Energy source balancer

Systems and methods for an energy source balancer are provided. The energy source balancer can predict an amount of electricity to charge a battery in a time interval. The energy source balancer can identify a charger connected to an electricity grid. The energy source balancer can determine, a portion of the electricity provided to the charger in the time interval via renewable energy sources. The charger can generate an action configured to offset any non-renewable energy provided.

INTRODUCTION

An energy grid, such as an electricity grid, can distribute energy from one or more energy sources to a load. The energy grid can include various energy sources, including renewable energy sources and non-renewable energy sources.

SUMMARY

Aspects of this technical solution can be directed to increasing a portion of renewable energy used to charge an electric vehicle, using an energy source balancer. A charge predictor of an energy source balancer can determine an amount of electricity to charge the battery of an electric vehicle. For example, the charge predictor can determine a difference between a current state of charge and a desired state of charge of the battery. A charger selector of the energy source balancer can identify a charger to charge the electric vehicle. The charger selector can determine additional charger information such as to ensure a compatibility or affiliation, or any energy offsets associated with the charger. For example, the charger selector can identify the charger based on the composition of an energy grid supplying electricity to the charger (e.g., can prioritize chargers having a higher content of renewable energy). The composition of the energy grid can be predicted by an emissions forecaster to identify the charger, or following identification of the charger. For example, the emissions forecaster can predict a future composition of an energy grid. The future composition of the energy grid can be at a time the electric vehicle is predicted to charge at the energy grid. The energy source balancer (e.g., the emissions forecaster) can reconcile the forecasted composition of the grid with another determined composition of the grid. For example, the emissions forecaster can determine the composition of the energy to charge the electric vehicle matched the predicted composition of the energy. An action generator can generate an action to offset any non-renewable portion of energy to charge the vehicle. For example, the action generator can adjust the operation (e.g., a route) of the electric vehicle to offset the non-renewable portion of the energy.

At least one aspect is directed to a system. The system can include one or more processors coupled with memory. The system can predict an amount of electricity to charge a battery of an electric vehicle. The prediction can be based at least in part on a state of charge of a battery of the electric vehicle. The system can identify a charger connected to an electricity grid configured to provide the amount of electricity to charge the battery. The system can determine a portion of electricity provided to the charger via renewable energy sources in the time interval. The system can generate an action to offset the difference between the amount of electricity to charge the battery and the portion of the amount of electricity provided via the one or more renewable energy sources in the time interval. The generation can be based on the portion less than the amount of electricity to charge the battery.

At least one aspect is directed to a method. The method can be performed by a data processing system that includes one or more processors coupled with memory. The method can include the data processing system predicting an amount of electricity to charge the battery of the electric vehicle in a time interval. The amount can be based least in part on a state of charge of a battery of the electric vehicle. The method can include the data processing system identifying a charger connected to an electricity grid configured to provide the amount of electricity to charge the battery. The method can include the data processing system determining, for the charger, a portion of the amount of electricity provided to the charger in the time interval via one or more renewable energy sources of the electricity grid. The method can include the data processing system generating an action configured to offset a difference between the amount of electricity to charge the battery and the portion of the amount of electricity provided via the one or more renewable energy sources in the time interval. The action can be based on the portion less than the amount of electricity.

At least one aspect is directed to an electric vehicle. The electric vehicle can include one or more processors coupled with memory. The electric vehicle can receive an indication from a user of the electric vehicle to charge a battery of the electric vehicle. The indication from the user can be received via a graphical user interface. The electric vehicle can determine an amount of electricity to charge the battery of the electric vehicle in a time interval. The determination can be responsive to the indication. The electric vehicle can identify a charger connected to an electricity grid configured to provide the amount of electricity to charge the battery. The electric vehicle can determine a portion of the amount of electricity provided to the charger in the time interval via one or more renewable energy sources of the electricity grid for the charger. The electric vehicle can provide an indication of an action configured to offset a difference between the amount of electricity to charge the battery and the portion of the amount of electricity provided via the one or more renewable energy sources in the time interval. The indication of the action can be provided via the graphical user interface. The indication of the action can be based at least in part on the portion less than the amount of electricity.

At least one aspect is directed to a graphical user interface. The graphical user interface can be presented, generated, or otherwise provided by one or more processors coupled with memory. The graphical user interface can display one or more chargers. A first portion of an amount of electricity provided to the one or more chargers is sourced from renewable energy sources. A second portion of the of the amount of electricity provided to the one or more chargers is sourced from non-renewable energy sources. The one or more chargers can be based on a location of a vehicle or an area of interest associated with the vehicle. The graphical user interface can receive a selection of a first charger. The graphical user interface can determine a portion of the amount of electricity provided to the first charger in a time interval via the second portion. The graphical user interface can generate an action configured to offset the second portion in the time interval.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems of energy source balancers. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways.

The present disclosure is directed to systems and methods of increasing the amount of electricity used to charge an electric vehicle that is generated from renewable energy sources as opposed to non-renewable energy sources. Information related to energy sources such as a composition of an energy grid associated with a charger or an offset program associated with a charger can be predicted, determined, or accessed (e.g., via an API from an energy grid operator or third party aggregator). The information can be presented to a user, such as by a graphical user interface. A prompt to perform an action to offset a non-renewable portion of the energy can be presented to the user. The user can accept the prompt to perform the action, or the action can be performed by the electric vehicle. For example, the user can adjust a vehicle speed, or provide peak demand energy to a grid (e.g., in the future, or apply a previous provision of peak demand energy in the past to the action).

The disclosed solutions have technical advantages of lowering non-renewable energy use relative to renewable energy use, such as an amount of non-renewable energy associated with various chargers at various times. The chargers can be presented to the user to allow the user to select chargers having additional renewable energy composition. The disclosed solutions associate potential actions which can be performed (e.g., automatically, or responsive to the prompt of a user) to offset any nonrenewable energy used by an electric vehicle. For example, the nonrenewable can be offset based on predictions of energy grid composition and use.

Systems and methods of the present technical solution can include, interface with, or otherwise communicate with an energy source balancer, an electric vehicle energy system, and an energy grid system including a charger. The electric vehicle energy system can monitor the battery system of the electric vehicle and interface with the energy source balancer to determine requested power (e.g., based on the location and route of the electric vehicle). The energy source balancer can determine a request for charging. The energy source balancer can identify a charger which is compatible with the electric vehicle, and otherwise desired. For example, the energy source balancer can base the identification of the charger on a charging station renewable energy credit policy. The charger can prompt the user to charge the electric vehicle at the charger and determine, based on the energy composition of the energy grid, a portion of the electricity that is not already associated with an offset. The energy source balancer can generate an action to offset the nonrenewable energy and prompt the electric vehicle or the user to perform the action.

FIG.1depicts an example system100to balance energy sources, in accordance with some aspects. The system100can include, interface with or otherwise communicate with one or more energy grid systems170. The system100can include, interface with or otherwise communicate with one or more energy source balancers102. In some cases, the energy source balancer102can be referred to or include a data processing system. The system100can include, interface with or otherwise communicate with electric vehicle energy systems152. The energy source balancer102, electric vehicle energy system152, and energy grid systems170can communicate via a network150. The network150can include computer networks such as the Internet, local, wide, metro, or other area networks, intranets, cellular networks, satellite networks, and other communication networks such as voice or data mobile telephone networks.

The energy grid system170can include at least one fossil fuel-based power plant172. The energy grid system170can include at least one renewable resource-based power plant174. The energy grid system170can include at least one grid storage176element. The energy grid system170can include at least one charging station178which is also referred to herein as a charger178. The energy grid system170can include at least one data repository180.

The energy grid system170can include at least one fossil fuel-based power plant172. The fossil fuel-based power plant172can be a plant intended to operate transiently, in response to a load (e.g., a gas peaker plant) or a plant intended for uninterrupted operation (e.g., a coal-fired plant). The fossil fuel-based power plant172can be associated with various emissions such as nitrous oxides and carbon dioxides. The energy grid system can include at least one renewable resource-based power plant174. The renewable resource-based power plant174can include solar, wind, nuclear, geothermal, hydroelectric or other renewable energies. The renewable resource-based power plants174can be grid scale (e.g., in the megawatt range) or can be residential scale (e.g., in the kilowatt range). For example, the renewable resource-based power plants174can include rooftop residential solar energy sources. The energy grid system can include at least one element for grid storage176. The grid storage can include grid scale or residential scale storage. For example, the grid storage176can include megawatt scale batteries, or fuels cells and kilowatt scale grid storage176such as electric vehicle batteries and residential energy storage devices.

The energy grid system170can include, interface with, or be connected to at least one charging station178. The charging station178can provide electricity to the electric vehicle that is generated from any one or more power sources of the energy grid system170. The charging station178can charge the electric vehicle using different characteristics of electricity. For example, a charging station178can be a high power AC or DC charger, a trickle charger (e.g., a 120 VAC charger), another electric vehicle, or a home charger. Some charging stations178can be associated with charging networks wherein certain parameters (e.g., renewable offsets, or plug type) can be inferred. Some charging stations178, such as home chargers can have information associated by user entry or inter-device access such as through a common application or an API. For example, a power grid associated with a home charger may be the rooftop solar associated with the home, and can be accessed by interfacing with the rooftop solar (e.g., the panels, the inverter, or an energy storage device associated therewith).

The data repository180can include one or more local or distributed databases, and can include a database management system. The data repository180can include computer data storage or memory and can store one or more of a historical grid composition182, a real time grid composition184, and forecast parameters186. The real time grid composition184can include current or most recent available records (e.g., estimates or measures) of the energy source composition of an energy grid. The historical grid composition182can include records of the energy source composition of an energy grid which is not real time grid composition184. The forecast parameters186include parameters used to forecast a future state of the energy grid such as weather, holidays, or planned maintenance.

Historical grid composition182and real time grid composition184can be determined according to an attributional framework or a consequentialist framework. An attributional framework considers the total power generation of an energy grid, while a consequentialist framework considers an incremental power generation of the energy grid. For example, if an energy grid includes only peaker gas plants to manage transient demand, then an incremental change in demand can be associated with the emissions of the peaker gas plants, and without regard to the rest of the grid (which is not impacted by the incremental demand). If an energy grid includes only grid storage to manage transient demand, then the incremental change in demand can be associated with the emissions to generate electricity to charge the grid storage176elements.

The energy source balancer102can include at least one charger selector104. The energy source balancer102can include at least one charge predictor106. The energy source balancer102can include at least one action generator108. The energy source balancer102can include at least one data repository112.

The charger selector104, charge predictor106, and action generator108can each include at least one processing unit or other logic device such as a programmable logic array engine, or a module configured to communicate with the data repository112or database. The charger selector104, charge predictor106, and action generator108can be separate components, a single component, or part of the energy source balancer102. The energy source balancer102can include hardware elements, such as one or more processors, logic devices, or circuits. For example, the energy source balancer102can include one or more components, structures of functionality of the computing device depicted inFIG.7.

The data repository112can include one or more local or distributed databases, and can include a database management system. The data repository112can include computer data storage or memory and can store one or more of charger data114, user preferences116, and vehicle state data118. The charger data114can include charger information such as a renewable offset policy associated with a charger. The user preferences116can include user preferences stored by the energy source balancer such as a preferred action to offset non-renewable energy use. The vehicle state data118can include vehicle state information such as current state of charge, or charge capacity.

Still referring toFIG.1, among others, the energy source balancer102can include at least one charger selector104designed, constructed and operational to identify a charger connected to an energy grid. The charger selector104can identify the charging station178by brand, location, or a unique code such as an electric vehicle supply equipment identifier (EVSEID). The charger selector104can access information regarding the energy supplied to the charger and other information disclosed herein via an application programming interface (API). For example, the charger selector104can access an API to a grid operator, charger operator, or third party data host. The charger selector104can identify the charger178based on a compatible charging standard (e.g., with or without the use of an adapter which can be specified according to a user preference116). The charger selector104can identify the charger178based on a charging network the vehicle is affiliated with. For example, a user preference116can specify one or more networks150that a user or an electrical vehicle is registered with. The charger selector104can rank (e.g., sort) or select a charger178based on a portion of renewable energy provided to the charger178. For example, the charger178with the highest portion of the amount of electricity provided via the one or more renewable energy sources can be selected. The charger178can be selected based on the portion during various times. For example, based on a historical highest portion, a current highest portion, or a predicted highest portion.

The charger selector104can associate one or more charging stations178with a portion of renewable energy used in the grid (e.g., with or without offsets). For example, the charger selector104can determine a charging station178can generate all electricity from renewable sources, such as a local micro-grid including wind, solar, or hydroelectric energy, and can be determined to be 100% renewable energy. The charger selector104can associate another charging station178with another energy grid having a varying portion of renewable energy (e.g., based on a forecast parameter186). Thus, the charger selector104can determine the renewable content of the charging station178according to the energy grid. A charging station178(e.g., a charging station type, brand, location, or other subset thereof) can be known to offset a portion of energy. For example, an operator of a charging station178can obtain renewable energy credits (RECs) to offset all or a portion of the energy delivered. The offset amount can be less than, equal to, or greater than the non-renewable portion of energy. For example, an operator can obtain RECs to offset 100% of energy used despite a lesser percent (e.g., 50%) of energy being non-renewable energy (e.g., can over-provision RECs), or can obtain RECs to offset 25% of energy used despite a greater percent (e.g., 50%) of energy being non-renewable energy.

The RECs can be associated with a total power use (e.g., 1 megawatt) so that 1 REC unit can offset 1 unit of used energy without regard to an energy intensity. For example, a REC can be used to offset coal or gas fired power plants based on an energy used. Some credits can offset carbon use generally, and can be used with the systems and methods provided herein. The renewable content can be a real time measure or a long term tabulated measure. For example, the charger selector104can associate a charger with a grid which is 50% renewable energy on an annualized basis; the grid can include 100% renewable energy during a windy Saturday in the summer, and 0% renewable energy during a still Tuesday night in the winter. The charger selector104can rank a plurality of charging stations178. The ranked chargers can be presented via an interface or can be ranked and selected according to an intermediate processing operation. For example, preferred chargers (e.g., closer, having a higher renewable energy content, or having a user affiliation) can be given a higher rank than non-preferred chargers.

The charger selector104can determine a portion of the amount of electricity provided to the charging station178in a time interval via renewable energy resources. The charger selector104can access data of a data repository180associated with an energy grid such as an energy grid operator or based on a third party aggregator via an API. For example, the charger selector104can receive information relating to the energy grid detailing the portion of renewable energy. The portion of the energy can be constant during the time interval of charging, or can vary according to two or more sub-intervals thereof. For example, the charger selector104can access a real time grid composition184for a first segment of charging wherein 60% of energy is solar, and during a second segment of charging wherein 50% of charging is solar. The determination of the renewable portion can be made prospectively or retrospectively. For example, the charger selector104can access or determine predictions of the future composition of the grid based on forecast parameters186, which can be accessible over the network150.

The energy source balancer102can include at least one charge predictor106to predict an amount of electricity to charge a battery of an electric vehicle in a time interval. The prediction can be based on the state of charge of the battery. For example, the charge predictor106can predict a difference between the state of charge of the battery and the desired state charge of the battery. For example, if an electric vehicle is on a route towards a home where the electric vehicle can be recharged with 100% renewable energy, the desired energy can be an amount of energy to allow the electric vehicle to reach the home. The charge predictor106can determine this amount according to a travel time, speed, or environment along a selected route. For example, if an electric vehicle is 200 miles from a home, and a battery pack has a state of charge of 80 kWh, the charge predictor106can predict a request to charge an additional 20 kWh, if the vehicle is presumed to travel 2 miles per kWh (e.g., according to a detected driver, temperature, traffic density, and other variables).

The charge predictor106can consider the charging efficiency of the battery and of the charging station178. For example, if a battery system156charges at an efficiency of 90%, and the battery state of charge should be increased by 20 kWh, the charge predictor106can predict an amount of electricity to charge the battery to be about 22.2 kWh. The charge predictor can determine a desired state of charge of the battery which can be a fully charged battery, or can be less than a full charged battery. For example, the amount of energy can vary based on a route of the electric vehicle. The electric vehicle can be associated with a route requiring two stops and a total charging of 120% of the battery capacity of the vehicle. The charge predictor106can determine a desired state of charge at a first charging station178based at least in part on the energy composition of the selected charging station178. For example, if the first charging station178sources a greater portion of energy from renewable energy sources, the charge predictor106can determine a desired charge to be to 100% for the first charger, and 20% for the second charger. The charge predictor106can reconcile one or more charging estimates with a measurement of an amount charged. For example, the charge predictor106can estimate a first total charge amount, and a first non-renewable charge amount. The first total charge amount, and first non-renewable charge amount can be reconciled by comparing or adjusting the prediction to the measured amount. For example, the charge predictor106can predict a total charge of 50 kWh, and an actual charge can be completed of 45 kWh. The reconciliation can update at least one element of the energy source balancer102of the actual energy use.

The energy source balancer102can include an emissions forecaster110to determine an amount of carbon emissions associated with a charging session. The emissions forecaster110can determine the carbon emissions associated with a current charging session (e.g., in real time), a historic charging session (e.g., a charging session that occurred in the past 24 hours, 48 hours, week, month, or other time interval), or predict the carbon emissions associated with a future charging session (e.g., a charging session that may occur in 24 hours, 48 hours, 1 week, 1 month, or other time interval). The emissions forecaster110can forecast emissions based on historical trends which can be determined or received from historical grid composition182. The trends can be adjusted or selected based on forecast parameters186. For example, the emissions forecaster110can estimate an energy grid composition based on weather or traffic conditions. The emissions forecaster110can consider scheduled maintenance associated with one or more fossil fuel-based power plants172or renewable resource based power plants174. For example, if a coal-fired plant is disengaged for maintenance, the emissions forecaster can predict a gas peaker plant can be engaged, or that a portion of the grid can be supplied from grid storage176. The emissions forecaster can associate one or more energy sources with an emissions amount based on the energy type or specific information related to the energy plant. The energy composition contribution of the grid storage176can be based on historical grid composition182, such as during a time of charging the grid storage176.

The emissions forecaster110can access an API of an energy grid operator (e.g., an operator of the energy grid system170) or a third party aggregator to obtain information used to forecast the amount of carbon emissions associated with a historical, current, or future charging session for an electric vehicle. For example, the emissions forecaster110can access historical grid composition182, real time grid composition184, or forecast parameters186. The emissions forecaster110can predict a portion of renewable resources or an amount of emissions based on the accessed information. The emissions forecaster110can access predictions from the energy grid operator or a third party aggregator. For example, the emissions forecaster110can receive one or more predications of future energy grid composition or emissions. The emissions forecaster110can select or refine the predictions based on available information and user preferences116. For example, the emissions forecaster110can receive a user preference116to prioritize energy source relative to route time (e.g., according to a weighted user preference116). The emissions forecaster110can determine an arrival time to the charging station based on the user preference116. The emissions forecaster110can provide (e.g., over the API) the arrival time, location, or amount of planned charge of one or more electric vehicles, which can further refine the prediction models.

The emissions forecaster110can be configured with a model trained using machine learning applied to historical charging session information and historical carbon emissions information. The training data used to the train the model can be obtained from the operator of the energy grid system170, the data repository180, or a third-party aggregator. The model can be trained with information associated with a particular charger, geographic location, energy grid system170, temperature, seasonality, or other information that can facilitate the emissions forecaster110to determine, predict, estimate, or otherwise identify an amount of carbon emissions associated with a charging session.

The energy source balancer102can include at least one action generator108to generate an action to offset a difference between the amount of electricity to charge the battery and the portion of the amount of electricity provided via the one or more renewable energy sources in the time interval. For example, the amount of electricity used to charge the battery may have been generated from multiple energy sources on in the energy grid system170, such as a fossil fuel-based power plant172and a renewable resource-based power plant174. The energy source balancer102can determine the portion of the amount of electricity generated from the renewable resource-based power plant174. The energy source balancer102can determine the portion of the amount of electricity generated from the fossil fuel-based power plant172. In this example, the difference between the amount of electricity to charge the battery and the amount of the amount of electricity provided via the one or more renewable energy sources in the time interval can refer to the amount of energy provided by the fossil fuel-based power plant172.

The energy source balancer102can generate the action to include a power demand adjustment (e.g., demand reduction) such as an adjustment to the operation of the electrical vehicle. For example, the action generator108can initiate a vehicle speed adjustment such as a decrease in highway speed, or a climate control setting such as a use of heated seats rather a passenger cabin heater. The action generator108can include a selection of a charger or charging time. For example, reaching a charger at a later or earlier time can result in a power demand adjustment, or selecting a lower charging time can lower the difference between the amount of electricity to charge the battery and the portion of the amount of electricity provided via the one or more renewable energy sources. The action generator108can cause a battery to be charged to a higher percent (e.g., 100%) than during normal operation at a first, 100% renewable charger to minimize a charge at second, 50% renewable charger, and charge a lower percent at the 50% renewable charger. For example, the action generator108can interface directly with the electric vehicle or the charging station178, can prompt a user to accept a proposed action, or can request the user perform an action.

The action generator108can generate an action of applying RECs. For example, the action can be or include using a charging station178which overprovisions RECs. Overprovisioning an REC can refer to or include a charging station178that offsets a greater portion of the energy received than is sources from renewable energy. For example, a charging station178that receives 50% of its energy from renewable resource-based power plants174, and offsets 100% of energy used can overprovision RECs by 50%. The action generator108can determine a number of offsetting RECs for an energy grid during the charging interval, and compare the number of offsetting RECs to a greater number of actual RECs associated with the charger. The action generator can obtain RECs such as by providing negative wattage demand (negawatts). For example, a battery system can accumulate RECs by alternatively sourcing a reception of energy by the battery (e.g., charging the battery) from a grid at a time when the energy grid includes a relatively high proportion of renewable energy, and discharging the battery into the energy grid when the energy grid includes a relatively low proportion of renewable energy. For example, the action generator108can perform the action in response to a previous authorization by a user (e.g., a user preference116), or by a prompt to the user to provide the energy. Energy credits can also be purchased. For example, the action generator can prompt the user to execute a transaction to make a purchase of RECs, or maintain a ledger of offsetting REC's to make purchases at regular intervals. The action generator can generate a demand reduction such as by causing a speed of the electrical vehicle to decrease, or adjusting a climate control setting (e.g., by displaying a prompt to take the action on an interface of the electric vehicle).

The electric vehicle energy system152can include at least one vehicle interface154. The electric vehicle energy system152can include at least one battery system156. The electric vehicle energy system152can include at least one data repository160.

The electric vehicle energy system152can include at least one vehicle interface154. The vehicle interface154can be or include a graphical user interface and a network connection to at least the energy source balancer. For example, the vehicle interface154can be or include a mobile device associated with the electric vehicle or a center information display (CID) of the electric vehicle which is communicatively coupled to a modem. The vehicle interface154can receive an indication from a user associated with the electric vehicle to charge a battery of the electric vehicle. Responsive to the receipt of the indication, the vehicle interface154can display a plurality of charging stations178along with energy grid compositions or energy intensities thereof. For example, the vehicle interface154can cause the energy source balancer102to determine information associated with various charging stations178, and communicate the information to the vehicle interface154.

The vehicle interface154can display the charging stations178with a greater portion of renewable energy with elevated prominence. For example, the vehicle interface154can depict a plurality of charging stations178in a map view wherein some charging stations can be bolded, starred, or shown with a green ring to denote a renewable option. The charging stations178selected for elevated prominence can be based on a minimum threshold of renewable energy supplied to the charger178, or a maximum amount of an offset for the charger. The thresholds can be dynamic (e.g., automatically varied according to chargers178available in a region or according to a user preference116), or fixed. The charging stations178selected for elevated prominence can be based on additional criteria. For example, a charging station178can be displayed based on a distance or affiliation.

The vehicle interface can infer the indication to charge the electric vehicle based on a route. For example, a 1,000 mile route selected for an electric vehicle with a 400 mile range, can provide an indication to charge the electric vehicle at least to complete the route. The vehicle interface154can include a routing function to locate charging stations178along a route of an electric vehicle or within a vicinity of the electric vehicle. The route or destination can be received by the vehicle interface154. For example, the route or destination can be entered by a user on a mobile device associated with the user, or the (CID) of the electric vehicle. The vehicle interface154can receive a route from a destination by determining the route by the electric vehicle or another resource, such as a service accessible to the electric vehicle over the network150(e.g., by a route determination module. The route can depend on various user preferences116. The distance can be based on an overall energy use (or overall carbon emissions), or based on a user preference116(e.g., for a shortest duration).

The electric vehicle energy system152can include at least one battery system156. The battery system156can include a battery and circuits to charge the battery, determine a state of charge of the battery, determine a capacity of the battery, or determine a charging speed of the battery. The battery can include a plurality of cells, cell balancing hardware, or a sensor suite reporting on the status of the battery and associated components. The battery can store energy, and the operations of the battery pack can be configured (e.g., in response to a user preference or another communication). For example, a maximum and minimum charge state can be established which can be relevant to the wear of the cells of the battery or of other components. The battery cells can include a thermal management system including a thermal management device. The battery can be, include, or be subdivided into modules or submodules which can include or be associated with battery cells and thermal management systems. Each battery, module, or submodule can include a plurality of cells such as prismatic, cylindrical, rectangular, square, cubic, flat, or pouch form factor cells.

The data repository160can include one or more local or distributed databases, and can include a database management system. The data repository160can include computer data storage or memory and can store one or more of a local user preferences162, location data164, or a battery data168. The local user preferences162can include user preferences116stored or accessibly by the electric vehicle energy system152such as a battery charge target. The location data164can include a location associated with the electric vehicle such as a location of the electric vehicle or a location along a route associated with the electric vehicle. The battery data168can include battery state information such as temperature and state of charge.

The local user preferences162can include a typical and maximum or minimum charge states of the battery of an electric vehicle, (e.g., 80% typical and 95% maximum). For example a local user preference162can include a charging efficiency preference, or a charging time threshold (e.g., the preference can result in the exclusion of low speed, low voltage charging because of efficiency and time thresholds or weights). The local user preferences162can include routing preferences, such as a routing selection which is optimized for time or for renewable energy use (or various weighted settings therebetween). The local user preferences162can include preferences for a use of charging stations178having renewable energy over charging stations178applying renewable energy offsets, or other preferences concerning renewable resource-based power plants174(e.g., a preference for solar relative to nuclear, or hydroelectric over wind). User preferences associated with the energy source balancer102can be stored as local user preferences162and conveyed to the energy source balancer102. For example, a user can prefer to accumulate information for an action to be taken at a regular interval such as monthly, quarterly or annually, and can be stored as a local user preference162or conveyed to the energy source balancer102.

FIG.2is an electric vehicle200, in accordance with some aspects. The electric vehicle can include a battery pack205. For example, the battery pack205can be or include one or more components of the battery system156. One or more local user preferences162or can affect the behavior of the battery pack205. For example, the battery pack can have a preferred charging rate or capacity. The rate or capacity can be varied according to energy use. For example, a preferred energy charge rate can be selected to maximize charging efficiency. A faster (or slower) charging speed can increase a portion of electricity supplied to the battery pack from renewable energy sources. For example, a faster charging speed can charge the vehicle prior to a sunset, a subsidence of wind, or another parameter associated with renewable energy production. Other parameters can affect the operation of the battery pack. For example, a temperature can affect a charging speed or efficiency of the battery pack. For example, at low temperatures, a battery pack205can be heated for charging, which can lower an efficiency relative to warmer temperatures (e.g., ambient temperatures or cell temperatures).

The battery pack can include or interface with one or more user preferences116. For example, a user preference116can indicate a choice to charge at home, or charge away from home. A user preference116can indicate a choice to maintain the battery within range of states of charge. For example, the user preference116indicate a preference to keep the battery between 20% and 80% state of charge. The range can be dynamic based on a time in range. For example, a user preference116can indicate a preference to maintain the battery of the electric vehicle in the range during nonoperation but permit excursions from the range during operation. For example, during or in advance of a trip, a charge of 90% or 100% can be preferred. For example, an increased charge for a trip can decrease carbon emissions, and reduce recharging time, such as when charging at home is based on renewable energy and charging during travel is variable, based on the state of one or more energy grids.

The electric vehicle200includes a charging circuit to interface the battery pack to a charging station178. The charging port210can interface, selectively, with one or more charging stations178. For example, a charging station178can be selected based on a physical connection standard, a membership or other affiliation, or an option to user a charger178without prior enrollment. The charging circuit and the charging station178can control the recharge rate, and total amount of the charge.

FIG.3depicts a graphical user interface (GUI)300, in accordance with some aspects. The GUI300can include one or more user inputs320such as audio, buttons, or wireless transceivers. The user inputs320can receive various selections, entries, responses, or requests from a user of the electric vehicle. The GUI can include one or more outputs such as audible outputs, display screens, or LEDs. The GUI300can be associated with one or more electric vehicles200. For example, the GUI300can be a center information display (CID) or other display of the electric vehicle200, or a mobile device associated with the electric vehicle200, such as a mobile device having a mobile application installed thereupon containing application data linking the mobile device to a unique identifier of the electric vehicle200.

The GUI300can display a location of the electric vehicle200relative to one or more roads, paths, or trails. The GUI300can include route information for the electric vehicle200. For example, the route information can be responsive to the state of the electric vehicle200or a state of the route. For example, a route can be selected based on a state of charge, a grid composition (e.g., a real time grid composition184), or a traffic speed along the route. The user can indicate, such as by a selection on the GUI300or an associated button, or based on a state of charge of the battery pack205(e.g., according to a user preference116), that the battery should be charged.

The GUI300can display one or more charging station locations along a route or associated with the route. For example, the GUI300can display a plurality of routes having charging stations178disposed there-along. The display can be automatic or responsive to the indication from the user. The GUI300can include information for one or more of the charging stations178or a predicted charge from the charging station178. For example, the GUI300can include a carbon intensity of a charge, a renewable portion of a charge, a cost of a charge (e.g., with or without any offsets), a time of a charge, or a time a charge can add to a total trip time. Some chargers178associated with a route can be disposed away from the route. The display of information can be adjusted to indicate the additional travel distance to reach the charger (e.g., the cost, portion of renewable energy, carbon intensity, etc.). For example, The GUI can display a charger associated with 90% renewable energy, but requiring a number of miles to drive to be doubled as being associated with 80% renewable energy to normalize the non-renewable energy use between the displayed chargers178.

Charging stations178can be depicted based on information associated therewith. For example, a first charging station location305can be shown at a first level of prominence because the first charging station location305extends the trip time of the vehicle by a shortest amount. A second charging station location310can be shown with a second, elevated prominence based on the combination of high renewable energy content and low increment to trip time. A third charging station location315can be depicted at a first level of prominence based on the highest portion of renewable energy content. Additional energy stations can be present, but not displayed, or displayed with another prominence. The GUI300can select a prominence for non-display based on the criteria to determine the prominence of the first charging station location305, second charging station location310, and third charging station location315. For example, a score exceeding a first threshold can cause the charging station locations to be displayed at a first level of prominence. A score exceeding a second threshold can cause the charging station to be displayed at the second level of prominence. According to various aspects, charging stations can be selected according to fixed or variable criteria. For example, a user preference116such as a local user preference162can determine a prominence of display for a charging station. The user preference116can specify criteria, priorities, and weights. For example, the user preferences116can include a weight for each of trip time, renewable portion, cost, and total carbon intensity.

The GUI300can depict one or more charging stations178in a map view without reference to a route. For example, the GUI300can depict charging stations178within a distance of the electric vehicle200or another distance defined by the GUI300(e.g., a distance specified by a local user preference162or by a user selection of a map area of interest). For example, the user can manipulate (e.g., scroll or zoom) the graphical user interface whereupon one or more charging stations can be displayed.

The GUI can depict one or more charging stations178in a list. For example, a list can be displayed visually or audibly. The list can include a depiction or a sorting of the charging station location according to one or more of the criteria used to determinate a prominence or display of the electric vehicle on a map view. For example, the charging stations178can be ranked according to a portion of renewable energy or a carbon intensity. The rank can generate a sorted list of chargers178which can be presented by the GUI300based on the sort order or a comparison to one or more thresholds. For example, a first (e.g., selected charger178) can have a highest portion of renewable energy or a lowest carbon intensity. For example, a greater percent of the renewable energy can be sources from renewable energy sources among a relevant set of chargers (e.g., chargers along a route or in proximity to an electric vehicle).

FIG.4illustrates the energy composition of an energy grid over time, in accordance with some aspects. The charge predictor106can record the energy composition of the grid over various discrete time segments410. The charge predictor106can define discrete time segments410as hourly, semi-hourly, minutely, or another discrete time segments410. The charge predictor106can define the discrete time segments410based on a processing or data capability of a system or device such as the energy source balancer102. For example, the charge predictor106can base the discrete time segments410on data selection practices, or the data available (e.g., available over an API from a grid operator or an aggregator of grid operators). The charge predictor106can associate each discrete time segment410with a non-renewable energy portion420and a renewable energy portion430. The charge predictor106can include additional information in a grid composition, such as a detail of energy sources which can be of interest to a user (e.g., a user can prefer to avoid a renewable energy source such as wind or nuclear, due to bird strikes or waste disposal concerns). Additional parameters can enable discrimination, by the charge predictor106or other elements of the energy source balancer102between power sources. For example, the action generator can discriminate between a grid use of a coal fired plant and a natural gas plant based on a carbon intensity.

The depicted grid composition can be a historical grid composition182, or can include forecast grid data. For example, the emission forecaster110can forecast grid demand. For example, the emissions forecaster110can evaluate a seasonality, time of day, temperature, day of week, or major events to forecast weather or energy demand or supply. The emissions forecaster can depend on forecast grid supply. For example, the emissions forecaster110can evaluate planned maintenance of any power plants, a state of grid storage176, and an expected contribution from variable sources. Some renewable resource-based power plants174can have varying but predictable outcomes. For example, the emissions forecaster110can predict solar power based on expected sunniness or cloudiness, or wind power based on an expected windiness or stillness. The emissions forecaster110can reconcile a measured condition with a predicted condition. For example, the reconciliation can improve future predictions, or update other elements of the energy balancing system102such as an action generator108(e.g., to offset a larger or smaller portion of the energy supplied to a battery of an electric vehicle).

FIG.5is a flow diagram of a method500to balance energy sources, in accordance with some aspects. The method500can be performed by one or more components or systems depicted inFIG.1-3or7, including, for example, an energy source balancer102. In brief summary, at ACT505, the energy source balancer102can predict an amount of electricity to charge a vehicle. At ACT510, the energy source balancer102can identify a charger178. At ACT515, the energy source balancer102can determine a portion of the electricity generated from renewable resources. At ACT520, the energy source balancer102can generate an action to offset at least some of the non-renewable portion.

At ACT505, the energy source balancer102can predict an amount of electricity to charge an electric vehicle200. The amount of electricity to charge the battery can depend on a current state of charge of a battery pack205of the electric vehicle200, as well as a location of the electric vehicle200. For example the energy source balancer102can determine a current state of charge of the battery pack205and compare the current state of charge to the maximum state of charge to determine an amount of energy. The amount of energy can include the amount of energy delivered to the battery pack205or any transmission power, auxiliary power (e.g., to condition the battery during or prior to charging), or charging station178power use. The energy source balancer102can receive an indication that the battery pack205will not be charged to a maximum capacity. For example, a user preference116can indicate a lower capacity, or a route associated with the electric vehicle200can be navigable with a lesser charge.

At ACT510, the energy source balancer102can identify a charger178. The charger178can be identified based on a proximity to the electric vehicle200, a proximity to the route of the electric vehicle200, or a composition of an energy grid or offset associated with the charger178. For example, the energy source balancer102can determine an approximate location for a charger178based on the state (e.g., position and state of charge) of the electric vehicle200. The energy source balancer102can access information related to a plurality of chargers178. For example, the information can be related to a type, or operator of the charger, and can include a compatibility with the charging port210of the electric vehicle. One or more chargers178can be identified. For example, the energy source balancer102can cause one or more chargers178and directions for travel thereto to be displayed by the GUI300.

At ACT515, the energy source balancer102can determine a portion of the electricity generated from renewable resources. The charger178can apply any renewable energy sourcing or renewable energy offsets known to be associated with the charger178. The energy source balancer102can access information related to the energy grid at the one or more chargers178. For example, the energy source balancer102can access predictions of energy grid composition at the time the electric vehicle200is expected at the charger178, or access forecast parameters186and predict the future composition of the energy grid. The predication can be based on (e.g., can be) the real time grid composition184. For example, if the electric vehicle200is approaching a charger178within a same discrete time segment410the vehicle is predicted to charge in, the last available recordation or estimate of real time grid composition184can be a forecasted grid composition.

At ACT520, the energy source balancer102can generate an action to offset some or all of the non-renewable portion of energy consumed during the charging session. Some offset portions can be time averaged or a ledger of actions can be generated. For example, an action can include charging the electric vehicle at a first charging station overprovisioning offsets following (or prior to) charging at a second charging station (or the first charging station at a second time) which under-provisions offsets. The action can be taken contemporaneously with charging. For example, the selection of an alternate route, or another vehicle demand adjustment can be elected at a time of charging. For example, the CID, another display of the electric vehicle the mobile device associated with the vehicle can prompt the user to take the action.

The amount of energy delivered to a battery, the composition of the electric grid, user, preferences, or a route can change between a time of prediction and the charging of the battery. The amount of electricity delivered to the vehicle or the composition of the grid can be reported to a user following the charging of the electric vehicle200, or the action can be generated in response to the completion of the charging. For example, a user can select a charger178(which may or may not be based on a reception of energy to charge the battery or a composition of the energy grid associated with the charger178) and the energy source balancer102can thereafter provide the user with an action to offset the non-renewable energy of the charger. For example, if the user maintains an REC ledger of over and under provisioned RECs, the user can be prompted to select one or more actions, which can include reducing a current REC ledger (e.g., from previous over-provisioned charges). In some implementations, an REC ledger is not maintained, or if a number of stored RECs are insufficient for the charge. The user can be prompted to execute a transaction for (e.g., purchase) an offsetting REC credit as a portion of the action or as the action. Additional variations based on this disclosure can be performed as a part of method500. Indeed, ACTs can be added (or substituted/omitted) which are not explicitly defined in the method500, based on the disclosure provided herein. For example, at least some of the various ACTs provided herein can be performed by a data processing system of the electric vehicle200, or the charger178.

FIG.6is a flow diagram of a method600to provide an electric vehicle200(ACT605), in accordance with some aspects. The electric vehicle200can be provided to a user or connected to an energy source balancer102. For example, the electric vehicle can interface with the energy source balancer102, a charger178, or another electric vehicle.

FIG.7depicts an example block diagram of an example computer system700. The computer system or computing device700can include or be used to implement one or more components of a data processing system, energy source balancer, or electric vehicle energy system. The computing system700includes at least one bus705or other communication component for communicating information and at least one processor710or processing circuit coupled to the bus705for processing information. The computing system700can also include one or more processors710or processing circuits coupled to the bus for processing information. The computing system700also includes at least one main memory715, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus705for storing information, and instructions to be executed by the processor710. The main memory715can be used for storing information during execution of instructions by the processor710. The computing system700may further include at least one read only memory (ROM)720or other static storage device coupled to the bus705for storing static information and instructions for the processor710. A storage device725, such as a solid state device, magnetic disk or optical disk, can be coupled to the bus705to persistently store information and instructions.

The computing system700may be coupled via the bus705to a display735, such as a liquid crystal display, or active matrix display, for displaying information to a user such as a driver of the electric vehicle or other end user. An input device730, such as a keyboard or voice interface may be coupled to the bus705for communicating information and commands to the processor710. The input device730can include a touch screen display735. The input device730can also include a cursor control, such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor710and for controlling cursor movement on the display735.

The processes, systems and methods described herein can be implemented by the computing system700in response to the processor710executing an arrangement of instructions contained in main memory715. Such instructions can be read into main memory715from another computer-readable medium, such as the storage device725. Execution of the arrangement of instructions contained in main memory715causes the computing system700to perform the illustrative processes described herein. One or more processors in a multi-processing arrangement may also be employed to execute the instructions contained in main memory715. Hard-wired circuitry can be used in place of or in combination with software instructions together with the systems and methods described herein. Systems and methods described herein are not limited to any specific combination of hardware circuitry and software.

Some of the description herein emphasizes the structural independence of the aspects of the system components or groupings of operations and responsibilities of these system components. Other groupings that execute similar overall operations are within the scope of the present application. Modules can be implemented in hardware or as computer instructions on a non-transient computer readable storage medium, and modules can be distributed across various hardware or computer based components.

For example, descriptions of positive and negative electrical characteristics may be reversed. For example, charging and discharging may be inverted. Elements described as negative elements can instead be configured as positive elements and elements described as positive elements can instead by configured as negative elements. For example, an amount of electricity a battery can provide can be determined instead of the amount of electricity the battery can be provided, or a predicted and measured energy portion can be substituted (e.g., an offset can be based on a predicted or measured value). Further relative parallel, perpendicular, vertical or other positioning or orientation descriptions include variations within +/−10% or +/−10 degrees of pure vertical, parallel or perpendicular positioning. References to “approximately,” “substantially” or other terms of degree include variations of +/−10% from the given measurement, unit, or range unless explicitly indicated otherwise. Coupled elements can be electrically, mechanically, or physically coupled with one another directly or with intervening elements. Scope of the systems and methods described herein is thus indicated by the appended claims, rather than the foregoing description, and changes that come within the meaning and range of equivalency of the claims are embraced therein.