Battery replacement optimization scheduling method of electric bus

Abstract

The method comprises the following steps: establishing a battery replacement demand model of an electric bus based on dynamics; the battery replacement demand model is used for calculating the energy consumption of the electric bus from the starting point to the terminal point; establishing a battery charging model of the battery swap station; the objective function of the battery charging model is used for minimizing the battery charging cost; the constraint condition of the battery charging model at least comprises one of a charging power constraint, a battery swap station available capacity constraint and a battery charge state horizontal constraint; the available capacity constraint of the battery swap station is determined through a battery swap demand model; and solving the battery charging model by using an optimization algorithm to obtain the minimum value of the charging cost and the charging power of each time period for taking the minimum value of the charging cost. According to the invention, through systematic modeling and optimization, the operation cost of the battery replacement station is minimized, and the battery replacement demand of the bus is guaranteed at the same time.

Description

Technical Field

[0001] The present disclosure relates to the field of demand-side management, and more specifically to a method for optimizing battery replacement scheduling for electric buses. Background Art

[0002] Growing awareness of the impacts of climate change and financial incentives to reduce greenhouse gas emissions are driving a global rise in electric vehicle ownership. However, factors such as limited range and high battery replacement costs are making potential buyers hesitant about purchasing an electric vehicle.

[0003] While fast-charging stations have made significant progress in reducing waiting times, one concern with their use is the accelerated aging of electric vehicle batteries. To address these shortcomings, battery swap stations have emerged as a promising solution, enabling high-speed service without placing excessive stress on batteries. At battery swap stations, electric vehicle batteries can be charged over a longer period of time, ensuring lower operating costs without overburdening the power system. Public transit bus services involve planned routes and scheduled stops that run continuously on a daily schedule. Consequently, public transit bus services do not allocate long rest breaks at main terminals, necessitating a quick and practical way to charge the batteries of electric buses to prevent delays and increased costs for public transit bus services. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides a battery swap optimization scheduling method for electric buses that reduces the operating costs of battery swap stations.

[0005] The present disclosure provides a battery swap optimization scheduling method for electric buses, comprising: establishing a battery swap demand model for electric buses based on dynamics; the battery swap demand model is used to calculate the energy consumption of the electric bus from a starting point to an end point; establishing a battery charging model for a battery swap station; the objective function of the battery charging model is used to minimize the battery charging cost; the constraints of the battery charging model include at least one of a charging power constraint, an available capacity constraint of the battery swap station, and a battery state of charge level constraint; the available capacity constraint of the battery swap station is determined by the battery swap demand model; and solving the battery charging model using an optimization algorithm to obtain the minimum charging cost and the charging power in each time period that minimizes the charging cost.

[0006] According to an embodiment of the present disclosure, a battery replacement demand model for an electric bus is established based on dynamics, including: calculating the traction component to obtain traction based on mechanical analysis; wherein the traction component includes at least one of air resistance, rolling friction, slope resistance and inertia; using the traction, integrally calculating the energy consumption of the electric bus from the starting point to the end point; and calculating the battery replacement demand of the electric bus based on the energy consumption of the electric bus from the starting point to the end point.

[0007] According to an embodiment of the present disclosure, the integral calculation of the energy consumption of an electric bus from a starting point to a terminal point includes: using traction to calculate the energy consumption between two consecutive bus stops by piecewise integral; wherein the traction component is calculated based on the road condition between the two consecutive bus stops; the road condition includes at least one of a slope angle, a number of stops, an average vehicle speed, and a rolling resistance coefficient; and adding the energy consumption between every two consecutive bus stops from the starting point to the terminal point to obtain the energy consumption of the electric bus from the starting point to the terminal point.

[0008] According to an embodiment of the present disclosure, the battery replacement demand of the electric bus is calculated based on the energy consumption of the electric bus from the starting point to the end point, including: for each electric bus, the battery replacement time and battery replacement demand capacity of the electric bus are obtained based on the battery capacity, operating shifts and energy consumption from the starting point to the end point of the electric bus.

[0009] According to an embodiment of the present disclosure, a battery charging model for a battery swap station is established, including: obtaining an objective function for solving the battery charging cost within a day based on the charging power and electricity price in each time period; establishing constraints based on the parameters of the battery swap station and the battery swap demand; wherein the charging power constraint is used to limit the maximum charging power; the available capacity constraint of the battery swap station is used to limit the available capacity of the battery swap station to meet the battery swap demand; and the battery state of charge level constraint is used to limit overcharging and over-discharging of the battery;

[0010] Among them, the objective function is as follows:

[0011]

[0012] Among them, P t represents the charging power during period t; c t represents the electricity price in the tth period; T represents the number of periods.

[0013] According to an embodiment of the present disclosure, constraints are established based on the parameters of the battery swap station and the battery swap demand, including:

[0014] Establish charging power constraints; the charging power constraints are as follows:

[0015] 0≤P t ≤P t max

[0016] P t max =nC char

[0017] Among them, P t represents the charging power during period t; P t max represents the maximum charging power during period t; Cchar Indicates the charging capacity of the charger; n indicates the number of chargers in the battery swap station;

[0018] Establish available capacity constraints for battery swap stations; available capacity constraints for battery swap stations are as follows:

[0019] 0≤C t ≤C max

[0020]

[0021] Among them, C t represents the available capacity during period t; C max Indicates the maximum energy capacity of the battery swap station; represents the battery replacement demand capacity at hour t;

[0022] Establish the battery state of charge level constraints; the battery state of charge level constraints are as follows:

[0023] S min ≤S t ≤S max

[0024] Among them, S t represents the state of charge level of the battery swap station at hour t, S min and S max Represent the minimum and maximum state of charge levels of the battery swap station respectively.

[0025] According to an embodiment of the present disclosure, an optimization algorithm is used to solve a battery charging model to obtain the minimum charging cost and the charging power for each time period that minimizes the charging cost, including: taking the charging power as a decision variable; using the optimization algorithm to iteratively optimize the charging power distribution output for each time period while satisfying constraints, so as to obtain the minimum charging power for each time period and the minimum charging cost; wherein the optimization algorithm includes at least one of a firefly algorithm and a particle swarm algorithm.

[0026] The second aspect of the present disclosure provides a battery swap optimization scheduling device for electric buses, which can be used to implement the above method. The device includes: an energy solution module, which is used to establish a battery swap demand model for electric buses based on dynamics; the battery swap demand model is used to calculate the energy consumption of the electric bus from the starting point to the end point; a cost solution module, which is used to establish a battery charging model of the battery swap station; the objective function of the battery charging model is used to minimize the battery charging cost; the constraints of the battery charging model include at least one of the charging power constraint, the available capacity constraint of the battery swap station and the battery state of charge level constraint; the available capacity constraint of the battery swap station is determined by the battery swap demand model; the battery charging module, which is used to solve the battery charging model using an optimization algorithm to obtain the minimum charging cost and the charging power in each time period that minimizes the charging cost.

[0027] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned battery replacement optimization scheduling method for electric buses.

[0028] The fourth aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned battery replacement optimization scheduling method for electric buses.

[0029] The battery swap optimization scheduling method for electric buses provided in this disclosure proposes an overall framework for optimizing battery swap scheduling for electric buses, including a battery swap demand model and a battery charging model. Because the charging power solution problem to be solved is described through systematic modeling, it at least partially solves the technical problem that battery swap stations have difficulty meeting the demand for electric buses and have high operating costs. Through systematic modeling and optimization, it achieves the technical effect of minimizing the operating costs of battery swap stations while ensuring the battery swap demand of buses. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flowchart schematically illustrates a method for optimizing battery replacement scheduling for an electric bus according to an embodiment of the present disclosure;

[0031] Figure 2 Schematically illustrates a battery replacement demand capacity diagram during a scheduling period according to an embodiment of the present disclosure;

[0032] Figure 3 Schematically shows an optimal operation diagram of a battery swap station during a scheduling period according to an embodiment of the present disclosure;

[0033] Figure 4 A diagram schematically illustrates a capacity situation of a battery swap station that can be used for regulating services according to an embodiment of the present disclosure;

[0034] Figure 5 The following schematically shows a structural block diagram of a battery replacement optimization scheduling device for an electric bus according to an embodiment of the present disclosure;

[0035] Figure 6 A block diagram of an electronic device suitable for implementing a battery replacement optimization scheduling method for electric buses according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0036] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0037] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0038] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0039] When expressions such as "at least one of A, B and C, etc." are used, they should generally be interpreted in accordance with the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0040] Figure 1 The flowchart of the battery replacement optimization scheduling method for electric buses according to the embodiment of the present disclosure is schematically shown. Figure 1As shown, an embodiment of the present disclosure provides a battery swap optimization scheduling method for electric buses, including: establishing a battery swap demand model for electric buses based on dynamics; the battery swap demand model is used to calculate the energy consumption of the electric bus from the starting point to the end point; establishing a battery charging model for the battery swap station; the objective function of the battery charging model is used to minimize the battery charging cost; the constraints of the battery charging model include at least one of the charging power constraint, the available capacity constraint of the battery swap station and the battery state of charge level constraint; the available capacity constraint of the battery swap station is determined by the battery swap demand model; and solving the battery charging model using an optimization algorithm to obtain the minimum charging cost and the charging power in each time period that minimizes the charging cost.

[0041] Through the embodiments of this disclosure, a modeling approach for optimizing battery swapping scheduling for electric buses is proposed. This approach includes three core modules: battery swapping demand modeling, charging model construction, and optimization algorithm solution. The goal is to optimize the battery swapping process for electric buses. By calculating the energy consumption of the vehicles and the charging costs of battery swapping stations, the operating costs of battery swapping stations are minimized while ensuring the battery swapping needs of buses.

[0042] On the basis of the above embodiments, a battery replacement demand model for electric buses is established based on dynamics, including: calculating the traction force component according to mechanical analysis to obtain traction; wherein the traction force component includes at least one of air resistance, rolling friction, slope resistance and inertia force; using the traction force, integrally calculating the energy consumption of the electric bus from the starting point to the end point; and calculating the battery replacement demand of the electric bus based on the energy consumption of the electric bus from the starting point to the end point.

[0043] In this embodiment, based on a common longitudinal dynamics model, the traction force F1 is expressed by the following relationship: F1 = F2 + F3 + F4 + F5.

[0044] Where F2 = K d v 2 (t);

[0045] Where, F2 represents air resistance; K d =0.5ρC d A; ρ represents the air density, unit is kg / m 3 ; C d is the drag coefficient; A is the frontal area of the vehicle in m 2 ; v(t) is the speed of the vehicle at time t, in m / s.

[0046] Where, F3 = Mgfcosα;

[0047] Where F3 represents rolling friction; f is the rolling resistance coefficient; M is the vehicle mass in kg; and g represents the acceleration due to gravity.

[0048] Where, F4 = Mgsinα;

[0049] Where F4 represents the slope resistance; α is the road slope.

[0050] Where, F5 = σMa(t);

[0051] where F5 represents the inertial force that causes changes in stored kinetic energy due to acceleration and deceleration. a(t) is the vehicle acceleration at time t, and σ is a coefficient that models the inertia of rotating components in the drivetrain.

[0052] Through the embodiments of this disclosure, traction is calculated based on mechanical analysis, the mechanical analysis process of the battery swap demand model is refined, and the core parameters of energy consumption calculation are clarified. This further estimates energy consumption, improves the accuracy of energy consumption prediction, avoids misjudgments of battery swap requirements due to differences in road conditions, and enables more accurate prediction of battery swap requirements for electric buses.

[0053] Based on the above embodiment, the energy consumption of the electric bus from the starting point to the end point is calculated by integrating the energy consumption between two consecutive bus stops using traction force, wherein the traction force component is calculated based on the road condition between the two consecutive bus stops; the road condition includes at least one of the slope angle, the number of stops, the average speed, and the rolling resistance coefficient; and the energy consumption between each two consecutive bus stops from the starting point to the end point is added to obtain the energy consumption of the electric bus from the starting point to the end point.

[0054] In this embodiment, E represents the energy demand due to traction between two consecutive bus stops, and is calculated as follows:

[0055] E=∫η[K d v 2 (t)+Mgf cosα+Mg sin a+σMa(t)]v(t)dt

[0056] Here, η is an efficiency factor that models the losses in the inverter, motor, and drive system; the meanings of the other parameters are as described above.

[0057] In this embodiment, in order to calculate the energy demand of the electric bus, a driving profile is created between consecutive stops on the bus route. The trip between two consecutive stops consists of η1+1 stages, and the length of each stage is D1=D / η1+1. η1 represents the number of intermediate stops between two bus stops. The first stage starts with a constant acceleration a + It starts by accelerating for a distance d0, then travels a distance d1 at a constant sliding speed v1, and finally travels a distance d2 at a constant deceleration a. -Slow down and travel a distance d2, satisfying d0 + d1 + d2 = D1. For simplicity, assume that there is an intermediate stop between the two stations, that is, η1 = 1, and the coasting speed between the two stations is equal to 1.5 times the average speed.

[0058]

[0059] E a(t)＝0 =ηd1(Mgf cosα+Mg sinα+K d v1 2 )

[0060] E a(t)＝a- =ηd2(Mgf cosα+Mg sinα-K d a - d2+σMa - )

[0061]

[0062]

[0063] in, Indicates the energy consumed in the acceleration phase; E a(t)＝0 Indicates the energy consumed during the uniform speed sliding stage; Indicates the energy consumed in the deceleration phase; a + Indicates the acceleration during the acceleration phase; a - Indicates the acceleration during the deceleration phase; the meanings of other parameters are as above.

[0064] In this embodiment, the energy demand Ec between consecutive bus stops is Ec=E. Then the energy demand Ec from the starting point to the end point is total is the sum of the energy requirements for travel between each station and is expressed by the following relationship:

[0065] E total =∑E c

[0066] Through the embodiments of the present disclosure, the energy consumption calculation is refined through segmented integration and associated with actual road condition parameters. The adaptability of the model to complex road conditions (such as frequent starts and stops, slope changes) is improved. The accuracy of energy consumption prediction is improved to avoid misjudgment of battery replacement needs due to differences in road conditions. This model can provide accurate calculation of battery consumption based on different road conditions, slopes, vehicle speeds and other conditions, thereby improving the accuracy and efficiency of battery replacement scheduling in practical applications.

[0067] On the basis of the above embodiment, the battery replacement demand of the electric bus is calculated according to the energy consumption of the electric bus from the starting point to the end point, including: for each electric bus, the battery replacement time and battery replacement demand capacity of the electric bus are obtained according to the battery capacity, operating schedule and energy consumption from the starting point to the end point of the electric bus.

[0068] Through the embodiments of the present disclosure, the time required for battery replacement and the required battery capacity can be calculated based on the vehicle's battery capacity, operating schedule, and energy consumption, and the energy consumption calculation results can be converted into battery replacement requirements (time and capacity). This process helps to rationally plan the vehicle's battery replacement cycle, avoid operational interruptions due to insufficient power, and improve the stability and reliability of the public transportation system.

[0069] Based on the above embodiment, the battery charging schedule is optimized in a centralized manner, with the goal of charging the batteries when the market electricity price is low, ensuring that the battery swap station continues to operate according to the operational constraints of the charging level, and that there are sufficient batteries to meet the battery demand in each time period. The goal of the optimization model is to minimize the total charging cost within 24 hours. For each time period, the optimization model considers the number of fully charged batteries in the battery swap station and evaluates the optimal charging power for each battery group by considering the estimated energy demand and the hourly electricity price. The optimization problem is stated as follows:

[0070] Establishing a battery charging model for a battery swap station involves: obtaining an objective function for solving the battery charging cost within a day based on the charging power and electricity price at each time period; establishing constraints based on the parameters of the battery swap station and the battery swap demand; among these, the charging power constraint is used to limit the maximum charging power; the available capacity constraint of the battery swap station is used to limit the available capacity of the battery swap station to meet the battery swap demand; and the battery state of charge level constraint is used to limit overcharging and over-discharging of the battery;

[0071] Among them, the objective function is as follows:

[0072]

[0073] Among them, P t represents the charging power during period t; c t represents the electricity price in the tth period; T represents the number of periods.

[0074] Through the embodiments of this disclosure, the objective function can improve the efficiency of power resource utilization, avoid waste, and reduce overall power consumption by optimizing charging costs. Constraints are also defined to ensure that the model conforms to actual operating conditions. By establishing a battery charging model, combining factors such as charging power and electricity price, and incorporating a time-of-use electricity pricing strategy, prioritizing charging during off-peak hours to save costs, charging costs can be minimized and the operating expenses of battery swap stations can be reduced.

[0075] Based on the above embodiment, constraints are established according to the parameters of the battery swap station and the battery swap requirements, including:

[0076] Establish charging power constraints; the charging power constraints are as follows:

[0077] 0≤P t ≤P t max

[0078] P t max =nC char

[0079] Among them, P t represents the charging power during period t; P t max represents the maximum charging power during period t; C char Indicates the charging capacity of the charger; n indicates the number of chargers in the battery swap station;

[0080] Establish available capacity constraints for battery swap stations; available capacity constraints for battery swap stations are as follows:

[0081] 0≤C t ≤C max

[0082]

[0083] Among them, C t represents the available capacity during period t; C max Indicates the maximum energy capacity of the battery swap station; represents the battery replacement demand capacity at hour t;

[0084] Establish the battery state of charge level constraints; the battery state of charge level constraints are as follows:

[0085] S min ≤S t ≤S max

[0086] Among them, S t represents the state of charge level of the battery swap station at hour t, S min and S max Represent the minimum and maximum state of charge levels of the battery swap station respectively.

[0087] Through the embodiments of the present disclosure, the constraints of charging power, capacity and state of charge (SOC) are clarified. These constraints ensure that the charging process of the battery swap station remains stable in terms of power supply, safety and battery health. For example, the maximum charging power constraint can prevent the station from being overloaded, and the state of charge level constraint can avoid overcharging or over-discharging of the battery, thereby extending the service life of the battery. The available capacity constraint of the battery swap station ensures that the battery swap station can meet the actual demand by evaluating the battery swap demand in real time, and there will be no shortage of supply.

[0088] Based on the above embodiment, an optimization algorithm is used to solve the battery charging model to obtain the minimum charging cost and the charging power for each time period that minimizes the charging cost, including: using the charging power as a decision variable; using the optimization algorithm to iteratively optimize the charging power distribution output for each time period while satisfying constraints, so as to obtain the minimum charging power for each time period and the minimum charging cost; wherein the optimization algorithm includes at least one of a firefly algorithm and a particle swarm algorithm.

[0089] In this embodiment, the firefly algorithm is suitable for multi-modal optimization problems, and the particle swarm algorithm has a fast convergence speed. Both can efficiently solve complex nonlinear models. It should be noted that the present disclosure does not impose any restrictions on the optimization algorithm.

[0090] Through the embodiments of the present disclosure, the optimal solution is found under complex constraints, thereby minimizing the cost of the charging process, and reasonably arranging the charging power in each time period, thereby improving the optimization level of the overall system.

[0091] Finally, a case study was conducted using data from 50 electric buses providing public transportation bus service on different bus routes in a certain area to demonstrate the application and effectiveness of the proposed method. The battery capacity of the electric buses was assumed to be 337 kWh. The battery swap station had 55 chargers, each with a charging power of 75 kW. The battery swap station selected to serve these 50 electric buses was equipped with 5 additional chargers to prevent interruptions in charging operations during emergencies. The dispatch period for the case study was 24 hours. It was assumed that all electric buses began service with a fully charged state at the beginning of the dispatch period. To evaluate the impact of the state of charge (SOC) level during battery swapping, three different scenarios were considered, assuming that the SOC of each electric bus battery could be reduced to a range of 5%-10%, 15%-20%, and 25%-30% before swapping. To facilitate the interpretation of the results, the following case numbers are used: Case 1 (5%-10% SOC); Case 2 (15%-20% SOC); and Case 3 (25%-30% SOC).

[0092] For these three situations, an optimal charging strategy is developed to minimize the charging cost of the battery swap station.

[0093] (1) Analysis of battery replacement demand

[0094] The battery replacement demand capacity that needs to be met by the battery replacement station is evaluated based on the battery replacement optimization scheduling method for electric buses, and the results are plotted on Figure 2 .from Figure 2 As can be observed, Case 1 generates the highest demand at every hour of the day due to its lower state of charge. Furthermore, none of the three cases generates demand for battery replacement before 4:00 AM. Battery replacement demand in each case exhibits a fluctuating curve starting at 4:00 AM and continuing until the end of the day. These results are expected, as each electric bus may have a different route, schedule, number of stops, and trip duration. It is also observed that the demand for battery replacement for electric buses decreases monotonically towards the end of the day.

[0095] (2) Charging dispatch results of battery swap stations

[0096] The battery swap station charging strategy proposed by the battery swap optimization scheduling method for electric buses is used to minimize the total charging cost of the battery swap station and Figure 3 The results for each scenario are presented in Figure 2. It is observed that in all three scenarios, the battery swap stations charge the batteries at full capacity between midnight and 4:00 AM, when there is no demand for battery swaps and electricity prices are low, allowing for battery swap demand later that day. Furthermore, with the exception of Case 1, due to higher electricity prices, the battery swap stations refrain from charging the batteries in the morning and evening hours. This also helps alleviate grid stress by reducing load during peak hours in residential areas. To meet high demand, Case 1 continues charging in the morning and evening, purchasing significantly more electricity than Cases 2 and 3, resulting in higher costs. Battery swap demand increases during work hours due to increased demand for public transportation bus services and traffic density. As a result, the battery swap stations in Cases 1 and 2 operate at full capacity from 9:00 AM to 4:00 PM. However, Case 3 operates at full capacity starting at 10:00 AM and reduces purchased power at 4:00 PM. In the evening, despite higher electricity prices, Case 1 employs a different charging schedule than the other two scenarios to meet the high demand for battery swaps at 9:00 PM, continuing to charge the batteries at high power. The capacity of the battery swap station available for regulation services is calculated by considering the demand in each time period during the dispatch period and the total energy of the battery swap station. Figure 4Figure 1 depicts the regulation capacity that a battery swap station can provide to the grid during each time period without disrupting public bus services. If this capacity is used to provide frequency regulation services, the battery swap station needs to re-determine the optimal charging schedule for subsequent time periods. Note that Case 1 purchases more electricity during most time periods due to the need to meet higher levels of demand, so the total energy stored in the battery swap station is generally greater in Case 1. Consequently, Case 1 has a higher total regulation capacity than the other two cases, as the batteries are charged immediately upon arrival at the battery swap station. The results show that the proposed method can reduce the total charging cost of the battery swap station by 10% by optimizing the battery charging schedule within a 24-hour scheduling period. It is expected that the cost savings achieved by the proposed method will be even greater if the charging capacity of the battery swap station is increased and the battery replacement demand is estimated on a monthly basis.

[0097] Based on the above-mentioned battery swap optimization scheduling method for electric buses, the present disclosure also provides a battery swap optimization scheduling device for electric buses. Figure 5 The device is described in detail.

[0098] Figure 5 The structural block diagram of the battery replacement optimization scheduling device for an electric bus according to an embodiment of the present disclosure is schematically shown.

[0099] like Figure 5 As shown, the battery replacement optimization scheduling device for electric buses in this embodiment can be used to implement the above method, and the device includes: an energy solving module, which is used to establish a battery replacement demand model for electric buses based on dynamics; the battery replacement demand model is used to calculate the energy consumption of the electric bus from the starting point to the end point; a cost solving module, which is used to establish a battery charging model of the battery replacement station; the objective function of the battery charging model is used to minimize the battery charging cost; the constraints of the battery charging model include at least one of the charging power constraint, the available capacity constraint of the battery replacement station and the battery state of charge level constraint; the available capacity constraint of the battery replacement station is determined by the battery replacement demand model; the battery charging module, which is used to solve the battery charging model using an optimization algorithm to obtain the minimum charging cost and the charging power in each time period that minimizes the charging cost.

[0100] According to an embodiment of the present disclosure, any multiple modules among the energy solution module, the cost solution module, and the battery charging module can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the energy solution module, the cost solution module, and the battery charging module can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of the energy solution module, the cost solution module, and the battery charging module can be at least partially implemented as a computer program module, which can perform the corresponding function when the computer program module is executed.

[0101] Figure 6 A block diagram of an electronic device suitable for implementing a battery replacement optimization scheduling method for electric buses according to an embodiment of the present disclosure is schematically shown.

[0102] like Figure 6 As shown, the electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for executing different actions of the method flow according to an embodiment of the present disclosure.

[0103] Various programs and data required for the operation of the electronic device 600 are stored in the RAM 603. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 602 and / or the RAM 603. It should be noted that the programs may also be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0104] According to an embodiment of the present disclosure, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the I / O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage portion 608 including a hard disk; and a communication portion 609 including a network interface card such as a LAN card or a modem. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed in the drive 610 as needed, so that a computer program read therefrom can be installed into the storage portion 608 as needed.

[0105] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0106] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM602 and / or RAM603 described above and / or one or more memories other than ROM602 and RAM603.

[0107] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method provided by the embodiments of the present disclosure.

[0108] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the processor 601 executes the computer program. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0109] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0110] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from a removable medium 611. When the computer program is executed by the processor 601, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0111] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, languages such as Java, C++, python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0113] Those skilled in the art will appreciate that the features described in the various embodiments and / or claims of this disclosure may be combined and / or coupled in various ways, even if such combinations and / or couplings are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or couplings are intended to fall within the scope of this disclosure.

[0114] The embodiments of the present disclosure are described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A battery replacement optimization scheduling method for electric buses, characterized in that: include: Based on dynamics, a battery replacement demand model for electric buses is established; The battery replacement demand model is used to calculate the energy consumption of the electric bus from the starting point to the end point; Establishing a battery charging model for a battery swap station; the objective function of the battery charging model is used to minimize the battery charging cost; the constraints of the battery charging model include at least one of a charging power constraint, a battery swap station available capacity constraint, and a battery state of charge level constraint; the battery swap station available capacity constraint is determined by the battery swap demand model; The battery charging model is solved by using an optimization algorithm to obtain the minimum value of the charging cost and the charging power in each time period that makes the charging cost take the minimum value.

2. The method according to claim 1, wherein Based on dynamics, a battery replacement demand model for electric buses is established, including: Calculating traction force components based on mechanical analysis to obtain traction force; wherein the traction force components include at least one of air resistance, rolling friction, slope resistance, and inertia; Using traction, the energy consumption of the electric bus from the starting point to the end point is calculated integrally; Calculate the battery replacement requirements of the electric bus based on the energy consumption of the electric bus from the starting point to the end point.

3. The method according to claim 2, wherein: The points calculate the energy consumption of an electric bus from its starting point to its destination, including: The energy consumption between two consecutive bus stops is calculated by piecewise integration using traction force; wherein the traction force component is calculated based on the road conditions between the two consecutive bus stops; the road conditions include at least one of a slope angle, a number of stops, an average vehicle speed, and a rolling resistance coefficient; The energy consumption of the electric bus from the starting point to the end point is obtained by adding up the energy consumption between each two consecutive bus stops from the starting point to the end point.

4. The method according to claim 2, wherein: Calculate the battery replacement requirements of electric buses based on their energy consumption from the starting point to the destination, including: For each electric bus, the battery replacement time and battery replacement capacity required are obtained based on the battery capacity, operating frequency and energy consumption from the starting point to the end point of the electric bus.

5. The method according to claim 1, wherein Establish a battery charging model for the battery swap station, including: According to the charging power and electricity price in each period, the objective function of solving the battery charging cost within a day is obtained; The constraint conditions are established based on the parameters of the battery swap station and the battery swap demand; wherein the charging power constraint is used to limit the maximum charging power; the available capacity constraint of the battery swap station is used to limit the available capacity of the battery swap station to meet the battery swap demand; and the battery state of charge level constraint is used to limit overcharging and over-discharging of the battery; Wherein, the objective function is as follows: Among them, P t represents the charging power during period t; c t represents the electricity price in the tth period; T represents the number of periods.

6. The method according to claim 5, wherein: According to the parameters of the battery swap station and the battery swap requirements, the constraints are established, including: Establish the charging power constraint; the charging power constraint is as follows: 0≤P t ≤P t max P t max =nC char Among them, P t represents the charging power during period t; P t max represents the maximum charging power during period t; C char Indicates the charging capacity of the charger; n indicates the number of chargers in the battery swap station; Establish the available capacity constraints of the battery swap station; the available capacity constraints of the battery swap station are as follows: 0≤C t ≤C max Among them, C t represents the available capacity during period t; C max Indicates the maximum energy capacity of the battery swap station; represents the battery replacement demand capacity at hour t; Establish the battery state of charge level constraint; the battery state of charge level constraint is as follows: S min ≤S t ≤S max Among them, S t represents the state of charge level of the battery swap station at hour t, S min and S max Represent the minimum and maximum state of charge levels of the battery swap station respectively.

7. The method according to claim 1, wherein Solving the battery charging model using an optimization algorithm to obtain the minimum charging cost and the charging power for each time period that minimizes the charging cost includes: Taking the charging power as a decision variable; Under the condition of satisfying the constraint conditions, the optimization algorithm is used to iteratively optimize the charging power distribution output of each time period so as to obtain the minimum value of the charging power and charging cost in each time period with the minimum charging cost; wherein the optimization algorithm includes at least one of the firefly algorithm and the particle swarm algorithm.

8. A battery replacement optimization scheduling device for electric buses, characterized in that: The device can be used to implement the method according to any one of claims 1 to 7, and the device includes: An energy solution module is used to establish a battery replacement demand model for electric buses based on dynamics; the battery replacement demand model is used to calculate the energy consumption of the electric bus from the starting point to the end point; a cost solving module for establishing a battery charging model for a battery swap station; the objective function of the battery charging model is to minimize the battery charging cost; the constraints of the battery charging model include at least one of a charging power constraint, a battery swap station available capacity constraint, and a battery state of charge level constraint; the battery swap station available capacity constraint is determined by the battery swap demand model; The battery charging module is used to solve the battery charging model using an optimization algorithm to obtain the minimum value of the charging cost and the charging power in each time period that minimizes the charging cost.

9. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to perform the method according to any one of claims 1 to 7.

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