Method and system for optimizing battery charging and replacing of battery replacing station

Abstract

The invention relates to a method for optimizing battery charging and replacement of a battery replacement station (BSS), comprising the following steps: predicting a battery replacement demand; -predicting the availability of the energy source; and-optimizing a battery charging and replacement strategy based on the predicted battery replacement demand and the predicted energy availability, wherein the optimizing comprises selecting a partially charged battery for replacement. According to the comprehensive solution provided by the invention, the importance of efficiency, environmental protection and customer experience is emphasized, and the overall performance of the battery exchange station is greatly improved through combination of an intelligent technology and a flexible operation strategy.

Description

Technical Field

[0001] The present invention relates to electric vehicle charging technology, and in particular to the field related to electric vehicle battery swapping stations (BSS). Background Art

[0002] The number of electric vehicles (EVs) worldwide has increased significantly over the past few decades. Compared to traditional internal combustion engine vehicles, EVs can reduce greenhouse gas emissions and prevent air pollution in urban areas. However, EVs have a drawback: charging can take several hours, while refueling a traditional internal combustion engine vehicle only takes minutes. This shortcoming is a major limitation to the growth of EVs.

[0003] To overcome this difficulty, a widely used approach is the battery swap station (BSS). A battery swap station consists of a battery charging device (e.g., a battery charging cabinet) and a batch of batteries. The batteries are charged in the charging device. When an electric vehicle arrives at the battery swap station with a low-charged battery, a station operator (manual or automated) removes the empty battery and places it inside the station. A fully charged battery from the batch is then inserted into the electric vehicle. The replaced empty battery is then charged inside the station, and the electric vehicle departs after the swap process is complete, which typically takes no more than five minutes and is comparable to refueling (gas) for traditional internal combustion engine vehicles.

[0004] In addition to the rapidity of battery replacement, battery swap stations can also unlock new business models for electric mobility. By decoupling the vehicle from the battery, new electric mobility services can be developed. For example, a fleet of batteries can be independently owned and managed, their charging, maintenance, secondary use, recycling, and more, with compensation based on the distance traveled or the energy provided to the vehicle. Furthermore, battery swap stations can enhance the synergy between electric mobility and the power system, reducing power constraints from the grid and providing potential ancillary services to the grid.

[0005] As a result, battery swap stations are becoming increasingly important as an alternative to traditional charging stations. Although battery swap stations offer a faster way to recharge than traditional charging, existing operating models still face significant challenges in terms of efficiency and sustainability.

[0006] Traditional battery swap stations typically rely on fixed charging patterns and strategies that fail to adequately account for grid load balancing, battery life management, and the volatility of renewable energy. Furthermore, existing charging and swap solutions often lack the ability to predict battery swap demand, leading to uneven allocation of charging resources and an inability to effectively address demand surges during peak periods. This not only increases operating costs but also limits service efficiency and user satisfaction.

[0007] Furthermore, existing battery swap stations often neglect environmental impact when managing battery charging. Most systems fail to effectively utilize renewable energy sources, such as solar or wind power, instead relying primarily on traditional electricity sources that can generate high carbon emissions. This practice is inconsistent with global trends and goals to reduce carbon emissions and urgently needs optimization and improvement.

[0008] The invention aims to address efficiency and sustainability issues in the operation of traditional battery swap stations, providing electric vehicle users with faster, more reliable and more environmentally friendly services. Summary of the Invention

[0009] The purpose of the present invention is to overcome the defects and problems existing in the background technology.

[0010] To this end, according to one aspect of the present invention, a method for optimizing battery charging and replacement at a battery swap station (BSS) is proposed, comprising the following steps:

[0011] - Forecasting battery replacement needs, for example based on historical usage data;

[0012] - Forecasting energy availability; and

[0013] -Optimizing battery charging and replacement strategies based on predicted battery replacement needs and predicted energy availability, where the optimization includes selecting partially charged batteries (i.e., below a battery full requirement, such as below 95%) for replacement.

[0014] By predicting battery replacement needs based on historical usage data, the method according to the present invention can more accurately reflect actual EV user behavior and demand patterns, thereby improving the service efficiency of battery swap stations (BSS). Predicting energy availability allows battery charging and replacement strategies to more effectively adapt to changes in the energy market. Optimization includes selecting partially charged batteries for replacement, increasing the flexibility and responsiveness of battery swap stations to meet diverse user needs.

[0015] The optimization can be performed using a variety of models. Preferably, the battery charging and replacement strategy is optimized using a mixed integer linear programming (MILP) model that uses the predicted battery replacement demand and the predicted energy source availability as inputs to determine the optimal charging schedule and battery allocation.

[0016] A mixed integer linear programming (MILP) model is used to optimize battery charging and replacement strategies, allowing for more refined and efficient resource allocation while improving overall energy efficiency. This approach can maximize energy cost savings while maintaining service quality.

[0017] Optionally, the MILP model includes an objective function based on CO2 emissions from electricity consumed during the optimization period. This allows the charging and swapping operations at the battery swap station to also consider environmental impacts, promoting environmental sustainability.

[0018] Furthermore, the MILP model takes into account the physical constraints of the battery and the grid conditions, ensuring the feasibility of the optimization scheme and the stability of the system, reducing the risks caused by overloading or incompatibility.

[0019] Optionally, the predicted energy source availability includes electricity price volatility and the availability of renewable energy sources, allowing the battery swap station to better adapt to grid and environmental changes, improving the system's adaptability and resilience to energy supply fluctuations.

[0020] Optionally, the method according to the present invention further comprises the following steps: - implementing the optimized battery charging and replacement strategy to ensure that the optimization results can be effectively implemented in actual operations. Preferably, this implementation step is automatically performed by a battery management system (BMS), thereby reducing the complexity and error potential of manual operations and improving operational efficiency and reliability.

[0021] According to a second aspect of the present invention, a system for optimizing battery charging and replacement at a battery swap station (BSS) is provided, comprising:

[0022] - A forecasting module for predicting battery replacement needs based on historical usage data;

[0023] - a forecasting module for predicting the availability of energy sources; and

[0024] - An optimization module for optimizing battery charging and replacement strategies based on predicted battery replacement needs and predicted energy source availability, wherein the optimization includes selecting partially charged batteries for replacement.

[0025] The system integrates prediction and optimization modules to provide a comprehensive solution for improving the overall performance of battery swap stations, including the selection of partially charged batteries for replacement, enhancing the system's ability to adapt to different customer needs.

[0026] Optionally, the optimization module for optimizing battery charging and replacement strategies uses a mixed integer linear programming (MILP) model that uses the predicted battery replacement needs and the predicted energy source availability as input to determine the optimal charging schedule and battery allocation. This improves the accuracy and efficiency of decision-making, making the battery charging and replacement strategies more in line with actual needs while optimizing overall energy efficiency.

[0027] According to a third aspect of the present invention, a method is provided, comprising instructions that, when executed by a computer, cause the computer to perform the steps of the method according to the present invention. This method enables the optimization operations according to the present invention to be easily deployed and executed on various computing platforms, thereby improving the method's universality and accessibility.

[0028] Therefore, this invention significantly improves the operational efficiency and adaptability of battery swap stations by applying prediction and optimization techniques. The key is to leverage historical usage data to accurately predict battery swap demand. Combined with energy availability forecasts, this allows battery charging and swap strategies to more effectively adapt to energy market fluctuations. This not only improves service efficiency but also optimizes energy utilization, particularly in addressing electricity price volatility and the uncertainty of renewable energy.

[0029] Strategy optimization using a mixed-integer linear programming (MILP) model makes battery charging and dispatching more refined and efficient while maintaining quality of service. A significant advantage of this approach is that it considers environmental impacts while optimizing economic benefits, such as promoting environmental sustainability through the CO2 emissions objective function in the MILP model. Furthermore, considering the physical constraints of the battery and grid conditions ensures system stability and security, reducing operational risks caused by overloads or incompatibilities.

[0030] Another key advantage of this invention is the efficiency and automation of operations. By automating the implementation steps through the battery management system (BMS), the complexity and potential for error associated with manual operations are significantly reduced, improving operational efficiency and reliability. The overall system design not only provides a comprehensive solution to enhance the overall performance of the BSS but also enhances the system's ability to adapt to diverse customer needs.

[0031] Overall, this invention not only achieves efficient operation of battery swap stations at a technical level, but also strikes a balance between environmental protection and economic benefits. Through its advanced prediction model and optimization algorithm, it provides strong support for the rapid development of electric vehicles and sets a new standard for energy management and environmental protection.

[0032] Therefore, building on existing technologies, this invention aims to provide a more efficient and environmentally friendly BSS operation method. This method predicts battery swap requirements, optimizes charging and swap strategies, and effectively integrates renewable energy to improve overall efficiency, reduce operating costs, and minimize environmental impact. This is achieved through the introduction of advanced prediction and optimization algorithms.

[0033] Other features and advantages of the present invention will be discussed in the following specific embodiments. Those skilled in the art will be able to clearly understand the content of the present invention and the technical effects obtained based on the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] It should be understood that in the present invention, except for obvious contradictory or incompatible situations, all features, variations and / or specific embodiments can be combined in various combinations.

[0035] Other features and advantages of the present invention will become apparent from the following non-limiting examples, taken in conjunction with the accompanying drawings, in which:

[0036] - Figure 1 is a flow chart of an embodiment of a method according to the present invention;

[0037] - Figure 2 The relationship curve between "battery replacement cost" and "charging level (SoC)" is shown;

[0038] - Figure 3 The relationship curve between "service kilometers" and "charge level (SoC)" is shown;

[0039] - Figure 4 is a flowchart of optimizing a battery swap station using a mixed integer linear programming (MILP) model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following are exemplary embodiments according to the present invention. The relevant definitions below are only used to describe exemplary embodiments and are not intended to limit the scope of the present invention. Since the embodiments described herein are exemplary, they can also be extended to modifications involving the functions, purposes and / or structures of the present invention.

[0041] Figure 1 An exemplary embodiment of the method according to the present invention is shown, which comprises the following steps:

[0042] Step S1: Battery replacement demand forecast

[0043] According to an embodiment of the present invention, first, in this step, battery replacement demand is predicted, including the predicted number of replacements required within a given time interval and the predicted energy level of batteries swapped out from vehicles entering the battery swap station. For example, historical data from similar weekdays can be used for prediction. Alternatively, battery replacement demand can be predicted based on information such as the user's scheduled battery replacement request on the mobile application and the user's specified minimum SOC required for the battery received.

[0044] Step S2: Predicting energy availability

[0045] Next, energy availability needs to be predicted, which can include two aspects.

[0046] The first aspect involves predicting grid usage. For example, in this example, electricity prices are predicted based on grid usage times, which typically include different prices for off-peak, flat, and peak periods. However, more dynamic pricing mechanisms, such as those based on spot electricity market prices, are also possible. Furthermore, data on the CO2 content of electricity is considered to more efficiently utilize grid resources when optimizing the clean kilometers of battery swap station services. "Clean kilometers" is defined here as the additional distance an electric vehicle can travel with the same carbon emissions level. This concept is based on a baseline: the distance an electric vehicle can typically travel with a given carbon emissions level. If, through optimization strategies, an electric vehicle can travel further with the same carbon emissions, this additional distance is considered a "clean kilometer." For example, suppose that, under normal circumstances, an electric vehicle can travel 100 kilometers for a given amount of carbon emissions. By using more efficient or cleaner energy sources, the same carbon emissions can enable the electric vehicle to travel 120 kilometers. These additional 20 kilometers are then considered "clean kilometers" achieved through optimization. "Clean kilometers" helps quantify and demonstrate the actual benefits of adopting environmentally friendly energy and technologies in reducing carbon emissions. It is an important indicator for evaluating and optimizing the environmental impact of electric vehicles and their battery swap stations.

[0047] Another aspect involves forecasting the production of renewable energy sources (such as photovoltaic and / or wind power) used by battery swap stations. This forecast involves not only estimating renewable energy production but also comprehensively considering environmental conditions, technical performance, and market dynamics. First, forecasting photovoltaic and wind energy production requires considering multiple environmental factors. For example, photovoltaic energy production is affected by sunlight intensity, duration, seasonal variations, and weather conditions (such as cloud cover). Similarly, wind energy production depends on wind speed, direction, topography, and climatic conditions. Therefore, accurate forecasting models must integrate meteorological data and historical weather patterns to predict renewable energy production at specific locations and time periods. Second, technical performance is also a significant factor influencing forecast accuracy. For example, the efficiency of photovoltaic panels may decrease with temperature changes and age. Similarly, the efficiency of wind turbines may be affected by maintenance status and technological aging. Therefore, forecasting models need to consider the performance parameters and potential degradation rates of these devices.

[0048] Due to the highly stochastic nature of renewable energy availability and battery replacement demand, forecast errors will inevitably increase over time. Therefore, for example, within a single operating day, it is necessary to regularly update forecast data for battery replacement demand and photovoltaic and wind power generation. This can be achieved using standard forecasting techniques using regression or machine learning methods. Each time new forecast data becomes available, it is used as new input to the optimization model in the subsequent S3 step.

[0049] Step S3: Optimize battery charging and replacement strategies

[0050] Next, in step S3, based on the exemplary method of the present invention, the battery charging and replacement strategy is optimized based on the predicted battery replacement demand and the predicted energy availability.

[0051] In the present invention, the construction of the optimization problem allows flexibility in the SOC of the rechargeable battery selected when swapping. That is, during periods of high demand for battery swapping, the method of the present invention can replace the incoming battery with a partially charged battery. Usually, a battery is considered fully charged when its SOC>95%, and the prior art only allows fully charged batteries to be replaced. According to the present invention, for example, during periods of high demand for battery swapping, batteries that are only 70% charged are allowed to be swapped.

[0052] In the present invention, the optimized battery charging and replacement strategy may include the following key factors:

[0053] 1. Select the battery to be replaced: This involves selecting the appropriate battery for each upcoming battery replacement in the charging cabinet at the battery swap station. This means the system needs to determine which battery is the most suitable for the next replacement, which may be based on the battery's state of charge, usage history, or other relevant parameters.

[0054] 2. Charging power per battery: This determines the specified charging power for each battery in the battery swap station. This is adjusted based on the battery type, current state, or predicted future usage needs to ensure that the battery is fully charged when needed while optimizing energy use.

[0055] 3. Management of Stationary Battery Energy Storage in Battery Swap Stations: If a battery swap station system includes a stationary battery energy storage system, this facility typically consists of a bank of large-capacity batteries whose purpose is to store energy for supply to the swap station when needed. The power input or output of this system also needs to be optimized. This may involve deciding when to draw energy from the energy storage system to charge the vehicle's mobile batteries, or when to store excess energy.

[0056] The present invention proposes calculating the values ​​of the variables for these key factors within each time step of the forecast cycle. As the forecast data is updated, these values ​​are recalculated to adapt to the latest information. This dynamic adjustment method enables battery swap stations to flexibly respond to changing demand and energy supply conditions, optimize battery usage and charging processes, thereby improving overall efficiency and reducing operating costs. With this approach, battery swap stations can more effectively manage their battery resources, ensuring sufficient rechargeable batteries are available during high-demand periods while maximizing battery charging efficiency when energy supply is sufficient.

[0057] According to the present invention, for optimization, it is possible, for example, to optimize an objective function. In the present invention, the objective function can be a mathematical expression for optimizing the operation of a battery swap station (battery charging and replacement). Specifically, the objective function is a mathematical formula or algorithm that defines specific parameters that need to be maximized or minimized in the optimization problem. In the context of the battery swap station of the present invention, the objective function may include but is not limited to goals such as maximizing clean kilometers, minimizing operating costs, minimizing energy consumption, or minimizing carbon emissions.

[0058] For example, as an embodiment, the goal of the optimization problem is to maximize the net revenue of the battery swap station:

[0059] Maximize the net income within a given optimization period (usually 24 hours), where net income = battery replacement cost - energy cost.

[0060] in,

[0061] - "Battery swap cost" is the sum of the swap costs of the swaps achieved during the optimization period. The "battery swap cost" of each swap and the "charge level (SoC)" can meet the following conditions: Figure 2 The relationship curve is shown in Figure 1. The vertical axis of the curve represents the cost of each battery swap, while the horizontal axis represents the battery charge level. As can be seen from the figure, as the battery charge level increases, the battery swap cost also increases. This shows that users need to pay different fees for the battery charge level, and the higher the battery charge level, the more fees users pay. It should be noted that this curve is not necessarily a straight line. It may have different shapes based on different pricing strategies and user behavior. This nonlinear relationship may reflect the complex influence of factors such as battery charging costs, market demand, or battery performance degradation. Through such a pricing model, battery swap stations can incentivize users to choose the appropriate charging level to balance costs and battery life.

[0062] - "Energy cost" is the total cost of energy consumed during the optimization. It can be composed of the following two parts:

[0063] 1. Local renewable electricity costs: This refers to electricity generated by the local power system (usually a photovoltaic system). This electricity is considered self-consumed at zero cost because it is produced by the exchange station's own renewable energy facilities and does not require external purchase. Therefore, no additional costs are included in the operating costs.

[0064] 2. Electricity cost from the public grid: This electricity is purchased by the battery swap station from the public grid, which may have a fixed or variable price. Fixed prices usually refer to the price of a long-term contract signed with the grid company, while variable prices may fluctuate based on market supply and demand, such as the difference in electricity prices during peak and off-peak hours.

[0065] When optimizing, battery swap stations need to consider how to balance these two power sources to reduce overall energy costs. For example, during peak photovoltaic power generation periods, battery swap stations may prioritize using their own clean electricity for battery charging, reducing electricity purchased from the public grid, thereby reducing costs and environmental impact. Furthermore, battery swap stations need to consider the impact of electricity price fluctuations on costs, rationally scheduling battery charging times and avoiding large-scale charging during periods of high electricity prices to maximize cost-effectiveness.

[0066] Alternatively, the objective function can also be based on CO2 emissions or renewable energy utilization. For example, the objective function becomes: maximize the service kilometers within a given optimization time period (usually 24 hours) - (1 / k)*(CO2 emissions).

[0067] in:

[0068] - "Service mileage" is the total mileage that the serviced vehicles can travel. The "service mileage" and "charge level (SoC)" of each battery replacement can meet the following requirements, for example: Figure 3 The vertical axis represents service kilometers, which is the total distance that an electric vehicle can be expected to travel after using a battery swap station. The horizontal axis represents the battery charge level, which is the percentage of battery charge completion.

[0069] As we can see from the graph, the curve starts from the origin and reaches zero service kilometers before a certain minimum charge level. This means that if the battery charge level is below this minimum percentage, it is considered to provide no useful service kilometers, because the energy consumed by the electric vehicle is the journey to and from the battery swap station, and is not enough to support any additional driving.

[0070] When the charge level exceeds this minimum point, the curve begins to rise, indicating that the service mileage it can provide increases with increasing battery charge level. This portion of the curve may initially rise slowly, then become steeper as the charge level increases further. This means that at low battery charge levels, the additional service mileage gained for each increase in charge level is relatively small; whereas at higher charge levels, the same increase in charge level yields more service mileage.

[0071] It's important to note that the curve isn't necessarily a straight line; its exact shape depends on the battery charging efficiency and the performance of the electric vehicle. For example, as the battery nears full charge, the growth in service mileage may slow due to a decrease in charging efficiency. This nonlinear curve helps reflect the complex variations in battery performance under real-world driving conditions and provides a basis for optimizing charging strategies at battery swap stations.

[0072] -CO2 emissions: is the total (indirect) CO2 emissions of the electricity consumed during the optimization period.

[0073] The term "indirect" here means that these emissions are not directly caused by the operation of the battery swap station, but are contributed by the emissions of the power plants that generate the consumed electricity. The energy consumed may include the following two parts:

[0074] 1. Local renewable electricity: This generally refers to electricity generated by local power systems near the BSS (e.g., photovoltaic systems). Since renewable energy sources like photovoltaics do not emit carbon dioxide when producing electricity, this energy has a zero CO2 content. This means that electricity obtained from these systems has no negative impact on the environment and is considered clean energy.

[0075] 2. Public Grid Electricity: This electricity comes from the public electricity supply network, and its CO2 content can be fixed or variable. This depends on the mix of sources within the grid. For example, electricity generated by fossil fuel power plants (coal, natural gas, etc.) will have higher CO2 emissions, while renewable energy sources such as wind and hydropower will have lower CO2 emissions. Therefore, the CO2 emissions of electricity obtained from the public grid will vary depending on the mix of different energy sources within the grid.

[0076] When optimizing the operation of battery swap stations, it is very important to consider CO2 emissions, as this affects the environmental footprint and sustainability of the power station. Optimization strategies aim to reduce CO2 emissions, which can be achieved by increasing the share of renewable energy, improving energy efficiency, or charging batteries when electricity prices are low (usually during periods of low carbon intensity). Through such optimization, battery swap stations can not only operate cost-effectively, but also reduce their impact on the environment, promote the use of clean energy, help achieve carbon reduction targets, and enhance the green image of the company.

[0077] -k is a fixed coefficient in [kg_CO2 / km], which is the mass of carbon dioxide produced per kilometer (kg_CO2 / km). It is used to quantify the average CO2 emissions corresponding to each kilometer traveled by electric vehicles.

[0078] A lower value for "k" means less CO2 emissions per kilometer driven. This is usually because some measures have been taken to reduce CO2 emissions, such as using cleaner energy or more efficient technology. In this case, the priority is to minimize CO2 emissions, even though this may result in fewer kilometers served.

[0079] Conversely, if the value of “k” is higher, then the CO2 emissions per kilometer will be higher. In this case, the strategy may focus more on maximizing the number of service kilometers, even if this may result in higher CO2 emissions.

[0080] If we use "k" as a reference value, then the expression (1 / k)*(CO2 emissions) represents the theoretical number of kilometers that can be driven (km_ref) based on the reference standard at a given CO2 emission level. In other words, if the total CO2 emissions are known, this formula can be used to calculate the expected number of kilometers that can be driven without considering any emission reduction strategies.

[0081] For example, if we set an approximate reference value for electric vehicles to 130 grams of CO2 emissions per kilometer (when the vehicle is charged without any emission reduction strategy), we can use this value to calculate the reference value km_ref.

[0082] Therefore, the optimization goal is to maximize the kilometers served (km_serviced) minus the reference kilometers (km_ref) within a given optimization period (usually 24 hours). The purpose of this is to maximize the "clean" kilometers, that is, more service kilometers are provided with the same CO2 emissions; these additional kilometers can be considered "clean" kilometers.

[0083] It should be noted that although kilometers (km) are used as the unit of measurement here, the stored useful energy (measured in kilowatt-hours, kWh) can be used instead and a similar method can be used for measurement and optimization.

[0084] After setting the objective function to be optimized, the next step is to optimize it. For this problem, there are different types of mathematical methods, including meta-heuristic methods: genetic algorithms, particle swarm optimization, ant colony optimization, etc.

[0085] In this exemplary embodiment, a mixed integer linear programming (MILP) is employed to formulate the charging and battery swapping problem of the battery swap station as a large MILP problem.

[0086] Mixed integer linear programming is a mathematical optimization technique used to handle complex decision problems involving both continuous and integer (usually binary) variables. In the battery swap station scenario, the charging and discharging operations are modeled as continuous variables, meaning they can take on any value, reflecting the amount of charge and discharge of the battery. The decision of whether to swap the battery is modeled as a binary variable, meaning it can only take on the value of 0 or 1, indicating whether to swap the battery or not.

[0087] The goal of optimization is to maximize the “clean kilometers” or net revenue provided by the battery swap station over a considered time period (which could be a few hours to a full day). Clean kilometers are the low-carbon or zero-carbon emission distance that an electric vehicle can travel using batteries swapped from a battery swap station.

[0088] Because large-scale MILP problems are often computationally intractable, even with state-of-the-art commercial solvers, in practice, the binary variables are kept only for the next few time steps, while the integer constraints for subsequent time steps are relaxed (that is, removed). This simplifies the problem and makes it easier to solve.

[0089] When the optimization model solves, it obtains the values ​​of the variables representing operational decisions that need to be made before the next forecast data update. Key results include the selection of batteries to swap, the battery charging strategy, and the use of stored energy. The battery swap station then operates according to these optimization results until the next forecast data update.

[0090] In short, in an exemplary embodiment of the present invention, the optimization method solves the operation problem of the battery swap station through MILP, aiming to intelligently manage the charging and swapping of batteries to improve operational efficiency, reduce environmental impact, and make the best operational decisions before the next data update.

[0091] For example, according to this embodiment, the battery replacement optimization problem is a MILP problem. Such problems can be solved by a dedicated solver.

[0092] Mixed-integer linear programming (MILP) problems are solved by optimizing a linear objective function subject to linear constraints, where some variables are required to take integer or binary values. MILP solvers employ branch and bound algorithms to systematically explore feasible solutions and prune the search space. Popular open-source MILP solvers include CBC (Coin-OR Branch and Cut), GLPK (GNU Linear Programming Kit), and SCIP (Solving Constraint Integer Programs). Commercial solvers such as CPLEX (developed by IBM) and Gurobi are widely used for their efficiency in handling large-scale MILP problems.

[0093] Note that the variable names and variable descriptions correspond to the "maximize revenue" objective. However, the optimization problem is exactly the same as the "maximize clean kilometers" objective.

[0094] index:

[0095] b: Charging cabinet ID

[0096] t: time period

[0097] n(t): number of batteries to be replaced at time t

[0098] l:Battery type

[0099] parameter

[0100] The (binary) type of battery n that was swapped at time t

[0101] Battery type Battery capacity l

[0102] Maximum power of charging cabinet b

[0103] The nth battery of SoC is swapped at time t

[0104] SOC 0 :When SoC is lower than this value, the battery swap income is zero

[0105] k: slope of the revenue vs. SoC curve

[0106] ELECP t : Electricity price at time t

[0107] ΔT: time step

[0108] variable

[0109] SOC b,t: SoC of the battery in charging cabinet b at time t

[0110] IS b,t,n : (binary) 1 if the battery n swapped at time t is placed in cabinet b, 0 otherwise

[0111] TY b,t,l : (binary) 1 if the battery in charging cabinet b at time t is type l, otherwise 0

[0112] P b,t : Charging power of cabinet b at time t

[0113] REV n,t : Revenue generated by the exchange of battery n at time t

[0114] Constraints:

[0115] 1. The capacity of the charging cabinet does not exceed the battery capacity:

[0116]

[0117] 2. Each cabinet can charge a maximum of one battery:

[0118]

[0119] 3. The battery must be charged every time it is replaced:

[0120]

[0121] if )

[0122] 4. The battery type of the charging station is continuous:

[0123]

[0124] 5. The SoC of the charging station is continuous:

[0125]

[0126] 6. The charging power of the charging station is limited:

[0127]

[0128] 7. Income generated by battery swapping, upper limit:

[0129]

[0130] 8. Revenue generated by battery swapping, lower limit: REV n,t ≥0

[0131] Maximize the objective function:

[0132]

[0133] Constraints 4, 5, and 7 contain quadratic terms. These constraints are reformulated as linear constraints.

[0134] Introducing a new variable ISN b,t

[0135]

[0136] Linearization Constraint 4

[0137] Introduce a new variable TISN b,t,l , represents the quadratic term TY b,t-1,l .ISN b,t

[0138] And replace constraint 4 with three new constraints: 4.1

[0140] 4.2

[0142] 4.3

[0144]

[0145] Linearization Constraint 5

[0146] Introduce a new variable SOCISN b,t Represents the quadratic term SOC b,t-1 .ISN b,t

[0147] And replace constraint 5 with four new constraints: 5.1

[0149] SOCISN b,t ≤SOC b,t-1 5.2

[0151] SOCISN b,t ≤M.ISN b,t 5.3

[0153] SOCISN b,t ≥SOC b,t-1 -M.(1-ISN b,t ) 5.4

[0155]

[0156] Where M is a constant parameter that is large enough and is always greater than SOC b,t , you can usually choose

[0157] Linearization constraint 7:

[0158] Introduce a new variable SOCIS b,n,t , represents the quadratic term SOC b,t .IS b,n,t

[0159] And replace constraint 7 with four new constraints: 7.1

[0161] SOCIS b,n,t ≤SOC b,t 7.2

[0163] SOCIS b,n,t ≤M.IS b,n,t 7.3

[0165] SOCIS b,n,t ≥SOC b,t -M.(1-IS b,n,t ) 7.4

[0167]

[0168] Therefore, in the above example, a mixed integer linear programming (MILP) model is constructed for the operation problem of the battery swap station. In this model, a series of indicators, parameters, variables and constraints are defined to optimize the battery charging and swapping process. Among them:

[0169] Indicators: These include charging cabinet identification, time period markings, and the number and type of batteries that need to be replaced at a specific time. These indicators help to specifically locate problems and decision points in the model.

[0170] Parameters: These include the battery swap type, maximum battery capacity, maximum charging cabinet power, initial battery state of charge, slope of the revenue vs. battery state of charge curve, and electricity price. These parameters are fixed values ​​in the model and are used to describe the physical characteristics of the battery swap station operation.

[0171] Variables: These are the values ​​that need to be solved in the model, such as the state of charge of the batteries in the charging cabinet, whether the batteries are placed in a specific charging cabinet, the identification of the battery type in the charging cabinet, and the charging power of the charging cabinet. The values ​​of these variables will be adjusted according to the optimization goal.

[0172] Constraints ensure that the optimization model's solution is within a reasonable range and meets actual operational constraints. For example, the state of charge of the batteries in the charging cabinet must not exceed the maximum capacity, each charging cabinet can only charge one battery at a time, each replacement battery must be charged, and the charging power of the charging station must be limited. These constraints ensure that the model's solution is both realistic and meets operational needs.

[0173] By setting these indicators, parameters, variables, and constraints, the MILP model aims to optimize the operational decisions of the battery swap station, such as which battery to swap, how to formulate the battery charging strategy, and how to utilize power storage, thereby maximizing revenue or clean kilometers within a given time period. In particular, the present invention proposes that battery swaps can be performed using partially charged batteries through optimization. The application of this model can make the operation of battery swap stations more efficient and environmentally friendly while ensuring economic benefits.

[0174] The battery charging and replacement strategies obtained after optimization will be used in subsequent possible implementation steps.

[0175] Step S4: Implement optimized battery charging and replacement strategies

[0176] After obtaining the optimized battery charging and replacement strategy, optionally, according to an embodiment of the present invention, a specific implementation process is further included, which mainly includes two methods:

[0177] 1. Manual implementation

[0178] In this approach, the operational plan is displayed on a screen and executed "manually" by the operator. In practice, the detailed operational solution is converted into a curve graph that shows the target number of fully charged batteries in the battery swap station in the next few hours. Using the solution to the optimization problem, i.e. the battery state of charge (SoC) at each time step in each charging cabinet, the operator extracts the number of fully charged batteries available in the battery swap station (SoC>95%) at each time step. The operator uses this curve as a guide and manually controls the battery swap station to match this curve. The operator operates according to the following rules (and adjusts them based on his own experience):

[0179] If the current number of fully charged batteries in the battery swap station is lower than the number shown on the graph, it is necessary to start charging more batteries, prioritizing batteries that are close to fully charged.

[0180] Whenever a vehicle arrives to swap batteries, the battery with the highest charge level at the battery swap station is selected for swapping.

[0181] The operator does not control the charging power; all batteries being charged are charged at the maximum power allowed by the Battery Management System (BMS).

[0182] While not optimal, this solution is simple to operate and provides operators with a clear view of operational strategy.

[0183] 2. Automatic Implementation

[0184] In an automated implementation, the battery swap station's operational plan is automatically executed by an integrated management system, which typically includes multiple advanced technology components and algorithms. This automated system may include the following elements:

[0185] Automated control system: The battery swap station will be equipped with an automated control system responsible for monitoring and executing all charging and swapping operations. The system will be able to receive the output of the optimization model in real time and convert it into specific operational commands.

[0186] Sensor network: To precisely control the battery charging and swapping process, battery swap stations are equipped with a series of sensors to monitor key parameters such as the battery's state of charge (SoC), temperature, and voltage in real time.

[0187] Robotic technology: During the battery replacement process, robotic arms or other robotic technologies can be used to automatically remove and insert batteries, thereby reducing manual operations and improving efficiency and safety.

[0188] Real-time communication: Battery swap stations need to communicate in real time with electric vehicles, power grids, weather forecast services, etc. to obtain the necessary data to support decision-making.

[0189] User interface: Although the operation is automated, the system still provides a user interface that allows operators to monitor the system status and intervene to make adjustments or handle abnormal situations when necessary.

[0190] Safety protocols: To ensure operational safety, the automated implementation system integrates multiple layers of safety protocols, including monitoring of abnormal conditions and automatic execution of emergency plans.

[0191] Data analysis and reporting: The system automatically collects operational data, analyzes it, and generates reports, which helps to continuously improve the performance and optimization strategies of battery swap stations.

[0192] Maintenance and self-diagnostics: Automated systems will have self-diagnostic capabilities that can predict maintenance needs and warn of problems before they occur.

[0193] These automated implementations can improve the operational efficiency of battery swap stations, reduce labor costs, and enhance the customer service experience. Furthermore, they help reduce operational errors and ensure system continuity and reliability. As technology develops, automated implementations may further integrate more innovative technologies, such as artificial intelligence and the Internet of Things, to achieve even higher levels of automation and optimization.

[0194] Figure 4 A more detailed flow chart according to the above embodiment of the present invention is exemplarily shown.

[0195] This flowchart illustrates the operational flow of one embodiment of the present invention, involving the charging and battery swap guidance algorithm for each operating day. As previously mentioned, the entire process is broadly divided into three steps: prediction, optimization, and implementation. Each step and decision point in the flowchart is described in detail below:

[0196] Initialization (setting the time to the start time of the battery exchange station): Among them, the current time is marked as h and set as the start time H_s of the battery exchange station operation.

[0197] Determine whether the operation end time has been exceeded: Check whether the current time h has exceeded the end time H_e of the battery swap station operation. If not, continue the operation process; if it has exceeded, end the operation.

[0198] Next, algorithms are used to predict upcoming battery swapping needs and renewable energy (such as photovoltaic and wind) production based on historical data and other relevant information.

[0199] Then, the charging and swapping strategies at the battery swap station are optimized. Based on the prediction results, a mixed integer linear programming (MILP) model is used to optimize battery charging and swapping. This may include determining which batteries need to be charged, which batteries are ready for swapping, and how to formulate the charging strategy.

[0200] Second, the operational strategy is executed based on the optimization results. The results of the optimization model are converted into practical operational steps and executed at the battery swap station. These steps include charging and swapping batteries, and possibly using stored energy.

[0201] Finally, perform a time update. Update the current time h to the next time interval, which is h plus the time interval length Δh. Then return to the judgment step to check whether it is still within the operating hours.

[0202] This flowchart illustrates the dynamic management process for battery swap station operations in this invention. Through continuous data updates and algorithm optimization, battery swap station operations are automated and intelligent. This approach ensures that battery swap stations operate efficiently while maximizing the use of renewable energy, minimizing environmental impact, and providing fast and reliable service to electric vehicle users.

[0203] The advantages of the present invention are mainly concentrated in providing a complete set of efficient operation solutions for battery swap stations through advanced prediction models, sophisticated optimization algorithms and flexible implementation strategies. Specifically, the present invention has the following advantages:

[0204] Operations based on precise forecasts: By leveraging historical data and real-time information to predict battery swapping needs and renewable energy production, the present invention can better plan battery charging and swapping, thereby improving resource utilization.

[0205] Efficient optimization strategy: A mixed integer linear programming (MILP) model allows battery swap stations to maximize clean kilometers or net revenue while meeting customer demand, thereby promoting environmental protection while ensuring economic benefits.

[0206] Flexible use of partially charged batteries: During high-demand periods, batteries can be swapped even if they are not fully charged. This not only reduces customer waiting time but also improves battery utilization efficiency and the service capacity of the battery swap station.

[0207] Combination of automated and manual implementation: The present invention provides both manual and automated implementation methods, which increases operational flexibility while ensuring that operational strategies can be quickly adjusted according to real-time conditions.

[0208] In general, the comprehensive solution provided by the present invention emphasizes the importance of efficiency, environmental protection and customer experience, and greatly improves the overall performance and market competitiveness of battery swap stations by combining intelligent technology with flexible operation strategies.

[0209] Those skilled in the art will readily appreciate numerous embodiments, variations, and improvements. In particular, it should be understood that, except where clearly contradictory or incompatible, the features, variations, and / or specific embodiments described herein may be combined with one another. All such embodiments, variations, and improvements are intended to fall within the scope of protection of the present invention.

Claims

1. A method for optimizing battery charging and replacement at a battery swap station (BSS), comprising the following steps: - Forecast battery replacement needs; - Forecasting energy availability; as well as -Optimize battery charging and replacement strategies based on predicted battery replacement needs and predicted energy availability, where the optimization includes selecting partially charged batteries for replacement.

2. The method according to claim 1, characterized in that The battery charging and swapping strategies are optimized by applying a mixed integer linear programming (MILP) model, which uses the predicted battery swapping demand and the predicted energy source availability as input to determine the optimal charging schedule and battery allocation.

3. The method according to claim 2, characterized in that The MILP model includes an objective function based on CO2 emissions from electricity consumed during the optimization period.

4. The method according to claim 2, characterized in that The MILP model takes into account the physical constraints of the battery and the grid conditions.

5. The method according to claim 1, characterized in that Forecasted energy availability includes electricity price volatility and the availability of renewable energy sources.

6. The method according to claim 1, characterized in that The following steps are also included: - Implement optimized battery charging and replacement strategies.

7. The method according to claim 6, characterized in that The implementation steps are automatically performed by the battery management system (BMS).

8. A system for optimizing battery charging and replacement at a battery swap station (BSS), comprising: - Forecasting module, used to predict battery replacement needs; - Forecasting module for predicting the availability of energy sources; as well as - An optimization module for optimizing battery charging and replacement strategies based on predicted battery replacement needs and predicted energy source availability, wherein the optimization includes selecting partially charged batteries for replacement.

9. The system according to claim 8, characterized in that The optimization module, which optimizes the battery charging and swapping strategy, uses a mixed integer linear programming (MILP) model that uses the predicted battery swapping needs and the predicted energy source availability as input to determine the optimal charging schedule and battery allocation.

10. A computer-readable medium comprising instructions which, when executed by a computer, cause the computer to perform the steps of the method of claim 1.

Images