WO2020208654A1 - Système et procédé de gestion de charge de véhicule électrique - Google Patents

Système et procédé de gestion de charge de véhicule électrique Download PDF

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Publication number
WO2020208654A1
WO2020208654A1 PCT/IN2020/050343 IN2020050343W WO2020208654A1 WO 2020208654 A1 WO2020208654 A1 WO 2020208654A1 IN 2020050343 W IN2020050343 W IN 2020050343W WO 2020208654 A1 WO2020208654 A1 WO 2020208654A1
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WO
WIPO (PCT)
Prior art keywords
charging
driver
geographic location
charging station
stations
Prior art date
Application number
PCT/IN2020/050343
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English (en)
Inventor
Atul ARYA
Praveen MALAV
Dhommata Naresh KUMAR
Yogesh Kumar
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Panasonic India Pvt. Ltd.
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Publication date
Application filed by Panasonic India Pvt. Ltd. filed Critical Panasonic India Pvt. Ltd.
Publication of WO2020208654A1 publication Critical patent/WO2020208654A1/fr

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Classifications

    • 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
    • B60L53/68—Off-site monitoring or control, e.g. remote control
    • 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

Definitions

  • the present subject matter relates, in general, to electric vehicles and, in particular, to electric vehicle charging.
  • Electric vehicles use motors for propulsion, instead of the common internal combustion engine.
  • EVs include, but are not limited to, road and rail vehicles, aircrafts, spacecrafts and underwater vessels.
  • EVs store electricity in an energy storing device, such as a battery, which can be charged from time to time.
  • EVs produce no emissions, have less maintenance costs and provide ease of operation.
  • advances in technologies such as storage cell design, braking regeneration, and motor efficiency have made electric vehicles a viable alternative to vehicles powered by internal combustion engines.
  • Figure 1 shows a network environment implemented for electrical vehicle (EV) charging, in accordance with an embodiment of the present subject matter.
  • Figure 2 illustrates a battery management system for an EV, according to an implementation of the present subject matter.
  • Figure 3 shows a charging management terminal for managing charging of EVs, according to an embodiment of the present subject matter.
  • Figure 4 illustrates a method for managing charging of EVs, in accordance with an implementation of the present subject matter.
  • Figure 5 depicts a method of receiving charging order for EVs, in accordance with an implementation of the present subject matter.
  • Electric vehicles are commonly powered by on-board battery packs.
  • Battery packs generally include multiple batteries to store charge, that is used to drive the EVs.
  • the battery packs lose their stored charge and have to be recharged.
  • the storage capability of an on-board battery packs used in the EVs is limited owing to different limitations, such as available space and allowable weight.
  • Such constrains are often dictated by design of the EVs. For example, in case of e-rickshaws, space for the battery may be constrained to allow more space for passengers. Consequently, EVs have a limited range of travel and have to be recharged frequently, for instance, on a daily basis, if not more frequently.
  • Electric charging stations are provided to supply electric energy to EVs for the recharging of battery packs. While some charging stations can be installed at local premise of users, public charging stations are also provided to support recharging of EVs during transit. Public transport EVs, such as e-busses, e-rikshaws and e-scooters rely on public charging stations to get their battery packs recharged. Public charging stations can be used by EV owners on payment of fees in proportion to consumption of the electricity or duration of charging.
  • the described techniques allow determination of information for charging the EV at a charging station based on various operating factors.
  • Determination of whether an EV requires recharging is based on the level of charge available in the battery pack.
  • the charge level of the battery pack of the EV may be monitored.
  • the charge of the battery pack of the EV may deplete with time and the depletion of charge may vary depending of various factors, such as duration of use of the EV, load of EV during operation, type of peripherals utilized during operation, temperature of operation of the EV, driving behavior of the driver, terrain on which the EV is utilized, etc. Thus, depending on such factors, sometimes the charge of the battery pack may last for one day, while in other situations, the charge may deplete earlier.
  • the charge level of the battery pack is determined to be below a threshold value, it is identified that the EV requires recharging of the battery pack.
  • geographic location of the EV may also be identified.
  • the geographic location of the EV may be identified based on various known techniques, such as through use of Global Positioning System (GPS), triangulation through mobile towers, assisted GPS (A-GPS), and the like.
  • GPS Global Positioning System
  • A-GPS assisted GPS
  • one or more charging stations in the vicinity of the geographic location of the EV may be identified. For example, charging stations within 2-3 Kilometer (Km) radius of the geographic location of the EV may be identified. It would be noted that the vicinity within which the charging stations are determined may depend on the charge left in the battery pack of the EV. For example, if the charge remaining in the EV can allow the EV to travel to about 10 Kms, charging stations within a radius of 7-8 Kms may be identified.
  • an optimum charging station may be identified for charging the EV.
  • the identification of the optimum charging station for the EV may depend of various factors that may include, but are not limited to, distance between the geographic location of the EV and the charging stations, amount of charge left in the EV, current congestion at the charging station or availability of the charging ports at the charging station, traffic congestion between the geographic location of the EV and the charging station, ease of reaching to the charging station, charging capabilities of the charging station, offers available at the charging station, fee of charging levied by the charging station, and driver categorization.
  • each of the factors may be assigned a weightage.
  • driver categorization may also be assigned a weightage for determination of the optimum charging station.
  • the driver categorization may be indicative of driving behavior of the driver.
  • driver categorization‘A’ may indicate a driving behavior that is better than a driver categorization‘B’.
  • driver categorizations such as driver categorization ‘G, driver categorization ‘2’, . , driver categorization‘n’ may exist where each driver categorization is indicative of a ranking provided to the driver of the EV based on the driving behavior of the driver.
  • the driving behavior may be monitored over a predefined period of time.
  • the driving behavior may be determined based on various operational parameters of the EV, where the various operational parameters are determined by monitoring the usage of the EV over a period of time. For instance, speed of the EV may be monitored continuously to identify the instances when the driver over sped. More than a predefined number of instances of over speeding over a predefined period of time may be indicate of a poor driving behavior. In another example, erratic driving actions, such as lane changing without activating a turn indicator may also be used together with the instances of over speeding to determine the driving behavior. In yet another example, information regarding any traffic violations and accidents that the driver may have been involved in may also be accounted for determination of the driver behavior and the driver categorization.
  • the driver may be assigned a driver categorization.
  • the drivers with similar driving behavior may be assigned a same driver categorization.
  • certain charging stations may only be provided to drivers who have been categorized in top categories based on their driving behavior. For example, drivers with driving behavior better than others, may be assigned a nearer charging station or preferable time slot for charging at a charging station for their respective EVs. Therefore, the determination of the optimum charging station may also be based on the category into which driver of the EV has been classified into.
  • a charging order may be issued to the EV, indicating a time slot and choice of charging station to be utilized for charging of the EV.
  • the present described techniques thus allow for timely and effective charging of the EVs, even in situations where driver of the EVs are unaware of nearby charging stations. Moreover, since the selection of the charging stations is based on various factors, an optimum charging station may be chosen effectively for the recharging of the EV.
  • FIG. 1 illustrates a network environment 100 implemented for EV charging, in accordance with an embodiment of the present subject matter.
  • the environment 100 includes multiple EVs, such as EVs 102-1 and 102-2.
  • the EVs 102-1 and 102-2 may be communicatively connected to an electric vehicle management system (EVMS) 104 through a network 106.
  • EVMS electric vehicle management system
  • the EVs 102-1 and 102-2 may be e-rickshaws, e- motorbikes or any other EVs that may have battery packs and may need to charge the battery packs from time to time by connecting to a charging station. Further, the EVs 102-1 and 102-2 may also include hybrid vehicles that are powered not only by battery packs but are also powered by oil. However, such hybrid vehicles may also require recharging of battery packs and may connect to the EVMS 104 for effective charging. Fir the ease of reference, the EVs 102-2 and 102-2 have been commonly referred to as EVs 102, hereinafter. Further, although two EV 102 have been depicted in the network environment 100, it would be noted that the multiple such EVs may ply on roads and may be connected to the EVMS 104 through the network 106.
  • the EVMS 104 may be implemented as any known computing, such as a rack server, a blade server, a mainframe computer, a desktop, a personal computer, a notebook or portable computer, a workstation, and a laptop. Further, in one example, the EVMS 104 may be a distributed or centralized network system in which different computing devices may host one or more of the hardware or software components of the EVMS 102. It would be noted that the EVMS 102 may either operate remotely or lie within the vicinity of the EVs.
  • the network 106 may be a single network or a combination of multiple networks and may use a variety of different communication protocols.
  • the network 106 may be a wireless or a wired network, or a combination thereof. Examples of such individual networks include, but are not limited to, Global System for Mobile Communication (GSM) network, Universal Mobile Telecommunications System (UMTS) network, Personal Communications Service (PCS) network, Time Division Multiple Access (TDMA) network, Code Division Multiple Access (CDMA) network, Next Generation Network (NON), Public Switched Telephone Network (PSTN).
  • GSM Global System for Mobile Communication
  • UMTS Universal Mobile Telecommunications System
  • PCS Personal Communications Service
  • TDMA Time Division Multiple Access
  • CDMA Code Division Multiple Access
  • NON Next Generation Network
  • PSTN Public Switched Telephone Network
  • the network 106 includes various network entities, such as gateways, routers; however, such details have been omitted for the sake of brevity of the present description.
  • the EVMS 104 may determine an optimum charging station for recharging of EVs 102 based on consideration of various factors.
  • the various factors may be stored in a database 108, communicatively coupled to the EVMS 104.
  • the database 108 may either be implemented within the EVMS 104 as an internal database or may be implemented as outside EVMS 104 as an external database.
  • the network environment 100 may further include charging stations
  • the charging stations are communicatively connected to the EVMS 104 through the network 106.
  • the charging stations 110-1, 110-2, 110-n have been commonly referred to as charging stations 110, hereinafter.
  • the charging stations 110 connected through the network 106 may support different payment methods, such as ChargePoint, Blink, Greenlots, eVgo, Aerovironment, Azra, SemaConnect, Circuit Electrique, ReseauVer, Sun Country Highway, BHIM, Bharat QR and Unified Payment Interface (UPI). Further, depending on the charging station, different rates of charging may apply, that may vary include session/monthly/yearly charges or flat charges. Some charging stations 110 may also provide free energy to charge the battery packs of the EVs 102.
  • different payment methods such as ChargePoint, Blink, Greenlots, eVgo, Aerovironment, Azra, SemaConnect, Circuit Electrique, ReseauVer, Sun Country Highway, BHIM, Bharat QR and Unified Payment Interface (UPI).
  • different rates of charging may apply, that may vary include session/monthly/yearly charges or flat charges.
  • Some charging stations 110 may also provide free energy to charge the battery packs of the EVs 102.
  • the EVs 102 may include, apart from other things, a battery management system (BMS) 112, one or more battery packs 114, at least one charging connector 116, and a sensor assemblyl l8.
  • BMS battery management system
  • the BMS 112 may monitor and control the utilization and charging of the battery packs 114. Further, the BMS 112 may also communicate with the charging connector 116 to control charging of the battery packs 114. Therefore, the BMS 112 can be understood to act as a control terminal of the battery packs 114. Among other functions, the BMS 112 also manages battery pack functions, such as monitoring current battery pack charge level, ensuring low consumption of the battery packs when in inactive mode, prevention from overcharging and under discharge. For the purpose of managing the battery functions, the BMS 112 measures various parameters of the battery packs 114, such as battery cell voltage, temperature, etc.
  • the BMS 112 may also communicate with the sensor assembly 118 to receive various operational parameters of the EV 102.
  • the BMS 112 may receive parameters, such as speed or location of the EV 102.
  • the sensor assembly 118 may include multiple sensors to monitor and gather data corresponding to various parameters of the EV 102.
  • the battery packs 114 may include lithium ion batteries.
  • Other examples of the battery pack 114 may include, nickel metal hydride batteries, graphene batteries, cadmium batteries, sodium or zebra batteries. Dur to their utility and advantages over other types of batteries, lithium ion batteries, may be used in the EVs 102.
  • a typical lithium ion battery cell yields 80-90% of discharge efficiency.
  • Examples of the types of lithium ion batteries that can be incorporated in the EVs 102 can be NCA, NMC, LMO, and LifeP04.
  • the EVs 102 includes a charging connector 116.
  • the charging connector 116 may be understood to be an input terminal to receive power and charge the battery packs 114. Examples of the charging connector 116 include but are not limited to mode 2, mode 3 charger or a plug type 1, or type 2. In accordance with an example embodiment of the present subject matter, the charging connector 116 may function in response to instructions given by a BMS 112.
  • FIG. 2 illustrates a battery management system (BMS) 112 for an EV, according to an implementation of the present subject matter.
  • the BMS 112 includes an interaction engine 202, a control engine 204 and a data store 206.
  • the BMS may act as a control to the battery packs 114 of the EV 102 and may communicate with other elements of the EV 102, such as sensor assembly 118 (not shown) and charging connector 116 (not shown).
  • the interaction engine 202 of the BMS 112 may facilitate communication between the BMS 112 and the sensor assembly 118 and charging connector 116. Further, the interaction engine 202 may also facilitate communication between the BMS 112 and the EVMS 104.
  • the communication may typically be based on known communication protocols.
  • the interaction engine 202 may use several methods of serial or parallel communication, for instance and not limited to, CAN bus communication, which is commonly used in automotive environments, DC-BUS for serial communication, and different types of wireless communication.
  • the communication protocols can vary as per the hardware implementation.
  • the interaction engine 202 communicates internally with various sensors of the sensor assembly 118 (not shown) that may be installed on the EVs 102 and transmits the inputs obtained from the sensors to the EVMS 104. Based on the inputs obtained from the sensor assembly 118 and other battery information communicated by the interaction engine 202 to the EVMS 104, the EVMS 104 provides control information to the control engine 204 of the BMS 112 to take actions to control the battery packs 114 and in turn the operation of the EVs 102. For example, the interaction engine 202 may receive charging orders from the EVMS 104 and may provide such charging orders to the control engine 204.
  • the control engine 204 may control the functioning of the battery packs
  • the control engine 204 may be responsible for controlling the trigger signals to the charging connector which may determine charging of the battery packs 114.
  • the control engine 204 controls the charging of the battery packs 114 as per predefined rule.
  • the control engine 204 instructs the charging connector 116 (not shown) of the EVs 102 to either connect to the supply terminals of the charging station 110 (not shown) or stop the charging process.
  • the control engine 204 also controls the charging schedule for the vehicle by ensuring the charging is done as per the charging orders received from the EVMS 104.
  • the control engine 204 also ensures that the charging of the battery packs 114 is in accordance with instructions provided by the EVMS 104, if any. For example, if the EVMS 104 determines a rate of charging for the battery packs 114, the control engine 204 ensures that the battery packs 114 charge at the specified rate by controlling the charging connecter 116 accordingly.
  • the engine(s) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement certain functionalities of the engine(s), such as transmitting signals.
  • programming for example, programmable instructions
  • engine(s) may be implemented in several different ways.
  • engine(s) may be implemented by electronic circuitry.
  • the data store 206 may be understood as a memory component to store various data collated, manipulated or otherwise used by the BMS 112 in its operation.
  • the memory component may include any computer- readable medium including, for example, volatile memory (e.g., RAM), and/or non volatile memory (e.g., EEPROM, flash memory, etc.).
  • the data store 206 may store information, for example and not limited to, inputs obtained from the sensor assembly 118 installed on the EVs 102, such as EV geographic location or current charge level information.
  • the data store 206 may also store instructions communicated to the BMS 112 by the EVMS 104, such as charging orders.
  • Other examples of information that the data store 206 may store are: customer rating in case of an electric rickshaw, battery maintenance data, drivers credit information, driver’s history information, information regarding driving actions.
  • FIG. 3 schematically depicts various components of an electric vehicle management system (EVMS) 104, according to an example of the present subject matter.
  • the EVMS 104 includes processor(s) 302 and memory 304 and interface(s) 306 coupled to the processor(s) 302.
  • the processor(s) 302 may be implemented as microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions.
  • the processor(s) 302 is configured to fetch and execute computer- readable instructions stored in a memory 304 of the EVMS 104.
  • the memory 304 may include any computer-readable medium including, for example, volatile memory (e.g., RAM), and/or non-volatile memory (e.g., EEPROM, flash memory, etc.).
  • processors may be provided through the use of dedicated hardware as well as hardware capable of executing software.
  • the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared.
  • explicit use of the term“processor” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), non-volatile storage.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • ROM read only memory
  • RAM random access memory
  • non-volatile storage non-volatile storage.
  • Other hardware conventional and/or custom, may also be included.
  • the interface(s) 306 may include a variety of software and hardware interfaces that allow the EVMS 104 to interact with BMS 112 of one or more EVs 102.
  • the EVMS 104 may further include engines 308 and data 310.
  • the engines 308 may either be implemented within the EVMS 104 in the memory 304, or may reside in an external database, such as database 108 (not shown).
  • the engines 308 include routines, programs, objects, components, data structures, and the like, which perform particular tasks or implement particular abstract data types.
  • the engines 308 may include an analysis engine 312, a communication engine 314, and other engines 316.
  • the communication engine 314 may allow the EVMS 104 to communicate with the EVs 102 and its components, such as BMS 112, and charging stations 110.
  • the analysis engine 312 may analyze different factors and data provided by the BMS 112 to determine charging order to the EVs 102.
  • the communication engine 314 of the EVMS 104 may communicate with the BMS 112 of the EVs 102 to receive different data, such as battery charge information, location of the EVs 102, battery usage patterns, and driver behavior.
  • the communication engine 314 may further communicate with the charging stations 110 to receive data, such as current congestion at the charging station or availability of the charging ports at the charging station, charging capabilities of the charging station, offers available at the charging station, fee of charging levied by the charging station, and their respective locations.
  • the analysis engine 316 may analyze different parameters received from the EVs 102 and the charging stations 110 along with different factors, such as distance between the geographic location of the EV and the charging stations, amount of charge left in the EV, traffic congestion between the geographic location of the EV and the charging station, ease of reaching to the charging station, and driver categorization to determine an optimum charging station for the EVs 102.
  • the analysis engine 312 may determine the driver categorization for a driver of an EV based on the driving behavior monitored over a predefined period of time.
  • the driving behavior may be determined based on various operational parameters of the EV, where the various operational parameters are determined by monitoring the usage of the EV over a period of time. For instance, speed of the EV may be monitored continuously to identify the instances when the driver over sped. More than a predefined number of instances of over speeding over a predefined period of time may be indicate of a poor driving behavior.
  • erratic driving actions such as lane changing without activating a turn indicator may also be used together with the instances of over speeding to determine the driving behavior.
  • information regarding any traffic violations and accidents that the driver may have been involved in may also be accounted for determination of the driver behavior.
  • the analysis engine 312 may assign a driver, a driver categorization.
  • the drivers with similar driving behavior may be assigned a same driver categorization.
  • the analysis engine 316 may generate a charging order for EVs 102 with charge level less than a threshold and may transmit the charging order to the BMS 112 of the EVs 102, through the communication engine 314.
  • the charging order may provide information for charging the EVs 102 at the optimum charging station, where the information may include details of the optimum charging station and an available time slot available for charging the EVs 102 at the optimum charging station. It would be noted that the optimum charging station may control a charging schedule of the EV s 102 by enabling the charging to be done only at the available time slot.
  • the engines 308 may also include other engines 316 that supplement functions of the EVMS 104.
  • the data 310 serves, amongst other things, as a repository for storing data that may be fetched, processed, received, or generated by the engines 308.
  • the data 310 comprises data provided by the BMS 112, such as battery charge data 318, EV location data 320, charging station data 322, driver categorization data 324, and other data 326 corresponding to the other engines 316.
  • Figure 4 and 5 illustrate methods 400 and 500 for managing charging of EVs, in accordance with an implementation of the present subject matter.
  • the methods 400 and 500 may be implemented in a variety of EVs, for the ease of explanation, the present description of the example methods 400 and 500 of managing charging of EVs is provided in reference to e-rickshaws.
  • the methods 400 and 500 may be implemented in a variety of computing devices, but for the ease of explanation, the present description of the example methods 400 and 500 for managing charging of EVs is provided in reference to the above-described BMS 112 and EVMS 104.
  • the order in which the methods 400 and 500 are described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the methods 400 and 500, or any alternative methods.
  • the methods 400 and 500 may be implemented by processor(s) or computing device(s) through any suitable hardware, non-transitory machine -readable instructions, or combination thereof.
  • blocks of the methods 400 and 500 may be performed by programmed computing devices.
  • the blocks of the methods 400 and 500 may be executed based on instructions stored in a non-transitory computer-readable medium, as will be readily understood.
  • the non-transitory computer-readable medium may include, for example, digital memories, magnetic storage media, such as magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media.
  • charge level of the battery pack of an electric vehicle is determined.
  • the charge of the battery packs of an EV may change from time to time and the availability of remaining charge in the battery packs of the EV may be ascertained regularly.
  • the BMS 112 of the EV may regularly determine the charge level of battery packs 114.
  • the control of the decision block 404 flows to block 402 and the charge level of the battery packs is again determined. However, if the charge level of the battery packs in the EV is below the threshold value, the control of the decision block 404 flows to block 406.
  • geographic location of the EV is determined. In an example, the geographic location of the EV may be determined based on known techniques, such as through use of Global Positioning System (GPS), triangulation through mobile towers, assisted GPS (A-GPS), and the like.
  • GPS Global Positioning System
  • A-GPS assisted GPS
  • one or more charging station(s) within the predefined area from the geographic location of the EV are identified.
  • the determination of the charging stations maybe made based on the charge level of the battery packs along with the geographic location of the EV.
  • an optimum charging station from the one or more charging stations is determined.
  • the determination may further be based on factors, such as distance between the geographic location of the EV and the charging stations, current congestion at the charging station or availability of the charging ports at the charging station, traffic congestion between the geographic location of the EV and the charging station, ease of reaching to the charging station, charging capabilities of the charging station, offers available at the charging station, fee of charging levied by the charging station, and driver categorization.
  • a charging order is issued to the electric vehicle.
  • the charging order may provide information for charging the EV at the optimum charging station, where the information may include details of the optimum charging station and an available time slot for charging the EV at the optimum charging station.
  • the optimum charging station may control a charging schedule of the EV by enabling the charging to be done only at the available time slot.
  • charge level of the battery packs of the electric vehicle is determined.
  • the BMS 112 may determine the charge level of the battery packs of the EV.
  • geographic location of the electric vehicle is determined.
  • the geographic location of the electric vehicle may be determined based on known techniques of determining location, such as through use of Global Positioning System (GPS), triangulation through mobile towers, assisted GPS (A-GPS), and the like.
  • GPS Global Positioning System
  • A-GPS assisted GPS
  • the charge level of the battery packs and the geographic location of the electric vehicle is communicated.
  • the charge level and the geographic location of the electric vehicle is communicated to a server, or a central electric vehicle management system, such as EVMS 104.
  • EVMS 104 a central electric vehicle management system
  • various other parameters of the electric vehicle such as battery usage pattern, driving patterns of the driver, speed, average distance travelled, and travelling pattern of the electric vehicle may also be communicated to the EVMS 104.
  • a charging order for the charging of the battery packs of the electric vehicle is received.
  • the charging order is provided by the EVMS 104.
  • the charging order for the electric vehicle may include details of an optimum charging station which may be utilized for charging the electric vehicle, along with an available time slot for charging the EV at the optimum charging station.
  • the charging station may be determined by the EVMS 104 based on consideration of different factors, such as distance between the geographic location of the EV and the charging stations, amount of charge left in the EV, current congestion at the charging station or availability of the charging ports at the charging station, traffic congestion between the geographic location of the EV and the charging station, ease of reaching to the charging station, charging capabilities of the charging station, offers available at the charging station, fee of charging levied by the charging station, and driver categorization.
  • factors such as distance between the geographic location of the EV and the charging stations, amount of charge left in the EV, current congestion at the charging station or availability of the charging ports at the charging station, traffic congestion between the geographic location of the EV and the charging station, ease of reaching to the charging station, charging capabilities of the charging station, offers available at the charging station, fee of charging levied by the charging station, and driver categorization.

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

Abstract

L'invention concerne des techniques de gestion de la charge d'un véhicule électrique (VE). Dans un exemple, des informations indiquant un niveau de charge d'un bloc-batterie du véhicule électrique conjointement avec un emplacement géographique du véhicule électrique sont reçues. Le niveau de charge du bloc-batterie est déterminé comme étant inférieur à une valeur de seuil. Sur la base de la détermination, une pluralité de stations de charge sont ensuite identifiées sur la base de l'emplacement géographique du véhicule électrique et du niveau de charge du bloc-batterie. Un ordre de charge du véhicule électrique est ensuite émis, l'ordre de charge indiquant des informations pour charger le véhicule électrique au niveau d'une station de charge parmi la pluralité de stations de charge.
PCT/IN2020/050343 2019-04-11 2020-04-10 Système et procédé de gestion de charge de véhicule électrique WO2020208654A1 (fr)

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IN201911014738 2019-04-11

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210178914A1 (en) * 2019-12-11 2021-06-17 Lyft, Inc. Modular bicycle designs
TWI748719B (zh) * 2020-10-30 2021-12-01 拓連科技股份有限公司 結合複數用電區域之電動車充電能源管理系統及方法
DE202022100283U1 (de) 2022-01-20 2022-01-28 Vandana Ahuja Intelligentes System zur automatischen Identifizierung der Ladespannung von Elektrofahrzeugen mit künstlicher Intelligenz und maschinellem Lernen

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010033517A2 (fr) * 2008-09-19 2010-03-25 Better Place GmbH Système et procédé de fonctionnement d’un véhicule électrique
US20140129139A1 (en) * 2012-11-07 2014-05-08 Intertrust Technologies Corporation Vehicle charging path optimization systems and methods
EP2792538A2 (fr) * 2013-04-19 2014-10-22 Honda Motor Co., Ltd. Système et procédé permettant de sélectionner une station de charge de véhicule électrique
US20180304759A1 (en) * 2017-04-19 2018-10-25 Arnold Chase Intelligent vehicle charging equipment

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010033517A2 (fr) * 2008-09-19 2010-03-25 Better Place GmbH Système et procédé de fonctionnement d’un véhicule électrique
US20140129139A1 (en) * 2012-11-07 2014-05-08 Intertrust Technologies Corporation Vehicle charging path optimization systems and methods
EP2792538A2 (fr) * 2013-04-19 2014-10-22 Honda Motor Co., Ltd. Système et procédé permettant de sélectionner une station de charge de véhicule électrique
US20180304759A1 (en) * 2017-04-19 2018-10-25 Arnold Chase Intelligent vehicle charging equipment

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210178914A1 (en) * 2019-12-11 2021-06-17 Lyft, Inc. Modular bicycle designs
TWI748719B (zh) * 2020-10-30 2021-12-01 拓連科技股份有限公司 結合複數用電區域之電動車充電能源管理系統及方法
DE202022100283U1 (de) 2022-01-20 2022-01-28 Vandana Ahuja Intelligentes System zur automatischen Identifizierung der Ladespannung von Elektrofahrzeugen mit künstlicher Intelligenz und maschinellem Lernen

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