33 results about "Energy management controller" patented technology

The invention discloses an optical storage diesel energy management system and method. The system comprises a photovoltaic power generation system, an energy storage system, a diesel power generation system, an alternating current bus, a load and an energy management controller. The photovoltaic power generation system, the energy storage system, the diesel power generation system and the load are all electrically connected with the alternating current bus, and the photovoltaic power generation system, the energy storage system and the diesel power generation system are all in communication connection with the energy management controller; and the energy management controller controls the operation states of the photovoltaic power generation system, the energy storage system and the diesel power generation system according to the photovoltaic power generation power of the photovoltaic power generation system, the total load power of the load, the energy storage charge state of the energy storage system and a priority control strategy. According to the invention, the photovoltaic power generation system is directly and electrically connected to the AC bus, so that the photovoltaic power loss can be avoided, the system operation efficiency is improved, and the real-time cooperative control of three paths of energy sources of the photovoltaic power generation system, the energy storage system and the diesel power generation system is realized through the photovoltaic power generation power, the total load power and the energy storage charge state.

The invention discloses a photo-thermal coupling self-powered tracking driving system, relates to the technical field of photovoltaic power generation, and solves the problems of energy consumption waste and use limitation in the prior art. The thermoelectric conversion module generates power by utilizing the temperature difference between waste heat generated by the photovoltaic panel in an illumination working state and the environment, the photovoltaic panel and the thermoelectric conversion module are electrically connected with the hybrid energy storage unit, and the hybrid energy storage unit comprises a capacitor and a rechargeable battery which are connected in parallel; the energy management controller is electrically connected with the photovoltaic panel, the thermoelectric conversion module and the hybrid energy storage unit and used for collecting the environment illumination intensity, the electric quantity state of the battery and the terminal voltage of the capacitor and generating a control instruction based on collected signals, and the driving mechanism is connected with the energy management controller and used for driving the hybrid energy storage unit to work. And driving the photovoltaic panel to adjust the azimuth angle and / or the elevation angle in response to the control instruction so as to perform sun tracking motion. The use adaptability is improved, and energy waste is reduced.

This invention discloses a fuel cell hybrid propulsion system and its power fluctuation-consumption synergistic optimization method. The system includes a fuel cell power generation unit, a lithium-ion battery unit, an electric drive unit, a sensing and data acquisition unit, and an energy management controller. Based on near-end strategy optimization, an intelligent agent is constructed with system state and operating condition information as input and power source power allocation commands as output. A reward function integrating total energy consumption penalty and power fluctuation penalty is designed to drive the intelligent agent to learn the optimal energy management strategy in a continuous action space. This invention can suppress frequent and drastic fluctuations in lithium-ion battery power, extend battery life, ensure system safety, and minimize overall hydrogen fuel consumption while meeting dynamic power demand and safety constraints. It achieves synergistic optimization of long-term operational economy and system output stability, enabling efficient, smooth, and long-life operation of the hybrid propulsion system in dynamic environments.

The invention discloses an MHC intelligent hybrid power system and a mobile port crane adopting the same. The hybrid power system comprises an internal combustion engine generator set, a lithium battery energy storage device, a driving device, an information acquisition unit and an energy management controller. The lithium battery energy storage device and the driving device are respectively connected with a direct current bus through a bidirectional DC / DC converter and a four-quadrant inverter, and the energy management controller adopts a management architecture in which outer ring rolling prediction and inner ring sliding mode control are coordinated; when the MHC works, the system predicts the load power demand in the future time domain in advance according to the current load information and selects the corresponding working mode, so that the power of the internal combustion engine and the lithium battery pack can be reasonably distributed in the heavy load stage, the internal combustion engine is always located in the optimal fuel economic area, fuel consumption and emission are reduced, the energy efficiency is improved, and the service life of the internal combustion engine is prolonged. Or in a load reduction or braking stage, the lithium battery pack recovers and stores the generated energy, so that the battery capacity and the cost are reduced.

This invention discloses a multi-system integrated control method for cold and heat sources based on heat recovery. The method relies on a four-tube combined cooling and heating architecture linked by a central energy management controller, a medical gas compression waste heat cascade recovery system, and a photovoltaic power generation system. This invention can achieve physical coupling of cooling of clean operating rooms and preheating of domestic hot water through a four-tube chiller unit, recovering condensation heat while outputting cooling capacity. It can also achieve energy saving and consumption reduction by uniformly scheduling photovoltaic green electricity, compression waste heat, and cooling and heating capacity through a central energy management controller.

The application discloses an energy management system of a UAV fuel cell hybrid power system, comprising a fuel cell group, a lithium battery group and a bidirectional DC-DC power supply controller; the fuel cell group is connected to a DC bus at a load end through the bidirectional DC-DC power supply controller, the lithium battery group is connected in parallel to the DC bus, the bidirectional DC-DC power supply controller collects bus voltage, fuel cell group output voltage and current, initial SOE, minimum SOE, thermodynamic theory output voltage and load power, solves real-time SOE of the fuel cell, and completes multi-working-condition energy management and control through multi-parameter threshold comparison. The application can directly manage and control fuel cell output power and SOE by relying on the bidirectional DC-DC power supply controller, without an additional energy management controller; the lithium battery works adaptively with the bus voltage, BMS control is saved, and the problems of cooperation obstacles and data delay caused by non-uniform interfaces and protocols of BMS and FCS are avoided.

The invention discloses a six-megawatt structural unit full-hydrogen new energy driving power generation method, and relates to the technical field of new energy power generation, and the method comprises the steps: collecting the wind-solar power generation power, the power grid demand power and the hydrogen storage state, executing the ultra-short-term prediction of the wind-solar power generation power, and obtaining a wind-solar power generation power prediction curve; inputting the wind-solar power generation power, the power grid demand power, the hydrogen storage state and the wind-solar power generation power prediction curve into a digital twinborn model equivalent to a physical unit through an energy management controller, carrying out multi-target look-ahead optimization simulation, and generating an optimal preset control instruction sequence; and issuing the optimal preset control instruction sequence to the electrolytic cell, the proton exchange membrane fuel cell unit and the hydrogen internal combustion engine to obtain a target operation state. By comparing the deviation between the actual operation data and the preset instruction sequence, a new round of data acquisition and optimization adjustment is triggered, closed-loop control is formed, and the look-ahead decision-making ability and the overall operation efficiency in coping with the wind and light volatility and the power grid demand variability are improved.

1. The name of the design product: heating energy controller. 2. The use of the design product: the design product is used for heating energy data collection, centralized management and control. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view. 5. The design includes colors. 6. The right view is symmetrical with the left view, and the right view is omitted.

The application provides a millisecond-level prediction suppression method and equipment for reverse power flow in a light storage and charging collaborative scenario, and relates to the technical field of smart grids. The method comprises the following steps: collecting in real time through an energy management controller; predicting a grid connection point according to multi-source operation data flow in combination with the light storage and charging collaborative scenario; presetting a reverse power flow threshold value, comparing the reverse power flow threshold value with a grid connection point net power trend; grading according to a reverse power flow risk index, formulating a collaborative suppression decomposition scheme; and respectively issuing power control instructions to perform millisecond-level prediction suppression of the reverse power flow. The application solves the technical problem in the prior art that only real-time power threshold values are relied on for triggering judgment, there is a lack of advanced perception of the grid connection point power change trend, protection actions can only be performed after the reverse power flow occurs, there is control lag, and the safety and stability of power grid operation are affected. The application changes reverse power flow control from post-disposal to proactive prevention and collaborative optimization, and ensures the stability of power grid operation.

This invention relates to the field of energy-saving technology for construction machinery, and discloses an energy recovery and reuse device for a hydraulic system of construction machinery. The device includes: an energy recovery interface for connecting to the regeneration oil port of the main hydraulic system to form a regeneration circuit; a composite energy storage module for receiving hydraulic energy of different pressure levels from the regeneration circuit and storing and releasing the energy in an optimal manner; an intelligent electro-hydraulic control valve group for controlling the on / off state, flow direction, and flow rate of the recovery and release oil circuits; and an intelligent energy management controller for predicting changes in the overall machine load in real time, dynamically coordinating energy recovery, storage, and release to achieve optimal overall energy efficiency of the hydraulic system. This invention upgrades the recovery of energy from in-situ utilization to global scheduling through multi-source energy recovery and prediction-based intelligent dynamic allocation, enabling precise release at the moment when auxiliary power is most needed, maximizing energy utilization; and significantly reducing fuel consumption.

This invention discloses a train energy management method, an energy management controller, and a train, relating to the field of energy management. The method includes determining the State of Charge (SOC) range within which the charging and discharging efficiency of the power battery reaches a preset efficiency. Before vehicle operation, if the SOC value of the power battery is less than the maximum value of the SOC range, the fuel cell is controlled to rapidly charge the power battery to the maximum SOC value. When the vehicle is in traction mode, the power battery and fuel cell are controlled to simultaneously supply power to the traction system and auxiliary system. When the vehicle is at a constant speed, if the SOC value of the power battery is greater than the maximum value of the SOC range, the power battery is controlled to supply power to the traction system and auxiliary system. The power battery achieves optimal charging and discharging efficiency only within the SOC range. Therefore, when the power battery's SOC value is below this range, the fuel cell needs to charge the power battery; when it is above this range and not in traction mode, the power battery supplies power to achieve optimal efficiency.

This invention discloses an integrated aviation power energy management controller and control method, comprising: a single modular housing; a battery management module, a power conversion module, a power distribution and protection module, and a microcontroller; wherein the battery management module, power conversion module, and power distribution and protection module are all integrated within the single modular housing; wherein the power conversion module is a bidirectional DC / DC converter using wide bandgap semiconductor devices; the power distribution and protection module includes a solid-state circuit breaker; the battery management module includes a multi-channel battery status monitoring circuit and an isolated current acquisition unit with redundant design; thereby achieving high physical integration of the aircraft while possessing aviation-grade functional safety, highly reliable redundant sensing, and efficient bidirectional energy management capabilities.

The invention discloses a central air conditioner energy management controller which comprises a main machine body shell, a control panel is arranged at the front end of the main machine body shell, a handle controls a gear shaft fixed to the lower end of the main machine body shell to rotate, and a ventilation groove is formed in the upper end of the main machine body shell. When the heat dissipation grid is installed, the protruding blocks are clamped in the clamping grooves, so that the heat dissipation grid is located in the ventilation grooves and prevented from falling into the inner position of the main machine body shell, a gear shaft is rotated by rotating a handle, a gear plate moves through a gear structure, and the heat dissipation grid is installed. According to the utility model, the square slot is formed in the shell, the square slot is inserted into the square slot, so that the heat dissipation grid is mounted, and through the connection mode, when the heat dissipation grid is damaged, the heat dissipation grid can be replaced independently, and the whole shell does not need to be replaced, so that the use cost is reduced.

This invention relates to an energy management strategy for series hybrid special vehicles incorporating the TD3 algorithm. Based on the vehicle's overall power performance requirements and the multi-source power coupling relationship, the total system power demand is calculated, and a joint simulation model of components such as the engine, generator, power battery, and drive motor is constructed. A joint control strategy for the engine and generator set is used to optimize the engine-generator set response hysteresis problem. Based on the vehicle dynamics model, vehicle speed, battery SOC, and power demand are constructed as the state space, and the engine-generator set output power is the action space, and an energy management controller based on the TD3 algorithm is designed. This invention can improve system response speed and fuel economy when facing dynamic power demand problems such as multi-step and wide-range fluctuations, reduce performance degradation caused by energy loss and matching errors, and enhance the operational stability and environmental adaptability of hybrid special vehicles.

The invention provides an energy system applied to a mobile test bed of an aviation hybrid electric power device, and belongs to the technical field of aviation power. The energy system comprises a test bed energy subsystem, the test bed energy subsystem comprises a first power battery pack, the first power battery pack is in bidirectional connection with a bidirectional converter and a bidirectional inverter which are connected in parallel, the bidirectional converter is in unidirectional connection with an energy management controller, and the bidirectional inverter is in bidirectional connection with a bidirectional permanent magnet synchronous motor; the hybrid electric power energy subsystem comprises a power generation unit, the power generation unit is in one-way connection with a first port of a confluence unit, a second port of the confluence unit is in two-way connection with the energy storage unit, a third port of the confluence unit is in one-way connection with the power output unit, and a fourth port of the confluence unit is in two-way connection with the two-way converter. The energy efficiency maximization and the use cost minimization of the whole energy system can be realized.

The application relates to a kind of dynamic high-precision map-based predictive energy management methods, the method comprises: step 1, dynamic high-precision map service module provides basic navigation map and energy consumption layer;Step 2, vehicle end energy management controller initiates data request to dynamic high-precision map service module, obtains the coordinate sequence of path, energy consumption layer, data precision requirement and vehicle model identification;Step 3, energy consumption simulation is carried out on the path, and a predicted energy consumption curve is generated;Step 4, based on energy consumption curve, global optimization algorithm is executed, so that the total energy consumption of vehicle is minimum, after optimization is completed, control instruction is generated;Step 5, cloud data aggregation platform collects the actual driving energy consumption data of vehicle in executing control instruction, and the precision of energy consumption layer is continuously calibrated and updated by using the energy consumption data through machine learning model, and a closed loop of data-driven optimization is formed.

This invention belongs to the field of intelligent control technology for unmanned platform power and new energy, specifically relating to a power supply unit for deployment in extreme environments and its energy management method. The energy management method includes the following steps: S1, acquiring the current physical state vector of the system in real time through a multi-dimensional state sensing network; S2, inputting the state vector into the pre-trained deep Q-network main network inside the energy management controller, and selecting the action command with the highest Q value; S3, sending the action command to the DC / DC conversion module, instructing the DC / DC conversion module to prioritize controlling the sodium-ion battery pack to release a high-rate current, and to connect in parallel with the hydrogen fuel cell stack for high-power output to compensate for the physical sluggishness of the dynamic response of the hydrogen fuel cell; S4, dynamically adjusting the opening of the temperature-controlled proportional directional valve to directionally transport the collected waste heat to the heat exchange branch of the sodium-ion battery pack for heating and insulation.

This invention provides an AI-based learning-based energy-saving control method and system for compressors, relating to the field of compressor control technology. By constructing a digital twin model of the compressor system, a more realistic offline training environment can be provided for deep reinforcement learning algorithms. An error correction module is introduced into the digital twin model, which can continuously correct the performance of each unit using measured data, solving the problem that relying solely on factory characteristic curves is insufficient to accurately reflect long-term operation and complex working conditions. By transforming the energy-saving operation task of the compressor system into multiple optimization objectives, task decomposition is achieved, reducing the difficulty of determining the target strategy controller and lowering the cost of energy-saving control for the compressor system. This method introduces a hierarchical structure in the initial strategy controller to achieve layer-by-layer control, reducing the difficulty of energy-saving control of the compressor system. Furthermore, through the upper-level energy management controller, it addresses the problem of unreasonable online / offline operation and unit number switching, avoiding energy waste and carbon emission pressure.

The invention discloses a fuel cell hybrid propulsion system and a power fluctuation-consumption collaborative optimization method thereof. The system comprises a fuel cell power generation unit, a lithium ion battery unit, an electric driving unit, a sensing and data acquisition unit and an energy management controller, based on near-end strategy optimization, an intelligent agent taking system state and working condition information as input and a power source power distribution instruction as output is constructed, a reward function fusing total energy consumption punishment and power fluctuation punishment is designed, and the intelligent agent is driven to learn an optimal energy management strategy in a continuous action space. On the premise of meeting dynamic power requirements and safety constraints, frequent and violent fluctuation of the power of the lithium ion battery is inhibited, the service life of the battery is prolonged, the safety of the system is guaranteed, meanwhile, the overall hydrogen fuel consumption is minimized, collaborative optimization of long-term operation economy and system output stability is achieved, and the safety of the system is improved. Therefore, efficient, smooth and long-life operation of the hybrid propulsion system in a dynamic environment is realized.

The invention discloses a vertical take-off and landing aircraft with tandem tilting wings and a hybrid power control method of the vertical take-off and landing aircraft, and belongs to the technical field of advanced air traffic. The aircraft comprises a tandem tilting wing structure, wherein a plurality of power units are distributed on a front wing and a rear wing and integrally tilt together with the units; the hybrid power system is integrated in the fuselage and comprises a hydrogen fuel cell system matched with cruise steady-state power and a lithium battery system matched with take-off and landing transient peak power; and the energy management controller is in signal connection with the hybrid power system and the tilt angle sensor, and is configured to dynamically adjust the power output ratio of the hydrogen fuel cell to the lithium battery in response to the wing tilt angle change. According to the invention, the power system and the tandem tilting wing configuration are deeply cooperated, so that the breakthrough improvement of the flight range is realized, the millisecond-level power response and safety redundancy in the transition flight stage are ensured, and the service life of the lithium battery is obviously prolonged through shallow charging and shallow discharging management of the lithium battery.

The invention discloses a ship battery container collaborative energy supply system supporting multi-energy complementation, and relates to the technical field of ship electric power, the whole energy supply system is divided into an input layer, a buffer layer and an output layer by constructing a layered energy flow mapping model of multi-energy hierarchical decoupling in a ship battery container system, and level-to-level management and structured acquisition of energy flow paths are realized. By arranging the layered acquisition unit and the data processing unit, the original data of each energy supply module can be subjected to unified acquisition and standardized processing, so that the originally discrete multi-energy supply data form a fusible standardized data set. According to the structure, the energy management controller can master the running state of each energy module in real time, and accurate input is provided for subsequent energy flow optimization. Compared with an existing single-layer energy supply structure, the system level energy flow data visualization management is realized, and the mapping precision and the state observability of an energy transmission link can be effectively improved.

This invention belongs to the field of power system energy management technology, specifically relating to an integrated energy management system for shipboard integrated power systems. It includes a power input interface for connecting to the ship's power generation system, and an energy storage conversion unit connected to the power input interface. The energy storage conversion unit includes a DAB converter and an energy storage device. The energy storage conversion unit is connected to an energy management controller, which monitors the shipboard integrated power system status in real time and controls the operating mode of the energy storage conversion unit. This invention can improve the operating efficiency of the DAB converter and control voltage and current stress by constructing a refined mathematical model of the DAB converter based on time-domain analysis and frequency-domain Fourier series expansion, combined with state machine management of complex operating mode switching, determining soft-switching boundary conditions and optimizing the return current power operating point. This improves the stability and energy utilization efficiency of the shipboard power system.

This invention relates to the field of aviation electrical systems technology, and discloses an intelligent energy management system for aviation electrical systems, including an intelligent energy management controller, a hybrid energy storage system, a main DC bus, and a solid-state power controller array. The intelligent energy management controller acquires flight scenario data and real-time electrical status data, and predicts future power demand curves based on a neural network prediction model to generate energy allocation control commands. The hybrid energy storage system includes a supercapacitor module and a lithium battery energy storage module, respectively handling transient high-frequency power demand and steady-state low-frequency power demand, and achieves energy interaction with the main DC bus through a bidirectional DC / DC converter module. Under conditions of limited power supply or abnormal operating conditions, the system dynamically adjusts the load weight according to the flight scenario, and achieves load reconfiguration through the solid-state power controller array. This invention enables prediction-based active energy scheduling, improving voltage stability, energy utilization efficiency, and system operational safety.

The invention discloses an underwater robot multi-source energy collaborative management method based on BA-PSO hybrid optimization, and relates to the technical field of multi-source energy collaborative management, and the method comprises the steps: constructing an underwater robot multi-source energy supply system; establishing a battery management system connected with the underwater robot multi-source energy supply system; a composite energy management controller is constructed, a structure combining a logic threshold control strategy and an improved fuzzy control strategy is adopted, and control modes are dynamically switched according to the positive and negative states of power required by the system; a BA-PSO hybrid optimization algorithm is adopted to optimize a membership function parameter and a logic threshold value of the improved fuzzy controller, and minimization of the comprehensive use cost of the system is taken as an optimization target. The comprehensive performance bottlenecks of the underwater robot in the aspects of energy endurance, dynamic response, system economy, environmental adaptability and the like are broken through through multi-source energy collaborative management, and long-time, high-reliability and self-adaptive operation under the complex and changeable marine environment is achieved.

The invention discloses a multi-target energy optimization algorithm for a series hybrid power vehicle based on a double depth Q network (DDQN), and belongs to the technical field of hybrid power vehicle energy management. According to the algorithm, the fuel economy is improved and the stability of the state of charge (SOC) of the power battery is kept while the dynamic property requirement of the whole vehicle is met. The method comprises the following steps: constructing a joint simulation model covering an engine, a generator, a power battery and a driving motor according to a coupling relationship between a whole vehicle power demand and multi-source power; a vehicle speed, a battery SOC and a system power demand are defined as a state space, engine generation power is used as an action variable, and an energy management controller based on a double-DQN architecture is designed. By separating action selection and value evaluation processes, the problem of Q value over-estimation in a traditional DQN algorithm is effectively suppressed. Meanwhile, a reward function fusing equivalent fuel consumption and SOC soft constraint is constructed, and tradeoff optimization between economy and a battery management target is achieved. A whole vehicle simulation platform is built in an MATLAB / Simulink environment, and training and verification are carried out in combination with typical cyclic working conditions. Results show that the algorithm can significantly improve fuel economy and SOC control performance under variable working conditions, and has good convergence characteristics and generalization ability.

The utility model belongs to the technical field of charging piles, and provides an automatic identification device for a hardware version of a charging pile, which comprises a hardware version 1 including a 12V power supply, an information processing controller and an EMS energy management controller, and the information processing controller is provided with a first pin and a second pin which are connected with the 12V power supply (1) through wires. The information processing controller judges the hardware version by sampling the voltage values of the first pin and the second pin; the hardware version 2 comprises an NTC thermistor, an information processing controller and an EMS energy management controller, one end of the NTC thermistor is grounded, and the other end of the NTC thermistor is connected with a first pin of the information processing controller through a wire; the information processing controller is in communication connection with the EMS energy management controller through a CAN bus; the charging pile version identification system does not need to depend on external identifications such as two-dimensional codes and bar codes, directly realizes charging pile version identification through a hardware circuit, is convenient to manage, and saves cost at the same time.

The invention discloses an intelligent ordered charging system and method, and relates to the field of charging infrastructure and energy management, and the system comprises a central energy management controller which is used for obtaining the real-time operation data and future demand data of a charging station, taking the total limit value of power distribution capacity as a constraint condition, and taking the optimization of the overall operation efficiency of the charging station as a target. Generating an optimized power distribution instruction for each charging interface and each energy storage unit; the energy storage unit stores and releases electric energy; the energy storage unit charging and discharging management module receives and executes the optimized power distribution instruction, and controls the energy storage unit to perform charging or discharging operation; the vehicle charging and discharging management module receives and executes the optimized power distribution instruction, and controls a specified charging interface to perform charging or V2G discharging operation on the electric vehicle; the real-time changing charging load requirement can be met, the charging cost can be controlled, the charging supply and demand contradiction of the charging station can be effectively relieved, and the non-sensitive intervention of orderly charging of the terminal user can be realized.

The application provides a hybrid power control system and method for a vehicle, which can be applied to the technical field of vehicle control. The hybrid power control system for the vehicle comprises an engine, a motor, a diesel supply system, an ammonia supply device, an ammonia catalytic decomposition device, a fuel cell, a super capacitor and an energy management controller; the energy management controller is connected with the engine, the motor, the super capacitor and the fuel cell through signals, and is used for sending control instructions to the engine, the motor, the super capacitor and the fuel cell according to current demand power of the vehicle and current electric quantity of the super capacitor, so as to control energy distribution among the engine, the motor, the super capacitor and the fuel cell; the diesel supply system, the ammonia supply device, the ammonia catalytic decomposition device, the engine and the fuel cell are connected through pipelines; and the fuel cell, the super capacitor and the motor are connected through circuits.

The invention relates to the technical field of mine electromechanical control, and provides a mine hoist emergency security power supply system and an emergency control method thereof. The system comprises a direct current collection bus; the AC end of the bidirectional converter is connected with the power grid, and the DC end is connected with the DC collection bus; the direct-current end of the four-quadrant frequency converter is connected with the direct-current collection bus, the alternating-current end of the motor side is connected with the mine hoist, and regenerative electric energy generated when the mine hoist is in a regenerative braking state is fed back to the direct-current collection bus; a fixed energy storage unit; the vehicle network interaction interface is connected with the direct current collection bus; and the energy management controller is used for controlling the fixed energy storage unit and / or the power battery to supply power to the mine hoist when the power grid fails. The bidirectional converter and a traditional frequency converter are deeply fused, the problems that a traditional emergency power supply is slow in response and complex in structure are fundamentally solved, and the safety requirement of emergency management is completely met.

The invention discloses an electric vehicle charging station energy management system and a scheduling method. The electric vehicle charging station energy management system comprises an energy management controller, and a wind power generation scheduling module, a photovoltaic power generation scheduling module, an energy storage system scheduling module and an electric vehicle charging control module which are in communication connection with the energy management controller; the energy management controller is used for dividing the operation process of the charging, storing and discharging integrated electric vehicle charging station into a plurality of discrete control periods, obtaining the operation state of each module in each control period, and generating a joint scheduling instruction based on a preset multi-agent constraint Markov decision process model. Cooperative control over wind power generation, photovoltaic power generation, battery energy storage and electric vehicle charging is achieved. According to the electric vehicle charging station energy management system and the scheduling method, the problem that the revenue maximization and the safety of the charging station cannot be considered in a random environment in the prior art is solved.