Intelligent efficient charging pile

Abstract

The invention discloses an intelligent efficient charging pile. Belongs to the technical field of electric vehicle charging, and provides energy for an electric vehicle through photovoltaic power generation and a power grid by adopting an alternating current and direct current combined charging mode-DC and AC fast charging technology so as to improve the charging speed, reduce the load of the power grid and realize energy conservation and emission reduction. A power distribution model is also established to optimize the charging process; and the performance index of the charging pile is optimized by adopting a red-mouth blue-pie algorithm, so that the charging pile is ensured to operate in an optimal working state, and an efficient and environment-friendly charging solution is provided for an electric vehicle user.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle charging, and specifically relates to electric vehicle fast charging technology, and relates to an intelligent and efficient charging pile; specifically, it relates to a new type of electric vehicle fast charging pile that integrates efficient charging technology, intelligent monitoring and advanced heat dissipation system. Background Art

[0002] As a clean energy means of transportation, electric vehicles (EVs) are developing and becoming increasingly popular. However, the widespread use of EVs cannot be separated from the support of efficient and reliable charging infrastructure. Currently, charging piles are the main way to replenish energy for electric vehicles, and their technological development level is directly related to the convenience and economy of using electric vehicles. Traditional charging pile technology has problems such as slow charging speed, low charging efficiency, and high grid load requirements, which limit the further promotion and application of electric vehicles.

[0003] In the current charging pile technology, most charging piles only support AC slow charging, which cannot meet the rapidly growing demand for fast charging. In addition, the impact of charging piles on the stability of the power grid when running at high power has also become an issue that cannot be ignored, especially during peak hours of electricity consumption, when large-scale electric vehicle charging demand may have an impact on the power grid and affect the stable operation of the power grid. At the same time, with the increase in the number of electric vehicles, how to improve the efficiency of charging piles, reduce operating costs, and reduce maintenance difficulties are also issues that need to be addressed in the development of charging pile technology. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a charging pile that integrates efficient charging technology, intelligent monitoring and advanced heat dissipation system to solve the problems of slow charging speed, heavy grid load and low charging efficiency in the prior art; through the present invention, electric vehicles can be quickly charged, the absorption capacity of green energy power generation can be improved, the grid load can be reduced, and the goal of energy conservation and emission reduction can be achieved.

[0005] The technical solution of the present invention is: an intelligent and efficient charging pile according to the present invention, the charging pile comprises a power supply unit, a collection unit, a charging unit, an adjustment unit and a control unit, etc. which are connected to each other;

[0006] The power supply unit includes a power grid, a photovoltaic panel, and a battery; the power grid and the photovoltaic panel provide AC and DC power to the charging pile, and the battery stores surplus power;

[0007] The acquisition unit includes a temperature sensor, a timer, an ammeter, a voltmeter, a battery management system (BMS), and a display screen; the temperature sensor monitors the temperature condition inside the charging pile in real time; the timer records the charging time, and the ammeter and voltmeter respectively measure the current and voltage parameters during the charging process; the battery management system (BMS) monitors the status of the connected battery; and the display screen collects the user's wishes;

[0008] The regulating unit includes a PI controller and a heat sink; the PI controller regulates the output, and the heat sink is used for heat dissipation of the charging pile;

[0009] The charging unit includes a bridgeless PFC controller, a DAB conversion controller, a DC / DC converter, and a charging power distribution model; a red-billed blue magpie algorithm is initialized using sin chaotic mapping to optimize the performance index function;

[0010] The bridgeless PFC controller in the charging unit is used for AC / DC conversion on the AC side; the DAB conversion controller is used for the second-stage DC / DC conversion on the AC side; the DC / DC converter is used for DC / DC conversion on the DC side; the charging power distribution model is used to respectively match the power usage status of the charging pile; the performance index function of the charging pile is established by combining these two parts, and the sin chaotic map is used to initialize the red-billed blue magpie algorithm to optimize the performance index.

[0011] Further, the workflow is as follows:

[0012] The system will integrate multiple energy inputs, advanced electric energy conversion and control technologies, intelligent power distribution strategies, and optimized performance regulation mechanisms to achieve fast and safe charging of electric vehicles and improve the overall energy utilization efficiency. The system uses the power grid and photovoltaic panels as the main power supply sources, where the power grid provides stable alternating current, while the photovoltaic panels output direct current. At the same time, a storage battery is configured to store surplus electric energy to provide additional power support for the charging pile when needed. During the power supply process, the system will collect real-time operating parameters such as current, voltage, and temperature of the charging pile, as well as information such as charging time and the state of charge (SOC) of the battery, and combine parameters such as user preferences (such as charging mode selection) to build a comprehensive and accurate charging control model. In the power conversion link, the system uses a bridgeless PFC controller for AC / DC conversion on the AC side. The voltage is regulated through a PI control strategy, and the duty cycle of the PI controller is dynamically adjusted according to the voltage signal difference, thereby effectively correcting the power factor, improving the power quality, and reducing harmonic pollution. Subsequently, a DAB conversion controller is used to perform the second-stage DC / DC conversion on the AC side. The current is also controlled by PI, and the power transmission is accurately controlled by the phase difference between the two bridges to ensure the high efficiency and stability of power conversion. In addition, a DC / DC converter is configured on the DC side to further adjust the duty cycle by detecting the error between the current and voltage in real time to achieve fine regulation of the output power. The system combines a charging power distribution model to intelligently optimize the charging time distribution according to the specific needs of users (such as expected charging time, vehicle battery status, etc.), ensuring that while maintaining the efficient operation of the system, it meets the personalized charging needs of different users. At the same time, a heat sink is used to effectively cool the key components of the charging pile and maintain its operating temperature within a reasonable range. Finally, the sin chaotic mapping is introduced to initialize the red-billed blue magpie algorithm to comprehensively optimize the overall performance indicators of the charging pile. The algorithm will comprehensively consider performance indicators in multiple dimensions such as charging efficiency, power quality, system stability, and user satisfaction.

[0013] Further, in the power conversion link, the total power expression of the charging pile is as follows:

[0014]

[0015] P dc =V pi I Lpi

[0016] In the formula, P av is the total charging power, P dc is the power on the DC side, is the average power on the AC side, is the maximum power on the AC side.

[0017] Further, the process of constructing the model of the bridgeless PFC controller is as follows:

[0018]

[0019] In the formula: i g , v g are the source current and source voltage respectively, C dc is the across-capacitance value, V dc is the voltage across the capacitor C dc R e is the simulated load, u s is the state of the switch, L i is the inductance value, and i is the index of the inductor;

[0020] Further, the following linear state-space model is obtained:

[0021]

[0022] In the formula: and are the perturbations of the state variables, is the average value of, D' = 1 - D, and D is the standard value of the duty cycle at the operating point;

[0023] Among them, the inductance L and the across-capacitor C in the bridgeless PFC controller dc have the following value ranges:

[0024]

[0025] In the formula: f sw and f g are the switching frequency and the grid frequency respectively, i o is the output current, Δi g,max and Δv dc,max are their allowed maximum fluctuation amounts.

[0026] Further, the process of constructing the model of the DAB conversion controller is as follows:

[0027] The output current i o (t) of the electric vehicle and the average output current I ac of the electric vehicle are expressed as follows:

[0028]

[0029] In the formula: i o1 (t) linearly decreases with time within the first T φ time period; i o2 (t) is from T φ to Between them, the output current continues to change linearly, but in the opposite direction, where V dc is the DC-link voltage, V bt is the voltage of the traction battery (EV battery), n is the turns ratio of the transformer, L D is the inductance value in the DAB, T φ is the phase shift time caused by the phase difference φ between the voltages u A and u B ; where f is the switching frequency of the DAB, T sD is the switching period of the DAB; sD

[0030] Among them, the initial value of the output current i o (0) and the output current iafter phase shift o1 (T φ ) are expressed as follows:

[0031]

[0032] In the formula: V bt is the battery voltage, V dc is the DC-link voltage, n is the turns ratio of the transformer, f sD is the switching frequency of the DAB, L D is the inductance value in the DAB, φ is the phase difference between the voltages u A and u B ; T φ is the phase shift time caused by the phase difference φ between the voltages u A and u B ;

[0033] Furthermore, the average output power P ac and the maximum output power are expressed as follows:

[0034]

[0035] In the formula: V bt is the battery voltage, V dc is the DC-link voltage, n is the turns ratio of the transformer, f sD is the switching frequency of the DAB, L D is the inductance value in the DAB, φ is the phase difference between the voltages u A and u B ;

[0036] Among them, the inductance L D in the DAB converter is expressed as follows:

[0037]

[0038] Where: V bt is the battery voltage, V dc is the DC link voltage, n is the turns ratio of the transformer, f sD is the switching frequency of the DAB, L D is the inductance value in the DAB, φ is the phase difference between the voltages u A and u B ;

[0039] Among them, the expression of the coil turns n is as follows:

[0040]

[0041] Where: the 1.1 factor provides an additional 10% turns ratio for the DAB converter to compensate for the voltage drop problem, V bt is the battery voltage, V dc is the DC link voltage.

[0042] Furthermore, the construction model process of the DC / DC converter is as follows:

[0043]

[0044] Where: I Lpi is the current passing through the input inductor, V pi is the DC power supply voltage, V bt is the battery terminal voltage, L pi is the input inductor, C b is the output capacitor, u s is the switching state, usually determined by PWM control, R e is the battery equivalent internal resistance or load resistance;

[0045] Furthermore, the linearized state space model of the bidirectional DC / DC converter interface:

[0046]

[0047] Where, and are the perturbations of the input current and the battery voltage relative to their nominal values, L pi is the input inductor of the boost converter, V bt is the battery voltage, C b is the output capacitor, R e is the equivalent internal resistance of the battery;

[0048] Among them, the value range of the input inductor L pi and the value range of the input capacitor C b are:

[0049]

[0050] Where: D is the duty cycle, ΔI Lpi is the allowable inductor current ripple, f sw is the switching frequency, V pi is the input voltage, I dc is the output current, ΔV bt is the allowable battery voltage ripple, f sw is the switching frequency.

[0051] Furthermore, the construction model process of the heat sink is as follows:

[0052]

[0053] Where: P c is the energy consumed by heat, that is, the power consumed during the charging process, Q in is the heat generated during the charging process, H in is the heat absorbed by the heat dissipation device, is the mass flow rate during the charging process, that is, the mass of the fluid flowing through the microchannel per unit time, C P is the specific heat capacity of the coolant, T in is the temperature of the fluid when it enters the microchannel, T out is the temperature of the fluid when it leaves the microchannel, and t is the charging time.

[0054] Furthermore, the construction model process of the charging power distribution model is as follows:

[0055]

[0056] Where, is the average charging power of electric vehicles, is the on-demand charging power of electric vehicles, A1 is an electric vehicle with the same battery capacity, A2 is an electric vehicle with different battery capacities, A3 is to turn on accelerated charging, and A4 is not to turn on accelerated charging;

[0057] (1), Average power distribution model:

[0058]

[0059] Where, is the power allocated to the kth electric vehicle connected to the fast charging pile i; is the maximum power of the fast charging pile, is the power of the electric vehicles that are charging and connected to the fast charging pile, is the fast charging power limit of the battery of the ith electric vehicle according to its charging state (SOC, that is, the remaining power of the battery), and n is the number of fast charging piles with multi-power output;

[0060] Among them, the calculation formula for the charging power limit of the battery is as follows:

[0061]

[0062] In the formula, P i max is the maximum charging power of the battery of the i-th electric vehicle. ε and γ are parameters related to the battery charging characteristics, representing the SOC levels at the start and end of the constant power charging stage respectively. β is the fitting parameter of the exponential function used to describe the change of charging power with SOC. SOC is the state of charge of the battery, ranging from 0 to 1, where 0 means the battery is fully discharged and 1 means the battery is fully charged;

[0063] (2) On-demand power distribution model:

[0064]

[0065] In the formula, is the allocated power of the EVi in the waiting state connected to the fast charging pile k, is the remaining power in the waiting state, is the fast charging power limit of the battery of electric vehicle i;

[0066] Among them, the calculation formula for the remaining power of the electric vehicle in the waiting state is as follows:

[0067]

[0068] In the formula, is the remaining power of the charging pile k for the electric vehicle i in the waiting state, is the maximum output power of the charging pile k, is the power consumed by the currently charging electric vehicle, is the maximum charging power that the previous electric vehicle can accept according to its SOC (state of charge of the battery), is the remaining power of the charging pile k after the previous electric vehicle i - 1;

[0069] Furthermore, the electric vehicle charging time model is as follows:

[0070]

[0071] In the formula, is the charging time of the i-th electric vehicle, is the initial SOC of the i-th electric vehicle, is the desired SOC of the i-th electric vehicle, and χ(SOC) is the battery charging characteristic function, which is related to the charging curve of the battery;

[0072] Among them, the calculation formula for the charging time required for an electric vehicle is as follows:

[0073]

[0074] In the formula, χ(SOC i ) is the charging time required to reach a certain target charging state from a specific initial state of charge (SOC). ε is the SOC value at the start of the constant-power charging stage in the battery charging curve, γ is the SOC value at the end of the constant-power charging stage in the battery charging curve. When entering the variable-power charging stage, t1, t2, and t3 are time parameters for different stages during the charging process, usually related to the charging characteristics of the battery. r is a scaling factor used to adjust the charging rate in the variable-power charging stage;

[0075] Among them, the calculation formulas for the parameters α and β are as follows:

[0076]

[0077] In the formula, α and β are charging parameters in the variable-power charging stage, used to describe the change of the battery charging power over time. t2 and t3 are specific time points during the charging process. t2 represents the time point at the end of the constant-power charging stage, and t3 represents the time point at the end of the entire charging process. P i max is the maximum charging power that the battery can accept during the constant-power charging stage, and γ is the state at the end of the constant-power charging stage in the charging curve, that is, the demarcation point between the constant-power charging stage and the variable-power charging stage in the battery charging curve;

[0078] Furthermore, the current battery state of the electric vehicle is as follows:

[0079]

[0080] In the formula, is the state when the i-th electric vehicle starts formal charging, is the initial state when the i-th electric vehicle arrives at the charging station. Q i is the battery capacity of the i-th electric vehicle, is the start charging time of the i-th electric vehicle, is the end charging time of the i-th electric vehicle. P i w is the charging power of the i-th electric vehicle at time t during the waiting time.

[0081] Furthermore, the objective function of the performance index is as follows:

[0082] maxQ = w1η + w2t + w3C ompat + w4E + w5T

[0083]

[0084] Where: Q is the performance index of the charging pile system, η is the utilization rate of the charging pile, C ompat is the fast charging technology compatibility, E is the fast charging revenue rate, T is the cooling rate, w1, w2, w3, w4, w5 are weight coefficients, w1 + w2 + w3 + w4 + w5 = 1, t is the charging efficiency, is the charging duration of the i-th vehicle, t total is the total time, n is the number of electric vehicles, n n is the total number of electric vehicle categories, n i is the number of electric vehicle categories, SOC is the battery capacity, E l is the electricity price, t other is the charging duration of other charging piles, E' is the additional cost, E” is the fast charging loss cost, N t is the total number of time periods, N c is the maximum number of charging piles, P t k is the power consumed by the operating pile k at time t, is the maximum output power of the charging pile k at time t, T low is the cooling value, T initial is the initial temperature.

[0085] Furthermore, the specific steps of the red-billed blue magpie algorithm initialized by the sin chaotic mapping are as follows:

[0086] (1) Establish a search agent position matrix, and the matrix model is as follows:

[0087]

[0088] Where X represents the position of the generated search agent, which is a candidate solution of RBMO and is randomly generated under the constraint conditions of the given problem and needs to be updated after each iteration. n represents the overall size, that is, the number of groups of experimental parameters, and dim represents the dimension of solving the problem, that is, the number of experimental parameters;

[0089] x i,j =(ub - lb)×Rand1 + lb

[0090] Where x i,j is each element in the X matrix, ub and lb are the upper and lower bounds of the problem respectively, and Rand1 represents a random number between 0 and 1;

[0091] (2) Calculate the evaluation function, and the evaluation function is as follows:

[0092] maxQ = w1η + w2t + w3C ompat+w4E+w5T

[0093]

[0094] Where: Q is the performance index of the charging pile system, η is the utilization rate of the charging pile, C ompat is the fast charging technology compatibility, E is the fast charging revenue rate, T is the cooling rate, w1, w2, w3, w4, w5 are weight coefficients, w1 + w2 + w3 + w4 + w5 = 1, t is the charging efficiency, is the charging duration of the i-th vehicle, t total is the total time, n is the number of electric vehicles, n n is the total number of electric vehicle categories, n i is the number of electric vehicle categories, SOC is the battery capacity, E l is the electricity price, t other is the charging duration of other charging piles, E' is the additional cost, E” is the fast charging loss cost, N t is the total number of time periods, N c is the maximum number of charging piles, P t k is the power consumed by the operating pile k at time t, is the maximum output power of the charging pile k at time t, T low is the cooling value, T initial is the initial temperature;

[0095] (3) Construct the algorithm search phase model, and the position update formula is as follows:

[0096]

[0097] Where: t represents the current iteration number, X i (t + 1) represents the position of the i-th new search agent, that is, the candidate solution of the charging pile performance index, p represents the number of search agents when the cluster explores food, X m represents the randomly selected m-th individual, X i represents the i-th individual, X rs represents the search agent randomly selected in the current iteration;

[0098] (4) Construct the algorithm attack phase model, and the position update formula is as follows:

[0099]

[0100] Where: t represents the current iteration number, X i (t + 1) represents the position of the i-th new search agent, that is, the optimal performance index of the charging pile, X food (t) represents the position of the food, Randn represents the random numbers used to generate the standard normal distribution (mean 0, standard deviation 1), and p represents the number of search agents of the cluster when exploring food;

[0101] (5), Construct the algorithm storage phase model, and the position update formula is as follows:

[0102]

[0103] In the formula: iffitness old and fitness new respectively represent the fitness values of the i-th red-billed blue magpie before and after position update;

[0104] (6), Determine whether the performance index meets the standard. If it meets the standard, store the data and jump to step (7). If it does not meet the standard, return to step (3);

[0105] (7), Output the optimal objective function value, that is, the optimal solution of the charging pile performance index.

[0106] Furthermore, the improvement steps of the red-billed blue magpie algorithm lie in initializing the population with the Sin chaotic map. Through the Sin chaotic map, the generated initial population can more evenly fill the entire solution space, improve the quality of the initial population, and facilitate the global search in the early stage. The specific improvement method is as follows:

[0107] x n+1 = μsin(πx n ) 0 ≤ x n ≤ 1

[0108] In the formula, x represents the iteration value, and μ is a random number in [0,1].

[0109] The beneficial effects of the present invention are as follows: 1. Improve charging efficiency and speed: The high-efficiency charging pile of the present invention significantly improves the charging speed of electric vehicles by integrating DC and AC fast charging technologies. Compared with traditional charging piles, this new type of charging pile can reduce the charging time, enabling electric vehicle users to complete charging in a shorter time, improving the charging efficiency, and thus enhancing the practicality and convenience of electric vehicles. Moreover, combined with the power distribution model, the charging strategy can be adjusted according to user needs, increasing the flexibility of the system. 2. Energy conservation and emission reduction: The charging pile of the present invention reduces the dependence on fossil fuels and thus reduces greenhouse gas emissions by optimizing the charging process and improving energy utilization efficiency. In addition, by integrating photovoltaic power generation, the charging pile can directly utilize clean energy, further promoting environmental protection and sustainable development, and reducing the grid load, improving the stability and reliability of the grid. 3. Use the sin chaotic mapping to initialize the population, and the generated initial population is relatively uniform, improving the quality of the initial population and facilitating the global search in the early stage. 4. Improve the performance of the charging pile: Use the red-billed blue magpie algorithm to optimize the performance indicators of the charging pile, and real-time monitor key parameters such as the temperature and power of the charging pile to ensure that the charging pile operates in the best state, improving the performance and reliability of the charging pile. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1 are the structural schematic diagrams of each device in the present invention;

[0111] Figure 2 is the technical detailed diagram of the present invention;

[0112] Figure 3 is the working flow chart of the red-billed blue magpie algorithm in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0113] The following further elaborates on the specific technical solutions of the present invention with reference to specific examples.

[0114] As Figure 1 shown, an electric vehicle fast charging system (intelligent and efficient charging pile) of the present invention includes a power supply unit, a collection unit, a charging unit, an adjustment unit, and a control unit.

[0115] Further, the power supply unit includes a power grid, a photovoltaic panel, and a storage battery; the collection unit includes a temperature sensor, a timer, an ammeter, a voltmeter, a battery management system (BMS), and a display screen; the charging unit includes a bridge-less PFC controller, a DAB conversion controller, a DC / DC converter, and a charging power distribution model; use the sin chaotic mapping to initialize the red-billed blue magpie algorithm to optimize the performance index function; the adjustment unit includes a PI controller and a heat sink.

[0116] In the power supply unit, the power grid and photovoltaic panels provide AC and DC power for the charging pile, and the storage battery stores surplus electric energy; the temperature sensor monitors the temperature condition inside the charging pile in real time; the timer records the charging time, and the ammeter and voltmeter measure the current and voltage parameters during the charging process respectively; the battery management system (BMS) monitors the state of the connected battery; the display screen collects the user's intention.

[0117] In the charging unit, the bridgeless PFC controller is used for AC / DC conversion on the AC side; the DAB conversion controller is used for DC / DC conversion in the second stage on the AC side; the DC / DC converter is used for DC / DC conversion on the DC side; the charging power distribution model is used to allocate the power usage status of the charging pile respectively; combining these two parts to establish the performance index function of the charging pile, and using the sine chaotic mapping to initialize the red-billed blue magpie algorithm to optimize the performance index.

[0118] In the adjustment unit, the PI controller adjusts the output, and the heat sink is used for the heat dissipation of the charging pile.

[0119] As Figures 2-3 shown, the present invention adopts the AC / DC charging method, combines the power distribution model, optimizes the charging method, and uses the red-billed blue magpie algorithm to optimize the performance index of the charging pile to ensure the efficient operation of the charging pile.

[0120] Furthermore, the high-efficiency charging pile adopts the charging method combining AC and DC power, and the total power calculation formula is as follows:

[0121]

[0122] Where: P av is the total charging power, P dc is the power on the DC side, is the average power on the AC side, is the maximum power on the AC side.

[0123] Furthermore, for the charging method combining AC and DC power: an on-vehicle charger model (OBC) is constructed on the AC side, and this model consists of two parts, a bridgeless PFC controller and a dual-active bridge converter (DAB). The model is as follows:

[0124] (1). The bridgeless PFC controller model can be used for AC / DC conversion on the AC side. It adopts voltage-mode control and maintains the DC-link voltage V dc stable through a proportional-integral (PI) controller, and adjusts the output of the PI through the error signal between the input voltage V g and the DC-link voltage V dc to control the on-off ratio of the switch and correct the power factor. The bridgeless PFC controller model is as follows:

[0125]

[0126] In the formula: and are the perturbations of the state variables, is the average value of; D' = 1 - D, where D is the standard value of the duty cycle at the operating point;

[0127] Among them, the inductor L and the across capacitor C in the bridgeless PFC controller dc Value range:

[0128]

[0129] In the formula: f sw and f g are the switching frequency and the grid frequency, i o is the output current, Δi g,max and Δv dc,max are their allowed maximum fluctuation amounts;

[0130] (2) The dual-active-bridge converter model uses phase-shift control to adjust the phase difference between the primary and secondary switching devices, change the switching time, and adjust the output power and voltage; the dual-active-bridge converter model is as follows:

[0131] The output current i o (t) of the electric vehicle and the average output current I ac of the electric vehicle are expressed as follows:

[0132]

[0133] In the formula: i o1 (t) linearly decreases with time within the first T φ time, and the output current; i o2 (t) is from T φ to and the output current continues to change linearly but in the opposite direction. V dc is the DC link voltage, V bt is the voltage of the traction battery (EV battery), n is the turns ratio of the transformer, L D is the inductance value in the DAB, T φ is the phase-shift time caused by the phase difference φ between the voltages u A and u B ; Among them, f sD is the switching frequency of the DAB, and T sD is the switching period of the DAB;

[0134] Among them, the initial value of the output current i o (0) and the output current i o1 (Tφ ) The expression is as follows:

[0135]

[0136] Where: V bt is the battery voltage, V dc is the DC link voltage, n is the turns ratio of the transformer, f sD is the switching frequency of the DAB, L D is the inductance value in the DAB, φ is the phase difference between the voltages u A and u B ; T φ is the phase shift time caused by the phase difference φ between the voltages u A and u B ;

[0137] Furthermore, the average output power P ac and the maximum output power are expressed as follows:

[0138]

[0139] Where: V bt is the battery voltage, V dc is the DC link voltage, n is the turns ratio of the transformer, f sD is the switching frequency of the DAB, L D is the inductance value in the DAB, φ is the phase difference between the voltages u A and u B ;

[0140] Among them, the inductance L in the DAB converter D is expressed as follows:

[0141]

[0142] Where: V bt is the battery voltage, V dc is the DC link voltage, n is the turns ratio of the transformer, f sD is the switching frequency of the DAB, L D is the inductance value in the DAB, φ is the phase difference between the voltages u A and u B ;

[0143] Among them, the expression of the number of turns n of the coil is as follows:

[0144]

[0145] Where: The 1.1 factor provides an additional 10% turns ratio for the DAB converter to compensate for the voltage drop problem; Vbt is the battery voltage, V dc is the DC link voltage.

[0146] Furthermore, in the AC-DC combined charging method, a DC / DC converter interface model is constructed on the DC side, and the model is as follows:

[0147]

[0148] In the formula: and are the perturbations of the input current and the battery voltage with respect to their nominal values, L pi is the input inductor of the boost converter, V bt is the battery voltage, C b is the output capacitor, R e is the equivalent internal resistance of the battery;

[0149] Among them, the input inductor L pi value range and the input capacitor C b value range:

[0150]

[0151] In the formula: D is the duty cycle, is the allowed inductor current ripple, f sw is the switching frequency, V pi is the input voltage, I dc is the output current, ΔV bt is the allowed battery voltage ripple, f sw is the switching frequency;

[0152] Furthermore, for the high-efficiency charging pile, the following cooling model is constructed:

[0153]

[0154] Q in = I 2 Rt

[0155]

[0156] In the formula: P c is the energy consumed by heat, that is, the power consumed during charging, Q in is the heat generated during charging, H in is the heat absorbed by the heat dissipation device, is the mass flow rate during charging, that is, the mass of the fluid flowing through the microchannel per unit time, C P is the specific heat capacity of the coolant, T in is the temperature of the fluid when it enters the microchannel, T outis the temperature when the fluid leaves the microchannel, and t is the charging time.

[0157] Further, the high-efficiency charging pile constructs a charging power distribution model, and the model is as follows:

[0158]

[0159] In the formula: is the average charging power of the electric vehicle, is the on-demand charging power of the electric vehicle, A1 is an electric vehicle with the same battery capacity, A2 is an electric vehicle with different battery capacities, A3 is to turn on accelerated charging, and A4 is not to turn on accelerated charging;

[0160] (1), Average power distribution model:

[0161]

[0162] In the formula: is the power allocated to the kth tram connected to the fast charging pile i, is the maximum power of the fast charging pile, The power of the electric vehicle being charged connected to the fast charging pile, is the fast charging power limit of the battery of the ith tram according to its charging state (SOC, that is, the remaining power of the battery), and n is the number of fast charging piles with multi-power output;

[0163] Among them, the calculation formula for the charging power limit of the battery is as follows:

[0164]

[0165] In the formula: P i max is the maximum charging power of the battery of the ith electric vehicle, ε and γ are parameters related to the battery charging characteristics, representing the SOC levels at the beginning and end of the constant power charging stage respectively, β is the fitting parameter of the exponential function used to describe the change of charging power with SOC, SOC is the charging state of the battery, ranging from 0 to 1, where 0 means the battery is completely discharged and 1 means the battery is completely charged;

[0166] (2), On-demand power distribution model:

[0167]

[0168] In the formula: is the allocated power of the EVi in the waiting state connected to the fast charging pile k, is the remaining power in the waiting state, R i max (SOC) is the fast charging power limit of the battery of the ith electric vehicle;

[0169] Among them, the calculation formula for the remaining power of an electric vehicle in the waiting state is:

[0170]

[0171] In the formula: is the remaining power of electric vehicle i in the waiting state for charging pile k, is the maximum output power of charging pile k, is the power consumed by the electric vehicle currently being charged, is the maximum charging power that the previous electric vehicle can accept according to its SOC (state of charge of the battery), is the remaining power of charging pile k after the previous electric vehicle i - 1.

[0172] Furthermore, the electric vehicle charging time model is as follows:

[0173]

[0174] In the formula, is the charging time of the i-th electric vehicle, is the initial SOC of the i-th electric vehicle, is the desired SOC of the i-th electric vehicle, and χ(SOC) is the battery charging characteristic function, which is related to the battery charging curve;

[0175] Among them, the calculation formula for the charging time required by the electric vehicle is as follows:

[0176]

[0177] In the formula, χ(SOC i ) is the charging time required to reach a certain target charging state from a specific initial charging state (SOC). ε is the SOC value at the start of the constant power charging stage in the battery charging curve, γ is the SOC value at the end of the constant power charging stage in the battery charging curve, entering the variable power charging stage, t1, t2, t3 are time parameters in different stages of the charging process, usually related to the charging characteristics of the battery, and r is a scaling factor used to adjust the charging rate in the variable power charging stage;

[0178] Among them, the calculation formulas for parameters α and β are as follows:

[0179]

[0180] In the formula, α and β are charging parameters in the variable power charging stage, used to describe the change of the battery charging power with time. t2 and t3 are specific time points in the charging process. t2 represents the time point at the end of the constant power charging stage, and t3 represents the time point at the end of the entire charging process. P imax is the maximum charging power that the battery can accept during the constant-power charging stage, and γ is the state at the end of the constant-power charging stage in the charging curve, that is, the demarcation point between the constant-power charging stage and the variable-power charging stage in the battery charging curve.

[0181] Furthermore, the current battery state of the electric vehicle is as follows:

[0182]

[0183] In the formula, is the state when the i-th electric vehicle starts formal charging, is the initial state when the i-th electric vehicle arrives at the charging station, and Q i is the battery capacity of the i-th electric vehicle, is the start charging time of the i-th electric vehicle, is the end charging time of the i-th electric vehicle, and P i w is the charging power of the i-th electric vehicle at time t during the waiting time.

[0184] Furthermore, the high-efficiency charging pile: constructs a charging pile performance index function, and the charging pile performance index function is as follows:

[0185] maxQ = w1η + w2t + w3C ompat + w4E + w5T

[0186]

[0187] In the formula: Q is the performance index of the charging pile system, η is the utilization rate of the charging pile, and C ompat is the fast charging technology compatibility, E is the fast charging revenue generation rate, T is the temperature reduction rate, w1, w2, w3, w4, w5 are weight coefficients, w1 + w2 + w3 + w4 + w5 = 1, t is the charging efficiency, is the charging duration of the i-th vehicle, and t total is the total time, n is the number of electric vehicles, and n n is the total number of electric vehicle categories, and n i is the number of electric vehicle categories, SOC is the battery capacity, and E l is the electricity price, and t other is the charging duration of other charging piles, E' is the additional cost, E” is the fast charging loss cost, and N t is the total number of time periods, and N c is the maximum number of charging piles, is the power consumed by the operating pile k at time t, is the maximum output power of the charging pile k at time t, and T low is the temperature reduction value, and Tinitial is the initial temperature.

[0188] Furthermore, the high-efficiency charging pile: adopts the red-billed blue magpie algorithm to optimize the performance indicators of the charging pile. The specific steps of the red-billed blue magpie algorithm are as follows:

[0189] (1) Establish a search agent position matrix, and the matrix model is as follows:

[0190]

[0191] In the formula: X represents the position of the generated search agent, which is a candidate solution for RBMO and is randomly generated under the constraint conditions of the given problem. It needs to be updated after each iteration. n represents the overall size, that is, the number of groups of experimental parameters. dim represents the dimension of solving the problem, that is, the number of experimental parameters;

[0192] x i,j =(ub - lb)×Rand1 + lb

[0193] In the formula: x i,j is an element in the X matrix. ub and lb are the upper and lower bounds of the problem respectively. Rand1 represents a random number between 0 and 1;

[0194] (2) Calculate the evaluation function to seek the optimal performance indicators of the charging pile. The evaluation function is as follows:

[0195] maxQ = w1η + w2t + w3C ompat + w4E + w5T

[0196]

[0197]

[0198] In the formula: Q is the performance index of the charging pile system, η is the utilization rate of the charging pile, C ompat is the fast charging technology compatibility, E is the fast charging revenue rate, T is the cooling rate, w1, w2, w3, w4, w5 are weight coefficients, w1 + w2 + w3 + w4 + w5 = 1, t is the charging efficiency, is the charging duration of the i-th vehicle, t total is the total time, n is the number of electric vehicles, n n is the total number of electric vehicle categories, n i is the number of electric vehicle categories, SOC is the battery capacity, E l is the electricity price, t other is the charging duration of other charging piles, E' is the additional cost, E” is the fast charging loss cost, N t is the total number of time periods, N c is the maximum number of charging piles, P tk The power consumed by the operating pile k at time t is the maximum output power of the charging pile k at time t, T low is the temperature decrease value, T initial is the initial temperature;

[0199] (3) Construct an algorithm search phase model, which aims to improve the efficiency of finding the optimal solution, simulate the habits of red-billed blue magpies to enhance the algorithm's ability in the search space, and the position update formula is as follows:

[0200]

[0201] In the formula: t represents the current iteration number, X i (t + 1) represents the position of the i-th new search agent, that is, the candidate solution for the charging pile performance index, p represents the number of search agents in the cluster when exploring food, X m represents the randomly selected m-th individual, X i represents the i-th individual, X rs represents the search agent randomly selected in the current iteration;

[0202] (4) Construct an algorithm attack phase model, which aims to enhance the positioning accuracy and local search ability of the algorithm for the optimal solution, and the position update formula is as follows:

[0203]

[0204] In the formula: t represents the current iteration number, X i (t + 1) represents the position of the i-th new search agent, that is, the optimal performance index of the charging pile, X food (t) represents the position of the food, Randn represents a random number used to generate a standard normal distribution (mean 0, standard deviation 1), p represents the number of search agents in the cluster when exploring food;

[0205] (5) Construct an algorithm storage phase model, which aims to judge whether the target value meets the requirements, and the position update formula is as follows:

[0206]

[0207] In the formula: iffitness old and fitness new respectively represent the fitness values before and after the position update of the i-th red-billed blue magpie;

[0208] (6) Judge whether the performance index meets the standard. If it meets the standard, store the data and jump to step (7). If it does not meet the standard, return to step (3);

[0209] (7) Output the optimal objective function value, that is, the optimal solution of the charging pile performance index.

[0210] Further, the improvement steps of the red-billed blue magpie algorithm lie in initializing the population with the sine chaotic map. Through the sine chaotic map, the generated initial population can more evenly fill the entire solution space, improve the quality of the initial population, and facilitate the global search in the early stage. The specific improvement method is as follows:

[0211] x n+1 = μsin(πx n ) 0 ≤ x n ≤ 1

[0212] In the formula: x represents the iteration value, and μ is a random number in [0, 1].

Claims

1. An intelligent and efficient charging pile, characterized in that, It includes a collection unit, a charging unit, a regulating unit, a control unit and a power supply unit which are connected to each other; The acquisition unit includes a temperature sensor, a timer, an ammeter, a voltmeter, a battery management system and a display screen; The charging unit includes a bridgeless PFC controller, a DAB conversion controller, a DC / DC converter and a charging power distribution model; The regulating unit includes a PI controller and a heat sink; The power supply unit includes a power grid, a photovoltaic panel and a battery.

2. The intelligent and efficient charging pile according to claim 1, characterized in that, The temperature sensor is used to detect the heating condition of the charging pile, the timer is used to record the charging time, the ammeter and voltmeter respectively measure the current and voltage parameters during the charging process, the battery management system is used to detect the status of the connected battery, and the display screen is used to read the user's charging intention and whether to perform fast charging; The PI controller adjusts the output, and the heat sink is used for heat dissipation of the charging pile; The power grid and photovoltaic panels provide AC and DC power for the charging piles, and the storage battery stores surplus electric energy.

3. An intelligent and efficient charging pile according to claim 1, characterized in that The bridgeless PFC controller is used for AC / DC conversion, adopts PI to control voltage, adjusts PI through voltage signal difference, controls duty cycle, and corrects power factor. The DAB conversion controller is used for the second stage DC / DC conversion of AC, adopts PI to control current, and controls power through the phase difference between the two bridges. The DC / DC converter is used for DC side DC / DC conversion, and adjusts duty cycle and power by detecting the error between current and voltage. The charging power distribution model is used to respectively match the power usage status of the charging pile. The performance index function of the charging pile is established by combining these two parts, and the sin chaotic map is used to initialize the red-billed blue magpie algorithm to optimize the performance index.

4. An intelligent and efficient charging pile according to claim 3, characterized in that, In the charging unit, the specific steps of constructing the bridgeless PFC controller model are as follows: Step 1: The bridgeless PFC controller model is as follows: Where i g , v g are the source current and source voltage respectively, C dc is the capacitance value across, V dc is the voltage across the capacitor C dc , R e is the analog load, u s is the state of the switch, L i is the inductance value, and i is the index of the inductor; Step 2: Convert to a linear state space model, as follows: wherein, and are disturbances of state variables, is average value; D' = 1 - D, where D is the standard value of the duty cycle at the operating point.

5. An intelligent and efficient charging pile according to claim 3, characterized in that, In the charging unit, the specific steps of constructing the DAB conversion controller model are as follows: Step 1: Establish the output current i o (t) of the electric vehicle and the average output current I ac of the electric vehicle, and the expressions are as follows: Where: i o1 (t) is the output current that linearly decreases with time within the first T φ time period; i o2 (t) is the output current that continues to linearly change but in the opposite direction between T φ and ; V dc is the DC link voltage, V bt is the voltage of the traction battery, n is the turns ratio of the transformer, L D is the inductance value in the DAB, T φ is the phase shift time caused by the phase difference φ between voltage u A and u B ; where f sD is the switching frequency of the DAB, T sD is the switching period of the DAB; Among them, the initial value of the output current i o (0) and the output current i after phase shift o1 (T φ ) are expressed as follows: Where: V bt is the battery voltage, V dc is the DC link voltage, n is the turns ratio of the transformer, f sD is the switching frequency of the DAB, L D is the inductance value in the DAB, φ is the phase difference between the voltages u A and u B The phase difference between them is T φ is the voltage u A and u B The phase shift time caused by the phase difference φ between them is Step 2: Average output power P ac and maximum output power The expressions are as follows: Where: V bt is the battery voltage, V dc is the DC-link voltage, n is the turns ratio of the transformer, f sD is the switching frequency of the DAB, L D is the inductance value in the DAB, φ is the phase difference between the voltages u A and u B therebetween.

6. An intelligent and efficient charging pile according to claim 3, characterized in that, In the charging unit, the specific steps of constructing the DC / DC converter model are as follows: Step 1: The DC / DC converter model is as follows: Wherein: is the current through the input inductor, V pi is the DC power supply voltage, V bt is the battery terminal voltage, L pi is the input inductor, C b is the output capacitor, u s is the switch state, usually determined by PWM control, R e is the battery equivalent internal resistance or load resistance; Step 2: Get the linearized state space model: wherein, and are the input current and battery voltage perturbations, L pi is the input inductor of the boost converter, V bt is the battery voltage, C b is the output capacitor, R e is the equivalent internal resistance of the battery; Step 3: Obtain the output power P dc The expression is as follows: Where, V in is the input voltage, d is the duty cycle, and R is the load size.

7. An intelligent and efficient charging pile according to claim 3, characterized in that, In the charging unit, the specific steps of constructing the charging power allocation model are as follows: Step 1: Construct the charging power distribution model as follows: Wherein, is the average charging power of the electric vehicle, is the on-demand charging power of the electric vehicle. A1 is an electric vehicle with the same battery capacity, A2 is an electric vehicle with different battery capacities, A3 is to enable accelerated charging, and A4 is not to enable accelerated charging; Step 2: Average power distribution model: In the formula, is the power allocated to the k-th tram connected to the fast charging pile i, is the maximum power of the fast charging pile, is the power of the electric vehicle being charged connected to the fast charging pile, is the fast charging power limit of the battery of the i-th tram according to its charging state, and n is the number of fast charging piles with multi-power output; The calculation formula for the charging power limit of the battery ground is as follows: where P i max is the maximum charging power of the battery of the i-th electric vehicle, ε and γ are parameters related to the battery charging characteristics, representing the SOC levels at the start and end of the constant power charging stage respectively, β is the fitting parameter of the exponential function used to describe the variation of the charging power with the SOC, SOC is the state of charge of the battery, ranging from 0 to 1, where 0 represents a fully discharged battery and 1 represents a fully charged battery; Step 3: On-demand power allocation model: In the formula, is the allocated power of the EVi in the waiting state connected to the fast charging pile k, is the remaining power in the waiting state, is the fast charging power limit of the battery of electric vehicle i; Among them, the calculation formula for the remaining power of an electric vehicle in the waiting state is as follows: Wherein, is the remaining power of the electric vehicle i in the waiting state at the charging pile k, is the maximum output power of the charging pile k, is the power consumed by the electric vehicle currently being charged, is the maximum charging power that the previous electric vehicle can accept according to its SOC, is the remaining power of the charging pile k after the previous electric vehicle i - 1; Among them, the electric vehicle charging time model is as follows: wherein, is the charging time of the i-th electric vehicle, is the initial SOC of the i-th electric vehicle, is the desired SOC of the i-th electric vehicle, and χ(SOC) is a battery charging characteristic function, which is related to the charging curve of the battery; Among them, the calculation formula for the charging time required for electric vehicles is as follows: where χ(SOC i ) is the charging time required to reach a certain target state of charge from a specific initial state of charge, ε is the SOC value at the start of the constant power charging phase in the battery charging curve, γ is the SOC value at the end of the constant power charging phase in the battery charging curve, entering the variable power charging phase, t1, t2, t3 are time parameters for different stages during the charging process, usually related to the charging characteristics of the battery, and r is a scaling factor used to adjust the charging rate during the variable power charging phase; The calculation formulas of parameters α and β are as follows: where α and β are the charging parameters in the variable power charging stage, used to describe the change of the battery charging power over time, t2 and t3 are specific time points during the charging process, t2 represents the time point when the constant power charging stage ends, t3 represents the time point when the entire charging process ends, P i max is the maximum charging power that the battery can accept during the constant power charging stage, and γ is the state at the end of the constant power charging stage in the charging curve, that is, the demarcation point between the constant power charging stage and the variable power charging stage in the battery charging curve; Among them, the current battery status of the electric vehicle is as follows: In the formula, is the state when the i-th electric vehicle starts formal charging, is the initial state when the i-th electric vehicle arrives at the charging station, Q i is the battery capacity of the i-th electric vehicle, is the charging start time of the i-th electric vehicle, is the charging end time of the i-th electric vehicle, P i w is the charging power of the i-th electric vehicle at time t during the waiting time.

8. An intelligent and efficient charging pile according to claim 3, characterized in that, In the charging unit, the performance indicator function is as follows: maxQ = w1η + w2t + w3C ompat + w4E + w5T Where: Q is the performance index of the charging pile system, η is the utilization rate of the charging pile, C ompat is the fast charging technology compatibility, E is the fast charging revenue generation rate, T is the cooling rate, w1, w2, w3, w4, w5 are weight coefficients, and w1 + w2 + w3 + w4 + w5 = 1; t is the charging efficiency, is the charging duration of the i-th vehicle, t total is the total time, n is the number of electric vehicles, n n is the total number of electric vehicle categories, n i is the number of electric vehicle categories, SOC is the battery capacity, E l is the electricity price, t other is the charging duration of other charging piles, E' is the additional cost, E” is the fast charging loss cost, N t is the total number of time periods, N c is the maximum number of charging piles, P t k is the power consumed by the operating pile k at time t, is the maximum output power of the charging pile k at time t, T low is the temperature drop value, T initial is the initial temperature.

9. The intelligent and efficient charging pile according to claim 3, wherein, In the charging unit, the specific steps of initializing the red-billed blue magpie algorithm using the sin chaotic map are as follows: (1) Establish a search agent position matrix. The matrix model is as follows: Wherein, X represents the position of the generated search agent, which is a candidate solution of RBMO and is randomly generated under the constraint conditions of the given problem and needs to be updated after each iteration; n represents the overall size, that is, the number of groups of experimental parameters, and dim represents the dimension of solving the problem, that is, the number of spray experimental parameters; x i,j = (ub - lb) × Rand1 + lb where x i,j is an element in the X matrix, ub and lb are the upper and lower bounds of the problem respectively, and Rand1 represents a random number between 0 and 1; (2) Calculate the evaluation function to seek the optimal charging pile performance index. The evaluation function is as follows: maxQ = w1η + w2t + w3C ompat + w4E + w5T Where Q is the performance index of the charging pile system, η is the utilization rate of the charging pile, C ompat is the fast charging technology compatibility, E is the fast charging revenue generation rate, T is the cooling rate, and w1, w2, w3, w4, w5 are weight coefficients, where w1 + w2 + w3 + w4 + w5 = 1; t is the charging efficiency, is the charging duration of the i-th vehicle, t total is the total time, n is the number of electric vehicles, n n is the total number of electric vehicle categories, n i is the number of electric vehicle categories, SOC is the battery capacity, E l is the electricity price, t other is the charging duration of other charging piles, E' is the additional cost, E” is the fast charging loss cost, N t is the total number of time periods, N c is the maximum number of charging piles, P t k is the power consumed by the operating pile k at time t, is the maximum output power of the charging pile k at time t, T low is the temperature drop value, T initial is the initial temperature; (3) Construct the algorithm search phase model, which improves the efficiency of finding the optimal solution and simulates the habits of red-billed blue magpies to enhance the algorithm's ability in the search space. The position update formula: where: t represents the current iteration number, X i (t + 1) represents the position of the i-th new search agent, that is, the candidate solution for the charging pile performance index, p represents the number of search agents when the cluster explores food, X m represents the m-th randomly selected individual, X i represents the i-th individual, X rs represents the search agent randomly selected in the current iteration; (4) Construct the algorithm attack phase model, which enhances the algorithm's positioning accuracy and local search ability for the optimal solution. The position update formula is as follows: where: t represents the current iteration number, X i (t + 1) represents the position of the i-th new search agent, that is, the optimal performance index of the charging pile, X food (t) represents the position of the food, Randn represents a random number used to generate a standard normal distribution, and p represents the number of search agents in the cluster when exploring food; (5) Construct the algorithm storage phase model, which judges whether the target value meets the requirements. The position update formula is as follows: where: iffitness old and fitness new represent the fitness values of the i-th red-billed blue magpie before and after position update, respectively; (6) Judge whether the performance index meets the standard. If it meets the standard, store the data and jump to step (7). If it does not meet the standard, return to step (3); (7) Output the obtained optimal objective function value, that is, the optimal solution of the charging pile performance index.

10. An intelligent and efficient charging pile according to claim 9, characterized in that, The improvement steps of the red-billed blue magpie algorithm lie in initializing the population with the sin chaotic map. Through the Sin chaotic map, the generated initial population can more evenly fill the entire solution space, improve the quality of the initial population, and facilitate the early global search. The specific improvement method is as follows: x n+1 = μsin(πx n ) 0 ≤ x n ≤ 1 Wherein, x represents the iteration value, and μ is a random number in [0, 1].

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