management and route planning process.

[0011] Summary

[0012] The present disclosure relates to a cargo box transportation system comprising an electric transportation vehicle and a plurality of cargo boxes. Each cargo box can be transported by the electric transportation vehicle and detached, and comprises an integrated battery configured to power local functions of the cargo box and a battery management system configured to measure and communicate a state of charge of the integrated battery. A central processing unit is configured to receive the state of charge from the battery management system from each cargo box and, based on the state of charge of the cargo boxes, calculate an optimized route for the electric transportation vehicle to place and / or collect the plurality of cargo boxes.

[0013] The cargo boxes can use the energy power internal function but also transfer some of their stored energy to the electric transportation vehicle during transport.

[0014] The disclosure further relates to a method for calculating a route for an electric transportation vehicle, comprising the steps of receiving a state of charge from a plurality of cargo boxes, each cargo box having an integrated battery and being capable of being transported by the electric transportation vehicle and detached, and calculating, based on the state of charge of the cargo boxes, an optimized route for the electric transportation vehicle to place and / or collect the plurality of cargo boxes. The cargo box transportation system may further include features such as: the central processing unit calculating how much of the buffer capacity will remain in each cargo box when it is to be retrieved, or the central processing unit taking into account the electric transportation vehicle’s energy consumption for driving when calculating the optimized route, or ensuring that there is enough energy in the cargo boxes to pick them up one by one according to the optimized route, or ensuring the electric transportation vehicle always picks up cargo boxes in an order that prevents the vehicle from running out of energy, thus enabling continuous operation, or calculating the order in which cargo boxes should be picked up and transported so that the electric transportation vehicle can be charged with the remaining buffer energy in the cargo boxes before returning to the central hub for charging.

[0015] The calculation of the optimized route can involve advanced algorithms and models, such as linear regression models for estimating energy consumption, machine learning algorithms for adaptive predictions, dynamic programming for buffer capacity calculations, Dijkstra’s algorithm for shortest path optimization, genetic algorithms for complex route optimization for dynamic route adjustments based on real-time data.

[0016] Additionally, reinforcement learning can be employed to continuously improve route efficiency based on feedback from real-time data.

[0017] The presently disclosed system offers several advantages, including efficient energy management by dynamically monitoring the state of charge of both the electric transportation vehicle and the cargo boxes, ensuring efficient use of energy and reducing downtime. It minimizes the need for oversized batteries in cargo boxes, thereby reducing costs. The system supports sustainability goals by optimizing energy usage, minimizing material usage and reducing the need for frequent recharging. The optimized route calculation ensures that the electric transportation vehicle can continuously operate without running out of energy, enhancing the reliability of the logistics system. Furthermore, the system can be integrated with autonomous electric transportation vehicles, further enhancing efficiency and reducing human intervention.

[0018] The system aims to ensure that the electric transportation vehicle can continuously operate by efficiently utilizing the buffer energy stored in cargo boxes without the need for over-dimensioning the batteries. By dynamically monitoring the State of Charge (SOC) of both the electric transportation vehicle and the cargo boxes, and calculating an optimized route based on real-time data, the invention overcomes the disadvantages of increased costs and logistical inefficiencies inherent in current systems.

[0019] Description of drawings

[0020] Various embodiments are described hereinafter with reference to the drawings. The drawings are examples of embodiments and are intended to illustrate some of the features of the presently disclosed cargo box transportation system and method for calculating a route for an electric transportation vehicle, and are not to be construed as limiting to the presently disclosed invention.

[0021] Fig. 1 shows an example of an embodiment of the presently disclosed cargo box transportation system.

[0022] Fig. 2 shows a flow chart of a method according to an embodiment of the presently disclosed method for calculating a route for an electric transportation vehicle.

[0023] Detailed description

[0024] The present disclosure relates to a cargo box transportation system, comprising: an electric transportation vehicle; a plurality of cargo boxes, each of which can be transported by the electric transportation vehicle and detached, wherein each cargo box comprises: an integrated battery configured to power local functions of the cargo box; a battery management system configured to measure and communicate a state of charge of the integrated battery; a central processing unit configured to: receive the state of charge from the battery management system from each cargo box; based on the state of charge of the cargo boxes, calculate an optimized route for the electric transportation vehicle to place and / or collect the plurality of cargo boxes.

[0025] An “electric transportation vehicle” may be any electric vehicle adapted for transporting cargo boxes. Such a vehicle is typically equipped with features and systems specifically tailored for handling and transporting detachable cargo boxes, which are integral to the operational efficiency and versatility of the logistics process.

[0026] The present disclosure encompasses both the placement and collection of cargo boxes by the electric transportation vehicle. That is, the optimized route calculated by the central processing unit may be used for delivering cargo boxes to designated locations, such as parking lots or customer sites, as well as for retrieving cargo boxes.

[0027] Fig. 1 shows an example of an embodiment of the presently disclosed cargo box transportation system 100. The central processing unit (CPU) 105 acts as the core of the system, coordinating various functions and processes. The system comprises an electric transportation vehicle 101 , which is responsible for transporting the cargo boxes 102; a number of cargo boxes 102, one of which is placed on the electric transportation vehicle 101. A network 106 in the form of a cloud-based solution provides communications means for communication between the components of the system. The electric transportation vehicle 101 comprises an integrated battery 103 and a battery management system 104 configured to measure and communicate a state of charge of the integrated battery.

[0028] The vehicle may be powered by an electric motor, drawing energy from onboard batteries. This system provides a clean, quiet, and efficient mode of transportation, reducing carbon emissions and operating costs compared to traditional internal combustion engines.

[0029] The vehicle may include a specialized mechanism for handling cargo boxes. This can include automated lifts and / or robotic arms that facilitate the loading and unloading of cargo boxes with minimal manual intervention. The system may be adapted to ensure secure attachment and detachment of cargo boxes during transport.

[0030] The vehicle may comprise an energy transfer interface that allows it to receive power from the integrated batteries in the cargo boxes. This system ensures that the vehicle can draw supplementary energy from the cargo boxes, extending its operational range and reducing the need for frequent charging stops.

[0031] The vehicle may be equipped with advanced communication systems that allow realtime data exchange between the vehicle, the cargo boxes, and the central logistics system. This includes transmitting SOC information, route updates, and operational status to optimize logistics planning and execution.

[0032] The central processing unit (CPU) may be further configured to calculate how much of the buffer capacity will remain in each cargo box when it is to be retrieved. This ensures efficient energy management and optimal utilization of resources within the logistics network.

[0033] The CPU continuously monitors the state of charge (SOC) of each cargo box through its integrated battery management system (BMS). By analyzing real-time data, the CPU can predict the energy consumption patterns of each cargo box based on historical usage data, current operational demands, and environmental factors such as temperature and placement duration. This predictive analysis allows the CPU to determine the remaining buffer capacity in each cargo box at any given point in time.

[0034] One advantage of this feature is that it enables precise planning for the retrieval and charging of cargo boxes. By knowing the exact buffer capacity remaining in each cargo box, the logistics system can schedule pickups and recharging operations more effectively, minimizing downtime and ensuring that cargo boxes are always available with sufficient energy reserves. This reduces the risk of cargo boxes running out of power and becoming non-operational, which can disrupt the delivery process and lead to inefficiencies.

[0035] Possible configurations of this feature include the use of advanced algorithms such as dynamic programming and machine learning models. These algorithms can enhance the accuracy of buffer capacity predictions by continuously learning from the data and adapting to changing conditions. For instance, machine learning models can identify patterns and anomalies in energy consumption that may not be apparent through traditional methods, allowing for more refined and reliable predictions.

[0036] Further details and implementations of this feature can involve integrating the CPU with loT (Internet of Things) devices and sensors installed in the cargo boxes. These sensors can provide additional data points such as temperature, humidity, and load weight, which can impact the energy consumption of the cargo boxes. By incorporating this sensor data, the CPU can make more informed decisions regarding buffer capacity calculations. Additionally, the CPU can be configured to perform real-time updates and adjustments to the buffer capacity calculations. For example, if a cargo box encounters unexpected energy consumption due to a change in its environment or operational demands, the CPU can immediately recalculate the buffer capacity and update the logistics plan accordingly. This dynamic adjustment capability ensures that the system remains flexible and responsive to real-world conditions.

[0037] In practical implementations, the CPU could use cloud-based computing resources to handle the intensive data processing required for these calculations. By leveraging cloud computing, the system can scale its processing power as needed, ensuring that it can handle large volumes of data and complex calculations efficiently. This approach also facilitates the integration of multiple data sources and allows for centralized management and coordination of the logistics network.

[0038] Overall, the feature of calculating the remaining buffer capacity in each cargo box when it is to be retrieved significantly enhances the efficiency and reliability of the cargo box transportation system. It enables better planning, reduces operational risks, and ensures that energy resources are used optimally, contributing to a more sustainable and effective logistics solution.

[0039] In one embodiment of the present cargo box transportation system, the central processing unit (CPU) takes into account the electric transportation vehicle’s energy consumption for driving when calculating the optimized route. This feature can ensure that the vehicle can complete its route without running out of energy, thereby maintaining efficient and uninterrupted delivery operations.

[0040] The CPU continuously monitors the state of charge (SOC) of the electric transportation vehicle and gathers data on its energy consumption. This data is influenced by several factors including distance, speed, terrain, and load weight. By analyzing this information, the CPU can predict the vehicle's energy requirements for different segments of its route. This predictive analysis allows the CPU to factor in the energy consumption of the vehicle when optimizing the route for placing and / or collecting cargo boxes. One significant advantage of this feature is that it enhances the reliability of the delivery system by ensuring that the vehicle does not deplete its energy reserves while on its route. By accurately accounting for the vehicle’s energy consumption, the CPU can plan routes that ensure the vehicle remains operational throughout its journey, avoiding delays and inefficiencies associated with emergency recharging stops.

[0041] Possible configurations of this feature include the use of energy consumption models such as linear regression or machine learning algorithms. Linear regression can provide a straightforward method to estimate energy consumption based on known variables such as distance and load weight. Machine learning algorithms, on the other hand, can offer more sophisticated predictions by learning from historical data and adapting to real-time changes in driving conditions.

[0042] Further details and implementations can involve integrating the CPU with real-time monitoring systems in the vehicle. Sensors placed within the vehicle can track various parameters such as speed, acceleration, road gradient, and load distribution. This sensor data can be fed into the CPU to continuously update the energy consumption model, ensuring that the route optimization remains accurate and responsive to current driving conditions.

[0043] Additionally, the CPU can incorporate external data sources such as traffic conditions, weather forecasts, and roadwork updates. By factoring in these external influences, the CPU can adjust the route to avoid energy-intensive conditions such as heavy traffic or steep inclines, thereby conserving the vehicle's energy.

[0044] The CPU may leverage cloud-based computing to handle the complex calculations required for energy consumption modeling and route optimization. Cloud computing resources allow for scalable and efficient processing, enabling the system to manage large datasets and real-time analytics seamlessly. This approach also facilitates the integration of multiple data sources, ensuring a comprehensive and holistic optimization process.

[0045] The CPU can also perform dynamic route adjustments based on real-time data. If the vehicle encounters unexpected changes in energy consumption due to unforeseen circumstances, the CPU can recalculate the route on-the-fly to ensure that the vehicle can still complete its deliveries without running out of power. This capability ensures that the logistics system remains flexible and resilient in the face of operational challenges.

[0046] In one embodiment of the presently disclosed cargo box transportation system, the central processing unit (CPU) is configured to ensure that there is enough energy in the cargo boxes to pick them up one by one according to the optimized route. This feature is useful for maintaining the operational continuity of the logistics system, ensuring that both the cargo boxes and the electric transportation vehicle have sufficient energy throughout the delivery process.

[0047] The CPU may continuously monitor the state of charge (SOC) of each cargo box and the electric transportation vehicle. By analyzing this data, the CPU calculates the energy requirements for picking up each cargo box in sequence along the optimized route. The CPU takes into account the energy consumption for driving to each pickup location, the energy required for any necessary loading and unloading operations, and the buffer capacity of each cargo box.

[0048] One advantage of this feature is that it prevents scenarios where the vehicle might run out of energy before completing its route or where a cargo box is picked up without sufficient energy to maintain its own operations. By ensuring that there is enough energy in each cargo box for sequential pickups, the CPU enhances the reliability and efficiency of the logistics system.

[0049] It is possible to use of predictive modeling and real-time analytics. Predictive models can estimate the energy consumption for each leg of the route based on historical data and real-time conditions such as distance, speed, terrain, and load weight. Real-time analytics can continuously update these predictions based on actual energy usage, allowing the CPU to make dynamic adjustments to the route as needed.

[0050] Further details and implementations involve integrating the CPU with advanced monitoring systems that provide detailed data on energy usage. Sensors in the cargo boxes and the vehicle can track SOC, energy consumption rates, and environmental factors that impact energy usage, such as temperature and road conditions. This data can be fed into the CPU to refine the energy consumption models and ensure accurate predictions. The CPU can also implement a priority system to manage energy distribution effectively. For instance, if the SOC of the vehicle or a critical cargo box drops below a certain threshold, the CPU can adjust the route to prioritize the pickup of cargo boxes with higher remaining energy or reroute to a charging station if necessary. This prioritization ensures that energy resources are used optimally, preventing disruptions in the delivery process.

[0051] The central processing unit (CPU) may be further configured to ensure the electric transportation vehicle always picks up cargo boxes in an order that prevents the electric transportation vehicle from running out of energy.

[0052] The CPU may continuously monitor the state of charge (SOC) of both the electric transportation vehicle and each cargo box through integrated battery management systems (BMS). By analyzing this data, the CPU can calculate the optimal sequence for picking up the cargo boxes along the vehicle's route. This calculation takes into account the current and projected SOC, the energy consumption for traveling between locations, and the buffer capacity available in each cargo box.

[0053] Possible configurations of this feature include the use of sophisticated algorithms such as dynamic programming, genetic algorithms, and Ant Colony Optimization (ACO). These algorithms can evaluate multiple factors and constraints to determine the most efficient pickup order that maximizes energy efficiency and minimizes the risk of the vehicle running out of power.

[0054] The central processing unit may be configured to calculate the buffer capacity of each cargo box, defining the amount of energy that can be transferred to the electric transportation vehicle without depleting the cargo box's operational needs. This feature ensures that energy resources are allocated optimally, maintaining the operational functionality of the cargo boxes while supporting the energy needs of the electric transportation vehicle. This balanced approach contributes to a more sustainable and effective logistics solution, maximizing operational efficiency and minimizing disruptions.

[0055] In one embodiment the central processing unit implements a route optimization algorithm that considers the state of charge of the electric transportation vehicle and cargo boxes, estimated energy consumption for driving, buffer capacity of the cargo boxes, and sequence of pickups to ensure continuous operation.

[0056] The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit uses linear regression models to estimate the energy consumption of the electric transportation vehicle for driving based on distance, speed, terrain, and load weight. Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The goal of linear regression is to find the best-fitting linear equation that describes how the dependent variable changes as the independent variables change. The process of linear regression involves several steps. First, data collection involves gathering data on the dependent variable and independent variables. Next, model specification involves identifying the dependent variable and the independent variables. Estimation involves using statistical techniques to estimate the coefficients (P values) that minimize the difference between the observed values and the values predicted by the linear model, typically using the least squares method. Evaluation involves checking the model’s performance by measuring how well it explains the variability in the dependent variable, often using the R-squared value. Finally, prediction involves using the estimated model to predict values of the dependent variable for given values of the independent variables. In the context of the presently disclosed cargo box transportation system, linear regression could be used to estimate the energy consumption of the electric transportation vehicle. By using independent variables such as distance, speed, terrain, and load weight, the system can predict the vehicle's energy usage. This information can then be used by the central processing unit (CPU) to optimize the vehicle's route, ensuring efficient energy management and preventing the vehicle from running out of power during its delivery route.

[0057] The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit employs dynamic programming to calculate the buffer capacity of each cargo box, ensuring the energy transferred to the vehicle does not deplete the cargo box’s operational needs. Dynamic programming is a method used in computer science and mathematics to solve complex problems by breaking them down into simpler subproblems. It is particularly useful for optimization problems where the goal is to find the best solution among many possible ones. Dynamic programming saves computational time by storing the results of subproblems and reusing these results in larger problems, thus avoiding the need to recompute them. In the context of the cargo box transportation system, dynamic programming can be used to optimize the route for the electric transportation vehicle. By breaking down the route optimization problem into smaller subproblems, such as determining the optimal sequence for picking up each cargo box, dynamic programming can efficiently find the best overall route. This ensures that the vehicle has enough energy to complete its deliveries while minimizing the total distance travelled and energy consumed. The CPU can use dynamic programming to store intermediate results and reuse them, significantly reducing the computational complexity and improving the efficiency of the route optimization process.

[0058] In one embodiment, the central processing unit uses Dijkstra’s algorithm to calculate the shortest and most energy-efficient route between different locations.

[0059] The processing unit may employ genetic algorithms to optimize the route by considering multiple factors such as SOC, energy consumption, and buffer capacity. Genetic algorithms are useful for optimizing routes, particularly in complex and dynamic environments like logistics and transportation, due to their ability to find high- quality solutions in large and complex search spaces. Genetic algorithms are well- suited for solving complex optimization problems where traditional methods might struggle. Routing problems, such as the Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP), involve numerous possible routes and constraints, making them difficult to solve optimally with conventional approaches. Genetic algorithms excel in such scenarios by efficiently exploring the solution space.

[0060] Moreover, genetic algorithms can handle multiple, and often conflicting, objectives. In route optimization, this might include minimizing total travel distance, reducing energy consumption, meeting delivery time windows, and balancing the load across multiple vehicles. Genetic algorithms can incorporate these various objectives into a fitness function that evaluates and guides the evolution of solutions. In the presently disclosed cargo box transportation system, genetic algorithms can be used to optimize the route of the electric transportation vehicle by: balancing the goals of minimizing travel distance, reducing energy consumption, and adhering to delivery time windows and / or adjusting the route dynamically based on real-time updates on traffic conditions, weather, and SOC of the vehicle and cargo boxes, and / or ensuring that the vehicle picks up cargo boxes in an order that maintains sufficient energy levels for continuous operation. By leveraging genetic algorithms, the CPU can efficiently manage the complexities of route optimization, enhancing the overall efficiency and reliability of the logistics system. This approach ensures that the electric transportation vehicle operates effectively within the dynamic and multifaceted environment of last-mile delivery.

[0061] In one embodiment, processing unit incorporates reinforcement learning to dynamically adjust the route based on real-time feedback and continuously improve route efficiency.

[0062] Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward. Unlike supervised learning, which learns from labeled data, reinforcement learning learns from the consequences of actions through trial and error, guided by the feedback (rewards or penalties) it receives from the environment.

[0063] In reinforcement learning, the agent is the entity that makes decisions, such as a CPU in a transportation system. The environment is the system within which the agent operates, such as the logistics network with cargo boxes and delivery routes. A state is a representation of the current situation of the agent in the environment, like the current location of the vehicle and the state of charge of batteries. An action is a set of choices the agent can make, such as which cargo box to pick up next. A reward is the feedback from the environment that evaluates the action taken, like energy efficiency or time saved. The policy is a strategy used by the agent to decide actions based on the current state, such as rules for choosing the next cargo box. The value function is a prediction of future rewards, used to evaluate the desirability of states. Using reinforcement learning has several advantages. RL algorithms can adapt to changes in the environment, making them suitable for dynamic and unpredictable logistics scenarios. RL can find near-optimal solutions in complex, multi-objective problems where traditional algorithms might struggle. The system improves over time by learning from past decisions and their outcomes. RL enables real-time route adjustments, improving responsiveness to unexpected events. For example, the CPU starts with a basic policy for route selection, perhaps based on simple heuristics. The vehicle follows the route determined by the CPU, picking up cargo boxes and moving between locations. After each action (e.g., picking up a cargo box), the CPU receives feedback on energy consumption, time taken, and remaining SOC. The CPU updates its policy to favor actions that resulted in better energy efficiency and timely deliveries. Over many delivery cycles, the CPU refines its policy to optimize the overall logistics process. By employing reinforcement learning, the cargo box transportation system can continuously improve its route optimization and energy management strategies, leading to enhanced efficiency, reduced operational costs, and better service quality.

[0064] The electric transportation vehicle may be an autonomous vehicle capable of selfdriving based on the optimized route calculated by the central processing unit.

[0065] An autonomous vehicle capable of self-driving based on the optimized route calculated by the central processing unit is an advanced electric transportation vehicle designed to enhance logistics efficiency and sustainability. This vehicle may integrate a variety of sophisticated technologies, including sensors, artificial intelligence, and communication systems, enabling it to navigate complex environments autonomously.

[0066] In one embodiment, the central processing unit is further configured to monitor the state of charge of at least one cargo box while the cargo box is located at a terminal during loading. During this period, the central processing unit receives SOC data via the battery management system integrated in the cargo box and evaluates the energy status in light of upcoming operational requirements.

[0067] Based on this information, the central processing unit may calculate the required charging time necessary to prepare the cargo box for deployment on an optimized route. The calculation may take into account the current SOC, the anticipated energy usage for the cargo box along the planned route, and, optionally, any buffer capacity needed for auxiliary functions or emergency fallback.

[0068] This approach ensures that each cargo box receives only the amount of charging necessary for its intended function, avoiding overcharging. Overcharging can lead to longer-than-necessary dwell time in the terminal, which negatively impacts the overall logistics chain.

[0069] The central processing unit may further consider additional factors when calculating the required charging time, such as whether the cargo box is expected to transfer power to the electric transportation vehicle during the route. The system can optionally incorporate real-time charging rate data to refine the calculation further, adapting the estimated time based on actual charge input as monitored during the charging session.

[0070] In a corresponding embodiment, the method for calculating a route for an electric transportation vehicle may include the steps of monitoring a state of charge of at least on cargo box while the cargo box is located at a terminal during loading; and calculating, based on the state of charge and anticipated energy usage of the cargo box for the optimized route, a required charging time to ensure sufficient charge for deployment without exceeding necessary energy levels.

[0071] The central processing unit (CPU) may continuously receive data from the vehicle and the logistics network, such as the state of charge (SOC) of the vehicle and cargo boxes, traffic conditions, delivery schedules, and real-time location information. Using advanced algorithms like genetic algorithms, dynamic programming, and reinforcement learning, the CPU calculates an optimized route for the vehicle. This route maximizes efficiency by minimizing energy consumption, reducing travel time, and ensuring timely deliveries while maintaining sufficient energy reserves for continuous operation. Once the optimized route is calculated, the CPU transmits this route data to the autonomous vehicle. The vehicle may be equipped with a suite of sensors, including LiDAR, radar, cameras, and ultrasonic sensors, which provide a comprehensive understanding of the surrounding environment. These sensors detect obstacles, road conditions, traffic signals, and other vehicles, allowing the autonomous system to make informed decisions.

[0072] The vehicle's onboard artificial intelligence system may be configured to process the sensor data in real-time, enabling it to follow the optimized route accurately. Machine learning algorithms may help the vehicle adapt to dynamic conditions, such as sudden changes in traffic or unexpected obstacles. The Al system can adjust the vehicle's speed, direction, and stopping points to ensure safety and efficiency. GPS technology provides precise location tracking, allowing the vehicle to navigate to each delivery point with high accuracy. The vehicle's navigation system integrates with the CPU to receive real-time updates, ensuring that the vehicle can respond to changes in the route or delivery priorities. The autonomous vehicle also includes an energy management system that works in conjunction with the CPU to monitor and manage the SOC. This system ensures that the vehicle and the cargo boxes maintain optimal energy levels throughout the journey. If necessary, the vehicle can draw energy from the cargo boxes to extend its operational range, ensuring that it can complete its route without the need for frequent recharging stops. Communication systems enable seamless data exchange between the vehicle, the CPU, and other components of the logistics network. This connectivity ensures that the vehicle remains updated with the latest route information, traffic conditions, and delivery requirements.

[0073] The present disclosure further relates to a method for calculating a route for an electric transportation vehicle, comprising the steps of: receiving a state of charge from a plurality of cargo boxes, each cargo box having an integrated battery and being capable of being transported by the electric transportation vehicle and detached; calculating, based on the state of charge of the cargo boxes, an optimized route for the electric transportation vehicle to place and / or collect the plurality of cargo boxes.

[0074] Fig. 2 shows a flow chart of a method 200 according to an embodiment of the presently disclosed method for calculating a route for an electric transportation vehicle. The method may comprise the step of receiving (201) a state of charge from a plurality of cargo boxes, each cargo box having an integrated battery and being capable of being transported by the electric transportation vehicle and detached, and calculating (202), based on the state of charge of the cargo boxes, an optimized route for the electric transportation vehicle to place and / or collect the plurality of cargo boxes.

[0075] A person skilled in the art will recognize that the presently disclosed method for calculating a route for an electric transportation vehicle may be performed using any embodiment of the presently disclosed cargo box transportation system, and vice versa.

[0076] The method may further comprise the step of communication the optimized route to the electric transportation vehicle. The method may further comprise the step of driving the electric transportation vehicle according to the optimized route.

[0077] The present disclosure further relates to a computer program having instructions which, when executed by a computing device or computing system, cause the computing device or computing system to carry out any embodiment of the presently disclosed method for calculating a route for an electric transportation vehicle. The computer program may be stored on any suitable type of storage media, such as non-transitory storage media.

[0078] As would be understood by a person skilled in the art, a central processing unit may be any type of processing circuitry, such as a single processor in a multi- core / multiprocessor system, or, for example, any suitable cloud-based processing solution.

Claims

Claims1. A cargo box transportation system, comprising: an electric transportation vehicle; a plurality of cargo boxes, each of which can be transported by the electric transportation vehicle and detached, wherein each cargo box comprises: an integrated battery configured to power local functions of the cargo box; a battery management system configured to measure and communicate a state of charge of the integrated battery; a central processing unit configured to: receive the state of charge from the battery management system from each cargo box; based on the state of charge of the cargo boxes, calculate an optimized route for the electric transportation vehicle to place and / or collect the plurality of cargo boxes.

2. The cargo box transportation system according to claim 1, wherein the central processing unit is further configured to calculate how much of the buffer capacity will remain in each cargo box when it is to be retrieved.

3. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit takes into account the electric transportation vehicle’s energy consumption for driving when calculating the optimized route.

4. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit ensures that there is enough energy in the cargo boxes to pick them up one by one according to the optimized route.

5. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit is configured to ensure the electric transportation vehicle always picks up cargo boxes in an order that prevents the electric transportation vehicle from running out of energy.

6. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit calculates the order in which cargoboxes should be picked up and transported so that the electric transportation vehicle can be charged with the remaining buffer energy in the cargo boxes before returning to the central hub for charging.

7. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit is configured to continuously monitor the state of charge of both the electric transportation vehicle and each cargo box.

8. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit estimates the energy consumption of the electric transportation vehicle for driving based on factors including distance, speed, terrain, and / or load weight.

9. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit calculates the buffer capacity of each cargo box, defining the amount of energy that can be transferred to the electric transportation vehicle without depleting the cargo box's operational needs.

10. The cargo box transportation system of according to any one of the preceding claims, wherein the central processing unit implements a route optimization algorithm that considers the state of charge of the electric transportation vehicle and cargo boxes, estimated energy consumption for driving, buffer capacity of the cargo boxes, and sequence of pickups to ensure continuous operation.

11. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit dynamically adjusts the optimized route based on real-time state of charge data and changes in energy consumption.

12. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit uses linear regression models to estimate the energy consumption of the electric transportation vehicle for driving based on distance, speed, terrain, and load weight.

13. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit uses machine learning algorithms to adaptively predict energy consumption for various driving conditions.

14. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit employs dynamic programming to calculate the buffer capacity of each cargo box, ensuring the energy transferred to the vehicle does not deplete the cargo box’s operational needs.

15. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit uses Dijkstra’s algorithm to calculate the shortest and most energy-efficient route between different locations.

16. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit employs genetic algorithms to optimize the route by considering multiple factors such as SOC, energy consumption, and buffer capacity.

17. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit incorporates reinforcement learning to dynamically adjust the route based on real-time feedback and continuously improve route efficiency.

18. The cargo box transportation system according to any one of the preceding claims, wherein the electric transportation vehicle is an autonomous vehicle capable of self-driving based on the optimized route calculated by the central processing unit.

19. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit is configured to communicate with the autonomous vehicle's navigation system to provide real-time updates and adjustments to the optimized route.

20. The cargo box transportation system according to any one of the preceding claims, wherein the central processing unit is further configured to monitor the state of charge of at least one cargo box while located at a terminal duringloading, and to calculate a required charging time based on the cargo box's current state of charge and anticipated energy usage for the optimized route.

21. A method for calculating a route for an electric transportation vehicle, comprising the steps of: receiving a state of charge from a plurality of cargo boxes, each cargo box having an integrated battery and being capable of being transported by the electric transportation vehicle and detached; calculating, based on the state of charge of the cargo boxes, an optimized route for the electric transportation vehicle to place and / or collect the plurality of cargo boxes.

22. The method according to claim 21 , further comprising the step of communicating the optimized route to the electric transportation vehicle.

23. The method according to any one of claims 21 to 22, further comprising the step of driving the electric transportation vehicle according to the optimized route.

24. The method according to any one of claims 21 to 23, further comprising the step of using an autonomous vehicle capable of self-driving based on the optimized route calculated.

25. The method according to any one of claims 21 to 24, further comprising the steps of monitoring a state of charge of at least one cargo box while the cargo box is located at a terminal during loading; and calculating, based on the state of charge and anticipated energy usage of the cargo box for the optimized route, a required charging time to ensure sufficient charge for deployment without exceeding necessary energy levels.

26. A computer program having instructions which, when executed by a computing device or computing system, cause the computing device or computing system to carry out the method for calculating a route for an electric transportation vehicle according to any one of claims 21 to 25.

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