Abstract: This disclosure relates generally to a method and system for optimal power allocation in large-scale electric vehicle (EV) charging network having cluster topology. Currently, methods based on mathematical and constraint programming provide solutions for optimal power allocation but are computationally slow on large problem sizes. Reinforcement learning (RL) based learning-to-optimize (L2O) are also used for solving, but the solutions these methods provide impact the solution quality. The present disclosure provides mathematical programming-based method for optimizing power allocation decisions. The power allocation problem for the EV charging network is modelled with constraints on energy demand, power supply capacity, available network capacity, customer types and their priority level. Further the optimization problem is formulated to maximize the power flow to prime customers in the EV charging network. The problem is formulated using a mixed-integer linear program.

Abstract: The embodiments of the present disclosure herein address unresolved problems of cyclic dependency between cost of power procurement and routing of one or more electric vehicles (EVs) of a logistic enterprise. Embodiments herein provide a method and system for an optimization framework to overcome a cyclic dependency between cost of power procurement and routing of electric vehicles (EVs) of a logistic enterprise. The optimization framework comprises two independent routines, coupled through an exchange of parameter values. First routine optimizes routing cost of the EVs to satisfy delivery constraints by assuming that the average EV charging cost is known. This is given as input to a second routine that optimizes electricity procurement cost from different energy sources by assuming that the EVs routes are fixed. The output from the second routine is then given back as input to first routine, and the procedure iterates till a stopping criteria is reached.

Abstract: Current approaches for minimizing energy requirement of buildings are not designed to handle multi-input multi-output systems, such as electric vehicle-heating, ventilation, and air conditioning (EV-HVAC) system. Further, scalability of the solutions is another challenge. Present disclosure provides method and system for jointly controlling EV-HVAC system of a building. The system utilizes the potential of electric vehicle (EVs) in building energy management by treating EVs as buffers with random availability. The system performs EV-HVAC joint control that scales seamlessly with increasing EVs while respecting both thermal constraints of HVAC and state of charge (SoC) constraints of EV users.

Abstract: Method and system of electric vehicle route planning for multi-service delivery and on-route energy replenishment is disclosed. Last mile delivery is a critical component of supply chains that impacts both customer experience and delivery cost. User request is received by a depot of a delivery environment. The user request comprising a current location of the user, one or more required services, and a time window rendered between each of the required services is processed and a graph is generated for the user request. Further, trained learning agent generates a route map indicating a plurality of waypoint locations for the electric vehicle to visit each node in accordance with minimized trip cost of fleet and time duration where each node has a state action pair for the electric vehicle. The learning agent learns continuously during the interaction with delivery environment and obtains feedback for every associated action.

Abstract: Accurate estimation of average procurement cost becomes increasingly pivotal to ensure optimal procurement of electricity for an enterprise owning EV fleets. Existing techniques available for joint optimization of EV routing and charging are not usable for enterprise with EV fleet charging loads as cost of electricity is assumed to be independent of the EV charging demand. Further, they require total demand to be known a priori. Present disclosure provides a method and a system for optimizing power procurement for enterprises with electric vehicle fleet charging load. The system first performs a route optimization by modelling set of routing constraints which are then used to obtain optimal EV routes. Thereafter, system performs a procurement cost optimization by modelling set of procurement constraints using the plurality of inputs and the optimal EV routes to obtain an allocation information of each external energy source which is then utilized to obtain final procurement cost.

Abstract: This disclosure relates generally to a bidirectional charging at an electric vehicle (EV) charging station by an energy model that uses electricity bought from the day-ahead market for charging the fleet of electric vehicles (EVs) and uses the intra-day market for arbitrage. The competitive pricing of wholesale electricity markets and distributed energy resource capability of EV fleets (in addition) provide a revenue channel through energy arbitrage. To effectively handle electricity price variations and the energy demand of the EV fleet, the present disclosure utilizes a graph representation-based learning agent (LA3_D) with two-stage encoding for day-ahead charge planning; and a priority order based greedy heuristic (GH_I) for intra-day arbitrage planning. Because the agent learns the planning policy of mapping EVs to charging operations over several problem instances, it is able to solve a given instance with limited sub-optimality when put to test at different levels of scale.

Abstract: Current approaches for minimizing energy requirement of buildings are not designed to handle multi-input multi-output systems, such as electric vehicle-heating, ventilation, and air conditioning (EV-HVAC) system. Further, scalability of the solutions is another challenge. Present disclosure provides method and system for jointly controlling EV-HVAC system of a building. The system utilizes the potential of electric vehicle (EVs) in building energy management by treating EVs as buffers with random availability. The system performs EV-HVAC joint control that scales seamlessly with increasing EVs while respecting both thermal constraints of HVAC and state of charge (SoC) constraints of EV users.

Abstract: This disclosure relates generally to method and system of electric vehicle route planning for multi-service delivery and on-route energy replenishment. Last mile delivery is a critical component of supply chains that impacts both customer experience and delivery cost. The method disclosed processes a received user request comprising a current location of the user, one or more required services, and a time window rendered between each of the required services and for the user request a graph is generated. Further, the trained learning agent generates a route map indicating a plurality of waypoint locations for the electric vehicle to visit each node in accordance with minimized trip cost of fleet and time duration where each node has a state action pair for the electric vehicle. The learning agent learns continuously during the interaction with the delivery environment and obtains feedback for every associated action.

Abstract: Disadvantage of state-of-the-art scheduling mechanisms for Electric Vehicle (EV) charging is that they fail to accommodate dynamic requirements in terms of charging needs. The disclosure herein generally relates to EV fleet charging, and, more particularly, to a method and system for Electric Vehicle (EV) fleet charging by accommodating one or more dynamic requirements. The system initially generates a base charging plan for a fleet of EVs. Further, the system checks if the base charging plan is to be modified to accommodate one or more dynamic charging requirements obtained. Upon determining that the base charging plan is to be modified, the system modifies the base charging plan till a) no more vehicles are left to charge, or b) all of a plurality of chargers have an assignment.

Abstract: This disclosure relates generally to method and system for detecting on-street parking violations. The method include capturing, by using an media capturing device embodied in an electronic device mounted in a vehicle, a video stream of a scene during a trip of the vehicle. The video stream is processed at the electronic device to identify target objects such as no-parking signage and vehicles parked in the vicinity thereof. A meta-information associated with the target objects is stored in form of a short-term historian in a repository associated with the electronic device. The absolute locations of the target objects are determined and the historian is updated with the values of the absolute locations. A set of unique target objects is determined from amongst the target objects and a meta-information associated with the unique objects is sent to a cloud server for determining parking violations.

Abstract: This disclosure relates generally to method and system to generate pricing for charging electric vehicles. With Electric vehicles becoming more mainstream, public chargers may not be able to match demand supply without extensive deployments. The present disclosure dynamically generates pricing policies to maximize aggregator revenue based on a stochastic model constraints and user behavioral models. The system involves three primary stake holders comprising a demand side having EV users requesting efficient charging at lowest possible price, a supply side which includes a public or private EVSE operators, and an EV charging aggregator which acts as intermediator between the demand side and the supply side. The EV charging aggregator having an RL agent receives user requests to generate pricing based on a tentative demand pool, an actual demand pool, and a service pool. The reward to the RL agent is the total revenue obtained in that timestep of the control action.

Abstract: This disclosure relates generally to method and system for detecting on-street parking violations. The method include capturing, by using an media capturing device embodied in an electronic device mounted in a vehicle, a video stream of a scene during a trip of the vehicle. The video stream is processed at the electronic device to identify target objects such as no-parking signage and vehicles parked in the vicinity thereof. A meta-information associated with the target objects is stored in form of a short-term historian in a repository associated with the electronic device. The absolute locations of the target objects are determined and the historian is updated with the values of the absolute locations. A set of unique target objects is determined from amongst the target objects and a meta-information associated with the unique objects is sent to a cloud server for determining parking violations.

Abstract: Detecting sound sources in a physical space-of-interest is challenging due to strong ego-noise from micro aerial vehicles (MAVs)' propeller units, which is both wideband and non-stationary. The present subject matter discloses a system and method for acoustic source localization with aerial drones. In an embodiment, a wideband acoustic signal is received from an aerial drone. Further, the wideband acoustic signal is splitted into multiple narrow sub-bands having cells. Moreover, from a measurement position corresponding to each of the multiple narrow sub-bands, power in each of the cells is measured by forming a beam to each of the cells. In addition, intra-band and inter measurement fusion of the measured power at each of the cells is performed. Also, geo-location of an acoustic source corresponding to the wideband acoustic signal is identified upon performing intra-band and inter measurement fusion of the measured power.

Abstract: Detecting sound sources in a physical space-of-interest is challenging due to strong ego-noise from micro aerial vehicles (MAVs)? propeller units, which is both wideband and non-stationary. The present subject matter discloses a system and method for acoustic source localization with aerial drones. In an embodiment, a wideband acoustic signal is received from an aerial drone. Further, the wideband acoustic signal is splitted into multiple narrow sub-bands having cells. Moreover, from a measurement position corresponding to each of the multiple narrow sub-bands, power in each of the cells is measured by forming a beam to each of the cells. In addition, intra-band and inter measurement fusion of the measured power at each of the cells is performed. Also, geo-location of an acoustic source corresponding to the wideband acoustic signal is identified upon performing intra-band and inter measurement fusion of the measured power.

Abstract: The present disclosure is related to a system and method for providing aerial communication services to users trapped in disaster conditions and need immediate attention. It includes an UAV mounted with a central base station to establish communication services with each communication equipment by modeling of an emergency communication network. It analyzes utility function of criticalities to assure an efficient resource allocation mechanism where critical users get preference over non-critical users. The users can be in a critical state either due to low remaining energy of at least one communication equipment of the one or more users or because of the criticality due to their physical surroundings and data rate component to ensure throughput for the communication services. An assisted global positioning system (A-GPS) is being used for obtaining information of physical criticality of the users distributed over a geographical area.