Urban rail hybrid energy storage optimization control method and system

Abstract

The present invention provides an urban rail hybrid energy storage optimization control method and system. An urban rail hybrid energy storage optimization control method includes: using a variable gain proportional control strategy to respond to voltage overshoot and power changes in the braking phase of the train, which is used to manage the energy recovery and output of the energy storage system. The control system will quickly increase the control action to respond to the voltage surge and pull the bus voltage back to a near normal range; when the train power demand changes, the power demand is smoothed according to a low-pass filter, and a filter distribution control strategy is used to optimize the power distribution between supercapacitors and lithium-ion batteries.

Description

Technical Field

[0001] The present invention relates to the field of hybrid energy storage technology, and more specifically, to an urban rail hybrid energy storage optimization control method and system. Background Art

[0002] Energy storage devices in urban rail transit systems are usually used to store braking energy and adjust bus voltage to improve energy utilization and reduce energy consumption. Traditional energy storage control systems usually use fixed-gain PID control strategies. These systems often fail to respond adequately to rapidly changing loads and energy feedback, resulting in energy loss and system instability. In addition, traditional systems fail to effectively solve the problem of energy circulation, increasing unnecessary energy consumption and equipment wear. Summary of the invention

[0003] The object of the present invention is to provide a method and system for optimizing control of hybrid energy storage in urban rail transit.

[0004] The present invention aims to solve the problems existing in the prior art.

[0005] Compared with the prior art, the technical solution of the present invention and its beneficial effects are as follows:

[0006] A hybrid energy storage optimization control method for urban rail transit includes: using a variable gain proportional control strategy to respond to voltage overshoot and power changes during the braking phase of a train, for managing energy recovery and output of an energy storage system, and the control system will rapidly increase control actions to cope with voltage surges, bringing the bus voltage back to a near normal range; when the train power demand changes, smoothing the power demand based on a low-pass filter, and using a filter distribution control strategy to optimize the power distribution between supercapacitors and lithium-ion batteries.

[0007] A hybrid energy storage optimization control system for urban rail transit includes: a variable gain proportional control module, which is configured to adopt a variable gain proportional control strategy to respond to voltage overshoot and power changes during the braking phase of a train, and is used to manage energy recovery and output of an energy storage system. The control system will quickly increase control actions to cope with voltage surges and pull the bus voltage back to a near normal range; a filter distribution control module, which is configured to smooth the power demand according to a low-pass filter when the train power demand changes, and adopt a filter distribution control strategy to optimize the power distribution between supercapacitors and lithium-ion batteries.

[0008] The beneficial effects of the present invention are:

[0009] The present invention relates to a hybrid energy storage system for urban rail transit, which optimizes energy management by adopting advanced variable gain proportional control strategy and filter distribution control strategy to improve system response speed, reduce energy loss, and enhance the economy and reliability of the system as a whole. The system is mainly composed of a software control module and a hardware interface, and can adjust and manage the charging and discharging process of supercapacitors and lithium-ion batteries in real time.

[0010] The present invention introduces a variable gain proportional control strategy to quickly respond to voltage overshoot and power changes during the braking phase, thereby more effectively managing the energy recovery and output of the energy storage system. Through the filter distribution control strategy, the energy distribution between the supercapacitor and the lithium-ion battery is optimized, the internal energy cycle is reduced, and the overall energy efficiency of the system is improved. The voltage stability of the system and the reliability of energy supply are improved to ensure the continuous operation and safety of urban rail transit. By optimizing energy use and reducing maintenance costs, the service life of the energy storage components is extended and the overall operating cost is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of an urban rail hybrid energy storage optimization control method provided by an embodiment of the present invention.

[0012] Figure 2 It is a schematic diagram of a hybrid energy storage system for urban rail transit provided by an embodiment of the present invention.

[0013] Figure 3 It is a schematic diagram of an urban rail hybrid energy storage optimization control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0015] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0016] Reference Figures 1 to 3 As shown, a hybrid energy storage optimization control method for urban rail transit includes: using a variable gain proportional control strategy to respond to voltage overshoot and power changes during the braking phase of the train, which is used to manage the energy recovery and output of the energy storage system. The control system will quickly increase the control action to cope with the voltage surge and pull the bus voltage back to a near normal range; when the power demand of the train changes, the power demand is smoothed by a low-pass filter, and a filter distribution control strategy is used to optimize the power distribution between the supercapacitor and the lithium-ion battery.

[0017] The variable gain proportional control strategy is used to respond to voltage overshoot and power changes during the braking phase of the train, and is used to manage energy recovery and output of the energy storage system, including: calculating the proportional gain based on the square of the error, and calculating the controller output in combination with the integral term.

[0018] The method of calculating the proportional gain based on the square of the error and calculating the controller output in combination with the integral term includes: calculating the proportional term based on the bus voltage measurement value and the no-load bus voltage value; calculating the proportional gain term based on the proportional gain and the proportional term; calculating the integral term based on the integral gain, the bus voltage measurement value and the charging threshold voltage; and calculating the controller output value based on the proportional gain term and the integral term.

[0019] The proportional term is calculated based on the bus voltage measurement value and the no-load bus voltage value, and the proportional term is (U dc -U0) 2 , where U dc is the measured value of bus voltage, and U0 is the no-load bus voltage.

[0020] The proportional gain term is calculated according to the proportional gain and the proportional term, and the proportional gain term is K p ·(U dc -U0) 2 , where K p is the proportional gain, U dc is the measured value of bus voltage, and U0 is the no-load bus voltage.

[0021] The integral term is calculated according to the integral gain, the bus voltage measurement value and the charging threshold voltage, and the integral term is K i ·(U dc -U char ), where K i is the integral gain, U char is the charging threshold voltage, U dc is the measured value of bus voltage.

[0022] The controller output value is calculated according to the proportional gain term and the integral term, and the controller output value is K(i)=K p ·(U dc-U0) 2 +K i ·(U dc -U char ), where K p is the proportional gain, U dc is the measured value of bus voltage, U0 is the no-load bus voltage, K i is the integral gain, U char is the charging threshold voltage, U dc is the measured value of bus voltage.

[0023] When the train power demand changes, the power demand is smoothed according to the low-pass filter, and the filter allocation control strategy is used to optimize the power distribution between the supercapacitor and the lithium-ion battery, including: when the train power demand decreases, a first-order low-pass filter is used to smooth the power demand change, the lithium-ion battery bears the main load, and the supercapacitor copes with high-frequency fluctuations.

[0024] The first-order low-pass filter is used to smooth the power demand change, and the calculation process is expressed as:

[0025]

[0026] Where T is the time constant of the filter and the value of T is set according to the expected frequency response.

[0027] On the other hand, during the braking phase, the traditional voltage control method (using fixed proportional and integral control parameters) has a slow response speed and is prone to voltage overshoot. Overshoot refers to the situation where the actual output exceeds the expected output, which is particularly obvious during energy-intensive braking processes and affects the effective recovery of energy and the stability of the system.

[0028] In order to improve the response speed and reduce overshoot, the present invention adopts a proportional gain adjustment strategy based on the square of the error, and the specific mathematical expression is as follows:

[0029] K(i)=K p ·(U dc -U0) 2 +K i ·(U dc -U char )

[0030] Among them, U dc is the current bus voltage; U0 is the bus voltage when no-load, which is used as a reference; U char is the charging threshold voltage, which is usually set within a safe range to prevent excessive voltage; K p and K i are the gain parameters for proportional and integral control respectively.

[0031] Dynamic adjustment: proportional gain adjustment (through (U dc -U0) 2 The square term of the voltage increases the speed of the control system's response to large deviations, allowing the system to quickly intervene and adjust when the voltage rises rapidly, reducing the possibility of overshoot.

[0032] Filter Allocation Control

[0033] When the train power demand decreases, conventional methods may lead to energy circulation within the energy storage system, that is, energy is ineffectively transferred between different energy storage elements, increasing energy losses and reducing system efficiency.

[0034] Low-pass filtering: A first-order low-pass filter is used to smooth the power demand changes, which is mathematically expressed as:

[0035]

[0036] Where T is the time constant of the filter, and its value is set according to the desired frequency response.

[0037] Power distribution: When the power demand drops, the lithium-ion battery takes on the main load after being processed by the filter, while the supercapacitor copes with high-frequency fluctuations. The purpose of this is to reduce frequent energy transfer and utilize the high power density characteristics of the supercapacitor to handle instantaneous loads.

[0038] Assumed system parameters

[0039] No-load bus voltage U0 = 600V;

[0040] Charging threshold voltage U char =650V;

[0041] Bus voltage measurement value U dc Rapidly rises to 680V during braking;

[0042] Proportional gain K p =0.1;

[0043] Integral gain K i =0.05;

[0044] The time constant T was set to 0.5 seconds;

[0045] Assume that during a braking event, the bus voltage quickly increases from 600V to 680V due to the rapid feedback of braking energy.

[0046] Application of variable gain proportional control strategy

[0047] Calculation steps

[0048] Calculate the proportional term:

[0049] (U dc -U0) 2 =(680V-600V) 2 =6400(V2)

[0050] K p ·(U dc -U0) 2 =0.1×6400=640(control units)

[0051] Compute the integral term:

[0052] K i ·(U dc -U char )=0.05×(680V-650V)=0.05×30V

[0053] =1.5 (control units)

[0054] Calculate the controller output:

[0055] K(i)=640+1.5=641.5(control units)

[0056] The output results show that the control system will quickly increase control actions to cope with the sudden increase in voltage, trying to pull the bus voltage back to a near normal range.

[0057] 1. Variable gain proportional control strategy

[0058] Purpose: To improve the system's response speed to voltage surges and reduce voltage overshoot by dynamically adjusting the proportional gain, thereby more effectively managing the energy recovery and output of the energy storage system.

[0059] Steps: Calculate the proportional gain based on the square of the error and combine it with the integral term to calculate the controller output.

[0060] Calculation steps and results:

[0061] Calculate the proportional term: (U dc -U0) 2 =(680V-600V) 2 =6400(V 2 )

[0062] Calculate the proportional gain term: K p ·(U dc -U0) 2 =0.1×6400=640(controlunits)

[0063] Compute the integral term:

[0064] K i ·(U dc -U char )=0.05×(680V-650V)=1.5(controlunits)

[0065] Controller output:

[0066] K(i)=640+1.5=641.5(controlunits)

[0067] Result meaning: The control system will quickly increase control actions to cope with the voltage surge, trying to pull the bus voltage back to a near normal range.

[0068] 2. Filter allocation control strategy

[0069] Purpose: When the train power demand changes, the power demand is smoothed by low-pass filtering, the energy distribution between supercapacitors and lithium-ion batteries is optimized, and energy loss and internal energy circulation are reduced.

[0070] Steps: Smooth the power variation through a low-pass filter and determine the power distribution between the lithium-ion battery and the supercapacitor.

[0071] Power demand change: Assume that the power demand rises rapidly to 1.5MW during braking.

[0072] Low pass filter output: Assuming the power variation frequency is low, the filter will smooth the power variation to 1.2MW.

[0073] Power Distribution:

[0074] Lithium-ion batteries bear the main load: 1.2MW

[0075] Supercapacitor handles instantaneous power peak: 1.5MW-1.2MW=0.3MW

[0076] Results and implications: The low-pass filter reduces the instantaneous power demand of the lithium battery, extending its life and improving energy efficiency. The supercapacitor handles the high power demand in a short period of time, ensuring system stability and responsiveness.

[0077] Calculation steps

[0078] Power demand change: Assume that the power demand rises rapidly to 1.5MW when the train brakes.

[0079] Low pass filter output:

[0080]

[0081] To simplify the calculation, if the power variation frequency is low, the filter will smooth the power variation to a more stable level. Assume that the power output is smoothed to 1.2MW.

[0082] Power Distribution:

[0083] Lithium battery bears the main load: 1.2MW

[0084] Supercapacitor handles instantaneous power peak: 1.5MW-1.2MW=0.3MW

[0085] The application of low-pass filters reduces the instantaneous power demand on lithium batteries, prolongs their life, and improves energy efficiency. At the same time, the presence of supercapacitors allows the system to cope with high power demands in a short period of time, ensuring system stability and responsiveness.

[0086] A hybrid energy storage optimization control system for urban rail transit includes: a variable gain proportional control module, which is configured to adopt a variable gain proportional control strategy to respond to voltage overshoot and power changes during the braking phase of a train, and is used to manage energy recovery and output of an energy storage system. The control system will quickly increase control actions to cope with voltage surges and pull the bus voltage back to a near normal range; a filter distribution control module, which is configured to smooth the power demand according to a low-pass filter when the train power demand changes, and adopt a filter distribution control strategy to optimize the power distribution between supercapacitors and lithium-ion batteries.

[0087] The above embodiments are only used to explain the technical solutions of the present invention rather than to limit them. Those skilled in the art should understand that any modification and equivalent substitution that does not depart from the spirit and scope of the present invention should fall within the protection scope of the claims of the present invention.

Claims

1. A hybrid energy storage optimization control method for urban rail, characterized in that: include: A variable gain proportional control strategy is used to respond to voltage overshoot and power changes during the train braking phase to manage energy recovery and output of the energy storage system. The control system will quickly increase control actions to cope with the voltage surge and pull the bus voltage back to a near normal range. When the train power demand changes, the power demand is smoothed by a low-pass filter, and the filter allocation control strategy is used to optimize the power distribution between the supercapacitor and the lithium-ion battery; The variable gain proportional control strategy is used to respond to voltage overshoot and power change during the braking phase of the train, and is used to manage energy recovery and output of the energy storage system, including calculating the proportional gain based on the square of the error and calculating the controller output in combination with the integral term, including: The proportional term is calculated based on the bus voltage measurement value and the no-load bus voltage value; the proportional term is ,in, is the measured value of bus voltage, is the no-load bus voltage; The proportional gain term is calculated based on the proportional gain and the proportional term; the proportional gain term is ,in, is the proportional gain; The integral term is calculated based on the integral gain, the bus voltage measurement value and the charging threshold voltage; the integral term is ,in, is the integral gain, is the charging threshold voltage; The controller output value is calculated according to the proportional gain term and the integral term; the controller output value is ; When the train power demand changes, the power demand is smoothed by a low-pass filter, and a filter allocation control strategy is used to optimize the power allocation between the supercapacitor and the lithium-ion battery, including: When the train power demand decreases, a first-order low-pass filter is used to smooth the power demand changes. The lithium-ion battery bears the main load, while the supercapacitor copes with high-frequency fluctuations. Wherein, the first-order low-pass filter is used to smooth the power demand change, and the calculation process is expressed as: ； in, is the time constant of the filter, The value is set according to the expected frequency response.

2. An urban rail hybrid energy storage optimization control system, characterized in that: include: A variable gain proportional control module is configured to adopt a variable gain proportional control strategy to respond to voltage overshoot and power changes during the braking phase of the train, and is used to manage energy recovery and output of the energy storage system. The control system will quickly increase control actions to cope with the voltage surge and pull the bus voltage back to a near normal range; A filter allocation control module is configured to optimize the power allocation between the supercapacitor and the lithium-ion battery by using a filter allocation control strategy when the train power demand changes according to the low-pass filter smoothing the power demand; The variable gain proportional control strategy is used to respond to voltage overshoot and power change during the braking phase of the train, and is used to manage energy recovery and output of the energy storage system, including calculating the proportional gain based on the square of the error and calculating the controller output in combination with the integral term, including: The proportional term is calculated based on the bus voltage measurement value and the no-load bus voltage value; the proportional term is ,in, is the measured value of bus voltage, is the no-load bus voltage; The proportional gain term is calculated based on the proportional gain and the proportional term; the proportional gain term is ,in, is the proportional gain; The integral term is calculated based on the integral gain, the bus voltage measurement value and the charging threshold voltage; the integral term is ,in, is the integral gain, is the charging threshold voltage; The controller output value is calculated according to the proportional gain term and the integral term; the controller output value is ; When the train power demand changes, the power demand is smoothed by a low-pass filter, and a filter allocation control strategy is used to optimize the power allocation between the supercapacitor and the lithium-ion battery, including: When the train power demand decreases, a first-order low-pass filter is used to smooth the power demand changes. The lithium-ion battery bears the main load, while the supercapacitor copes with high-frequency fluctuations. Wherein, the first-order low-pass filter is used to smooth the power demand change, and the calculation process is expressed as: ； in, is the time constant of the filter, The value is set according to the expected frequency response.

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