11results about How to "Maximize energy efficiency" patented technology

This utility model discloses a thermal management system suitable for containerized energy storage systems, comprising an air conditioning refrigerant circuit and a battery thermal management circuit, both of which include plate heat exchangers. Through this structure, the integration of the air conditioning refrigerant circuit and the battery thermal management circuit reduces system complexity and cost, while also reducing space requirements and maintenance costs. The battery thermal management system utilizes the plate heat exchanger to form a heat exchange with the air conditioning refrigerant circuit, effectively dissipating the heat absorbed from the energy storage battery module through the air conditioning system. The air conditioning refrigerant circuit dynamically adjusts the refrigerant distribution to regulate the heat exchange distribution between the air conditioning refrigerant circuit and the battery thermal management circuit. The battery thermal management circuit includes a phase change thermal storage unit connected in parallel with the plate heat exchanger, releasing stored heat / cold energy during charging and discharging intervals, reducing the frequent start-stop of the compressor and PTC heater.

The application relates to a heat management system, a control method and a controller of the heat management system, the heat management system comprising an expansion valve, the heat management system being used for heat management of a cabin, the method comprising the following steps: determining a current heat exchange capacity of the heat management system; determining a required heat exchange capacity of the cabin in a working mode and a preset capacity interval in which the required heat exchange capacity is located, obtaining a control strategy of the heat management system, wherein the control strategies associated with different preset capacity intervals are different; and adjusting the opening degree of the expansion valve of the heat management system according to the control strategy, so that the current heat exchange capacity approximates to the required heat exchange capacity. According to the application, the heat management system can be controlled in intervals, and the optimal efficiency in the intervals under various working conditions is considered, so that the energy efficiency of the heat management system is maximized.

A hybrid drive mode independent water loop semiconductor chip heating system comprises a water inlet tank, a main water tank and an auxiliary water tank, the water inlet tank is connected with a water inlet, the main water tank is provided with a main water outlet, the auxiliary water tank is provided with an auxiliary water outlet, and the main water outlet and the auxiliary water outlet are jointly connected with an adjusting valve. The water outlet side of the adjusting valve is communicated with the main water outlet; the water inlet tank is communicated with at least one wide water channel and at least one narrow water channel, the wide water channel is communicated with the main water tank, and the narrow water channel is communicated with the auxiliary water tank; the outer wall of the wide water channel is tightly attached to the heat dissipation metal thin plate. The narrow water channel is connected with the cold end of the semiconductor chip, and the hot end of the semiconductor chip is connected with the radiating metal sheet. The semiconductor chip is combined for heating, the characteristics of different driving modes are fully considered, the heating mode is flexible and controllable, the energy utilization efficiency is maximized, the practicability and applicability of a warm air system are improved, and comfortable, energy-saving and efficient heating experience is brought to users.

This invention proposes a rapid braking control strategy for multiple units in a ramp-type gravity energy storage system. By constructing an energy efficiency evaluation function, the optimal delay time for multi-stage braking that can balance small-power impacts and rapid shutdown braking is determined. This invention is based on the braking delay time Δt, the number of grid-connected units N, and the total braking time T of the entire unit in the ramp-type gravity energy storage system. brake Braking peak power P max By identifying the relationship between parameters and the speed curve of a single unit during braking, the delay time t of a single unit can be determined. brake By setting weight factors for the braking time and peak braking power of the entire unit and the number of units in the system, and fitting the braking time curve and peak braking power curve of the entire unit using the basic interpolation method, a multi-level braking energy efficiency evaluation function model for the sloped gravity energy storage system is constructed, and the optimal delay time setting scheme under different weight factors is studied.

The invention is applicable to the technical field of vehicle control, and provides an amphibious vehicle control method based on stability and economy collaborative optimization, which comprises the following steps: solving additional torque required for maintaining the stability of a vehicle attitude through real-time working condition identification and adaptive model prediction control, and calculating a stable power demand in combination with multi-target optimization distribution; superposing the stability power and the longitudinal propulsion power to obtain the total power demand of the system; on the basis of the total power demand, a hybrid power system energy management model is constructed, and global optimization distribution of engine and battery power is realized by using a neural network-based adaptive equivalent factor iteration updating strategy; and converting the optimized power instruction into torque and distributing the torque to a corresponding driving unit. By deeply coupling the full-scene stability constraint to the energy management closed loop, the problem that stability and economical efficiency optimization are disjointed is solved, and the energy economical efficiency of the whole vehicle is improved on the premise that all-working-condition driving safety and attitude stability of the amphibious vehicle are guaranteed.

The invention provides a base station energy efficiency evaluation optimization method, which comprises the following steps of: obtaining the economic total energy consumption cost of a coding multicast system based on the placement energy consumption of the coding multicast system in a content placement stage, the delivery energy consumption of the coding multicast system in a content delivery stage and real-time electricity price; taking the ratio of the user file request load to the total cost of actual economics energy consumption as the economics energy efficiency of the coding multicast system, and taking the economics energy efficiency as an energy efficiency evaluation index of the coding multicast system; and when the delivery delay of the user request is satisfied, the content placement transmission rate and the content delivery transmission rate with the economic energy efficiency maximization are realized, and the coding multicast system is optimized. According to the method, a network peak-valley period differential pricing strategy is considered, an economics energy consumption cost model is established, a base station energy efficiency index based on economics is provided, a base station economics energy efficiency optimization mechanism based on communication delay sensitivity is provided, and the base station economics energy efficiency is maximized under the constraint of communication delay, so that the service satisfaction degree of a user is guaranteed.

The invention provides a photovoltaic module adaptive monitoring and early fault early warning method and system. The method comprises the following steps: constructing a three-layer intelligent monitoring network architecture; the photovoltaic module intelligent monitoring node PV-SN executes a multi-mode cooperative sensing and self-adaptive sampling strategy, collects multi-dimensional state parameters of the photovoltaic module, and calculates a local health index LHI (t); the ECN executes edge side data fusion and diagnosis, and operates a lightweight anomaly detection algorithm to identify suspicious components; the cloud digital twinborn platform CDTP runs a high-precision fault diagnosis model, performs component performance degradation prediction and fault propagation simulation by using a digital twinborn model, and generates a predictive maintenance decision; and based on the suspicious component and the predictive maintenance decision, optimizing sensing parameters and network communication strategies of intelligent monitoring nodes PV-SN of each photovoltaic component to form a closed-loop intelligent optimization system. The technical problems that the monitoring dimension is single, the communication mode is limited, the diagnosis capability is insufficient, and the system intelligence degree is low are solved.

An ammonia complete combustion system according to an embodiment of the present application can include an ammonia complete combustor configured as a primary combustor or a secondary combustor to heat ammonia supplied to the inside of a combustion chamber to a preset temperature, an ammonia water spray nozzle to spray ammonia water into the inside of the combustion chamber heated by the ammonia complete combustor and to vaporize the ammonia water, a combustion cavity to accommodate combustion gas and ammonia vaporized from the ammonia water spray nozzle for maintaining an internal temperature and creating a stable combustion environment, an ammonia oxidation catalyst to completely combust trace amounts of ammonia generated from the combustion cavity and to completely combust ammonia including combustion gas generated in the combustion process before being discharged to the atmosphere, and a combustion air heating preheater to recover heat of combustion gas discharged from the ammonia oxidation catalyst to preheat combustion air required for the ammonia complete combustor.

The invention relates to the technical field of wireless communication, in particular to an energy efficiency maximization method for a reconfigurable intelligent surface-assisted movable antenna system. The method comprises the following steps of: constructing an energy efficiency maximization objective function with double overhead, and taking an antenna position, a reconfigurable intelligent surface reflection coefficient matrix and a base station transmitting beam forming matrix as joint optimization variables; taking a three-variable initial value as a starting point, combining channel state information iteration from a base station to a reconfigurable intelligent surface and from the reconfigurable intelligent surface to a user, in the first round, fixing an initial antenna position, optimizing a transmitting beam forming matrix and a reflection coefficient matrix, then optimizing the antenna position, and carrying out subsequent cyclic updating; and when the energy efficiency converges or reaches the maximum number of iterations, outputting an optimal parameter and controlling the antenna to move to a target position and then transmitting data, and realizing system energy efficiency maximization in a real scene by explicitly incorporating double overhead and cooperatively optimizing a core variable.

The utility model relates to air dehumidification field, concretely is a kind of energy-saving dehumidification system based on phase change PVT technology, and dehumidification system includes rotary dehumidifier, and the processing section of rotary dehumidifier extracts external space and is injected target dehumidification space after dehumidification, and the regeneration section of rotary dehumidifier extracts external air and restores the dehumidification capacity of rotary dehumidifier after heating;Its characterized in that, along gas conveying direction, front regeneration fan, heat exchanger, electric auxiliary heater, regeneration section and rear regeneration fan are sequentially arranged, and heat exchanger is connected with the heat storage tank in heat supply system and exchanges heat;Heat supply system includes photovoltaic panel and the solar energy evaporator of setting with the back of photovoltaic panel, and solar energy evaporator, compressor, condenser are sequentially communicated to form refrigerant circuit, and the cooling water in condenser is communicated with heat storage tank to store heat energy.The utility model keeps production environment humidity control, and greatly reduces the energy consumption of dehumidification system.

The invention belongs to the field of integrated circuits, and particularly relates to a 9T-SRAM unit, a floating point memory computing circuit and a CIM chip. In the 9T-SRAM unit, P1, P2 and N1-N4 form a latch structure with inverted storage nodes Q and QB; the grid electrode of the N6 is connected with the Q; the source electrode of the N6 is connected with the VSS; the drain electrode of the N6 is connected with the source electrode of the N5; the grid electrode of the N5 is used as an input port INPUT of multiplication; the grid electrode of the P3 is connected with a switch signal line KEY; drain electrodes of the N5 and the P3 are connected and serve as an output port OUT of a multiplication result. The floating point in-memory calculation circuit comprises a memory calculation array, a finger tail precision mapper, a sensitive amplifier array and a shift adder. The storage array is formed by arranging a 9T-SRAM unit array; and the finger tail precision mapper is used for converting the input index difference value into a multi-bit precision mask for controlling the starting state of each column of the storage and calculation array, and carrying out bit-by-bit adjustment on the precision mask in a subsequent period. According to the invention, the problems of high power consumption and large area commonly existing in the existing floating point memory calculation can be solved.