# US 2021/0276447 A1 · Design and control of electric vehicle charging infrastructure > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0276447 A1 - Document ID: US20210276447A1 - Status: Published application - Kind: A1 - Application number: 16/771,041 - Filed: 2018-12-21 - Published: 2021-09-09 - Earliest claimed: 2017-12-22 - Assignees: Microgrid Labs Inc.; The Regents of the University of California - Applicants: Microgrid Labs Inc; The Regents of the University of California - Inventors: Arun KUMAR; Namit SINGH; Scott MOURA; Sharabh SHUKLA; Narayanan SANKAR - Web page: https://patentgrep.com/patent/US20210276447A1 ## CPC - B60L 53/68: Off-site monitoring or control, e.g. remote control - B60L 53/63: in response to network capacity - B60L 53/64: Optimising energy costs, e.g. responding to electricity rates - B60L 53/66: Data transfer between charging stations and vehicles - B60L 2240/80: Time limits - B60L 2250/20: by driver identification - Y02T 10/70: Energy storage systems for electromobility, e.g. batteries - Y02T 10/7072: Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors - Y02T 90/12: Electric charging stations - Y02T 90/16: Information or communication technologies improving the operation of electric vehicles - B60L: PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES (arrangements or mounting of electrical propulsion units or of plural diverse prime-movers for mutual or common propulsion in vehicles B60K1/00, B60K6/20; arrangements or mounting of electrical gearing in vehicles B60K17/12, B60K17/14; preventing wheel slip by reducing power in rail vehicles B61C15/08; dynamo-electric machines H02K; control or regulation of electric motors H02P); SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES (electric coupling devices combined with mechanical couplings of vehicles B60D1/64; electric heating for vehicles B60H1/00); ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL (control or regulation of electric motors H02P); MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES - Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION ## Abstract A system for configuring an Electric Vehicle (EV) charging infrastructure, the apparatus comprising. The system receives user direction and inputs regarding a custom EV charging infrastructure, submits the data an optimal planning module which is configured for performing optimization based on objectives in view of a set of constraints; and generates a recommendation from the optimal planning module for a custom EV charging infrastructure based on charging needs and behavior of the consumers toward deferring upgrades in electric infrastructure without compromising energy required for electrical transportation. The system validates the results from the optimal planning module using discrete event-based simulator and generates charging schedules from the controlling module for custom EV charging based on information about EV data, distributed energy resource (DER), and the electric infrastructure without compromising energy required for electrical transportation and causing undue impact on the electrical infrastructure. ## Claims ### Claim 1 An apparatus for configuration and control of Electric Vehicle (EV) charging infrastructure, the apparatus comprising: (a) a processor; and (b) a non-transitory memory storing instructions executable by the processor; (c) wherein said instructions, when executed by the processor, perform steps comprising: (i) receiving user input data associated with one or more parameters of an EV charging infrastructure; (ii) storing inputs as system data for various aspects of the EV charging infrastructure configuration; (iii) submitting the system data to an optimal planning module; which is configured for performing optimization based on objectives in view of a set of constraints; and (iv) generating a recommendation from the optimal planning module for the custom EV charging infrastructure based on one or more factors comprising: charging needs, behavior of EV end users, interchange time and cost, instances of impatience, waiting time, and penalty for unfulfillment both at an aggregated demand level and at an individual vehicle level. ### Claim 2 (depends on 2) The apparatus of claim 2, wherein said instructions when executed by the processor are further configured for performing steps comprising: (v) validating the recommendation from the optimal planning module using a discrete event-based simulator to assess the extent of queuing characteristics present in the EV charging infrastructure configuration and if needed adjust the recommendation from the optimal planning module to minimize issues determined from the simulator; and (vi) generating one or more charging schedules via a controlling module for custom EV charging based on one or more input parameters. ### Claim 3 (depends on 2) The apparatus of claim 2, wherein the queuing characteristics are selected from the group comprising: waiting time, queue length, impact of interchange or transition times, and instances of impatience. ### Claim 4 (depends on 2) The apparatus of claim 2, wherein the one or more input parameters are selected from the group comprising: EV facility infrastructure, EV facility preferences, EV mobility behavior and preferences, EV end user preferences, EV data, distributed energy resource (DER) specifications, utility and market data, weather data, master data, financial data, business model data. ### Claim 5 (depends on 1) The apparatus of claim 1, wherein said instructions when executed by the processor are configured to generate the recommendation by generating solutions in response to mathematical models, machine learning, and convex optimization to determine optimal component sizes and control set-points within the EV charging infrastructure. ### Claim 6 (depends on 1) The apparatus of claim 1, wherein said instructions when executed by the processor are configured to generate the recommendation by utilizing objective functions to support a selected objective optimization goal with respect to the EV charging infrastructure. ### Claim 7 (depends on 6) The apparatus of claim 6, wherein said objective function comprises a deterministic algorithm in which uncertainty in any input parameters is not considered when modeling the system. ### Claim 8 (depends on 6) The apparatus of claim 6, wherein said objective function comprises a stochastic algorithm that accounts for uncertainty in at least a portion of the input parameters. ### Claim 9 (depends on 1) The apparatus of claim 1, wherein said instructions when executed by the processor are configured to generate the recommendation for the EV charging infrastructure via generating outputs selected from the group of outputs comprising: financial data output, technical operations data output, and business model data output. ### Claim 10 (depends on 9) The apparatus of claim 9, wherein said financial output is selected from the group of financial data outputs comprising: investment decisions, type, number and size of EV chargers in the EV charging infrastructure, investment for EV charging infrastructure upgrades, annualized investment cost of the EV charging infrastructure, and operating costs of the EV charging infrastructure. ### Claim 11 (depends on 9) The apparatus of claim 9, wherein said technical operations data output is selected from the group of technical operations data outputs comprising: per unit time load profile for EV facility load with contribution from residential load and each EV charger load, charger utilization, queue length, queue waiting time and space usage statistics as a function of unit time for an individual EV being charged, and fulfilled and unfulfilled charging requirements. ### Claim 12 (depends on 9) The apparatus of claim 9, wherein said business model data output is selected from the group of business model data outputs comprising: suggested pricing schemes, financial analysis of the EV charging infrastructure, cash flow with respect to the EV charging infrastructure, and net present value of the EV charging infrastructure. ### Claim 13 (depends on 1) The apparatus of claim 1, wherein the one or more input parameters are selected from the group comprising: battery capacity, state of charge upon arrival, facility operating hours, queue management strategy, queue length, waiting time, and transition time. ### Claim 14 (depends on 2) The apparatus of claim 2, wherein said charging schedules are generated from the controlling module based on short term forecasting, rolling unit commitment, optimal dispatch and error correction for generating a custom EV charging profile; and wherein the custom EV charging profile is generated based on one or more input parameters are selected from the group comprising: weather conditions, EV charging needs, EV queue management strategy, cost of energy, pricing strategy, and behavior of EV end users to minimizing operating cost of the EV charging infrastructure. ### Claim 15 (depends on 2) The apparatus of claim 2, wherein the optimal planning module comprises a pre-processing module that processes the one or more input parameters and outputs an aggregated or individual EV charging demand profile for input by an optimization module. ### Claim 16 (depends on 15) The apparatus of claim 15, wherein the EV charging demand profile is adjusted for statistical variance. ### Claim 17 (depends on 15) The apparatus of claim 15, wherein the pre-processing module further generates one or more facility profiles. ### Claim 18 A method for configuration and control of an Electric Vehicle (EV) charging infrastructure, the apparatus comprising: receiving user input data associated with one or more parameters of an EV charging infrastructure; storing inputs as system data for various aspects of the EV charging infrastructure configuration; submitting the system data to an optimal planning module; which is configured for performing optimization based on objectives in view of a set of constraints; and generating a recommendation from the optimal planning module for the custom EV charging infrastructure based on one or more factors comprising: charging needs, behavior of EV end users, interchange time and cost, instances of impatience, waiting time, and penalty for unfulfillment both at an aggregated demand level and at an individual vehicle level; wherein said method is performed by a processor executing instructions stored on a non-transitory medium. ### Claim 19 (depends on 18) The method of claim 18, wherein said instructions when executed by the processor are further configured for performing steps comprising: validating the recommendation from the optimal planning module using a discrete event-based simulator to assess the extent of queuing characteristics present in the EV charging infrastructure configuration and if needed adjust the recommendation from the optimal planning module to minimize issues determined from the simulator; and generating one or more charging schedules via a controlling module for custom EV charging based on one or more input parameters. ### Claim 20 (depends on 19) The method of claim 19, wherein the queuing characteristics are selected from the group comprising: waiting time, queue length, impact of interchange or transition times, and instances of impatience. ### Claim 21 (depends on 19) The method of claim 19, wherein the one or more input parameters are selected from the group comprising: EV facility infrastructure, EV facility preferences, EV mobility behavior and preferences, EV end user preferences, EV data, distributed energy resource (DER) specifications, utility and market data, weather data, master data, financial data, business model data. ### Claim 22 (depends on 18) The method of claim 18, wherein generating the recommendation comprises generating solutions in response to mathematical models, machine learning, and convex optimization to determine optimal component sizes and control set-points within the EV charging infrastructure. ### Claim 23 (depends on 18) The method of claim 18, wherein generating the recommendation comprises generating the recommendation by utilizing objective functions to support a selected objective optimization goal with respect to the EV charging infrastructure. ### Claim 24 (depends on 23) The method of claim 23, wherein said objective function comprises a deterministic algorithm in which uncertainty in any input parameters is not considered when modeling the system. ### Claim 25 (depends on 23) The method of claim 23, wherein said objective function comprises a stochastic algorithm that accounts for uncertainty in at least a portion of the input parameters. ### Claim 26 (depends on 18) The method of claim 18, wherein generating the recommendation comprises generating outputs selected from the group of outputs comprising: financial data output, technical operations data output, and business model data output. ### Claim 27 (depends on 26) The method of claim 26, wherein said financial output is selected from the group of financial data outputs comprising: investment decisions, type, number and size of EV chargers in the EV charging infrastructure, investment for EV charging infrastructure upgrades, annualized investment cost of the EV charging infrastructure, and operating costs of the EV charging infrastructure. ### Claim 28 (depends on 26) The method of claim 26, wherein said technical operations data output is selected from the group of technical operations data outputs comprising: per unit time load profile for EV facility load with contribution from residential load and each EV charger load, charger utilization, queue length, queue waiting time and space usage statistics as a function of unit time for an individual EV being charged, and fulfilled and unfulfilled charging requirements. ### Claim 29 (depends on 26) The method of claim 26, wherein said business model data output is selected from the group of business model data outputs comprising: suggested pricing schemes, financial analysis of the EV charging infrastructure, cash flow with respect to the EV charging infrastructure, and net present value of the EV charging infrastructure. ### Claim 30 (depends on 18) The method of claim 18, wherein the one or more input parameters are selected from the group comprising: battery capacity, state of charge upon arrival, facility operating hours, queue management strategy, queue length, waiting time, and transition time. ### Claim 31 (depends on 19) The method of claim 19, wherein said charging schedules are generated from the controlling module based on short term forecasting, rolling unit commitment, optimal dispatch and error correction for generating a custom EV charging profile; and wherein the custom EV charging profile is generated based on one or more input parameters are selected from the group comprising: weather conditions, EV charging needs, EV queue management strategy, cost of energy, pricing strategy, and behavior of EV end users to minimizing operating cost of the EV charging infrastructure. ### Claim 32 (depends on 19) The method of claim 19, wherein the optimal planning module comprises a pre-processing module that processes the one or more input parameters and outputs an aggregated or individual EV charging demand profile for input by an optimization module. ### Claim 33 (depends on 32) The method of claim 32, the EV charging demand profile is adjusted for statistical variance. ### Claim 34 (depends on 32) The method of claim 32, wherein the pre-processing module further generates one or more facility profiles.