Heavy-haul train group operation control system hybrid energy storage management method and device

Abstract

The application discloses a heavy-load train group operation control system hybrid energy storage management method and device, which comprises the following steps: acquiring the speed and position of all heavy-load trains in the group, planning the speed and acceleration of each train, and then calculating the future power demand of each train; according to the future power demand of the train, the cloud digital twin is simulated online to output the sliding mode surface parameter, the control law parameter and the event trigger threshold; the train bus voltage is collected, the given bus voltage is combined, and the PI controller is used to generate the total reference current of the future power demand; the real-time state of the super capacitor and the lithium battery is acquired, and the event trigger threshold is compared to determine whether to start the sliding mode controller; if the sliding mode controller is started, the total reference current is distributed to the lithium battery and the super capacitor to generate corresponding DC / DC drive control signals. The application can reduce the frequency of on-board calculation and communication, and improve the flexibility and robustness of energy management of the heavy-load train group.

Description

Technical Field

[0001] This invention belongs to the field of hybrid energy storage management technology, specifically relating to a hybrid energy storage management method and device for a heavy-haul train group operation control system. Background Technology

[0002] Urban rail transit, with its advantages of large carrying capacity, punctuality, speed, and low environmental pollution, is being vigorously promoted in many cities. Electricity is a clean energy source with no waste pollution, which is highly beneficial for environmental protection and clean air. Batteries, as commonly used energy storage components, are most notable for their outstanding energy density. However, facing the fluctuating power demands in actual energy supply, battery energy storage systems struggle to cope with large instantaneous output power, and battery life is affected by cyclic charging and discharging. Therefore, considering the power density and discharge characteristics of batteries has become a key issue and research hotspot in hybrid energy storage systems.

[0003] Supercapacitors, as an energy storage element, possess advantages over lithium batteries, including high power density, long cycle life, and clean, pollution-free operation. Because the energy storage process of supercapacitors is independent of chemical reactions, they can quickly respond to the power demands of electric vehicles and effectively absorb and regenerate power. However, considering the relatively low energy density of supercapacitors, relying solely on this energy storage element is insufficient to meet the range requirements of vehicles. Currently, no single energy storage technology can simultaneously meet the demands for high power density, high energy density, long lifespan, safety, and technological maturity. Given the diverse energy storage needs of systems, no single energy storage technology can be entirely satisfactory. Therefore, it is essential to select a matching energy storage method based on specific requirements. This approach leverages the strengths of each method to achieve multiple requirements in terms of energy and power, and significantly increases the cycle life of energy storage elements. This has become a new trend in energy storage research.

[0004] In the real-time operation of electric vehicles, energy management strategies play a crucial role in the power allocation of hybrid energy storage systems. There are three main types of energy management strategies: rule-based, optimization-based, and AI-based. Rule-based energy management strategies for hybrid energy storage systems include rules such as setting logical thresholds or fuzzy logic for power allocation. These strategies may be simple to implement and work well in certain situations, but they largely depend on the designer's expertise and may not be suitable for all circumstances. AI-based energy management strategies require a large dataset of optimal control data for training. Researchers utilize AI algorithms such as neural networks and machine learning to obtain energy management strategies for online power allocation in hybrid energy storage systems. Optimization-based methods struggle to solve all hybrid energy storage problems simultaneously with a single optimization objective. Therefore, research on energy management strategies for hybrid energy storage systems still faces many unresolved issues.

[0005] Some existing patents, such as the patent application "Control Method for Hangzhou Power Hybrid Energy Storage Device" (Chinese Patent Publication No. CN119834430A), only use droop control to stabilize the bus voltage, without utilizing future driving information or introducing sliding surface or event triggering mechanisms. Control updates are continuous, resulting in a heavy computational burden. The patent application "Ceres Suspension Energy Distribution Method" (Chinese Patent Publication No. CN119749232A), although using LSTM to predict short-term speeds, uses offline fixed weights, still requiring rolling ECMS solutions and lacks online adaptive updates. The patent application "Huaneng Taicang Hybrid Energy Storage Coupling" (Chinese Patent Publication No. CN119315579A) further illustrates this. The current "AGC frequency regulation" method continuously recalculates the operating time and target power in each regulation cycle, lacking forward-looking control of future operating conditions and failing to employ a sliding mode robust strategy. The patent application "Power Allocation Method for GAC Vehicles" (Chinese Patent Publication No. CN118770175A) relies on the equivalent factor MAP and driving style recognition, with offline MAP calibration and online table lookup + ECMS solution, failing to adjust weights in real-time according to future power demands. The patent application "High-Voltage Cascaded Grid-Type Hybrid Energy Storage" (Chinese Patent Publication No. CN118281934A) only widens the supercapacitor voltage by adding a half-bridge, representing hardware topology innovation but lacking event triggering, sliding mode, or predictive control. In conclusion, it is necessary to design a more adaptable hybrid energy storage management method for heavy-haul train group operation control systems. Summary of the Invention

[0006] This invention provides a hybrid energy storage management method and device for a heavy-haul train group operation control system, which can reduce the frequency of onboard computing and communication, and improve the flexibility and robustness of energy management for heavy-haul train groups.

[0007] To achieve the above technical objectives, the present invention adopts the following technical solution:

[0008] A hybrid energy storage management method for a heavy-haul train group operation control system includes:

[0009] Obtain the speed and location of all heavy-load trains in the group, and plan the speed and acceleration of each train based on the obtained information, and then calculate the future power requirements of each train;

[0010] Based on the train's future power requirements, online simulation is performed using a cloud-based digital twin to output sliding surface parameters, control law parameters, and event trigger thresholds;

[0011] The train's bus voltage is collected, and based on the bus voltage and a given bus voltage, a PI controller is used to generate the total reference current for the train's future power requirements.

[0012] The system acquires the real-time status of the supercapacitor and lithium battery, and determines whether to activate the sliding mode controller based on event trigger thresholds.

[0013] If the sliding mode controller is activated, the total reference current for the train's future power demand will be allocated to the lithium battery and supercapacitor.

[0014] Based on the reference current allocated to each of the lithium battery and the supercapacitor, corresponding DC / DC drive control signals are generated.

[0015] Furthermore, the future power demand is calculated based on the planned train speed and acceleration, using the following formula:

[0016] ;

[0017] in, It is the calculated future power requirement of the train. It is the train's speed. The total mechanical traction force required for train operation is defined as follows:

[0018] ;

[0019] In the formula, It is the total mass of the train. Represents gravitational acceleration. Indicates the rolling resistance coefficient. Indicates the slope of the incline. It is air density. It is the area in front of the train. It is train acceleration. It is the air drag coefficient. It refers to the efficiency of the train's hybrid energy storage system. It is energy conversion efficiency. It refers to the traction motor efficiency. It is the average energy feedback efficiency.

[0020] Furthermore, online simulation using a cloud-based digital twin is conducted to output sliding surface parameters, control law parameters, and event trigger thresholds, specifically including:

[0021] The power demand model of the train, the SOC dynamic model of the lithium battery, the SOC dynamic model of the supercapacitor, and the capacity loss model of the lithium battery will be integrated into the cloud-based digital twin.

[0022] In a cloud-based digital twin integrating various models, a multi-objective particle swarm optimization algorithm is used to search for the sliding surface parameters corresponding to the optimal objective function. }, Control Law Parameters { } and trigger threshold { }

[0023] Furthermore, the objective function is specifically expressed as:

[0024] ;

[0025] in, This indicates the real-time capacity decay of the lithium battery. This indicates the maximum permissible value for lithium battery capacity decay. This indicates the change in the output power of the lithium battery. This represents the maximum permissible variation in lithium battery capacity. This indicates the real-time state of charge of the supercapacitor. This is the reference state of charge for the supercapacitor. and These are the upper and lower limits of the safe operating range of the supercapacitor; These are the weighting coefficients.

[0026] Furthermore, the sliding surface and the control law are respectively expressed as:

[0027] ;

[0028] ;

[0029] in, Represents the sliding surface function; The feedback gain coefficient representing the state-of-charge deviation of a supercapacitor; The feedback gain coefficient representing the current deviation of the supercapacitor; This serves as the reference state of charge for the supercapacitor. This is the reference current for the supercapacitor; This represents the sliding mode control law, i.e., the duty cycle command for the bidirectional DC / DC converter on the supercapacitor side; For the constant velocity term gain of the sliding mode control law; This is the gain of the proportional term in the sliding mode control law.

[0030] Furthermore, the triggering condition for determining whether to activate the sliding mode controller is one of the following: (1) (2) (3) Twin warning flag = 1; where, This indicates the real-time state of charge of the supercapacitor. This is the reference state of charge for the supercapacitor. This indicates the change in the output power of the lithium battery. Indicates the system sampling time interval. and These represent the trigger thresholds for supercapacitors and lithium batteries, respectively.

[0031] Furthermore, the SOC dynamic model of lithium batteries:

[0032] ;

[0033] in, This indicates the state of charge of the lithium battery. express rate of change, This indicates the coulombic efficiency of a lithium battery. This indicates the rated capacity of the lithium battery. This indicates the current of the lithium battery.

[0034] Furthermore, the SOC dynamic model of a supercapacitor:

[0035] ;

[0036] in, This indicates the state of charge of the supercapacitor. express rate of change, This indicates the discharge efficiency of the supercapacitor. This represents the current in the supercapacitor. This indicates the rated capacitance of the supercapacitor. This indicates the upper limit of the safe operating range of a supercapacitor. This indicates the charging efficiency of the supercapacitor.

[0037] Furthermore, the capacity loss model for hybrid energy storage:

[0038] ;

[0039] ;

[0040] ;

[0041] in, This represents the capacity loss of hybrid energy storage, ignoring the capacity decay of supercapacitors and consisting only of the capacity loss of lithium batteries; It refers to the discharge rate of the lithium battery. It is the discharge ampere-hour flux of a lithium battery. It is a lithium battery based on discharge rate Activation energy, in units of ; It is a lithium battery based on discharge rate The pre-index factor, It is the ideal gas constant. It's the temperature of the lithium battery. It is a power-law factor.

[0042] A hybrid energy storage management system for heavy-haul train group operation control system includes:

[0043] The data acquisition module is used to: acquire the speed and position of all heavy-load trains in the group, acquire the bus voltage of the trains, and acquire the real-time status of the supercapacitors and lithium batteries.

[0044] The power prediction module is used to: plan the speed and acceleration of each train based on the acquired speed and position of all heavy-haul trains, and then calculate the future power demand of each train;

[0045] The cloud-based digital twin platform is used to: simulate and output sliding surface parameters, control law parameters, and event trigger thresholds online based on the future power requirements of the train;

[0046] A voltage loop PI controller is used to generate a total reference current for the future power demand of the train based on the train's bus voltage and a given bus voltage.

[0047] The event triggering module is used to determine whether to activate the sliding mode controller based on the real-time status of the supercapacitor and lithium battery, as well as the event triggering threshold.

[0048] Sliding mode controller, used to: allocate the total reference current for the train's future power demand to the lithium battery and supercapacitor;

[0049] The drive signal generation module is used to generate corresponding DC / DC drive control signals based on the reference current allocated to the lithium battery and the supercapacitor, respectively.

[0050] This invention addresses the power allocation and management of a hybrid energy storage system integrating supercapacitors, bidirectional DC / DC converters, and power batteries. It constructs a multi-objective optimization problem with varying performance characteristics, predicts future power demands based on planning, and adjusts sliding surface parameters and trigger thresholds in real time through an event-triggered sliding mode control and digital twin collaborative mechanism to achieve adaptive energy management. On one hand, the hybrid energy storage system integrates lithium-ion power batteries, supercapacitors, and bidirectional DC / DC converters, allowing for simultaneous energy supply from both energy sources, avoiding the supply-demand mismatch and high losses associated with relying on a single energy source. On the other hand, the event-triggered sliding mode control and digital twin collaboration significantly reduce onboard computing and communication frequency and provide pre-control capabilities, further extending the lifespan of power batteries and improving the flexibility and robustness of energy management for heavy-haul train groups. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart of one embodiment of the method for energy management and control of hybrid energy storage for urban rail trains based on expert experience learning, according to the present invention.

[0053] Figure 2 This is a block diagram of the energy management algorithm provided in an embodiment of the present invention. Detailed Implementation

[0054] The embodiments of the present invention will be described in detail below. These embodiments are based on the technical solutions of the present invention and provide detailed implementation methods and specific operation processes to further explain the technical solutions of the present invention.

[0055] Example 1

[0056] This embodiment provides a hybrid energy storage management method for a heavy-haul train group operation control system. The hybrid energy storage system consists of a lithium battery and a supercapacitor. The lithium battery and the supercapacitor are respectively connected to one end of two DC / DC converters. The other end of the DC / DC converters is connected to a DC bus. The lithium battery and the supercapacitor are connected to the bus in parallel, and a filter capacitor is connected in parallel.

[0057] This hybrid energy storage management method adopts an architecture that combines cloud collaboration with vehicle-side control, and specifically includes the following steps:

[0058] (I) Group Unified Planning and Optimization Phase (Executed by Cloud / Ground Control Center):

[0059] Step 1: Obtain the speed and location of all heavy-load trains in the group. Based on the obtained information, combined with constraints such as line gradient, speed limit and scheduling timetable, use existing train operation optimization algorithms (such as dynamic programming, pseudospectral method or model predictive control method commonly used in this field) to plan the speed and acceleration of each train, and then calculate the future power demand of each train.

[0060] The future power demand is calculated based on the planned train speed and acceleration. The specific calculation formula is as follows:

[0061] ;

[0062] in, It is the calculated future power requirement of the train. It is the train's speed. The total mechanical traction force required for train operation is defined as follows:

[0063] ;

[0064] In the formula, It is the total mass of the train. Represents gravitational acceleration. Indicates the rolling resistance coefficient. Indicates the slope of the incline. It is air density. It is the area in front of the train. It is train acceleration. It is the air drag coefficient. It refers to the efficiency of the train's hybrid energy storage system. It is energy conversion efficiency. It refers to the traction motor efficiency. It is the average energy feedback efficiency.

[0065] Step 2: Based on the train's future power requirements, online simulation is performed using a cloud-based digital twin to output sliding surface parameters, control law parameters, and event trigger thresholds.

[0066] Step 2.1: Integrate the train's power demand model (used to construct dynamic load conditions during the simulation process), the lithium battery's SOC dynamic model, the supercapacitor's SOC dynamic model, and the lithium battery's capacity loss model into the cloud-based digital twin.

[0067] Among them, the SOC dynamic model of lithium batteries:

[0068] 1;

[0069] in, This indicates the state of charge of the lithium battery. express rate of change, This indicates the coulombic efficiency of a lithium battery. This indicates the output power of the lithium battery. This indicates the current of the lithium battery.

[0070] Among them, the SOC dynamic model of supercapacitors:

[0071] ;

[0072] in, This indicates the state of charge of the supercapacitor. express rate of change, This indicates the discharge efficiency of the supercapacitor. This represents the current in the supercapacitor. This indicates the rated capacitance of the supercapacitor. This indicates the upper limit of the safe operating range of a supercapacitor. This indicates the charging efficiency of the supercapacitor.

[0073] Among them, the capacity loss model of hybrid energy storage:

[0074] ;

[0075] ;

[0076] ;

[0077] in, This represents the capacity loss of hybrid energy storage, ignoring the capacity decay of supercapacitors and consisting only of the capacity loss of lithium batteries; It is the discharge rate of a lithium battery, which is the ratio of the battery discharge current to its rated capacity. Its value is a dynamic variable that changes in real time with the train's operating conditions. It is the discharge ampere-hour flux of a lithium battery. It is a lithium battery based on discharge rate Activation energy, in units of ; It is a lithium battery based on discharge rate The pre-index factor, It is the ideal gas constant. It's the temperature of the lithium battery. It is a power-law factor. In this embodiment, .

[0078] Step 2.2: Run the integrated models on the cloud-based digital twin platform and use a multi-objective particle swarm optimization algorithm to search for the sliding surface parameters corresponding to the optimal objective function. }, Control Law Parameters { } and trigger threshold { }

[0079] In this embodiment, the sliding surface and the control law are constructed as follows:

[0080] ;

[0081] ;

[0082] in, Represents the sliding surface function; The feedback gain coefficient representing the state-of-charge deviation of a supercapacitor; The feedback gain coefficient representing the current deviation of the supercapacitor; This serves as the reference state of charge for the supercapacitor. This is the reference current for the supercapacitor; This indicates the output of the sliding mode controller, i.e., the duty cycle command of the power converter; For the constant velocity term gain of the sliding mode control law; This is the gain of the proportional term in the sliding mode control law.

[0083] Then, the optimal sliding surface parameters are searched by minimizing the following objective function: }, Control Law Parameters { } and trigger threshold { }:

[0084] ;

[0085] in, This indicates the real-time capacity decay of the lithium battery. This indicates the maximum permissible value for lithium battery capacity decay. This indicates the change in the output power of the lithium battery. This represents the maximum permissible variation in lithium battery capacity. This indicates the real-time state of charge of the supercapacitor. This is the reference state of charge for the supercapacitor. and These are the upper and lower limits of the safe operating range of the supercapacitor; These are the weighting coefficients.

[0086] In this embodiment, the search space for searching the optimal parameters using the multi-objective particle swarm optimization algorithm is: , , , , , Weighting coefficient (Can be calibrated offline).

[0087] The cloud-based digital twin platform will search for the optimal sliding surface parameters { }, Control Law Parameters { } and trigger threshold { The data is packaged and sent to the vehicle controller, with an update cycle of 10 seconds or triggered by significant changes in operating conditions.

[0088] (II) Real-time control and execution phase for individual trains (executed independently by the onboard controller of each train):

[0089] Step 3: Collect the train's bus voltage, form a voltage loop based on the bus voltage and the given bus voltage, and then use general voltage loop PI control technology to generate the total reference current for the train's future power demand.

[0090] Step 4: Obtain the real-time status of the supercapacitor and lithium battery, and determine whether to start the sliding mode controller based on the event trigger threshold.

[0091] The on-board controller determines whether to activate the sliding mode controller based on one of the following trigger conditions: (1) (2) (3) Twin warning flag = 1; where, This indicates the real-time state of charge of the supercapacitor. This is the reference state of charge for the supercapacitor. This indicates the change in the output power of the lithium battery. Indicates the system sampling time interval. and These represent the trigger thresholds for supercapacitors and lithium batteries, respectively.

[0092] Step 5: If the sliding mode controller is activated, the total reference current for the train's future power demand will be allocated to the lithium battery and supercapacitor through the sliding mode controller.

[0093] The new duty cycle command of the bidirectional DC / DC converter on the supercapacitor side is calculated based on the sliding mode control law. By adjusting this duty cycle, the output current of the supercapacitor is directly controlled, thereby changing the ratio of the load current borne by the lithium battery and the supercapacitor, that is, updating the power distribution state, and maintaining this command before the next trigger, thereby achieving a balance between precise control and online load calculation by the vehicle controller.

[0094] Step 6: Generate corresponding DC / DC drive control signals based on the reference current allocated to the lithium battery and the supercapacitor, respectively.

[0095] The reference current allocated to the lithium battery and the supercapacitor, as well as their respective current currents, are input into their respective current loops to obtain the duty cycle of their respective DC / DC converters. This generates drive control signals to control the DC / DC converters to complete energy distribution.

[0096] Example 2

[0097] This embodiment provides a hybrid energy storage management system for a heavy-haul train group operation control system, including:

[0098] The data acquisition module is used to: acquire the speed and position of all heavy-load trains in the group, acquire the bus voltage of the trains, and acquire the real-time status of the supercapacitors and lithium batteries.

[0099] The power prediction module is used to: plan the speed and acceleration of each train based on the acquired speed and position of all heavy-haul trains, and then calculate the future power demand of each train;

[0100] The cloud-based digital twin platform is used to: simulate and output sliding surface parameters, control law parameters, and event trigger thresholds online based on the future power requirements of the train;

[0101] A voltage loop PI controller is used to generate a total reference current for the future power demand of the train based on the train's bus voltage and a given bus voltage.

[0102] The event triggering module is used to determine whether to activate the sliding mode controller based on the real-time status of the supercapacitor and lithium battery, as well as the event triggering threshold.

[0103] Sliding mode controller, used to: allocate the total reference current for the train's future power demand to the lithium battery and supercapacitor;

[0104] The drive signal generation module is used to generate corresponding DC / DC drive control signals based on the reference current allocated to the lithium battery and the supercapacitor, respectively.

[0105] The specific implementation methods of each module included in the hybrid energy storage management system described in this embodiment are the same as those described in Embodiment 1.

[0106] The above embodiments are preferred embodiments of this application. Those skilled in the art can make various changes or improvements based on them. Without departing from the overall concept of this application, these changes or improvements should fall within the scope of protection claimed in this application.

Claims

1. A hybrid energy storage management method for a heavy-haul train group operation control system, characterized in that, include: Obtain the speed and location of all heavy-load trains in the group, and plan the speed and acceleration of each train based on the obtained information, and then calculate the future power requirements of each train; Based on the train's future power requirements, online simulation is performed using a cloud-based digital twin to output sliding surface parameters, control law parameters, and event trigger thresholds; The train's bus voltage is collected, and based on the train's bus voltage and a given bus voltage, a PI controller is used to generate the total reference current for the train's future power requirements. The system acquires the real-time status of the supercapacitor and lithium battery, and determines whether to activate the sliding mode controller based on event trigger thresholds. If the sliding mode controller is activated, the total reference current for the train's future power demand will be allocated to the lithium battery and supercapacitor. Based on the reference current allocated to each of the lithium battery and the supercapacitor, corresponding DC / DC drive control signals are generated.

2. The hybrid energy storage management method for heavy-haul train group operation control system according to claim 1, characterized in that, The future power demand is calculated based on the planned train speed and acceleration. The specific calculation formula is as follows: ； in, It is the calculated future power requirement of the train. It is the train's speed; The total mechanical traction force required for train operation is defined as follows: ； In the formula, It is the total mass of the train. Represents gravitational acceleration. Indicates the rolling resistance coefficient. Indicates the slope of the incline. It is air density. It is the area in front of the train. It is train acceleration. It is the air drag coefficient. It refers to the efficiency of the train's hybrid energy storage system. It is energy conversion efficiency. It refers to the traction motor efficiency. It is the average energy feedback efficiency.

3. The hybrid energy storage management method for heavy-haul train group operation control system according to claim 1, characterized in that, Online simulation using a cloud-based digital twin is used to output sliding surface parameters, control law parameters, and event trigger thresholds, specifically including: The power demand model of the train, the SOC dynamic model of the lithium battery, the SOC dynamic model of the supercapacitor, and the capacity loss model of the lithium battery will be integrated into the cloud-based digital twin. In a cloud-based digital twin integrating various models, a multi-objective particle swarm optimization algorithm is used to search for the sliding surface parameters corresponding to the optimal objective function. }, Control Law Parameters { } and trigger threshold { } 4. The hybrid energy storage management method for heavy-haul train group operation control system according to claim 3, characterized in that, The objective function is specifically expressed as follows: ； in, This indicates the real-time capacity decay of the lithium battery. This indicates the maximum permissible value for lithium battery capacity decay. This indicates the change in the output power of the lithium battery. This represents the maximum permissible variation in lithium battery capacity. This indicates the real-time state of charge of the supercapacitor. This is the reference state of charge for the supercapacitor. and These are the upper and lower limits of the safe operating range of the supercapacitor; These are the weighting coefficients.

5. The hybrid energy storage management method for heavy-haul train group operation control system according to claim 3, characterized in that, The sliding surface and the control law are respectively expressed as follows: ； ； in, Represents the sliding surface function; The feedback gain coefficient representing the state-of-charge deviation of a supercapacitor; The feedback gain coefficient representing the current deviation of the supercapacitor; This serves as the reference state of charge for the supercapacitor. This is the reference current for the supercapacitor; This represents the sliding mode control law, i.e., the duty cycle command for the bidirectional DC / DC converter on the supercapacitor side; For the constant velocity term gain of the sliding mode control law; The gain of the proportional term in the sliding mode control law; This indicates the real-time state of charge of the supercapacitor. This indicates the current in the supercapacitor.

6. The hybrid energy storage management method for heavy-haul train group operation control system according to claim 1, characterized in that, The trigger condition for determining whether to activate the sliding mode controller is one of the following: (1) (2) (3) Twin warning flag = 1; where, This indicates the real-time state of charge of the supercapacitor. This is the reference state of charge for the supercapacitor. This indicates the change in the output power of the lithium battery. Indicates the system sampling time interval. and These represent the trigger thresholds for supercapacitors and lithium batteries, respectively.

7. The hybrid energy storage management method for heavy-haul train group operation control system according to claim 3, characterized in that, Dynamic model of SOC of lithium battery: ； in, This indicates the state of charge of the lithium battery. express rate of change, This indicates the coulombic efficiency of a lithium battery. This indicates the rated capacity of the lithium battery. This indicates the current of the lithium battery.

8. The hybrid energy storage management method for heavy-haul train group operation control system according to claim 3, characterized in that, Dynamic model of SOC of supercapacitor: ； in, This indicates the state of charge of the supercapacitor. express rate of change, This indicates the discharge efficiency of the supercapacitor. This represents the current in the supercapacitor. This indicates the rated capacitance of the supercapacitor. This indicates the upper limit of the safe operating range of a supercapacitor. This indicates the charging efficiency of the supercapacitor.

9. The hybrid energy storage management method for heavy-haul train group operation control system according to claim 3, characterized in that, Capacity loss model for hybrid energy storage: ； ； ； in, This represents the capacity loss of hybrid energy storage, ignoring the capacity decay of supercapacitors and consisting only of the capacity loss of lithium batteries; It refers to the discharge rate of the lithium battery. It is the discharge ampere-hour flux of a lithium battery. It is a lithium battery based on discharge rate Activation energy, in units of ; It is a lithium battery based on discharge rate The pre-index factor, It is the ideal gas constant. It's the temperature of the lithium battery. It is a power-law factor.

10. A hybrid energy storage management system for a heavy-haul train group operation control system, characterized in that, include: The data acquisition module is used to: acquire the speed and position of all heavy-load trains in the group, acquire the bus voltage of the trains, and acquire the real-time status of the supercapacitors and lithium batteries. The power prediction module is used to: plan the speed and acceleration of each train based on the acquired speed and position of all heavy-haul trains, and then calculate the future power demand of each train; The cloud-based digital twin platform is used to: simulate and output sliding surface parameters, control law parameters, and event trigger thresholds online based on the future power requirements of the train; A voltage loop PI controller is used to generate a total reference current for the future power demand of the train based on the train's bus voltage and a given bus voltage. The event triggering module is used to determine whether to activate the sliding mode controller based on the real-time status of the supercapacitor and lithium battery, as well as the event triggering threshold. Sliding mode controller, used to: allocate the total reference current for the train's future power demand to the lithium battery and supercapacitor; The drive signal generation module is used to generate corresponding DC / DC drive control signals based on the reference current allocated to the lithium battery and the supercapacitor, respectively.

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