2128 results about "Charging station" patented technology

The invention discloses a wind-solar complementary energy storage direct current charging method and system based on liquid cooling heat dissipation, and relates to the technical field of energy storage charging. Comprising the following steps: S1, collecting wind-solar complementary energy storage data in real time, and carrying out data preprocessing; s2, converting wind-solar power generation into direct current, judging the flow direction of wind-solar power generation energy, and performing corresponding wind-solar power generation energy transmission power supply; s3, judging the limit of the output power of the charging pile, and controlling and adjusting the output of the charging pile in stages; s4, quantifying the liquid cooling flow required by heat dissipation, and performing heat source heat dissipation on the energy storage system and the charging pile; and S5, carrying out coordinated regulation and control, strategy optimization and multi-dimensional visualization on the flow direction of wind and light power generation energy, charging pile output and heat source heat dissipation. The problems that an existing charging station is difficult to deploy in remote and power transmission difficult areas, high-power charging heat dissipation and energy efficiency bottlenecks are prominent, and the efficiency of traditional air cooling and alternating current conversion is low, so that efficient, safe and green energy supplementation is difficult to achieve are solved.

The invention discloses an electric heavy truck cooperative balance control system and method based on a large-scale charging group, and the method comprises the steps: constructing a power grid load prediction model, and obtaining a power grid load prediction value; establishing a multi-objective optimization model, wherein the objective function is to minimize the deviation between the planned charging power and the actual demand power of each electric heavy truck; setting a constraint function, and constraining the planned charging power and charging time of each electric heavy truck and the power grid load of each time period; collecting multi-source data and extracting multi-source features; constructing a demand prediction model based on the multi-source features to obtain a charging demand prediction result, and performing charging priority dynamic evaluation on the electric heavy truck to obtain a charging sequence of the electric heavy truck; and based on the charging sequence and the planned charging power, power distribution is carried out on the to-be-charged electric heavy truck, and an optimal charging station and an optimal charging path are recommended. According to the actual conditions of the vehicle and the power grid, the charging power can be dynamically adjusted, the charging time is shortened, the charging efficiency is improved, and the power resource of the charging pile is fully utilized.

The invention relates to the technical field of distributed computing, and discloses a heavy truck charging load scheduling-oriented distributed energy intelligent matching method, which comprises the following steps of: establishing a charging service computing power mapping table at a scheduling node; maintaining a shadow counter in a local memory, extracting a pre-estimated computing power consumption value according to an event type and accumulating the pre-estimated computing power consumption value to the shadow counter, and executing linear numerical deduction on the shadow counter according to a reference logic subtraction rate so as to simulate a scheduling data throughput evolution process; adjusting a linear deduction rate parameter according to the deviation between the state feedback data and the numerical value of the shadow counter; and distributing the charging matching task to a charging station edge computing node of which the shadow counter value does not exceed a preset logic saturation threshold value. According to the invention, through an open-loop estimation and closed-loop calibration mechanism of a local logic state, an instantaneous congestion risk caused by physical feedback lag is eliminated; and logic state consistency and self-adaptive distribution of the whole network computing power resources are realized.

The invention relates to the technical field of unmanned aerial vehicle charging and the technical field of energy storage, and discloses an unmanned aerial vehicle intelligent wireless quick charging platform based on echelon utilization batteries, which comprises a battery health assessment module, a battery management module and a battery management module, performing dynamic health assessment on each echelon battery in a charging and discharging state; the charging resource scheduling module obtains charging request information sent when the unmanned aerial vehicle prepares to return to the charging station, and dynamically allocates charging resources; the wireless charging control module is used for identifying the position information when the unmanned aerial vehicle lands, adjusting the wireless charging power and adaptively optimizing the working frequency; the energy optimization configuration module is used for performing energy optimization configuration; the safety protection module is used for identifying potential safety hazards and carrying out safety protection and fault self-healing; the optimization maintenance module is used for evaluating the performance of the charging platform and carrying out closed-loop optimization and decision updating; according to the invention, precise matching between the performance of the echelon battery and the charging demand of the unmanned aerial vehicle is realized.

The invention discloses a charging pile power supply switching method and equipment based on a micro-grid, and relates to the technical field of electric vehicle charging, and the method comprises the steps: determining a target user according to a preset price threshold value and a region requirement based on the vehicle information of each vehicle in a target charging station before power supply switching; according to the energy storage state parameter and the power supply quality parameter of the current power supply of the target charging station, judging whether the charging demand of the target user is met; if yes, generating and executing a power supply switching strategy based on a power supply in the energy storage system and the first power supply switching rule; and if the power supply does not meet the preset high-quality power supply switching threshold, determining a standby power supply and a charging power supply, and after the charging power supply supplements energy storage to the standby power supply to a safety threshold, executing switching operation according to a preset switching sequence and an energy storage allocation result. By implementing the application, accurate switching and dynamic adaptation of the power supply can be realized, the problems of blind switching of the power supply and mismatching of the power supply capability and the high demand of the user in the prior art are solved while the resource utilization efficiency is improved, and finally the stability and reliability of switching of the power supply of the charging pile in the micro-grid environment are realized.

The invention discloses an electric vehicle charging guide strategy based on hierarchical hypergraph reinforcement learning, and aims to solve the problem of supply and demand mismatching between massive electric vehicle charging demands and limited charging resources. The method comprises the following steps: firstly, constructing a feature extraction model based on a space-time hypergraph convolutional network, predicting charging demand time distribution by using a time convolutional network, and realizing relevance learning of space-time features of a traffic-power heterogeneous coupling network through a hypergraph structuring method and the hypergraph convolutional network; secondly, constructing a double-layer finite Markov decision process; the upper layer aggregates the environment state to output the target charging station and is embedded into the lower layer, and the lower layer dynamically adjusts the path by combining the real-time road network and the upper layer decision. And finally, a multi-agent reinforcement learning solution strategy is adopted, and the robustness of the strategy is enhanced through a probability distribution mechanism and Q variance constraint.

The invention discloses a photovoltaic-energy storage-charging multi-stage scheduling and market bidding optimization method and a photovoltaic-energy storage-charging multi-stage scheduling and market bidding optimization device. The method comprises the following steps: constructing a data-driven random environment model reflecting photovoltaic output, electricity price fluctuation and charging load uncertainty by adopting a mode of combining time sequence clustering and a non-homogeneous Markov chain based on historical operation data; modeling a scheduling and bidding problem of the optical storage and charging integrated station into a multi-stage Markov decision process model which comprises day-ahead decision and joint optimization of multiple intra-day rolling adjustment; a deep reinforcement learning algorithm is utilized to train the network, and a strategy regulation and control network which can adapt to various uncertain scenes and meet equipment physical constraints is obtained; and deploying the trained strategy regulation and control network in an energy management system to realize global coordinated scheduling and bidding of the optical storage and charging integrated station. According to the method, the economic benefit is remarkably improved, the robustness is greatly enhanced, the decision is globally coordinated and optimized, the real-time decision capability is strong, and the expandability and portability are good.

The invention discloses an electric vehicle charging station recommendation method based on multi-agent reinforcement learning and user preference, and the method comprises the following steps: S1.1, constructing a dynamic intelligent electric vehicle charging station recommendation (EVCSR) frame based on a Markov decision process (MDP), determining a decision period, a state space, an action set, a reward function and a state-action transition probability of the frame, according to the electric vehicle charging station recommendation method based on multi-agent reinforcement learning and user preference, through an integrated framework, the problems that a traditional method is insufficient in individual demand, weak in multi-vehicle cooperation and low in high-dimensional state decision-making efficiency are effectively solved, the accuracy, the real-time performance and the user experience of electric vehicle charging station recommendation under the urban scale are remarkably improved, and the method is suitable for popularization and application. And support can be provided for charging facility optimization and power grid dispatching.

The invention discloses a new energy automobile dynamic charging power adjustment method considering a power grid load, and aims to solve the problems of power grid overload and non-fine regulation and control caused by concentrated charging of new energy automobiles. The method is executed by a regional charging coordination server and comprises the following steps: acquiring a real-time load value and a safety threshold value of a target line of the power distribution network; collecting real-time charging aggregation data of each charging station in the jurisdiction, wherein the real-time charging aggregation data comprises the power of each charging pile and the state of charge of a vehicle battery; calculating the load margin of the power grid and determining the total charging power needing to be adjusted; the charging urgency degree coefficient of each charging pile is calculated based on the state of charge of the vehicle battery, the total adjustment amount is distributed to each charging station according to the coefficient and the total power of the charging stations, and the initial power adjustment amount is generated; according to the invention, the real-time, collaborative and refined regulation and control of the charging load from the global perspective of the power grid is realized, under the premise of ensuring the safe operation of the power grid, the highly urgent charging demand is preferably met, and the resource allocation and user experience are optimized.

The invention discloses a mixed traffic flow-oriented automatic driving taxi dynamic scheduling system and a collaborative decision-making method, belongs to the field of intelligent traffic systems, and solves the problem that in the coexistence environment of manual driving vehicles and automatic driving taxies, the automatic driving taxies cannot be automatically scheduled. The technical problem of how to efficiently and cooperatively dispatch vehicles, accurately predict demands, optimize energy management and improve the overall operation efficiency of the system is solved. The system comprises a scheduling server which is connected with a road side sensing unit, a vehicle-mounted control unit and a charging station management platform. The scheduling server comprises a traffic flow analysis module; a demand prediction module; a dynamic scheduling module; and an energy collaboration module. The system is mainly used for realizing real-time, dynamic and intelligent scheduling and energy management of the automatic driving taxis in the mixed traffic flow, improving the operation efficiency, relieving the traffic jam and optimizing the charging resource utilization.

The present invention relates to a privacy-preserving EV charging authorization and billing system, and a method for the same. The proposed system is configured to integrate permissioned blockchain with fully homomorphic encryption. The present invention aims to eliminate plaintext exposure mitigates single point of failure, by performing all authorization and billing computations on encrypted data and recoding transactions immutably, wherein an EV user securely generates encrypted authorization and billing requests using FHE-based public keys. The charging station routes these encrypted requests to the blockchain network, which records immutable encrypted transactions and verifies them via consensus. The FHE computation layer performs secure operations on the encrypted data for authorization and billing, while smart contracts execute automated verification and billing computations, ensuring transparency and auditability.

The invention discloses an electric vehicle charging station energy storage scheduling method based on a multi-agent system. The method comprises the following steps: obtaining and standardizing operation basic data of a plurality of new energy vehicle charging stations; setting five types of agents, defining observation variables and action space, and constructing a multi-agent system model; constructing a global collaborative scheduling network, and executing strategy evaluation and strategy generation; setting a constraint boundary, and constructing a linear programming scheduling model; constructing a training sample, and performing offline training and updating of a global collaborative scheduling network; solving a local optimal scheduling amount based on the real-time operation basic data, and analyzing to generate a control instruction; and issuing an energy storage control instruction and a computing power unit control instruction, acquiring a cooperative scheduling result to generate a final scheduling result set, and submitting the final scheduling result set to an upper-layer scheduling system. According to the method, a multi-agent collaborative scheduling system is constructed, strategy optimization and linear programming are fused, and efficient, stable and executable global collaborative scheduling of energy storage and computing power tasks of the charging station is achieved.

The invention belongs to the field of urban traffic management, and provides a hierarchical dynamic routing system and method for a computing power-energy fusion network, and the method comprises the steps: dividing a fusion network graph into a plurality of region sub-graphs, and dividing a path table and a connection point path table based on each region sub-graph; monitoring the running states of the road, the edge computing node and the charging station in real time, and predicting the road congestion intensity, the edge computing node queuing estimation information and the charging constraint; and determining a candidate path set in the partition path table and the connection point path table according to an online request of the vehicle, screening each candidate path in the candidate path set based on the road congestion intensity, the edge computing node queuing estimation information and the charging constraint, and determining an optimal path. According to the method, online retrieval is converted into rapid combination and evaluation of small candidate sets from full-graph traversal; traffic, energy and computing power are incorporated into the same decision framework, and systematicness deviation caused by evaluation of the future at present is avoided.

An illustrative system includes a location sensor configured to sense location of an electric vehicle and to generate a signal indicative of an electric vehicle's location. An electric vehicle battery cooling system is configured to provide cooling to an electric vehicle's battery. A controller is electrically couplable to receive the signal indicative of an electric vehicle's location from the location sensor and is configured to determine battery energy for the electric vehicle to reach a location of a charging station. The battery energy is based on the location of the electric vehicle and an amount of electrical energy remaining in the electric battery. The controller is configured to initiate cooling of the electric battery based on the amount of electrical energy remaining in the electric battery and the battery energy for the electric vehicle to reach the location of the charging station.

The invention provides a multi-scene urban electric vehicle charging station stochastic planning method and system based on an improved Huff-weighted Voronoi diagram, and the method comprises the steps: simulating the multi-scene travel behaviors of an electric vehicle in different typical days and urban functional areas through employing a Monte Carlo method, and generating charging demand space-time distribution considering the randomness of pile selection; establishing an improved Huff model fusing three types of attraction of power consumption, charging duration and electricity price, calculating a user station selection probability, constructing an improved weighted Voronoi diagram by taking the probability as a weight, and dividing the service range of each charging station; establishing a comprehensive expected cost minimization objective function, and applying a charging station service range constraint, a charging waiting time constraint and a charging pile number constraint; and solving the model by using an improved particle swarm algorithm combining multi-dimensional chaotic mapping and a linear decline strategy, and outputting an optimal charging station position and an optimal charging pile number. According to the invention, the randomness of user behaviors can be effectively described, and the economy and applicability of a charging station planning scheme are improved.

This invention introduces a next generation of Efficient Electric Vehicle (EEV) charging system, comprising two core components: a high-speed, intelligent, and automatic charger, and a fully automated recharge station. Together, they enable battery recharge within 2-5 minutes, minimizing downtime and eliminating the need for user intervention. The station includes robotic systems that detect the vehicle inlet, align the connector, and autonomously complete the recharge process. Designed primarily for Efficient Electric Vehicles (EEVs), the system is also adaptable to other EVs using modular adapters that support various inlet geometries. The combined solution defines a High-Speed Robotic Charger integrated into an Automatic Mega Battery Recharge Station (AMBRS), delivering a user-friendly, rapid, and efficient charging infrastructure.

A charging coupling for connecting a robotic mower to a charging station, comprising a first charging contact provided on the robotic mower and a second charging contact provided on the charging station. The first charging contact comprising a recess with two side walls substantially perpendicular to a mowing surface of the robotic mower, each side wall is provided with an essentially rectangular resilient metal plate. The second charging contact comprising a protruding part configured to fit in the recess of the first charging contact and has two sides facing the two side walls of the recess of the first charging contact when in contact. Each side of the protruding part is provided with metal plates with its sharp side facing the surface of the resilient metal plates of the first charging contact, when in contact.

The invention discloses a method for jointly determining a taxi scheduling strategy and a charging station pricing strategy. The method comprises the following steps: constructing a double-layer multi-agent reinforcement learning model; obtaining state information of interaction between the taxi intelligent agent and / or the charging station intelligent agent and the environment at the current moment; the strategy network calculates and generates a scheduling action of the taxi intelligent agent based on the state information at the current moment, and determines a scheduling strategy of the taxi intelligent agent; and the improved double-delay depth deterministic strategy model calculates and generates a charging action of the charging station intelligent agent based on the state information at the current moment and the historical state information, and determines a charging strategy of the charging station intelligent agent. According to the invention, the scheduling strategy and the charging pricing strategy of the electric taxi are combined to realize collaborative optimization of the two strategies.

The invention discloses an unmanned aerial vehicle battery replacement scheduling system and method based on multi-state battery prediction, particularly relates to the technical field of unmanned aerial vehicle battery management, and is used for solving the problems of insufficient energy of an inspection unmanned aerial vehicle and mismatching of battery replacement decisions. According to the method, multi-source data such as voltage, current, temperature, internal resistance and environment wind speed are collected, key states such as charge, health, power, energy and temperature are extracted, state feature vectors related to task context are formed, and task energy consumption and return energy are estimated based on a prediction model. A scheduling instruction for continuing tasks, going to a charging station or a battery swap station, maintaining charging and cooling fast charging is generated, in a battery swap scene, an improved ant colony algorithm of a battery health factor is introduced to construct a path cost function, distance, energy consumption and time are considered, optimal battery swap station selection and safe path planning are achieved, and the optimal battery swap efficiency is improved. And the energy management precision and the task stability of the unmanned aerial vehicle inspection task are obviously improved.

The invention provides a dual-time-scale optimization scheduling system and method considering diesel storage insurance supply and new energy consumption, and the method comprises the steps: collecting a plurality of data in real time, and outputting a historical data set and real-time monitoring data to a scheduling module; based on the wind and light output predicted values and load predicted values of the day before dispatching and the dispatching day, a multi-target optimization model is constructed, an energy storage charging and discharging plan, a diesel generator start-stop plan and an electric vehicle dispatching instruction are output, and cross-day energy transfer is achieved; on the basis of a model prediction control principle, 15 minutes are taken as a scheduling step length, a day-ahead scheduling plan is corrected in a rolling manner in combination with ultra-short-term source load prediction data, and prediction deviation is stabilized; controlling charging and discharging switching of the energy storage system, dynamic adjustment of the charging power of the electric vehicle and the operation state of the charging pile; through cooperative operation of day-ahead pre-scheduling and intra-day rolling optimization, source-load balance and low-carbon operation are realized. According to the scheduling system, long-period energy planning and short-period dynamic correction are both considered, and the self-consistency and low-carbon property of the off-grid charging station are improved.

The invention discloses a cross-cabinet power scheduling method, device and system applied to a charging station, the cross-cabinet power scheduling method can be applied to the technical field of energy management of the charging station, and the method comprises the following steps: when the charging demand of a first power cabinet exceeds the rated total power thereof, the first power cabinet is switched to the second power cabinet; obtaining dynamic schedulable power of at least one second power cabinet and a peak power demand in a future time window; wherein the first power cabinet is any one power cabinet of the charging station, and the second power cabinets are other power cabinets; the dynamic dispatchable power is determined based on the rated total power of the power cabinet, the current load power and the safety margin adjusted according to the real-time state of the charging gun in the power cabinet; determining a resource occupation priority of each second power cabinet based on the dynamic schedulable power and the peak power demand; and calling the power resource of the corresponding second power cabinet through the corresponding cross-cabinet contactor group according to the priority. The operation stability and the power resource utilization rate of the charging station are improved.

The invention discloses an electric vehicle collaborative optimization scheduling method based on a'network-station-vehicle 'layered architecture. The method comprises the following steps: constructing a double-layer distributed scheduling model; in the upper-layer model, modeling is carried out on aggregation flexibility of the charging stations, and an aggregation charging and discharging plan of each charging station is formulated by taking maximization of the total benefit of the system as a target; in the lower-layer model, the charging and discharging behaviors of the individual electric vehicle are scheduled by following an upper-layer plan instruction and taking the comprehensive satisfaction degree of a vehicle owner as a target; a second-order cone programming relaxation technology is adopted to convert a non-convex power flow constraint equation in the upper-layer model into convex constraint; decomposing the global optimization problem into a plurality of independent sub-problems which can be solved by each charging station in parallel by adopting an improved Lagrange relaxation dual method; and iteratively updating the Lagrangian multiplier through a sectional self-adaptive step length strategy until the algorithm is converged and a global optimal scheduling plan is generated. According to the method, the scheduling effect close to centralized optimization can be obtained while the privacy of the user is protected; and the problem of curse of dimensionality is effectively solved.

The present disclosure describes systems for providing electrical power that are modular, upgradable, and customizable. Systems described herein can be used to create modular, upgradable, and customizable charging stations for charging electric vehicles. In one example, a sub panel module can be electrically connected to a main input line that provides an alternating current power supply, and includes a plurality of electrical outlets, and a plurality of breaker switches Each breaker switch is electrically connected to one or more of the plurality of electrical outlets and can be electrically connected to the main input line.

The invention discloses a charging strategy optimization method considering the competitive relationship of charging stations, and the method comprises the following steps: constructing a finite rationality value model considering the risk attitude and price sensitivity of a user, and depicting the selection preference of an electric vehicle user among different charging stations; establishing a master-slave game model for a plurality of charging stations with a competitive relationship; iteratively solving each sub-problem in the master-slave game model by adopting an adaptive alternating direction multiplier (ADMM) method to obtain the optimal time-of-use electricity price of the charging station; based on the optimal time-of-use electricity price of a charging station, an electric vehicle charging elastic clustering optimization model is established in a sliding time window, a large number of vehicles in different states are subjected to aggregation optimization according to the remaining charging duration, and finally an optimal charging strategy is obtained. Compared with a traditional strategy based on a fixed electricity price, the strategy optimization of price signal-user selection-charging power-load curve is realized in a competitive environment, and the utilization rate of renewable energy sources and the operation income of the charging station are improved.

The invention discloses a charging station network cooperative control method and system based on an improved particle swarm optimization algorithm, and the method comprises the following steps: S1, constructing a multi-dimensional feature fusion deep learning model, predicting a load demand of the charging station network in a future first preset time period based on a multi-dimensional feature fusion deep learning model according to the multi-source feature data, so as to obtain a short-term load demand result; s2, on the basis of a short-term load demand result, establishing a dynamic scheduling optimization model containing multiple constraint conditions; and S3, introducing an adaptive inertia weight adjustment mechanism into the improved particle swarm optimization algorithm, and cooperatively solving a short-term load prediction result and the dynamic scheduling optimization model in combination with a probability acceptance criterion of a simulated annealing algorithm so as to dynamically allocate and cooperatively control power resources in the charging station network in real time. Therefore, through the deep coupling prediction and scheduling process, global optimization and real-time response are balanced, and the operation efficiency of the charging station network and the compatibility of the power grid are improved.

The invention relates to the technical field of power system optimization, and discloses a charging load estimation and charging mode optimization method based on an electric vehicle. According to the method, a source load storage-medium voltage distribution network collaborative planning framework is initialized, and charging station geographical distribution and user charging behavior modes are integrated; collecting charging load sequence data in real time, and judging a charging load control state through a volatility analysis algorithm; when the charging load control is in an unstable state, synchronously analyzing voltage fluctuation characteristics and charging power change characteristics of the power distribution network, and generating a charging efficiency evaluation value; dividing the charging effect into three levels of high efficiency, conventional and low efficiency based on the evaluation value; for a conventional grade charging effect, a time sequence decomposition technology is adopted to extract a load trend component, and an overall load stability index is calculated; and fusing the charging efficiency evaluation value and the overall load stability index, and outputting a charging power adjustment instruction. The method effectively improves the operation stability and the charging efficiency of the power distribution network.

Systems and method to minimize conditions that cause sub-synchronous oscillation (SSOs) associated with series compensated transmission grids. The implementations ensures the correct development of methods and systems that can mitigate sub-synchronous oscillation (SSOs) associated with a series compensated transmission grid and enhance the series compensation efficiency. An example implementation is a network-based SSO mitigation technology, and another example implementation is a generator-based SSO mitigation technology. The discovery and example implementations are applicable to series compensated transmission networks with synchronous generators, induction generators, wind turbines, solar photovoltaic generators, charging stations, battery storage systems, high-voltage DC transmission systems, STATCOMs, inverter-based data centers, and other inverter-based resources.

A power distribution method for charging piles in a charging station relates to the technical field of power distribution, and comprises the following steps: periodically acquiring charging pile state data, vehicle battery state data, user order state data and station level and power grid information data; the obtained data is preprocessed; constructing a comprehensive weight model according to the preprocessed data, and calculating to obtain a comprehensive weight; the average power planned to be allocated to a certain charging pile in a future period is defined as a decision variable; constructing a target function based on the decision variable and the comprehensive weight; an effective set method or an interior point method is adopted to solve the target function on the edge calculation controller, and then an optimal decision variable set is obtained; outputting each decision variable in the optimal decision variable set to the corresponding charging pile, and determining the output power of each charging pile; the problems that a charging station is low in overall operation efficiency and poor in safety are solved.

An electrical on-board power supply of a vehicle includes a traction battery, a charging connection for coupling to a DC charging station, a positive potential line, a negative potential line, reference potential line, and a voltage measuring device between the positive potential line and the reference potential line and / or between the negative potential line and the reference potential line. A DC converter is arranged in the positive potential line and / or in the negative potential line. An isolating element is arranged in the positive potential line and / or in the negative potential line. A processing unit is configured to evaluate the measured voltage(s) and to control opening of the respective isolating element. The processing unit is configured to cause the respective isolating element to open when the triggering criterion or one of the multiple or all of the triggering criteria is / are determined to occur.

A modular utility system integrates clean power generation, thermal management, and security monitoring within a standardized shipping container format. The system comprises a generator set (70 kW-600 kW) configured for operation with multiple fuel sources including natural gas, propane, methane, or hydrogen, coupled to a water-fired absorption chiller utilizing waste heat recovery. A closed-circuit evaporative cooler enhances thermal efficiency without chemical additives. The system incorporates a 360-degree security camera and remote monitoring capabilities for tracking operational parameters. A power distribution panel (460V, 3-phase) supports both building power supply and electric vehicle charging stations. The modular architecture enables scalable deployment through dual-module configurations, with separate power generation and thermal management units. The system provides sustainable, off-grid power generation while maximizing energy efficiency through integrated waste heat utilization and advanced thermal management protocols.