# US 12,224,592 B2 · Methods and systems for green energy charging of electrical vehicles

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- Number: US 12,224,592 B2
- Document ID: US12224592B2
- Status: Granted
- Kind: B2
- Application number: 18/351,533
- Filed: 2023-07-13
- Published: 2023-11-09
- Granted: 2025-02-11
- Earliest claimed: 2020-10-30
- Assignees: Hygge Energy Inc.
- Applicants: Hygge Energy Inc.
- Inventors: Sundara Raju Giridhar Bangalore; Rajagopalan Krishnamurthy
- Web page: https://patentgrep.com/patent/US12224592B2

## CPC

- H02J 3/381: {Dispersed generators}
- H02J 7/342: {The other DC source being a battery actively interacting with the first one, i.e. battery to battery charging (with circuits for polarity protection H02J7/68)}
- B60L 53/51: Photovoltaic means
- B60L 53/53: Batteries
- B60L 53/63: in response to network capacity
- B60L 53/64: Optimising energy costs, e.g. responding to electricity rates
- B60L 53/665: {Methods related to measuring, billing or payment}
- B60L 53/80: Exchanging energy storage elements, e.g. removable batteries
- B60L 55/00: Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
- B60L 58/12: responding to state of charge [SoC]
- H02J 3/32: using batteries or super capacitors with converting means
- H02J 3/322: {the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging}
- H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
- B60L 2240/72: Charging station selection relying on external data
- B60L 2240/80: Time limits
- B60L 2260/54: Energy consumption estimation
- H02J 2101/20: Dispersed power generation using renewable energy sources
- H02J 2101/22: Solar energy
- H02J 2105/10: Local stationary networks having a local or delimited stationary reach
- H02J 2105/37: exchanging power with electric vehicles [EV] or with hybrid electric vehicles [HEV]
- Y02E 70/30: Systems combining energy storage with energy generation of non-fossil origin
- 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
- Y02T 90/167: Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
- Y04S 30/12: Remote or cooperative charging
- H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS

## Abstract

A microgrid system that supports EV charging. The microgrid system powers the load with one or more renewable energy sources (for example, solar, wind), battery storage, in combination with optional access to limited utility power and automatically-controlled generator power as a backup. The system can deliver clean uninterrupted power. The system provides flexibility to support the export of power for net metering, gross metering or peer-peer trading, as permitted by local regulations. The system includes a main storage sub-system and an optional secondary storage that may be brought online and increased or decreased as needed, and optimized based on guidance provided by the microgrid controller. The system also includes modules that decide how to optimally use the energy from the various sources, in order to maximize renewable energy usage, minimize the cost of electricity, while maximizing the life of the system.

## Claims

### Claim 1

A microgrid system comprising a microgrid controller;
wherein the microgrid controller is configured to receive a plurality of input energy sources comprising: a grid source; a battery; and one or more renewable energy sources;
wherein the microgrid controller is configured to direct output power from the grid source, and the one or more renewable energy sources to: the battery for charging the battery;
wherein the microgrid controller is configured to direct power to an EV charging service by a blend of the one or more renewable energy sources, the battery, the grid source; and
wherein the microgrid controller includes a dynamic storage module configured to determine when and from which of the plurality of input energy sources is used to power the EV charging service, using as input: an EV predictor module based on EV charging load patterns, and historical EV arrival patterns to consume energy from a cheapest source possible of the plurality of input energy sources; and
wherein the dynamic storage module also uses as input:
a renewable source of energy predictor module based on one or more of a location-based historical model, local weather prediction, short-term historical production data, historical geographic records, local climatic conditions, seasonal adjustments, and site specific performance history;
a system behavior module based on one or more of a historical effectiveness of storage at a given site for a given system sizing; site load patterns; and EV charging load patterns; and
a cost function module providing values representing a time-based cost of buying electricity and of selling, storage and retrieval for the input energy sources.

### Claim 2 (depends on 1)

The microgrid system of claim 1, wherein the renewable source of energy predictor module comprises a solar predictor module based on one or more of a location-based historical photovoltaic model, the local weather prediction, short-term historical solar production data, the historical geographic records for solar illumination, the local climatic conditions, the seasonal adjustments, and the site-specific performance history.

### Claim 3 (depends on 2)

The microgrid system of claim 2, wherein:
the EV predictor module provides a first prediction, for a given day, of at least one of: one or more EV occurrences and EV power requirements for each occurrence;
the renewable source of energy predictor module provides a second prediction for the given day, of the one or more renewable energy sources; and
the system behavior module provides a third prediction, for the given day, of at least one of:
one or more energy shortfalls;
power consumption of the site; and
occurrences of a change of the input energy sources occurs; and
the cost function module provides information about at least one of:
time-based electricity rates;
selling rates for each of the input energy sources;
storage rates for each of the input energy sources; and
retrieval rates for each of the input energy sources.

### Claim 4 (depends on 3)

The microgrid system of claim 3, wherein at least one of the EV predictor module, the renewable source of energy predictor module and the system behavior module is a machine-learning based module.

### Claim 5 (depends on 4)

The microgrid system of claim 4, further comprising a database storing:
one or more source data structures, each source data structure associated with a respective input energy source, each data structure comprising information about the respective input energy source, the information comprising: one or more time intervals during which the respective input energy source provides energy to the battery; and an amount of power provided by the respective input energy source to the battery during each time interval; and
a blend ratio buffer, the blend ratio buffer comprising: a state of charge of the battery and a split factor of each input energy source during each time interval.

### Claim 6 (depends on 5)

The microgrid system of claim 5, wherein a billing module is used to determine billing for an episode of charging an electrical vehicle, the billing module based on:
billing information for each input energy source;
a direct contribution of each input energy source towards the episode;
a blended contribution of each input energy source towards the episode, the blended contribution based on the blend ratio buffer; and
one or more parameters of the battery.

### Claim 7 (depends on 1)

The microgrid system of claim 1, wherein the microgrid controller is configured to provide guidance on when a second battery is connected as input based on the dynamic storage module to increase capacity, wherein the second battery is detachable from the microgrid system.

### Claim 8 (depends on 7)

The microgrid system of claim 7, further comprising a secondary storage guidance module for predicting second battery specifics selected from at least one of:
how many times a day to use the second battery;
a time of day when to use the second battery; and
a storage capacity of the second battery;
the secondary storage guidance module using as input:
the EV predictor module;
the renewable source of energy predictor module; and
the system behavior module.

### Claim 9 (depends on 8)

The microgrid system of claim 8, wherein:
the EV predictor module provides a first prediction, for a given day, of at least one of: one or more EV occurrences and EV power requirements for each occurrence;
the renewable source of energy predictor module provides a second prediction for the given day of availability of renewable energy; and
the system behavior module provides a third prediction, for the given day, of at least one of:
one or more energy shortfalls;
power consumption of the site; and
occurrences of a change of the input energy sources occurs.

### Claim 10

A method of powering an electric vehicle (EV) charging service, the method comprising:
providing as input to a microgrid controller of a microgrid, a plurality of input energy sources selected from grid source; a battery; and one or more renewable energy sources;
providing as output by the microgrid controller: the battery, and the EV charging service;
directing output power with the microgrid controller at selected times from the grid source, and the one or more renewable energy sources to the battery for charging the battery;
directing output power with the microgrid controller at selected times to the EV charging service by a blend of the one or more renewable energy sources, the battery, and the grid source; and
determining with a dynamic storage module of the microgrid controller when and from which of the plurality of input energy sources is used to power the EV charging service using as input an EV predictor module based on EV charging load patterns, and historical EV arrival patterns to consume energy from a cheapest source possible of the plurality of input energy sources; and
wherein a renewable source of energy predictor module based on one or more of a location-based historical model, local weather prediction, short-term historical production data, historical geographic records, local climatic conditions, seasonal adjustments, and site specific performance history;
a system behavior module based on one or more of a historical effectiveness of storage at a given site for a given system sizing; site load patterns; and EV charging load patterns; and
a cost function module providing values representing a time-based cost of buying electricity and of selling, storage and retrieval for the input energy sources.

### Claim 11 (depends on 10)

The method of claim 10, wherein the renewable source of energy predictor module comprises a solar predictor module based on one or more of a location-based historical photovoltaic model, the local weather prediction, short-term historical solar production data, historical geographic records for solar illumination, the local climatic conditions, the seasonal adjustments, and the site specific performance history.

### Claim 12 (depends on 11)

The method of claim 11, wherein:
the EV predictor module provides a first prediction, for a given day, of at least one of: one or more EV occurrences and EV power requirements for each occurrence;
the renewable source of energy predictor module provides a second prediction for the given day, of the one or more renewable energy sources; and
the system behavior module provides a third prediction, for the given day, of at least one of:
one or more energy shortfalls;
power consumption of the site; and
occurrences of a change of the input energy sources occurs; and
the cost function module provides information about at least one of:
time-based electricity rates;
selling rates for each of the input energy sources;
storage rates for each of the input energy sources; and
retrieval rates for each of the input energy sources.

### Claim 13 (depends on 12)

The method of claim 12, wherein at least one of the EV predictor module, the renewable source of energy predictor module and the system behavior module is a machine-learning based module.

### Claim 14 (depends on 13)

The method of claim 13, further comprising storing in a database:
one or more source data structures, each source data structure associated with a respective input energy source, each data structure comprising information about the respective input energy source, the information comprising: one or more time intervals during which the respective input energy source provides energy to the battery; and an amount of power provided by the respective input energy source to the battery during each time interval; and
a blend ratio buffer, the blend ratio buffer comprising: a state of charge of the battery and a split factor of each input energy source during each time interval.

### Claim 15 (depends on 14)

The method of claim 14, further comprising:
determining, by a billing module, billing for an episode of charging an electrical vehicle, the billing module based on:
billing information for each input energy source;
a direct contribution of each input energy source towards the episode;
a blended contribution of each input energy source towards the episode, the blended contribution based on the blend ratio buffer; and
one or more parameters of the battery.

### Claim 16 (depends on 10)

The method of claim 10, further comprising: connecting a second battery as input to the microgrid controller based on the dynamic storage module to increase capacity, and detaching the second battery from the microgrid when not in use based on the dynamic storage module.

### Claim 17 (depends on 16)

The method of claim 16, further comprising:
predicting, by a secondary storage guidance module, one or more second battery specifics selected from at least one of:
how many times a day to use the second battery;
a time of day when to use the second battery; and
a storage capacity of the second battery;
the secondary storage guidance module using as input at least one of:
the EV predictor module;
the renewable source of energy predictor module; and
the system behavior module.

### Claim 18 (depends on 17)

The method of claim 17, wherein:
the EV predictor module provides a first prediction, for a given day, of at least one of: one or more EV occurrences and EV power requirements for each occurrence;
the renewable source of energy predictor module provides a second prediction for the given day, of renewable energy; and
the system behavior module provides a third prediction, for the given day, of at least one of:
one or more energy shortfalls;
power consumption of the site; and
occurrences of a change of the input energy sources occurs.