CN-121989714-A - Hybrid energy storage management method and device for operation control system of heavy-load train group

CN121989714ACN 121989714 ACN121989714 ACN 121989714ACN-121989714-A

Abstract

The invention discloses a hybrid energy storage management method and device for a heavy-duty train group operation control system, wherein the method comprises the steps of obtaining the speeds and positions of all heavy-duty trains of a group, planning the speeds and accelerations of all the trains, and further calculating the future power demands of all the trains; the method comprises the steps of simulating and outputting slip mode surface parameters, control law parameters and event trigger thresholds on line through a cloud digital twin body according to future power demands of a train, collecting train bus voltage, combining given bus voltage and adopting a PI controller to generate total reference current of the future power demands, obtaining real-time states of a super capacitor and a lithium battery, comparing the real-time states with the event trigger thresholds to judge whether to start the slip mode controller, and if the slip mode controller is started, distributing the total reference current to the lithium battery and the super capacitor to generate corresponding DC/DC driving control signals. The invention can reduce the frequency of vehicle-mounted calculation and communication and improve the flexibility and robustness of energy management of the reloaded train group.

Inventors

HUANG ZHIWU
SU XUAN
PENG JUN
LI HENG
LIU WEIRONG
ZHANG XIAOYONG
LIU YONGJIE
ZHANG ZHUOZHUO
WANG ZIXUAN
SONG ZONGYING
PENG HUI
WANG WENBIN
WU YUE
WANG XINGZHONG
JIANG FU

Assignees

中南大学
中国神华能源股份有限公司

Dates

Publication Date: 20260508
Application Date: 20260409

Claims (10)

1. The hybrid energy storage management method of the heavy-duty train group operation control system is characterized by comprising the following steps of: Acquiring the speed and the position of all heavy-duty trains in the group, planning the speed and the acceleration of each train according to the acquired information, and further calculating the future power demand of each train; according to the future power demand of the train, online simulation is carried out through a cloud digital twin body, and slip form surface parameters, control law parameters and event triggering thresholds are output; Collecting bus voltage of a train, and generating total reference current of future power demand of the train according to the bus voltage of the train and the given bus voltage and by adopting a PI controller; Acquiring real-time states of the super capacitor and the lithium battery, and judging whether to start the sliding mode controller or not based on an event trigger threshold; if the sliding mode controller is started, distributing the total reference current of the future power demand of the train to the lithium battery and the super capacitor; And generating a corresponding DC/DC driving control signal according to the reference currents respectively distributed by the lithium battery and the super capacitor.
2. The hybrid energy storage management method of a re-loaded train consist operation control system of claim 1, wherein future power demands are calculated from planned train speeds and accelerations, and the specific calculation is: ; Wherein, the Is the calculated future power demand of the train, Is the train running speed; the total mechanical traction force required for train operation is defined as follows: ; in the formula, Is the total mass of the train and is used for controlling the total mass of the train, Indicating the acceleration of gravity and, The coefficient of rolling resistance is represented by, Indicating the slope of the ramp, Is the air density of the air, and the air is compressed, Is the area in front of the train, Is the acceleration of the train and, Is the air resistance coefficient, and the air resistance coefficient, Is the efficiency of the hybrid energy storage system of the train, Is the efficiency of the energy conversion and, Is the efficiency of the traction motor and is characterized in that, Is the average energy feedback efficiency.
3. The hybrid energy storage management method of the heavy-duty train group operation control system according to claim 1, wherein the slip form surface parameters, the control law parameters and the event trigger threshold are simulated and output on line through a cloud digital twin, and specifically comprising: integrating a power demand model of a train, an SOC dynamic model of a lithium battery, an SOC dynamic model of a super capacitor and a capacity loss model of the lithium battery in a cloud digital twin body; Searching sliding mode surface parameters { corresponding to an optimized objective function in a cloud digital twin integrating each model by adopting a multi-objective particle swarm algorithm { Control law parameters }, { { And trigger threshold } }。
4. The heavy-duty train group operation control system hybrid energy storage management method of claim 3, wherein said objective function is specifically expressed as: ; Wherein, the Represents the real-time capacity fade of the lithium battery, A maximum allowable value indicating the capacity attenuation of the lithium battery; indicating the amount of change in output power of the lithium battery, The maximum allowable variation of the lithium battery; Represents the real-time state of charge of the super-capacitor, Is the reference state of charge of the super-capacitor, And The upper limit and the lower limit of the safe working interval of the super capacitor are respectively set; is a weight coefficient.
5. The heavy-duty train group operation control system hybrid energy storage management method of claim 3, wherein the slip plane and control law are respectively expressed as: ; ; Wherein, the Representing a sliding mode surface function; A feedback gain coefficient representing the super-capacitor state of charge deviation; A feedback gain coefficient representing the current deviation of the super capacitor; the reference charge state of the super capacitor; the reference current is the reference current of the super capacitor; the method is characterized by expressing a sliding mode control law, namely a duty ratio instruction of the super capacitor side bidirectional DC/DC converter; constant term gain for sliding mode control law; proportional term gain for the sliding mode control law.
6. The hybrid energy storage management method for a re-loaded train consist of the method of claim 1, wherein the trigger condition for determining whether to activate the slip-form controller is one of (1) ;(2) (3) Twin early warning sign=1, wherein, Represents the real-time state of charge of the super-capacitor, Is the reference state of charge of the super-capacitor, Indicating the amount of change in output power of the lithium battery, The system sampling time interval is represented as a function of the time, And Respectively representing trigger thresholds of the super capacitor and the lithium battery.
7. The hybrid energy storage management method of a re-loaded train consist operation control system of claim 1, wherein the SOC dynamic model of the lithium battery: ; Wherein, the Indicating the state of charge of the lithium battery, Representation of Is used for the rate of change of (a), Indicating the coulombic efficiency of the lithium battery, Indicating the rated capacity of the lithium battery, Representing the current of the lithium battery.
8. The hybrid energy storage management method of a re-loaded train consist operation control system of claim 1, wherein the SOC dynamic model of the super capacitor: ; Wherein, the Representing the state of charge of the super-capacitor, Representation of Is used for the rate of change of (a), Indicating the discharge efficiency of the super-capacitor, The current representing the current of the super-capacitor, Representing the rated capacitance of the super-capacitor, Represents the upper limit of the safe working interval of the super capacitor, Indicating the charging efficiency of the supercapacitor.
9. The method of hybrid energy storage management for a re-loaded train consist operation control system of claim 1, wherein the capacity loss model for hybrid energy storage: ; ; ; Wherein, the The capacity loss of the hybrid energy storage is represented, the capacity attenuation of the super capacitor is ignored, and the hybrid energy storage consists of the capacity loss of the lithium battery only; is the discharge rate of the lithium battery, Is the discharge ampere-hour flux of the lithium battery, Is based on discharge rate of lithium battery The unit of the activation energy is ; Is based on discharge rate of lithium battery Is used for the pre-exponential factor of (c), Is an ideal gas constant for the gas, Is the temperature of the lithium battery and, Is a power law factor.
10. A heavy-duty train group operation control system hybrid energy storage management system, comprising: the data acquisition module is used for acquiring the speed and the position of all heavy-duty trains in the group, acquiring the bus voltage of the trains and acquiring the real-time states of the super capacitor and the lithium battery; The power prediction module is used for planning the speed and the acceleration of each train according to the acquired speeds and positions of all the heavy-duty trains so as to calculate the future power demand of each train; The cloud digital twin platform is used for online simulation and output of sliding mode surface parameters, control law parameters and event trigger thresholds according to future power demands of the train; The voltage loop PI controller is used for generating total reference current of future power demands of the train according to the bus voltage of the train and the given bus voltage; The event triggering module is used for judging whether to start the sliding mode controller according to the real-time state of the super capacitor and the lithium battery and the event triggering threshold value; The sliding mode controller is used for distributing the total reference current of the future power demand of the train to the lithium battery and the super capacitor; And the driving signal generation module is used for generating a corresponding DC/DC driving control signal according to the reference currents respectively distributed by the lithium battery and the super capacitor.

Description

Hybrid energy storage management method and device for operation control system of heavy-load train group Technical Field The invention belongs to the technical field of power hybrid energy storage management, and particularly relates to a hybrid energy storage management method and device for a heavy-duty train group operation control system. Background Urban rail transit has been widely popularized in many cities at present due to the advantages of large carrying capacity, quick quasi-points, less environmental pollution and the like. The electric power is clean energy without waste pollution, and is very beneficial to protecting environment and clean air. Batteries are commonly used as energy storage elements, most notably their outstanding energy density. However, in face of varying power demands in actual energy supply, battery energy storage systems have difficulty coping with large instantaneous output power, and battery life is affected by cyclic charge and discharge. Therefore, considering the power density and discharge characteristics of the battery has become a key issue and research hotspot in hybrid energy storage systems. Compared with a lithium battery, the super capacitor serving as an energy storage element has the characteristics of high power density, long cycle life, cleanliness, no pollution and the like. Because the energy storage process of the super capacitor is irrelevant to chemical reaction, the power demand of the electric automobile can be responded quickly, and meanwhile, feedback power can be absorbed effectively. However, considering that the energy density of the super capacitor is low, the requirement of the locomotive on the cruising performance is difficult to meet only by relying on the energy storage element. At present, any energy storage technology cannot meet the requirements of high power density, high energy density, long service life, safety, technical maturity and the like. In view of the diversity of energy storage requirements of the system, none of the energy storage technologies is fully adequate. Therefore, the matched energy storage mode must be selected according to specific requirements, so that the energy storage device can be increased in favor of each other, the respective advantages are exerted, multiple requirements on energy, power and the like are realized, the cycle life of the energy storage element can be remarkably prolonged, and the energy storage device has become a new trend of energy storage research. In real-time operation of an electric vehicle, an energy management strategy plays a vital role in power distribution of a hybrid energy storage system. The energy management strategies mainly comprise three types of methods, namely a rule-based energy management strategy, an optimization-based energy management strategy and an artificial intelligence-based energy management strategy. The rule-based hybrid energy storage system energy management strategy comprises rules such as logic threshold setting or fuzzy logic setting for power distribution. These strategies may be simple to implement and perform well in some situations, but they are largely dependent on the expertise of the designer and may not be suitable for all situations. The energy management strategy based on artificial intelligence needs to prepare a large number of data sets for optimal control for training, and researchers perform online power distribution on the hybrid energy storage system by using the energy management strategy obtained by artificial intelligence algorithms such as neural network and machine learning. It is difficult for optimization-based methods to solve all hybrid energy storage problems simultaneously with a single optimization objective. Thus, there are many still unresolved issues in the research of hybrid energy storage system energy management strategies. The existing patent application scheme of China patent publication No. CN119834430A is a control method of a Hangzhou electric hybrid energy storage device, which only adopts sagging control stable bus voltage, does not utilize future driving information, does not introduce a sliding mode surface or an event triggering mechanism, and is continuously controlled to update, the calculation burden is large, the patent application scheme of China patent publication No. CN119749232A is a Sailis suspension energy distribution method, although LSTM is used for predicting short-term speed, the distribution weight is solidified offline, ECMS still needs to be solved in a rolling way, and is not adaptively updated on line, the patent application scheme of China patent publication No. CN119315579A is a AGC frequency modulation mode, working time and target power are continuously calculated in each regulation period, the forward looking pre-control on future working conditions is lacked, and a sliding mode robust strategy is not adopted, the patent application scheme of China patent publication No. CN118770175A