Direct-current bus type electric vehicle charging system with source-load-storage collaborative management capability

Abstract

The application provides a direct-current bus type electric vehicle charging system with source-load-storage collaborative management capability, and belongs to the technical field of electric vehicle charging. The system has the following technical advantages: bidirectional energy flow, modular expansion, flexible deployment in multiple scenarios, and is suitable for typical application scenarios such as intelligent buildings, residential areas, industrial areas, microgrids, and grid energy storage nodes, and can effectively overcome the defects of the existing electric vehicle charging system in collaborative management with comprehensive energy systems.

Description

Technical Field

[0001] This application relates to the field of electric vehicle charging technology, and in particular to a DC bus-type electric vehicle charging system with source-load-storage collaborative management capabilities. Background Technology

[0002] In recent years, with the rapid development of the new energy vehicle industry, the number of electric vehicles (EVs) has continued to rise, placing higher demands on the coverage, power density, and intelligent scheduling capabilities of charging infrastructure. Simultaneously, to address issues such as renewable energy consumption and insufficient grid regulation capacity, various distributed energy storage systems, microgrid systems, and source-load-storage collaborative management technologies have been widely applied. Against this backdrop, traditional charging systems have revealed several limitations, making it difficult to meet the comprehensive operational needs of future energy systems.

[0003] Traditional electric vehicle charging systems typically employ AC power distribution, resulting in a decentralized structure and independent management. This leads to problems such as low space utilization, numerous energy conversion stages, and significant energy losses. Furthermore, they lack integration mechanisms with energy storage, the power grid, and smart building energy management systems, hindering dynamic adjustment and optimized allocation of charging loads. In addition, traditional electric vehicle charging systems often rely on single-point control and low-frequency response, making it difficult to support intelligent scheduling scenarios such as simultaneous high-power charging of multiple vehicles, load forecasting and regulation, and bidirectional energy flow.

[0004] Specifically, existing electric vehicle charging systems still face the following technical challenges and bottlenecks in terms of multi-scenario deployment and integrated energy system collaborative management:

[0005] 1. Large space occupation and low layout efficiency: Traditional charging facilities mostly adopt a distributed AC power distribution structure, with each charging terminal independently connected and controlled, resulting in redundant equipment configuration and low space utilization efficiency, making it difficult to meet the needs of intensive deployment in high-density urban areas or building scenarios.

[0006] 2. Lengthy and inefficient energy conversion links: Multi-stage AC / DC and DC / DC conversions result in significant energy loss during energy transmission, especially in high-power fast charging or multi-vehicle parallel charging scenarios, where energy utilization efficiency drops significantly, affecting the system's economics.

[0007] 3. Fragmented systems and lack of coordinated control capabilities: Existing charging systems typically lack effective coupling and linkage with renewable energy systems, energy storage units, grid dispatching systems, and intelligent building energy management systems, making it impossible to achieve coordinated operation mechanisms such as dynamic power adjustment, peak-valley load migration, and energy storage-assisted charging.

[0008] 4. Poor adaptability and scalability, unable to support unified deployment in multiple scenarios: Most existing solutions are designed for single scenarios (such as public buildings or community parking lots), with rigid system structures and difficult expansion, making it difficult to quickly adapt to diversified deployment needs such as new energy access nodes, grid-side energy storage stations, and microgrid systems.

[0009] 5. Lack of bidirectional power control and flexible response mechanism: Under the future trend of "integrated source, load and storage", the charging system of traditional electric vehicles lacks the ability to respond to the power system and cannot realize functions such as energy storage frequency regulation, peak shaving and valley filling, and rapid power regulation, making it difficult to participate in higher-level energy management systems.

[0010] With the development of the concepts of "integrated generation, grid, load, and storage" and "virtual power plants," future electric vehicle charging systems will no longer be simple energy consumption terminals, but rather new energy nodes requiring bidirectional energy exchange, high scalability, and intelligent scheduling optimization capabilities. Therefore, developing an electric vehicle charging system that integrates a DC common bus as its core, renewable energy access, energy storage units, charging units, and a scheduling and control system, while also possessing the ability for coordinated scheduling of generation, load, and storage, and modular expansion, has become a crucial area requiring breakthroughs in the current power electronics and new energy application fields. Summary of the Invention

[0011] To address the aforementioned technical issues, this application proposes a DC bus-type electric vehicle charging system with source-load-storage collaborative management capabilities.

[0012] The technical solution adopted in this application is: a DC bus-type electric vehicle charging system with source-load-storage collaborative management capability, including an interface connected to a medium- and low-voltage distribution network, a bidirectional AC / DC converter, a common DC bus, several bidirectional DC / DC converters and several unidirectional DC / DC converters. The medium- and low-voltage distribution network is connected to the AC side of the bidirectional AC / DC converter through a transformer. The bidirectional AC / DC converter realizes the conversion between AC and DC. The DC side of the bidirectional AC / DC converter is connected to each bidirectional DC / DC converter and unidirectional DC / DC converter through a common DC bus.

[0013] One part of the bidirectional DC / DC converter is connected to the electric vehicle and used as a charging station, while the other part of the bidirectional DC / DC converter is connected to the battery energy storage system;

[0014] A unidirectional DC / DC converter is connected to a renewable energy unit;

[0015] The bidirectional AC / DC converter adopts a hybrid bridge arm AC / DC converter, which includes an A-arm and a B-arm. The A-arm adopts a five-level active midpoint clamped topology as a level generation unit, and the B-arm adopts a two-level topology as a polarity conversion unit, converting the level of the A-arm into a multi-level with positive and negative polarities.

[0016] Furthermore, bridge arm A includes eight power switches S1-S8 and one flying capacitor C. f And two DC-side support capacitors C1 and C2, which are connected by power switches S1-S4 and flying capacitor C. f A flying capacitor structure is formed to perform multi-level combination; a clamping structure is formed by power switches S5-S8 and DC-side support capacitors C1 and C2 to clamp the DC bus voltage.

[0017] The B-arm consists of two diodes connected in series. The B-arm is cascaded on the A-arm, and the two diodes conduct alternately within one power frequency cycle.

[0018] Furthermore, the control strategy of the hybrid bridge arm AC / DC converter adopts a combination of current inner loop and voltage outer loop control strategy.

[0019] Furthermore, the bidirectional AC / DC converter has a built-in AC-side pre-charge circuit and a DC-side pre-charge circuit. The DC-side pre-charge circuit is used when the DC output terminal of the bidirectional AC / DC converter is physically disconnected from the common DC bus, and the system requires that the electrical connection be restored after the pre-charge is completed.

[0020] Furthermore, the AC-side pre-charging circuit includes a main contactor KM2 connected to the AC busbar of the medium- and low-voltage distribution network connected to the transformer. A pre-charging contactor KM1 and a pre-charging resistor Rac are connected in series in parallel with the main contactor KM2. The AC-side pre-charging circuit is used for the interlocking charging stage and the unlocking charging stage. The AC-side pre-charging process is as follows:

[0021] 1) When the pre-charge contactor KM1 is closed, the medium and low voltage distribution network performs uncontrolled rectified charging through the pre-charge resistor Rac. As charging proceeds, the charging current will drop rapidly and approach zero.

[0022] 2) When the precharge voltage reaches more than 90% of the target voltage, close the main contactor KM2 until the bridge arm voltage stabilizes;

[0023] 3) Start the DC voltage control with droop control, unlock the bidirectional AC / DC converter, set the initial setpoint of DC voltage to the peak value of AC line voltage, and set the setpoint of reactive power to zero;

[0024] 4) The pre-charging process ends when the given DC voltage rises to the rated value.

[0025] Furthermore, the DC-side pre-charge circuit includes a main contactor KM3 connected to the positive DC output of the bidirectional AC / DC converter. A pre-charge contactor KM4 and a pre-charge resistor R are connected in series in parallel with the main contactor KM3. The DC-side pre-charge circuit is used for the lockout charging phase and the unlock charging phase. The DC-side pre-charge process is as follows:

[0026] 1) Close the pre-charge contactor KM4, and the DC power supply will perform uncontrolled charging until the charging current approaches zero;

[0027] 2) Unlock the bidirectional AC / DC converter and engage the passive inverter control. At this time, the reference value of the given AC voltage is 0.

[0028] 3) Close the pre-charge resistor R of the main contactor KM3 bypass;

[0029] 4) All DC-side support capacitors are charged to near their rated values, and the charging process ends.

[0030] Furthermore, the bidirectional DC / DC converter adopts a bidirectional full-bridge LLC resonant converter topology. When the bidirectional full-bridge LLC resonant converter is running in the forward direction, frequency conversion control is used, and when the bidirectional full-bridge LLC resonant converter is running in the reverse direction, phase shift control is used.

[0031] Furthermore, it also includes a coordination and control unit, which integrates a collaborative energy management algorithm for the charging system and the smart building. The smart building includes several residential users. There is not only an energy flow between the DC bus-type electric vehicle charging system with source-load-storage collaborative management capabilities and the residential users, but also a bidirectional information flow. Each residential user is equipped with a home energy management system. The specific implementation steps of the collaborative energy management algorithm are as follows:

[0032] Step 1: Construct mathematical models of renewable energy units, battery energy storage systems, and electric vehicle charging load prediction models in a DC bus-type electric vehicle charging system with source-load-storage collaborative management capabilities.

[0033] Step 2: Using the configuration capacity of renewable energy units, battery energy storage systems, and charging stations as the solution particles, and taking the optimal economic benefits of the charging system and the highest user satisfaction as the solution objectives, an improved multi-objective particle swarm optimization algorithm is used to solve for the optimal configuration of the capacity of renewable energy units, battery energy storage systems, and charging stations in the charging system.

[0034] Furthermore, the steps for constructing the electric vehicle charging load prediction model are as follows:

[0035] The driving range of an electric vehicle follows a log-normal distribution, and its probability density function is... Represented as:

[0036] ;

[0037] In the formula: This refers to the mileage traveled. Standard deviation; This is the expected value;

[0038] Assuming electric vehicles can be charged immediately upon arrival, the above probability density function can be expressed as:

[0039] ;

[0040] In the formula: This is the start of the charging process; , All are standard deviations; , All are expected values;

[0041] Considering that there is a certain distance between the electric vehicle and the charging station entrance, let's assume the state of charge of the electric vehicle's battery before entering the charging station. Following a uniform distribution of [60%, 90%], the state of charge of the electric vehicle battery upon arrival at the service area is... for:

[0042] ;

[0043] In the formula: Electricity consumption per 100 kilometers; Battery capacity;

[0044] Therefore, the charging time for electric vehicles is... for:

[0045] ;

[0046] In the formula: For charging efficiency; The rated charging power of the charging station;

[0047] The hourly load of the charging station was calculated using the Monte Carlo method. Users' mileage, initial state of charge, and arrival time at the charging station were randomly sampled to obtain the hourly charging load of the charging station. :

[0048] ;

[0049] In the formula: Daily traffic flow of electric vehicles on highways; The percentage of users who need charging services; Let n be the charging load of the nth electric vehicle during the i-th time period.

[0050] Furthermore, the improved multi-objective particle swarm optimization algorithm applies the Logistic-Tent dual chaotic mapping to the population initialization process and uses the entropy weight-TOPSIS method to select the optimal solution set obtained from solving the Logistic-Tent dual chaotic mapping model.

[0051] The beneficial effects of this application compared to the prior art are as follows: The DC bus-type electric vehicle charging system with source-load-storage collaborative management capability proposed in this application, focusing on four core design goals—"unified DC bus platform, modular charging structure (bidirectional DC / DC converter connected to the battery), bidirectional energy flow, and intelligent control coordination"—significantly improves the energy efficiency, system synergy, and deployment adaptability of charging infrastructure. The main technical effects include:

[0052] 1. Improve energy efficiency and reduce system losses: By adopting a centralized DC bus architecture, the multi-stage energy conversion links and redundant inverter equipment in traditional charging systems are effectively reduced, significantly reducing energy losses and improving overall energy efficiency from the grid side to the load side. Simultaneously, the DC bus supports direct access to and internal on-site consumption of renewable energy sources such as wind and solar power, further improving system energy conversion efficiency.

[0053] 2. Enhance system synergy and achieve integrated optimization management of energy sources, loads, and storage: This system has multi-source access and bidirectional energy flow control capabilities, enabling dynamic power adjustment among electric vehicle loads, battery energy storage systems (such as BESS), and renewable energy units. Through integration with building energy management systems (BEMS), park EMS, microgrid controllers, or grid dispatching platforms, it can participate in multi-level energy management tasks such as demand response, load balancing, and peak shaving, achieving integrated collaborative operation of energy sources, loads, and storage.

[0054] 3. Enhanced scenario adaptability and modular expansion capabilities: The system adopts a standardized and modular design, possessing excellent scalability and reusability. Capacity and topology can be flexibly configured according to deployment scenarios (such as smart buildings, residential parks, urban fast charging stations, grid energy storage nodes, or microgrid systems). Each module has independent control and hot-swappable capabilities, supporting online maintenance and rolling deployment, effectively extending the system lifecycle and reducing construction and operation costs.

[0055] 4. Supports bidirectional energy flow and future new grid interfaces: The system supports bidirectional power control for both charging and rectification, and can feed power back to the grid (V2G) or supply power to building loads (V2B / V2H) when needed, and has the ability to participate in power ancillary services (such as frequency regulation and voltage regulation). The system is compatible with the requirements of future power systems for high-frequency dispatch, virtual power plant access, and flexible load management, and has strong foresight and adaptability.

[0056] 5. Optimize charging experience and ensure operational safety: The system introduces a bidirectional DC / DC converter commissioning and decommissioning management system and a pre-charging circuit, which effectively prevents abnormal phenomena such as start-up shock of the bidirectional DC / DC converter and sudden changes in bus voltage, thereby improving the reliability of system operation. Attached Figure Description

[0057] The following description, in conjunction with the accompanying drawings, further illustrates this application:

[0058] Figure 1 This is a schematic diagram of the system structure provided in the embodiments of this application;

[0059] Figure 2 The hybrid bridge arm AC / DC converter circuit topology provided in the embodiments of this application;

[0060] Figure 3 Operating mode diagram of the five-level active midpoint clamped (5L-ANPC) topology provided in the embodiments of this application;

[0061] Figure 4 Operating mode diagram of the hybrid bridge arm AC / DC converter provided in the embodiments of this application;

[0062] Figure 5 This is a block diagram of the current inner loop control model provided in an embodiment of this application;

[0063] Figure 6 This is a block diagram of the voltage outer loop control model provided in the embodiments of this application;

[0064] Figure 7 This is a pre-charging circuit topology diagram provided in an embodiment of this application;

[0065] Figure 8 A bidirectional DC / DC converter circuit topology diagram provided for embodiments of this application;

[0066] Figure 9 This is a control principle diagram of frequency converter control provided in an embodiment of this application;

[0067] Figure 10 This is a control principle diagram of phase-shift control provided in an embodiment of this application;

[0068] Figure 11 This is a schematic diagram illustrating the energy and information exchange between the charging system and residential users provided in an embodiment of this application.

[0069] Figure 12 This is a flowchart of electric vehicle charging load prediction provided in an embodiment of this application;

[0070] Figure 13 The flowchart for solving the MOPSO algorithm provided in the embodiments of this application is shown. Detailed Implementation

[0071] like Figures 1 to 13 As shown, this application provides a DC bus-type electric vehicle charging system with source-load-storage collaborative management capabilities. The system takes the DC bus as its core and integrates a distribution network interface, a renewable energy unit, a battery energy storage system, a charging station, and a coordination control unit. It has technical advantages such as bidirectional energy flow, modular expansion, and flexible deployment in multiple scenarios, and is suitable for various typical application scenarios such as smart buildings, residential parks, industrial plants, microgrids, and grid energy storage nodes.

[0072] like Figure 1 As shown, the overall system architecture includes: an interface for connecting to the medium- and low-voltage distribution network, bidirectional AC / DC converters, a common DC bus (DC BUS), several bidirectional DC / DC converters, and several unidirectional DC / DC converters. It can optionally connect to renewable energy units such as wind and solar power and battery energy storage systems (BESS), and is equipped with a coordination and control unit (which can be used to realize collaborative energy management between smart buildings and charging facilities). Through a unified DC bus platform, it achieves unified access to multiple energy sources, load sharing, and dynamic scheduling. Specifically, the medium- and low-voltage distribution network provides AC power to the system through transformers, and the bidirectional AC / DC converters convert AC to DC, connecting to each bidirectional and unidirectional DC / DC converter via the common DC bus.

[0073] The common DC bus also integrates renewable energy units such as wind and solar power, as well as battery energy storage systems. Different bidirectional DC / DC converters can be connected to battery energy storage systems and electric vehicles, with the bidirectional DC / DC converter connected to electric vehicles serving as a charging station. Unidirectional DC / DC converters can be connected to renewable energy units such as photovoltaic or wind power systems. The flexible interaction and efficient utilization of energy through unidirectional and bidirectional DC / DC converters improves the overall system's economy and energy efficiency.

[0074] The system's common DC bus manages power distribution in a unified manner, effectively reducing system energy consumption and space occupation. In addition, it provides stable and efficient charging services for electric vehicles through bidirectional DC / DC converters, which is suitable for the high-density electric vehicle charging needs in smart buildings. The system as a whole has good scalability and coordination control capabilities.

[0075] In this embodiment, the bidirectional AC / DC converter adopts a hybrid bridge-arm AC / DC converter, and its topology is as follows: Figure 2As shown in the diagram, bridge arm A is a five-level active midpoint clamped (5L-ANPC) topology, and bridge arm B is a simple diode bridge arm, i.e., a two-level topology. The hybrid bridge arm AC / DC converter consists of DC-side supporting capacitors C1 and C2, and a flying capacitor C f Diodes D1 and D2, power switches S1-S8, and filter inductor L s Composition. v in the diagram s i s These represent the mains power supply voltage and current, respectively, using a filter inductor L. s It connects the medium and low voltage distribution network and the converter, and plays the role of transmitting electrical energy and suppressing high-order harmonics. The A arm is a level generation unit, which is used to generate five levels; the B arm is a polarity conversion unit, which mainly converts the level of the A arm into a multi-level with positive and negative polarities.

[0076] The five-level active midpoint clamped (5L-ANPC) topology includes eight power switches S1-S8 and one flying capacitor C. f And two DC-side support capacitors C1 and C2. The first part consists of power switches S1-S4 and flying capacitor C. f The first part consists of a flying capacitor structure for multi-level combination; the second part, composed of power switches S5-S8 and DC-side support capacitors C1 and C2, forms a clamping structure to clamp the DC bus voltage. The normal operating condition of this topology is: under steady-state conditions, the DC-side voltage remains constant at V. dc The voltages of the two DC-side supporting capacitors remain essentially the same, i.e., V... C1 =V C2 =0.5V dc And ensure the voltage across the flying capacitor remains stable, i.e., V. Cf =0.25V dc .

[0077] Figure 3 This demonstrates the conduction status and current path of all switches V1-V8 in the A-arm of a five-level active midpoint clamped (5L-ANPC) topology. Let V be the voltage across the bridge arm. n This indicates that the current at the midpoint of the bridge arm is i. n The positive direction of the current at the midpoint of the bridge arm is as follows: Figure 3 As shown by the dashed arrow, the flying capacitor current i f The positive direction is taken during discharge. From Figure 3 It can be seen that, under balanced conditions, with the DC side midpoint as the zero potential reference point, the voltage across power switches S1-S8 and capacitors C1, C2, and C6 is... f The combination of capacitor voltages, single-phase bridge arm voltage V n It can generate 0.5V dc 0.25V dc 0, -0.25V dc-0.5V dc There are five voltage levels in total. During normal operation, the voltage stress on the power switches S1-S4 on the flying capacitor side and the power switches S5-S8 on the DC side is 0.25V each. dc 0.5V dc .

[0078] The hybrid bridge-arm AC / DC converter used in this application cascades a diode bridge arm onto a five-level active midpoint clamped (5L-ANPC) topology. By combining a two-level topology with a five-level topology, the number of voltage levels in the converter is doubled: two high-voltage rated diodes, D1 and D2, conduct alternately within one power frequency cycle; diode D2 conducts during the positive half-cycle of the mains voltage, and diode D1 conducts during the negative half-cycle. The five-level bridge arm operates at the switching frequency and provides five voltage levels. Due to the unidirectional conduction of the diodes, the converter terminal voltage v... ab It is also related to the polarity of the bridge arm current. Specific switching states are affected by it and reverse as the converter current direction flips. Figure 4 A typical output voltage of V is given. dc (State 1 shown in Figure (4a)) 0.75V dc The conduction path and switching conditions in states 2 (shown in Figure (4b)) and 0 (states 5 and 6 shown in Figures (4c) and (4d)) are shown. For both states with an output level of 0, the current polarity of the two bridge arms is reversed.

[0079] Compared to traditional converter structures, the hybrid bridge-arm AC / DC converter proposed in this application reduces the rated voltage and DC-side capacitor voltage required to generate bridge-arm voltages with the same peak level by half, thereby improving output voltage quality. It also offers advantages in structural simplicity and control over other multilevel converter topologies, effectively reducing converter switching losses.

[0080] The control strategy of the aforementioned hybrid bridge-arm AC / DC converter adopts a combination of current inner loop and voltage outer loop. The current inner loop can be extracted and analyzed separately based on circuit control principles. Its control model block diagram is shown below. Figure 5 As shown in the figure. G i (s) represents the current loop controller transfer function, d' represents the output reference value of the duty cycle feedforward, and G p (s) represents the PWM comparator transfer function, G id (s) represents the transfer function of duty cycle and input current, H i This represents the input current sampling feedback coefficient. Actual input current i s After input current sampling feedback coefficient H i Then, the input feedback current i is obtained. s’ Input current command value i s * With input feedback current i s ’ After subtraction, Δi is obtained; Δi is then passed through the current loop controller transfer function G. i After (s), the duty cycle command value d is obtained. s * After being added to the output reference value d' of the duty cycle feedforward, Δd is obtained; Δd is transferred through the PWM comparator unit transfer function G. p After (s), we obtain Δd s * ;Δd s * Through duty cycle and input current transfer function G id (s) realizes the input current i s Control.

[0081] After correcting the current inner loop transfer function, the voltage outer loop uses the closed-loop transfer function of the current inner loop as its control object, let the latter be G. icl (s). The command signal output by the outer voltage loop is a DC signal, which is multiplied by the sine of the grid-side voltage phase angle to obtain the given current signal required by the inner current loop. The control block diagram of the outer voltage loop is as follows: Figure 6 As shown in the figure, G v (s) represents the voltage loop controller transfer function, G r (s) represents the transfer function between the input current and the output voltage, v s H represents the AC input sampling voltage. v This represents the output voltage sampling feedback coefficient. Actual output voltage V o After output voltage sampling feedback coefficient H v Then, the output feedback voltage V is obtained. o ’ Output voltage command value V dc * With output feedback voltage V o ’ After subtraction, ΔV is obtained; ΔV is then passed through the voltage loop controller transfer function G. v After (s), the required current amplitude I for the inner current loop is obtained. sm * AC input sampling voltage v s The sine value of the grid-side voltage phase angle, sinθ, is obtained through the voltage conversion coefficient K, and then compared with I. sm * After multiplication, the given current signal i required for the inner current loop is obtained. s * i s *Through the closed-loop transfer function G of the inner current loop icl After (s), the actual input current i is obtained. s G icl For detailed logic of (s), see the current inner loop design; i s Through the transfer function G of input current and output voltage r (s) to achieve control over the output voltage V o Control.

[0082] The control principle of the bidirectional AC / DC converter's on / off process is as follows:

[0083] To address the impact issues during the commissioning and decommissioning of bidirectional AC / DC converters, in practical applications, hybrid bridge-arm AC / DC converters connected to AC active networks typically employ an AC-side pre-charging method. Alternatively, a DC-side pre-charging method can be used. The DC-side pre-charging method is suitable for situations where the DC output terminals of the hybrid bridge-arm AC / DC converter are physically disconnected from the common DC bus, and the system requires that the electrical connection be restored after pre-charging is completed.

[0084] In order to study different pre-charging methods, and to design corresponding pre-charging circuits for these two methods, such as... Figure 7 As shown, Figure (7a) is the AC side pre-charging circuit and Figure (7b) is the DC side pre-charging circuit. In the figures, KM1, KM2 and Rac form the AC side pre-charging circuit, where KM1 is the pre-charging contactor, KM2 is the main contactor, and Rac is the pre-charging resistor, which is used as a current-limiting resistor. KM3, KM4 and R form the DC side pre-charging circuit (and are connected to the DC side pre-charging circuit on both the positive and negative terminals of the DC side), where KM4 is the pre-charging contactor, KM3 is the main contactor, and R is the pre-charging resistor, which is used as a current-limiting resistor.

[0085] The AC-side pre-charge circuit addresses the DC voltage establishment issue in two phases: latch-up and unlock-up. In the latch-up charging phase, all power switches of the hybrid bridge arm AC / DC converter are turned off, and the DC-side support capacitor is charged only through the anti-parallel body diodes of the hybrid bridge arm AC / DC converter power switches. The capacitor is unlocked when it reaches its maximum value. In the unlock-up charging phase, charging is performed by controlling the DC voltage.

[0086] The AC side pre-charging process can be briefly described as follows:

[0087] 1) When the pre-charge contactor KM1 is closed, the medium and low voltage distribution network performs uncontrolled rectified charging through the pre-charge resistor Rac. As charging proceeds, the charging current will drop rapidly and approach zero.

[0088] 2) When the precharge voltage reaches more than 90% of the target voltage, close the main contactor KM2 until the bridge arm voltage stabilizes;

[0089] 3) Start the DC voltage control with droop control, unlock the hybrid bridge arm AC / DC converter, enable the power switch of the hybrid bridge arm AC / DC converter, set the initial setpoint of DC voltage to the peak value of AC line voltage, and set the setpoint of reactive power to zero.

[0090] 4) The pre-charging process ends when the given DC voltage rises to the rated value.

[0091] DC-side pre-charging can solve the impact problem when the module is put into operation. The process can be divided into two stages: the latch-up charging stage and the unlock-up charging stage. The process can be briefly described as follows:

[0092] 1) Close the pre-charge contactor KM4, and the DC power supply will perform uncontrolled charging until the charging current approaches zero;

[0093] 2) Unlock the hybrid bridge arm AC / DC converter and engage passive inverter control. At this time, the given AC voltage reference value is 0.

[0094] 3) Close the main contactor KM3;

[0095] 4) All DC-side support capacitors are charged to near their rated values, and the charging process ends.

[0096] The bidirectional DC / DC converter of this application is described below.

[0097] The bidirectional DC / DC converter topology of this application is as follows: Figure 8 As shown, a bidirectional full-bridge LLC resonant converter topology is adopted. V1 and V2 are DC power supplies, and the left side of the converter is called the primary side, and the right side is called the secondary side. The primary side switches Q1-Q4 and the secondary side switches Q5-Q8 form a full-bridge network, and the resonant inductor L... r Resonant capacitor C r Magnetizing inductance L m A resonant network is formed, with a transformer turns ratio of n:1. The diagram also shows the body diodes and parasitic capacitances of the primary and secondary side switches. When the bidirectional DC / DC converter operates in the forward direction, energy flows from left to right, i.e., energy is transferred from V1 to V2. At this time, the primary side switches Q1-Q4 operate as an inverter network, and the secondary side switches Q5-Q8 operate as a rectifier network. When the bidirectional DC / DC converter operates in the reverse direction, energy flows from right to left, i.e., energy is transferred from V2 to V1. At this time, the primary side switches Q1-Q4 operate as a rectifier network, and the secondary side switches Q5-Q8 operate as an inverter network.

[0098] When the bidirectional full-bridge LLC resonant converter is operating in the forward direction, frequency conversion control is employed. Frequency conversion control achieves precise regulation of the equivalent impedance of the resonant cavity by changing the switching frequency of the drive pulses of the primary-side switching transistors, thereby controlling the output voltage and power. The control principle of frequency conversion control is as follows: Figure 9 As shown, the output voltage V2 of the bidirectional full-bridge LLC resonant converter under forward operation is compared with the command value V. ref2 The input error is calculated by performing a difference operation, and the resulting error value is sent to a PI controller. A PI controller is a combination of a proportional controller and an integral controller. Based on the magnitude of the input error signal, the PI controller adjusts the output signal accordingly to reduce the error. By continuously adjusting the output signal, the PI controller gradually brings the actual value of the controlled object closer to the set value, thereby achieving the goal of controlling the output voltage. The output value of the PI controller is then sent to a voltage-controlled oscillator (VCO) after passing through a limiter. The VCO outputs drive pulses of different frequencies according to the input value, thereby changing the operating frequency and regulating the output voltage.

[0099] When the bidirectional full-bridge LLC resonant converter operates in reverse, phase-shift control is employed. Phase-shift control increases the midpoint voltage v of the secondary-side bridge arm by changing the phase difference of the drive pulses between the two bridge arms in the same network. CD The zero-level time is used to regulate the voltage. The control principle of phase-shift control is as follows: Figure 10 As shown, the output voltage V1 of the bidirectional full-bridge LLC resonant converter in reverse operation is compared with the reference value V. ref1 The difference is calculated and fed into the PI controller. The PI controller functions similarly to that in frequency converter control, outputting a suitable control value based on the error. In phase-shift control, the PI controller ultimately outputs a phase-shift angle, which needs to be limited to within 0°~180°. This ultimately changes the phase difference of the bridge arm drive pulses, achieving the goal of adjusting the voltage gain.

[0100] The coordination and control unit implemented in this application integrates a collaborative energy management algorithm between the charging system and the smart building. This algorithm is designed for a smart building environment, where the proposed DC bus-type electric vehicle charging system with source-load-storage collaborative management capabilities and residential users have a relationship such as... Figure 11 The network shown illustrates a DC bus-type electric vehicle charging system with source-load-storage collaborative management capabilities. There is not only energy flow but also bidirectional information flow between the system and residential users. Each residential user is equipped with a home energy management system (HEMS), which monitors the status of electrical equipment, including electric vehicles, and controls their operation using a multi-objective particle swarm optimization algorithm. The HEMS exchanges energy and information with the outside world through smart meters.

[0101] The specific implementation steps of this algorithm are as follows:

[0102] Step 1: Construct mathematical models of renewable energy units, battery energy storage systems, and electric vehicle charging load prediction models in a DC bus-type electric vehicle charging system with source-load-storage collaborative management capabilities.

[0103] Step 2: Using the configuration capacity of renewable energy units, battery energy storage systems, and charging stations as the solution particles, and taking the optimal economic benefits of the charging system and the highest user satisfaction as the solution objectives, the improved multi-objective particle swarm optimization (MOPSO) algorithm is used to solve for the optimal configuration of the capacity of renewable energy units, battery energy storage systems, and charging stations in the charging system.

[0104] In this embodiment, the renewable energy unit is selected as a photovoltaic power generation system. The output power of the photovoltaic power generation system has certain fluctuations and regularities, and its output power can be expressed as:

[0105] (1);

[0106] In the formula: This represents the actual output power of the photovoltaic power generation system, expressed in kW. This refers to the number of photovoltaic units installed in a photovoltaic power generation system. This represents the maximum output power of the photovoltaic unit, in kW. This represents the hourly average surface solar irradiance of the area to be developed at latitude φ, on day b of month m, at hour h, in W / m². 2 ; The standard irradiance is 1 kW / m². 2 ; The power temperature coefficient of the photovoltaic power generation system is taken as -0.35% / ℃; The actual temperature of the photovoltaic power generation system is expressed in °C. The reference temperature for the photovoltaic power generation system is 25℃.

[0107] In this embodiment, the battery energy storage system uses a battery energy storage device. Therefore, the operating model of the battery energy storage device considering self-discharge can be expressed as follows:

[0108] (2);

[0109] In the formula: The real-time energy storage capacity of the battery energy storage system at time t is expressed in kWh. It is related to the energy stored in the battery at the previous time and the net input power, output power and charge / discharge efficiency at the current time. The self-discharge rate of the battery energy storage system is taken as 0.001 / h; , The ratio represents the charging and discharging efficiency of the battery energy storage system, taken as 90%. , The ratio represents the charging and discharging power of the battery energy storage system at time t, in kW. For time intervals.

[0110] The electric vehicle charging load prediction model is constructed as follows: In a DC bus-type electric vehicle charging system with source-load-storage collaborative management capabilities, the hourly load of a charging station is the sum of the charging demand of electric vehicles per unit time. Therefore, the accuracy of charging load prediction directly affects the planning of charging station capacity. The daily mileage of electric vehicles directly affects the battery capacity and user charging choices. Electricity consumption is directly proportional to the mileage, and the mileage of electric vehicles follows a log-normal distribution with a probability density function... It can be represented as:

[0111] (3);

[0112] In the formula: The distance traveled is expressed in kilometers. The standard deviation is taken as 0.8; The expected value is set to 4.1.

[0113] Assuming electric vehicles can be charged immediately upon arrival, their probability density function can be expressed as:

[0114] (4);

[0115] In the formula: This is the start of the charging process; , All are standard deviations, of which =2.8, =4.3; , All are expected values, among which =14, =19.

[0116] Considering that there is a certain distance between the electric vehicle and the charging station entrance, let's assume the state of charge of the electric vehicle's battery before entering the charging station. Following a uniform distribution of [60%, 90%], the state of charge of the electric vehicle battery upon arrival at the service area is... for:

[0117] (5);

[0118] In the formula: The power consumption per 100 kilometers is expressed in kWh / (100km). This refers to the battery capacity, expressed in kWh.

[0119] Therefore, the charging time for electric vehicles is... for:

[0120] (6);

[0121] In the formula: For charging efficiency, we take 0.9; The rated charging power of the charging station is expressed in kW.

[0122] The hourly load of the charging station was calculated using the Monte Carlo method. Users' mileage, initial state of charge, and arrival time at the charging station were randomly sampled to obtain the hourly charging load of the charging station. The specific solution process is as follows: Figure 12 As shown.

[0123] (7);

[0124] In the formula: Daily traffic flow of electric vehicles on highways; The percentage of users who need charging services; This represents the charging load of the nth electric vehicle within the i-th time period, expressed in kW.

[0125] The initial positions and velocities of the traditional MOPSO particle swarm are generated randomly. To improve the quality of the initial solution and ensure the randomness and diversity of the population, this embodiment proposes to apply the Logistic-Tent dual chaotic mapping to the population initialization process, as shown in Equation (8). The specific solution steps of the improved MOPSO algorithm are as follows: Figure 13 As shown.

[0126] (8);

[0127] In the formula: Let d be the value of the i-th particle in the d-th dimension; r This is called the "bifurcation parameter". r The value of (usually between 0 and 4) determines the final behavior of the system, allowing it to transition from a stable, periodic "bifurcation" to a chaotic state; l is the mapping parameter of the Logistic-Tent dual chaotic mapping model, l∈(0,4), and is set to 4.

[0128] The optimal solution set obtained by solving the Logistic-Tent dual chaotic mapping model is selected using the entropy weight-TOPSIS method, and a source-load-storage collaborative scheme that compromises the economic cost of the charging system and user satisfaction is formed under the premise of taking into account the decision-maker's preferences.

[0129] In this embodiment, the bidirectional AC / DC converter can also be replaced by other types of multilevel converter topologies or traditional bidirectional converter structures to achieve different cost and efficiency requirements. Furthermore, the type of renewable energy unit can be selected according to the actual usage environment, such as wind power or solar energy, to adapt to different geographical and environmental conditions.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A DC bus-type electric vehicle charging system with source-load-storage collaborative management capability, characterized in that: It includes an interface for connecting to medium and low voltage distribution networks, a bidirectional AC / DC converter, a common DC bus, several bidirectional DC / DC converters and several unidirectional DC / DC converters. The medium and low voltage distribution network is connected to the AC side of the bidirectional AC / DC converter through a transformer. The bidirectional AC / DC converter realizes the conversion between AC and DC. The DC side of the bidirectional AC / DC converter is connected to each bidirectional DC / DC converter and unidirectional DC / DC converter through a common DC bus. One part of the bidirectional DC / DC converter is connected to the electric vehicle and used as a charging station, while the other part of the bidirectional DC / DC converter is connected to the battery energy storage system; A unidirectional DC / DC converter is connected to a renewable energy unit; The bidirectional AC / DC converter adopts a hybrid bridge arm AC / DC converter, which includes an A-arm and a B-arm. The A-arm adopts a five-level active midpoint clamped topology as a level generation unit, and the B-arm adopts a two-level topology as a polarity conversion unit to convert the level of the A-arm into a multi-level with positive and negative polarities. Bridge arm A includes eight power switches S1-S8 and one flying capacitor C. f And two DC-side support capacitors C1 and C2, which are connected by power switches S1-S4 and flying capacitor C. f A flying capacitor structure is formed to perform multi-level combination; a clamping structure is formed by power switches S5-S8 and DC-side support capacitors C1 and C2 to clamp the DC bus voltage. The B-arm consists of two diodes connected in series. The B-arm is cascaded on the A-arm, and the two diodes conduct alternately within one power frequency cycle. The bidirectional DC / DC converter adopts a bidirectional full-bridge LLC resonant converter topology. When the bidirectional full-bridge LLC resonant converter is running in the forward direction, frequency conversion control is used, and when the bidirectional full-bridge LLC resonant converter is running in the reverse direction, phase shift control is used. It also includes a coordination and control unit, which integrates a collaborative energy management algorithm for the charging system and the smart building. The smart building includes several residential users. There is not only energy flow between the DC bus electric vehicle charging system with source-load-storage collaborative management capabilities and the residential users, but also bidirectional information flow. Each residential user is equipped with a home energy management system.

2. The DC bus-type electric vehicle charging system with source-load-storage collaborative management capability according to claim 1, characterized in that: The control strategy of the hybrid bridge-arm AC / DC converter adopts a combination of current inner loop and voltage outer loop.

3. The DC bus-type electric vehicle charging system with source-load-storage collaborative management capability according to claim 1, characterized in that: The bidirectional AC / DC converter has a built-in AC-side pre-charge circuit and a DC-side pre-charge circuit. The DC-side pre-charge circuit is used when the DC output terminal of the bidirectional AC / DC converter is physically disconnected from the common DC bus, and the system requires that the electrical connection be restored after the pre-charge is completed.

4. A DC bus-type electric vehicle charging system with source-load-storage collaborative management capability according to claim 3, characterized in that: The AC-side pre-charging circuit includes a main contactor KM2 connected to the AC busbar of the medium- and low-voltage distribution network connected to the transformer. A pre-charging contactor KM1 and a pre-charging resistor Rac are connected in parallel with the main contactor KM2. The AC-side pre-charging circuit is used for the interlocking charging phase and the unlocking charging phase. The AC-side pre-charging process is as follows: 1) When the pre-charge contactor KM1 is closed, the medium and low voltage distribution network performs uncontrolled rectified charging through the pre-charge resistor Rac. As charging proceeds, the charging current will drop rapidly and approach zero. 2) When the precharge voltage reaches more than 90% of the target voltage, close the main contactor KM2 until the bridge arm voltage stabilizes; 3) Start the DC voltage control with droop control, unlock the bidirectional AC / DC converter, set the initial setpoint of DC voltage to the peak value of AC line voltage, and set the setpoint of reactive power to zero; 4) The pre-charging process ends when the given DC voltage rises to the rated value.

5. A DC bus-type electric vehicle charging system with source-load-storage collaborative management capability according to claim 3, characterized in that: The DC-side pre-charge circuit includes a main contactor KM3 connected to the positive DC output of the bidirectional AC / DC converter. A pre-charge contactor KM4 and a pre-charge resistor R are connected in parallel with the main contactor KM3. The DC-side pre-charge circuit is used for the lockout charging phase and the unlock charging phase. The DC-side pre-charge process is as follows: 1) Close the pre-charge contactor KM4, and the DC power supply will perform uncontrolled charging until the charging current approaches zero; 2) Unlock the bidirectional AC / DC converter and engage the passive inverter control. At this time, the reference value of the given AC voltage is 0. 3) Close the main contactor KM3; 4) All DC-side support capacitors are charged to near their rated values, and the charging process ends.

6. A DC bus-type electric vehicle charging system with source-load-storage collaborative management capability according to any one of claims 1-5, characterized in that: The specific implementation steps of the collaborative energy management algorithm are as follows: Step 1: Construct mathematical models of renewable energy units, battery energy storage systems, and electric vehicle charging load prediction models in a DC bus-type electric vehicle charging system with source-load-storage collaborative management capabilities. Step 2: Using the configuration capacity of renewable energy units, battery energy storage systems, and charging stations as the solution particles, and taking the optimal economic benefits of the charging system and the highest user satisfaction as the solution objectives, an improved multi-objective particle swarm optimization algorithm is used to solve for the optimal configuration of the capacity of renewable energy units, battery energy storage systems, and charging stations in the charging system.

7. A DC bus-type electric vehicle charging system with source-load-storage collaborative management capability according to claim 6, characterized in that: The steps for constructing an electric vehicle charging load prediction model are as follows: The driving range of an electric vehicle follows a log-normal distribution, and its probability density function is... Represented as: ； In the formula: This refers to the mileage traveled. Standard deviation; This is the expected value; Assuming electric vehicles can be charged immediately upon arrival, the above probability density function can be expressed as: ； In the formula: This is the start of the charging process; , All are standard deviations; , All are expected values; Considering that there is a certain distance between the electric vehicle and the charging station entrance, let's assume the state of charge of the electric vehicle's battery before entering the charging station. Following a uniform distribution of [60%, 90%], the state of charge of the electric vehicle battery upon arrival at the service area is... for: ； In the formula: Electricity consumption per 100 kilometers; Battery capacity; Therefore, the charging time for electric vehicles is... for: ； In the formula: For charging efficiency; The rated charging power of the charging station; The hourly load of the charging station was calculated using the Monte Carlo method. Users' mileage, initial state of charge, and arrival time at the charging station were randomly sampled to obtain the hourly charging load of the charging station. : ； In the formula: Daily traffic flow of electric vehicles on highways; The percentage of users who need charging services; Let n be the charging load of the nth electric vehicle during the i-th time period.

8. A DC bus-type electric vehicle charging system with source-load-storage collaborative management capability according to claim 6, characterized in that: The improved multi-objective particle swarm optimization algorithm applies the Logistic-Tent dual chaotic mapping to the population initialization process and uses the entropy weight-TOPSIS method to select the optimal solution set obtained by solving the Logistic-Tent dual chaotic mapping model.

Images