Electric vehicle energy scheduling method in V2G scene based on battery recession modeling

Abstract

The invention discloses an electric vehicle energy scheduling method in a V2G scene based on battery recession modeling, and belongs to the technical field of new energy and smart power grid crossing. The invention aims to provide an electric vehicle energy scheduling method in a V2G scene based on battery recession modeling by dynamically adjusting in a rolling range and minimizing charging and discharging cycles while ensuring that a charging demand of a user is met. In order to realize power scheduling optimization of the EV in the V2G participation process, two objective functions are taken as optimization objectives, an SOC dynamic updating model considering the battery recession characteristic is introduced, and constraint conditions of the charging and discharging power and the SOC of the electric vehicle are set. According to the invention, the battery loss cost when the user participates in the V2G service is reduced, and the service life of the battery is effectively prolonged. The long-term economy and popularization feasibility of the V2G system are improved, and the overall operation efficiency and reliability of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the cross - technical field of new energy and smart grid. Background Art

[0002] With the rapid development of new energy technologies and the continuous promotion of the "dual - carbon" goal, the large - scale deployment of electric vehicles (EVs) can effectively reduce carbon emissions and is widely promoted as a representative of green travel. At the same time, the power system also faces the opportunities and challenges brought about by the large - scale deployment of EVs. It is necessary to seize the opportunity of optimizing the energy structure brought about by the high - proportion access of EVs and systematically address the technical challenges in aspects such as load management, grid stability, and dispatching mechanisms. The large - scale access of EVs will have a profound impact on the structure and operation mode of the power system, promoting the transformation of the power system from "unidirectional power supply" to "two - way interaction".

[0003] As an important means to achieve two - way energy interaction between electric vehicles and the grid, vehicle - to - grid (V2G) technology regulates the energy storage characteristics of in - vehicle power batteries through a two - way charging and discharging system. The charging and discharging behavior of EVs provides ancillary services such as peak shaving and frequency modulation for the grid, realizes the intelligent interaction between distributed energy storage units and the power network, and improves the flexibility and stability of grid operation.

[0004] Currently, a large number of studies have been carried out on energy scheduling and control strategies in the V2G environment, especially multi - level and multi - objective optimal scheduling based on optimization algorithms such as model predictive control (MPC).

[0005] However, in the V2G scenario, the existing EV charging and discharging control strategies mainly have the following defects, which have become the main obstacles to the widespread adoption of V2G services: 1. Ignoring the feedback effect of battery degradation on the control strategy The frequent charging and discharging behavior of EVs in V2G interaction will increase the number of battery cycles, thus accelerating battery aging and shortening the service life. Existing V2G control strategies generally regard the battery as an ideal energy storage unit and do not explicitly consider the performance degradation of the battery during frequent charging and discharging processes, resulting in difficulty in balancing battery health and system goals in long - term operation of control decisions.

[0005] 2. Lack of coordinated optimization between battery degradation and grid stability In existing scheduling models, the battery aging process and grid operation goals are often independent of each other, and no effective coupling mechanism is constructed. Traditional control strategies usually only take power balance or grid indicators as optimization goals, do not incorporate battery degradation into the objective function of the scheduling model, and lack a protection mechanism for battery health. The dynamic characteristics of battery life and performance degradation will damage the battery life of users, and further affect user participation willingness and system stability. Summary of the Invention

[0006] The objective of the present invention is an energy scheduling method for electric vehicles in a V2G scenario based on battery degradation modeling, which dynamically adjusts within the rolling range to minimize the charge-discharge cycles while ensuring the satisfaction of users' charging demands.

[0007] The steps of the present invention are as follows: S1. To optimize the power scheduling of EVs during their participation in the V2G process, two objective functions are proposed as the optimization targets, which are specifically defined as follows: min(f1(x), f2(x)) (2) Among them, the objective function f1(x) represents the sum of the squares of the system power tracking errors, and the objective function f2(x) represents the charge-discharge cycle behavior of the EVs during the scheduling period; H represents the length of the optimization time domain; N currtet (i) represents the number of currently connected EVs at the i-th time step; represents the charge-discharge power of the k-th vehicle at the i-th moment,, then represents the charging of this vehicle, then represents the discharging of this vehicle; C load represents the difference between wind energy and load, and represents the target curve; represents the number of charge-discharge state switching times of the k-th EV at the i-th moment; S2. The judgment logic is as follows: S3. Introduce a dynamic update model of SOC considering battery degradation characteristics: Among them, represents the state of charge of the k-th electric vehicle at the i-th time step; represents the charge-discharge power of the k-th electric vehicle at the i-th time step; h represents the sampling time step; η represents the charge-discharge efficiency, when it is the charging efficiency η c , when it is the charging efficiency η d ; S4. Constraints on the charge-discharge power and SOC of electric vehicles: The charge-discharge power of electric vehicles during any scheduling period needs to meet the rated power range limit: Among them, P rat is the rated charge-discharge power; The following constraint conditions are set for the SOC state of electric vehicles:

[0008] The beneficial effects of the present invention are as follows: 1. Explicitly considering the battery degradation process to improve the battery life management ability: The present invention introduces a battery charge and discharge status flag variable into the V2G energy scheduling model, and explicitly incorporates the battery degradation control objective into the optimization framework by minimizing the number of cycles. Different from the existing strategies that only target power balance, it realizes the dynamic protection of the battery health state, reduces the battery loss cost faced by users participating in V2G services, effectively extends the battery service life, and improves the long-term economy and promotion feasibility of the V2G system; 2. Achieving the coordinated optimization of battery degradation and grid scheduling objectives: The present invention incorporates key factors related to battery degradation into the optimization objective function and constraints, and combines the status information of individual EVs to construct a model for the impact of charging demand on the battery degradation characteristics with differentiation for each vehicle, improving the scheduling accuracy and enhancing the personalization and precision of the control strategy. Moreover, the battery health state and the grid supply-demand balance objective jointly drive the scheduling decision, improving the overall operation efficiency and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a flowchart of the EV energy scheduling strategy considering battery degradation; Figure 2 is a comparison result graph of the number of charge and discharge state switches in the public charging area; Figure 3 is a comparison result graph of the number of charge and discharge state switches in the home charging station. DETAILED DESCRIPTION OF THE INVENTION

[0010] The present invention relates to the field of interactive control between EV (Electric Vehicle) and microgrid, and particularly to an electric vehicle energy management and scheduling control method considering the impact of battery degradation in V2G (Vehicle-to-Grid) technology.

[0011] The present invention proposes an EV energy scheduling strategy considering battery degradation, a control method that explicitly considers the impact of battery degradation, taking into account both grid objectives and battery health. This strategy aims to jointly optimize the SOC (State of Charge), grid load, and battery degradation cost. By dynamically adjusting within a rolling range, this strategy minimizes charge and discharge cycles while ensuring the satisfaction of users' charging demands. Compared with traditional strategies, this method incorporates battery life degradation into the global optimization objective, effectively delaying battery degradation while maintaining the stable operation of the microgrid.

[0012] The present invention provides an EV energy scheduling strategy considering battery degradation in the context of V2G technology. By introducing the influence of battery degradation characteristics on the charging demand model and cost function into the control model, an economic scheduling strategy considering battery life protection is constructed, effectively improving the operational safety, economy, and sustainability of the V2G control system.

[0013] The implementation method of the present invention includes the following parts: 1. Construct an energy scheduling optimization framework including a battery degradation model: Based on the original V2G scheduling model, introduce charge and discharge status flag variables, minimize the number of cycles, and incorporate them into a multi-objective optimization function for unified modeling. Dynamically protect the battery health to avoid impacts on the battery caused by frequent and large-amplitude power fluctuations.

[0014] 2. Introduce a dynamic SOC constraint and charge and discharge cycle control mechanism: Construct a model considering the influence of battery degradation on the charging demand, add degradation factors related to the battery health status, such as depth of discharge, capacity attenuation rate, etc. By restricting the SOC change range of the EV battery at each time step, it can reduce the power fluctuation of the power grid and delay battery degradation while meeting the final charging demand of the EV.

[0015] While ensuring the realization of the power grid scheduling objective, the present invention effectively inhibits the battery degradation process, extends the battery life, improves the long-term operation economy and reliability of the system, and is applicable to the actual scenarios of future large-scale electric vehicle access to the microgrid or the main grid.

[0016] In summary, the difference between the present invention and the prior art lies in that the influence of battery degradation is explicitly incorporated into the multi-objective scheduling optimization framework in the form of dynamic parameters, and the coordinated control of scheduling accuracy, battery life, and system economy is realized in the V2G control scenario.

[0017] The experimental part of the present invention is realized through Matlab simulation.

[0018] The objective of the present invention is to coordinate the power relationship between the cluster of EVs, wind energy, and the base load, minimize the power exchange between the MG and the power grid, and at the same time minimize the charge and discharge cycle times of each EV to delay battery degradation. On this basis, by modeling the dynamic charging demand characteristics of EVs considering battery degradation, the charging demands of different EVs are met.

[0019] The specific implementation steps taken by the present invention are as follows: 1. Electric vehicle charge and discharge control strategy 1.1 Influence of battery degradation characteristics on battery capacity Battery life is affected by various factors, mainly including temperature, depth of discharge, SOC, and cycle frequency, etc. Generally speaking, the life of an electric vehicle battery can be measured from two dimensions: one is the calendar life, which refers to the number of years that the battery can be used under natural aging conditions; the other is the cycle life, which refers to the number of charge and discharge cycles that the battery can withstand.

[0020] In an operating environment where it frequently participates in V2G services, the battery is not only affected by time aging, but its life is also closely related to the power fluctuations during actual use. To more accurately reflect the battery health state during scheduling, the present invention adopts a battery life degradation model constructed based on the battery degradation mechanism and semi-empirical modeling method, which is used to characterize the performance degradation trend of the battery under continuous charge and discharge conditions, as shown in formula (1): Where: S soc_acc represents the cycle discharge interval acceleration factor; α represents the empirical coefficient used to adjust the sensitivity of the SOC influence factor; F represents the Faraday constant; R represents the gas constant; SOC cyc represents the SOC during the current cycle; SOC cyc_ref represents the SOC reference value during the cycle; S DOD_acc represents the battery degradation acceleration factor related to the depth of discharge; D DOD represents the depth of discharge of the current charge and discharge cycle; D DOD_ref represents the depth of discharge reference value; β represents the empirical constant used to describe the non-linear influence degree of the depth of discharge on battery degradation; Q site represents the degradation rate of the battery capacity; c ref represents the capacity degradation coefficient reference value; N cyc represents the cumulative number of charge and discharge cycles of the battery; C EV represents the initial rated capacity of the battery; C evq represents the current remaining effective capacity.

[0021] This model comprehensively considers the influence of key factors such as the average SOC, depth of discharge, and number of cycles of the battery on capacity degradation. By introducing the acceleration factors S DOD_acc and S soc_acc , it is used to evaluate the battery life change trend and further guide the design of charge and discharge control strategies in the V2G scenario.

[0022] Aiming at the degradation characteristics of EV batteries, on the premise of meeting the normal power consumption requirements of the vehicle, it is necessary to effectively control the number of charge and discharge cycles of electric vehicles during the V2G process, so as to slow down the battery performance degradation caused by frequent cycling.

[0023] 1.2 Charge and Discharge Control Strategy To achieve power scheduling optimization of EVs during their participation in the V2G process, the present invention proposes the following multi-objective optimization model, as shown in Formulas (2) and (3). This optimization model aims to minimize the number of EV charge-discharge cycles while ensuring system power balance, thereby slowing down battery degradation and reducing system operation costs.

[0024] This model takes two objective functions as the optimization targets, and the specific definitions are as follows: min(f1(x), f2(x)) (2) Where H represents the length of the optimization time domain; N currtet (i) represents the number of currently connected EVs at the i-th time step; represents the charge-discharge power of the k-th vehicle at the i-th moment, and, then represents the charging of this vehicle, then represents the discharging of this vehicle; C load represents the difference between wind energy and load, representing the target curve; represents the number of charge-discharge state switching times of the k-th EV at the i-th moment.

[0025] The objective function f1(x) represents the sum of the squares of the system power tracking errors, aiming to minimize the difference between the EV population power and wind power and load, and improve the utilization rate of renewable energy. The objective function f2(x) represents the charge-discharge cycling behavior of EVs during the scheduling period.

[0026] Its judgment logic is as shown in Formula (4). When the power direction changes (from charging to discharging or vice versa) in two consecutive time steps, it is considered that a cycle switch occurs:

[0027] This optimization problem comprehensively considers power tracking accuracy and battery cycling behavior. By introducing a dual-objective function modeling method, while meeting the system's dynamic regulation requirements, it effectively controls the battery degradation rate and improves the practicality and sustainability of the control strategy.

[0028] To accurately describe the change trend of the battery SOC of electric vehicles during their participation in the V2G process, the present invention introduces a dynamic SOC update model considering battery degradation characteristics, as shown in Formula (4). This model comprehensively considers multiple factors such as charge-discharge efficiency, battery degradation coefficient, SOC historical state, DOD (depth of discharge), etc., and can more realistically reflect the SOC evolution process of the battery during long-term operation: Where, represents the state of charge of the k-th electric vehicle at the i-th time step; denotes the charging and discharging power of the k-th electric vehicle at the i-th time step; h represents the sampling time step; η represents the charging and discharging efficiency, which is the charging efficiency η when ; and is the charging efficiency η when c ; and the meanings of the remaining variables are the same as those in formula (1) of 1.1. ; and is the charging efficiency η when d ; and the meanings of the remaining variables are the same as those in formula (1) of 1.1.

[0029] This model not only realizes the fine dynamic tracking of SOC, but also tightly couples the battery health state with the operation history information, providing a reliable state variable basis for the optimization control strategy proposed in the present invention. By embedding this model into the EV energy scheduling strategy considering battery degradation, it can ensure that the system takes into account battery degradation control and energy supply-demand balance when optimizing V2G behavior, thereby improving the sustainability and practicality of the overall control system.

[0030] In the centralized electric vehicle V2G control framework of the present invention, in order to ensure the feasibility of the control strategy and the safety of the battery system operation, the following constraint conditions for the charging and discharging power and SOC of electric vehicles are designed:

[0031] As shown in formula (6), the charging and discharging power of an electric vehicle in any scheduling period needs to meet the rated power range limit: where P rat is the rated charging and discharging power. This power symmetric limit is used to ensure that the charging and discharging power of the electric vehicle during the scheduling process is always within its acceptable range, thereby ensuring the physical feasibility and operation safety of the scheduling scheme.

[0032] In addition, in order to ensure battery life and user travel needs, the following constraint conditions are set for the SOC state of electric vehicles:

[0033] As shown in formula (7), during the intermediate period within the rolling prediction time domain, the SOC of the electric vehicle should be between 0.1 and 1; this constraint prevents the battery from being over-discharged and avoids a sharp decline in life caused by deep discharge.

[0034] As shown in formula (8), at the final prediction step of the rolling time domain (i.e., when the vehicle is about to leave), it is required that the SOC shall not be lower than 0.8; this constraint is used to ensure that the electric vehicle has sufficient power before leaving the V2G platform to meet the minimum travel power demand of users. This mechanism can significantly improve the willingness of users to participate, and at the same time improve the practicality and acceptability of the V2G system.

[0035] The above power and SOC constraints ensure the engineering feasibility of the scheduling command and the satisfaction of the user side, reflecting the control principle of taking into account safety, reliability and practicability. The flow schematic block diagram of the present invention is as shown in Figure 1 shown.

[0036] 3. Simulation Analysis The present invention is carried out for a microgrid system including a 2.3MW wind turbine, a basic load in a fixed area and 150 EVs. All parameters involved in the experiment are shown in Table 1.

[0037] Table 1 Simulation-related parameters

[0038] The simulation scenarios adopted by the present invention cover two typical electric vehicle charging environments, including two charging scenarios of public charging areas and home charging piles, so as to comprehensively evaluate the adaptability and effectiveness of the proposed control strategy under different application scenarios.

[0039] Figure 2 shows the comparison results of the number of charge and discharge state switches of electric vehicles over time under two scheduling strategies in the public charging area scenario. Among them, curve (a) corresponds to the average model predictive control strategy that does not consider the impact of battery degradation in the prior art; curve (b) corresponds to the EV energy scheduling strategy proposed by the present invention that takes into account the battery degradation situation.

[0040] As can be seen from the figure, the traditional control strategy triggers a large number of charge and discharge cycles in the early stage, the number of switches rises rapidly, and reaches the saturation value in a short time, showing a high-frequency and unconstrained scheduling behavior. Although this strategy can quickly respond to system load fluctuations, it ignores the risk of battery performance degradation during frequent charge and discharge processes, which may cause the shortening of battery life.

[0041] In contrast, the EV energy scheduling strategy proposed by the present invention that takes into account the battery degradation situation maintains a low charge and discharge cycle frequency throughout the operation cycle. Although curve (b) has certain fluctuations in some time periods, the overall fluctuation amplitude is controlled, and the number of switches is stable at a low level, reflecting that while optimizing the scheduling target, the strategy actively restricts the battery usage behavior and reduces the degradation effect caused by frequent charge and discharge.

[0042] Figure 3 shows the comparison results of the number of charge and discharge state switches of electric vehicles over time when different control strategies are adopted in the home charging pile charging scenario. In the figure, curve (a) represents the traditional control strategy, that is, the control method that does not consider the impact of battery degradation during the scheduling process; curve (b) corresponds to the EV energy scheduling strategy proposed by the present invention, which introduces a modeling and constraint mechanism for battery degradation behavior during the scheduling process.

[0043] As can be observed from the figure, the number of charge-discharge cycles under the traditional control strategy (a) increases rapidly in the initial stage and reaches the saturation value in a relatively short time. Although this high-frequency switching behavior enhances the system's response ability to load fluctuations to a certain extent, it also significantly increases the charge-discharge times of the battery, exacerbates the performance degradation, and is likely to cause a substantial shortening of the battery life.

[0044] In contrast, the strategy (b) proposed by the present invention exhibits a more stable cycling behavior during operation. The number of switches mainly remains between 50 and 70, and the overall fluctuation range is small, indicating that this control strategy effectively suppresses unnecessary frequent switching operations. Through the dynamic assessment of the battery health state and the modeling of the degradation risk, the system can actively limit the overuse behavior while ensuring the basic response performance, thereby improving the battery usage efficiency and extending its service life.

[0045] Therefore, the simulation results fully demonstrate that, on the basis of ensuring the V2G scheduling response ability, the present invention effectively suppresses unnecessary state switching, takes into account both battery life protection and system operation stability, provides a solid technical support for constructing an efficient, economical, and long-life V2G system, and has significant engineering practical value and promotion potential.

[0046] Project number: in part by the Natural Science Foundation of Jilin Province under Grant 20230101065JC.

Claims

1. An electric vehicle energy scheduling method in a V2G scenario based on battery degradation modeling, characterized in that: The steps are as follows: S1. To optimize the power scheduling of EVs during V2G participation, two objective functions are proposed as the optimization objectives, which are specifically defined as follows: min(f1(x), f2(x)) (2) Among them, the objective function f1(x) represents the sum of squares of the system power tracking errors, and the objective function f2(x) represents the charge-discharge cycling behavior of the EVs during the scheduling period; H represents the length of the optimization time domain; N currtet (i) represents the number of EVs currently connected at the i-th time step; represents the charge-discharge power of the k-th vehicle at the i-th moment, then represents that the vehicle is charging, then represents that the vehicle is discharging; C load represents the difference between the wind energy and the load, representing the target curve; represents the number of charge-discharge state switches of the k-th EV at the i-th moment; S2. The judgment logic is as follows: S3. Introduce a dynamic SOC update model considering battery degradation characteristics: wherein, represents the state of charge of the k-th electric vehicle at the i-th time step; represents the charging and discharging power of the k-th electric vehicle at the i-th time step; h represents the time step of sampling; η represents the charging and discharging efficiency, when it is the charging efficiency η c , when it is the charging efficiency η d ; S4. Constraints on the charging and discharging power of EVs and SOC: The charging and discharging power of EVs in any scheduling period needs to meet the rated power range limit: Among them, P rat is the rated charge and discharge power; Set the following constraint conditions for the SOC state of EVs:

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