Classifications

• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
  • H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
  • B60L53/60—Monitoring or controlling charging stations
  • B60L53/63—Monitoring or controlling charging stations in response to network capacity
• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
  • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
  • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
• • H—ELECTRICITY
  • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
  • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
  • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
  • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
  • H02J2310/54—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads according to a pre-established time schedule
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
  • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
  • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
  • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
  • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T10/00—Road transport of goods or passengers
  • Y02T10/60—Other road transportation technologies with climate change mitigation effect
  • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T10/00—Road transport of goods or passengers
  • Y02T10/60—Other road transportation technologies with climate change mitigation effect
  • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
  • Y02T90/10—Technologies relating to charging of electric vehicles
  • Y02T90/12—Electric charging stations
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
  • Y02T90/10—Technologies relating to charging of electric vehicles
  • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
  • Y02T90/167—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]
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
  • 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
  • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
  • Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
  • Y04S10/126—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation the energy generation units being or involving electric vehicles [EV] or hybrid vehicles [HEV], i.e. power aggregation of EV or HEV, vehicle to grid arrangements [V2G]
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
  • 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
  • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
  • Y04S20/20—End-user application control systems
  • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
  • 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
  • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
  • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
  • Y04S30/12—Remote or cooperative charging

Definitions

• the invention relates to a method of allocating electrical power grid capacity to vehicles for optimal charging, and components of a corresponding system, and also to a network communications concept to allow vehicles to guide the provisioning by the power grid to the vehicles and to allow the power grid to optimize load usage by a vehicle or vehicles connected to the grid.
• An ideal network grid might be configured to receive and automatically analyze various types of information, including, without limitation, information from energy generators, information on the status of different facilities for transporting power, and also information on renewable generation including expected or estimated generating capacity profiles in coming hours or days. Generating capacity for renewables may be more or less predictable and subject to change, e.g. in the case of solar or wind generation.
• information on user behavior e.g. over-ride any algorithm due to immediate charging need
• information on expected short-term future requirements can be used to further improve load distribution and load levelling for a power grid.
• Today's power grid is generally managed top-down. As distributed, local, and renewable generation and non-generating loads proliferate across territories, markets may change to use these resources to contribute to the supply-demand balance needed for the safe and effective operation of the electrical power grid. This includes but is not limited to the battery/load states of connected electric vehicles and charging stations.
• a typical Electric Vehicle may consume from 5 to 20 KWh of energy daily to recharge batteries.
• Other electric propulsion means such as e-bikes or electrical scooters may use less, and commercial vehicles may use substantially more.
• the time to transfer that amount of energy from the AC grid to the vehicle's battery for a typical recharge may be 90 minutes. However, the amount of time available for such a recharge is generally over 23 hours during a typical 24-hour day.
• the knowledge of how much electrical power a vehicle may require and the knowledge of when and where this power will be needed, is most likely to be available at the vehicle - if it is known at all.
• charging time may be available at destinations such as restaurants, stores and cinemas. The delta between time available and actual time required for charging may create an opportunity to reduce the instantaneous charging power load requirements and still insure a sufficiently charged battery for normal use.
• This ability to reduce instantaneous charging power can be used for the benefit of the power grid, to modulate the instantaneous electrical power drawn by a fleet of EVs by modulating charging current for each EV in cooperation with other EV’s and charging stations. Such modulation capability can then be used to provide various stabilization services to the electrical power grid (e.g., Demand Management, Frequency Regulation, Peak Shaving, Economic Demand Response, quick response for renewable energy sources, etc.). Similar to EVs, charging of other energy storage devices, such as home energy storage batteries, may also be modulated to stabilize the electrical power grid.
• energy storage devices such as home energy storage batteries
• Modulation must consider the objectives of each stakeholder, including the larger power grid, connected and islanded micro-grids, substations, renewable generation facilities, local premises, energy customers, and the generation or storage needs of each.
• Equipment that provides charging services may be improved by capability to communicate and receive inputs to identify optimal charging needs. Means are needed to collect usage pattern data from a specific user or groups of users. Likewise, information about existing conditions such as climate or weather, current pricing of each stakeholder, CO2 associated with the generation of electrical power, or environmental conditions may be relevant for modulating power consumption.
• objectives which can be achieved by improved modulation or load-levelling include grid energy balancing, revenue maximization for the operator of the grid on both the wholesale and retail levels, protection or deferment of critical infrastructure investments, or environmental goals such as greenhouse gas emission mitigation or CCte-based pricing.
• a sole charger or selected, aggregated groupings of chargers are configured to start, modulate or stop charging, or start, modulate (down) or stop discharging over specific time intervals based on the electrical grid needs as automatically determined based on the totality of the available information.
• a system and an associated method are provided to perform complete electrical charging load distribution.
• Information is collated and analyzed where it is available, to reduce data transfer volume, increase reaction speed, and minimize transfer of data which is difficult to transfer due to volume, coding requirements, legal restrictions, etc.
• participating vehicles with storage capability share a requirements profile.
• the object of the invention is attained with a method according to claim 1 .
• Advantageous embodiments of the invention are subject-matter of the dependent claims.
• Fig. 1 illustrates a daily State of Charge of a battery
• Fig. 2 shows a local power grid with participating vehicles
• Fig. 3 shows a charger set-up for a participating vehicle
• Fig. 4 shows a requirements profile
• Fig. 5 shows the steps of the inventive method
• Fig. 6 shows an exemplary computation of a charging schedule.
• FIG. 1 a possible State of Charge (SOC) 141 for the battery of an electric vehicle.
• SOC State of Charge
• the average charge may be 1/2 of the maximum capacity 140, and in this example the charge level 141 never drops below 1/4 of the battery’s rated capacity.
• a participating vehicle determines its profile or prediction for a coming time window and offers this as an electrical requirements profile to a scheduler or schedulers for the portion of the power grid to which the participating vehicle is connected.
• the electrical requirements profile may also include information on user's short-term energy needs (e.g. ignore any optimization due to immediate charging need), information on renewable generation, including, without limitation, solar, wind, biomass and/or hydro, and information on environmental conditions including, without limitation, barometric pressure, temperature, ambient light intensity, humidity, air speed, and air quality.
• a sole novel charger or selected, aggregated groupings of the aforesaid novel chargers are configured to start, modulate or stop charging, or start, modulate (down) or stop discharging over specific time intervals based on the electrical power grid needs.
• the requirements profile of one or more vehicles is provided as an additional factor in order to optimize the charging.
• the requirements profile should provide no more than or nearly the minimum of data to allow the power grid or grid scheduler to create a composite view of the electrical demand profile.
• a central station or scheduler will generate charging profiles for participating vehicles to satisfy the request of the requirements profile while respecting - to the extent possible - the constraints of the power grid.
• the scheduler provides a corresponding charging profile to each vehicle charger.
• a system and an associated method are envisaged which perform complete electrical charging load modeling to optimize power grid objectives.
• One example objective might be to minimize “fast” charging, i.e. charging with the maximum rate or maximum capacity.
• Another such objective might be to use lower-cost electricity, or to use renewable electricity, or to load supplemental electricity which can be returned to the grid later for load-levelling, i.e. local short-term storage.
• a vehicle which may be a battery-electric vehicle (BEV) including plug-in hybrids.
• BEV battery-electric vehicle
• plug-in hybrids plug-in hybrids
• FIG. 2 shows a local power grid or electrical grid and connection to vehicles 241 , 242, 243, 244.
• the physical grid 221 is at least conceptually distinct from the supplier participants in a stock market 201 , a grid provider 202, or an energy provider or electricity producer 203. All information to operate the grid and supply to customers is centralized over a central scheduler 210.
• the platform is linked to a smart grid interface 220 via communications links 231 such as SIO 15118 or OCCP. Electricity physically transits to the charger 225, and from the charger to the battery of the participating vehicle 244.
• the charger may be located in the vehicle, or it may be located in a Wall Box. In one embodiment, all vehicles shown are plugged into the local grid, and all are participating vehicles.
• One or more schedulers collect a requirements profile from each participating vehicle, and in return provide a charging profile to each participating vehicle.
• the local grid capabilities are determinative for the power grid capacity.
• the local grid is connected to a larger general power grid, whose capacities are also to be respected.
• Information about the local and or general power grid may be provided to one or more schedulers, which in turn provide an estimated or improved charging profile to one or more vehicles which will be charging from the local grid.
• One objective may be charging profiles which are configured such that the combined demand or load due to all the charging profiles of all participating vehicles still respects both requirements profiles and the capabilities and capacities of the local grid and/or the general grid.
• Fig. 3 shows a charger set-up for a participating vehicle.
• a charger 325 cooperates with both the power grid 321 (at the local grid level) and with the participating vehicle 344.
• the participating vehicle receives a charging profile from the power grid in the form of the scheduler 310 in a platform, which communicates with the physical grid 321 via a link 331 , and with the charger 325 via the charger interface 320 and the link 332.
• the charger 325 charges the battery 326 in the participating vehicle.
• the platform provides the information which the vehicle and the charger need to determine the instantaneous and average power transfer.
• the charger may be located in the vehicle 344 (as shown), or may be in a separate Wall Box. Communications over the links 331 , 332 may, in one embodiment, used the protocol ISO 15118 or OCCP.
• the link 322 between charger interface and the scheduler 310 may use powerline communications, internet communications, wireless communications, or any combination of these.
• the participating vehicle or the corresponding charger first indicates that it is or corresponds to a vehicle participating in load distribution.
• the vehicle or charger either establishes a time window for distributing load, or receives this from a central scheduler. Then the vehicle or charger sends an electrical requirements profile and receives a charging profile for the participating vehicle via the charger interface. The participating vehicle or charger then charges according to the charging profile during the time window.
• the vehicle and charger may transfer power to the power grid if an additional supply of power is needed, using the same links and connections.
• FIG 4 is shown an example of an electrical requirements profile 400.
• This embodiment includes the fields with a minimum of information for scheduling the charging.
• Total Power to Add to the battery 410 gives the number of Kilowatt Hours of charge to be added during charging.
• Total Power Deliver-by Time 420 gives the anticipated or expected time by which this charge should be added.
• the Deliver-by time may be an exact value, or may be estimated based e.g. on past events.
• the Max Power Delivery Rate 430 gives the maximum instantaneous amount of power that the battery or vehicle can accept. This may also be a curve or a function which changes as the level of charge of the battery increases.
• the Power Storage Capability 440 give the total amount of power which the battery or the vehicle can accept or store. This too may be a curve or a function which changes over time or other parameters, or a set of values.
• the participating vehicle charger develops a requirements profile based both on the physical characteristics of the battery to be charged, and on an estimate or approximation of what the future power requirements will be during a time window. Future power requirements might be determined based on considerations which include a known or estimated departure time, a known or estimated distance before the next opportunity for charging, or a known or estimated heating or cooling requirement.
• the requirements profile contains a minimum of information, which might be the maximum instantaneous charging capacity of the battery in amps, volts, and/or watts.
• the considerations derived from the heating or cooling requirements might use information about weather conditions, but also information about preferences of a driver or occupant. For example, a certain passenger may be transported on weekday evenings after work, and prefer that the interior of the vehicle be warm. In this case, the consideration used to determine or estimate the future power requirements might include additional power in the winter, and a slight reduction in power requirements in the summer. If this information is maintained at the vehicle and used at the vehicle, then the disadvantages of transferring this data can be avoided: less data volume, less data to encode, and no concerns about transferring data which must be protected if it is personal data.
• Another example might be an estimated departure time based on the location and the day of the week.
• the participating vehicle may be parked at work on workdays from 9 am to 5 pm.
• the participating vehicle may estimate the departure time on weekdays to be 5 pm, starting at arrival at 9 am at the parking spot.
• the vehicle may not estimate the departure time to be 5 pm if the vehicle arrives at the same parking spot on Saturday.
• the driver may wish to override the estimated departure time if the driver plans to leave early.
• the requirements profile provides information from a participating vehicle for an 8-hour period. It is estimated that at the end of 5 hours, the participating vehicle will need a minimum of 5 kWh additional power and after that will no longer be a participating vehicle. Before then, the vehicle would like to add electrical power to the battery, or charge, at an optimal rate of 1 kW per hour. The vehicle is capable of charging at a maximum rate of 3 kW per hour.
• the participating vehicle may be parked in a garage where it is plugged in to the power grid, and it predicts to leave the garage in 5 hours. After leaving the garage, it predicts to need 5 kWh for driving until the next occasion where it will be connected to the power grid for charging.
• the scheduler may provide a charging profile of constant 3 kW in a first hour time window, to provide a minimum of power to the battery, then an hour time window of no charge, then 1 kW until the battery is full, and then no charge.
• the requirements profile of Figure 4 may be updated continuously, or it may be updated at regular intervals. In another embodiment, the requirements profile may only be updated when there is a change, for example the predicted time of unplugging changes. The requirements profile may also be updated when a new vehicle becomes a participating vehicle or is no longer a participating vehicle.
• the requirements profile provided by the vehicle should contain the minimum of data needed by the power grid.
• the requirements profile comprises a charge-by time field and a minimum and a maximum amount of electrical power (e.g. kWh) which the vehicle would like to receive by the time in the charge-by field.
• the requirements profile may include additional information, such as preference levels for the amount of electrical power and the respective charge-by time, or pricing information which is to be used in determining how much power the vehicle would like to receive.
• the charging profile for a time window may also provide a simple constant charge, or it may be a complex curve or function. Likewise, the time window may be updated continuously or be for a longer period.
• the requirements profile may not include all detailed information or information with a personal content, such as the exact time of departure or the next destination or destinations.
• the detailed information will be kept with the vehicle and used at the vehicle to create a requirements profile. Only the information necessary for the generation of charging profiles need be transferred.
• a requirements profile is generated by a participating vehicle and collected by the platform.
• the requirements profile may be passed to one or more schedulers.
• the Power Grid availability & capabilities are collected.
• a scheduler combines at least one requirements profile with information about the requirements of the power grid.
• the charging profiles are distributed to participating vehicles. The scheduler generates a charging profile for at least one participating vehicle in the fourth step. Based on the charging profile, the participating vehicle receives current and charges according to the charging profile in step 550.
• the instantaneous and average power drawn by the participating vehicle in the fifth step may be controlled at a charging station, or at the vehicle, or by the two working in cooperation under the direction of the Charger 325.
• Figure 6 shows the computation of an exemplary charging schedule.
• the user Via user interface 615, the user set the preferred parameters. That includes, but is not limited to minimum range, preferred charging spots, preferred cabin temperature, usual departure times, special charging tariffs. Furthermore, he can grant access to external data sources that may include but is not limited to smart home data, charging spot availability, price indications for parking and charging spots 622 as well as information arising from loT devices 621 . All user-centric parameters can be set manually but can also be inferred by machine learning techniques. Additionally, the user can stop, proceed, initiate, adjust and overwrite a computed charging schedule or any inferred parameters via the user interface. The computation of the charging schedule starts with the storage of historical and recent mobility and car data by modules 601 and 602.
• the mobility pattern may be detected by GPS signals, plug/unplug events at particular charging spots or connection/disconnection events in case of a wireless charging system. At the least, any event may be tagged with a timestamp and a location label. Between any unplug and consecutive plug event may lie a time period which consists of at least a driving and optionally idle periods. The parameters that describe the driving and idle period or periods are logged as well. This may include, but is not limited to driving time, idle time, velocity profile, averages of the latter and distance traveled.
• the pattern analysis module 604 is accessed by the prediction modules 605, 606, 607 and 608.
• the pattern analysis module 604 enriches the data from module 601 and 602 with external customer data. That may include, but is not limited to calendar data, public holidays, planned holidays, and loT devices. Thus, the consecutive mobility pattern prediction modules can detect if a certain pattern has its origin in external events.
• the module 605 predicts the next location among a set of feasible next locations. In a further embodiment it is also feasible to assign a probability value to any feasible next location.
• the prediction is based on historical and recent vehicle pattern as well as live parameters.
• the live parameters may include, but are not limited to, the current vehicle location and the current timestamp.
• the prediction can be implemented by, but is not limited to, a Markov model, a hidden Markov model, or Al techniques like classification with feedforward neural network, a recurrent neural network or convolutional neural network in combination with the latter ones.
• module 606 predicts the departure time from the current location. That could be an absolute value, as well as a probability distribution.
• the prediction can be implemented by, but is not limited to, a regression model, kernel density estimation or Al techniques like (recurrent) mixture density networks.
• module 607 predicts the corresponding driving and idle parameters. That may include, but is not limited to, driving distance, driving time, average velocity profiles, idle time until reaching the next location. All parameters may be absolute values or probability distributions. Some parameters, like average velocity may be influenced by external sources like the traffic flow. Thus, module 607 access information content that may be provided by external sources. That may include, but is not limited to, external information sources like in module 616-622.
• the prediction can be implemented by regression models, kernel density estimation or Al techniques like (recurrent) mixture density networks.
• the arrival time prediction module 608 predicts the arrival time for every location.
• the prediction can be an absolute value as well as a probability distribution, and is based on the driving and idle parameters prediction from module 607.
• the arrival time is made up of the departure time, the driving time and the idle times that may be spend at locations that are not in the feasible set of next locations.
• the predictions from 605-608 form a mobility pattern that compromises of a sequence of locations and the corresponding arrival and departure times.
• the sequence is not limited to a pre-defined length and could stretch from 1 to any value, that is feasible in terms of optimization horizon and price fluctuations.
• the energy demand between any pair of locations is inferred by the vehicle energy consumption module 610.
• the prediction is based on parameters from module 607 and external data.
• the external data may include, but is not limited to, ambient temperature and average velocity profiles from real time traffic providers.
• the energy consumption compromises the energy demand due to driving as well as auxiliary demands.
• the prediction can be implemented by vehicle consumption maps, classical regression models, as well as Al techniques such as feedforward networks or mixture density networks.
• Module 611 assigns to every mobility pattern from module 609 an energy demand for driving and auxiliaries between any pair of locations.
• the optimization module 612 computes the charging schedule on the basis of the mobility pattern from module 609, the energy demand between locations from module 611 , as well as external sources from module 616-622.
• the optimization may be guided but is not limited to, a time-dependent price signal, smart home data, like surplus of photovoltaic capacity or home storage batteries 617, loT devices 621 , hardware efficiency at different charging powers and/or levels 623 as well as flexible offers from the energy market 616 or aggregators 618.
• the optimization may be constrained by, but is not limited to, a maximum load signal by the electrical grid 619 or grid providers, as well as user constraints 615 like departure time or minimum range.
• the vehicle parameters of module 623 may impose other constraints, like maximum charging rate with respect to SOC, SOH or any other feasible hardware constraint.
• the optimization can be implemented by any feasible optimization technique like linear programming, dynamic programming, stochastic dynamic programming or Al techniques such as reinforcement learning.
• the computed charging schedule from module 613 can be visualized by module 614.
• the visualization may include, but is not limited to, any end user device as well as vehicle HMI 615.
• the charging schedule can be fully or partly shared with other vehicles 620, loT devices 621 , Aggregators 618, Energy Markets 616, Grid Providers 619, Smart Home 617 and Smart Infrastructue Providers 622 to enable iterative optimization.
• the computed charging schedules can be an assistance for the reservation of charging spots.
• the user may be briefed via the user interface 615. This enables the booking of a charging spot in coordination with the smart infrastructure module 622. It indicates the booking plan, maximum charging rates as well as price indications.

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• Engineering & Computer Science (AREA)
• Power Engineering (AREA)
• Transportation (AREA)
• Mechanical Engineering (AREA)
• Charge And Discharge Circuits For Batteries Or The Like (AREA)
• Electric Propulsion And Braking For Vehicles (AREA)

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