TECHNICAL FIELD
[001] The present subject matter relates, in general, to electric vehicles and,
5 in particular, to utilization of the electric vehicles.
BACKGROUND
[002] The advancement of technology in the field of automobile industry has
not only improved human lives but has also enabled efficient and safe transportation.
10 With the increasing demand of oil and increasing air pollution, an increased focus is
on renewable energy and alternative vehicles that can provide a reliable mode of
transportation. Electric vehicles (EVs) 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
15 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. Also,
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.
20
BRIEF DESCRIPTION OF DRAWINGS
[003] The detailed description is described with reference to the
accompanying figures. In the figures, the left-most digit(s) of a reference number
identifies the figure in which the reference number first appears. The same numbers
25 are used throughout the drawings to reference like features and components.
3
[004] Figure 1 shows a network environment implemented for utilization of
an electrical vehicle (EV), in accordance with an embodiment of the present subject
matter.
[005] Figure 2 illustrates a battery management system for an EV, according
5 to an implementation of the present subject matter.
[006] Figure 3 shows a charging management terminal for managing charging
of EVs, according to an embodiment of the present subject matter.
[007] Figure 4 illustrates a method for utilizing EVs, in accordance with an
implementation of the present subject matter.
10
DETAILED DESCRIPTION
[008] Electric vehicles (EVs) are commonly powered by on-board battery
packs. Battery packs generally include multiple batteries to store charge, that is used to
drive the EVs. During operation of the EVs, the battery packs lose their stored charge
15 and have to be recharged. Further, the storage capability of 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
20 recharged frequently, for instance, on a daily basis, if not more frequently.
[009] Public transport EVs, such as e-busses, e-rikshaws and e-scooters travel
from one location to other, to meet their engagements and provide public transport
services. Since public EVs also have a limited range of travel, driving public EVs
without engagements lead to wastage of EV resources. For example, driving an e25 rikshaw from point A to point B without any passenger amounts to wastage of battery
of the e-rikshaw. Similarly, travelling from a location to a charging station for charging
of the battery packs, without any engagement, may lead to ineffective utilization of
resources of the e-rikshaw.
4
[0010] According to examples of the present subject matter, techniques of EV
utilization are described. In an example of the present subject matter, the described
techniques allow effective utilization of the EVs by identifying available and suitable
engagements for EVs during their travel.
5 [0011] In an example of the present subject matter, to provide effective
utilization of EVs, current location of the EVs is determined. The location of the EV
may indicate a current geographic position, providing whereabouts of the EV. In an
example, the current location of the EV may be identified based on various known
techniques, such as through use of Global Positioning System (GPS), triangulation
10 through mobile towers, use of assisted GPS (A-GPS), and the like.
[0012] Upon determination of the current location of the EV, a destination
corresponding to the EV is determined. The destination of the EV may either be based
on current engagements that are being undertaken by the EV or may be based on future
reserved engagements of the EV. The destination of the EV may also be provided by
15 the driver of the EV. For example, based on an engagement of an e-rikshaw, the erikshaw may be ferrying a passenger from location ‘L’ to location ‘M’. In such a
situation, location ‘M’ may be identified as the destination of the e-rikshaw. Similarly,
another e-rikshaw may have a reserved engagement and may have to travel to a location
‘X’, at a predefined time, to pick-up a passenger. In such situation, the destination of
20 the e-rikshaw for the reserved engagement may be identified as the location ‘X’.
[0013] In an example of the present subject matter, based on the determination
of the current location of the EV and the destination of the EV, possible engagements
between the current location of the EV and the destination are determined. The possible
engagements may allow utilization to the EV, during its travel from its current location
25 to the destination. In an example, the possible engagements between the current
location of the EV and the destination of the EV may include new fulfilment for the
EV, such as fulfillment to ferry passengers, fulfillment to deliver one or more products,
5
or fulfilment of other certain services between EVs current location and the destination
of the EV.
[0014] In an example of the present subject matter, a set of engagements from
amongst the possible engagements between the current location of the EV and the
5 destination of the EV are determined and provided to the driver of the EV. The set of
engagements may be chosen from all possible engagements based on engagement
parameters. The engagement parameters may indicate the state of the EV along with
its past plying statistics. In an example, the engagements parameters may include
parameters, such as time of possible engagement, availability of the EV for the possible
10 engagement, pre-registered engagements of the EV, amount of charge left in the EV,
traffic congestion between the current location of the EV and the destination of the
possible engagement, ease of reaching to the destination of the possible engagement,
offers available at the possible engagement, fee applicable for the possible engagement,
and driver categorization.
15 [0015] For example, between the current location of the EV and the destination
of the EV, 10 different possible engagements may be identified. From amongst these
10 possible engagements, a set of 7 engagements may be identified based on
engagement parameters. For example, any possible engagement where traffic condition
is poor may not be included the set of engagements. Further, any engagement which
20 has an additional offer for the driver of the EV may be included. Furthermore, if the
possible engagement takes the EV closer to a charging station, such an engagement
may be included in the set of the engagements.
[0016] Also, in an example of the present subject matter, a possible
engagement may be included in the set of engagements based on the categorization of
25 the driver of the EV. That is, depending on the category into which the driver of the
EV has been categorized into, possible engagement may be included or removed from
the set of engagements to be provided to the driver.
6
[0017] In an example implementation, certain possible engagements 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 possible engagement with maximum offers or preferable time slot.
5 [0018] The driver categorization may be indicative of driving behavior of the
driver. In an example, driver categorization ‘A’ may indicate a driving behavior that is
better than a driver categorization ‘B’. In another example, driver categorizations, such
as driver categorization ‘1’, driver categorization ‘2’, ……………, driver
categorization ‘n’ may exist where each driver categorization is indicative of a ranking
10 provided to the driver of the EV based on the driving behavior of the driver.
[0019] In an example, for determining the driver categorization for a driver of
an EV, the driving behavior may be monitored over a predefined period of time. In an
example, the driving behavior may be usage of a battery of the EV by the driver. For
instance, when a driver drives the EV at very high speed, a rate of battery drainage
15 maybe high. The output of the battery may be monitored to determine instances of over
speeding. 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 battery usage to determine the driving
20 behavior. Similarly, information regarding any traffic violations and accidents that the
driver may have been involved in may also be accounted for determination of driver
behavior. Accordingly, a variety of parameters may be monitored over a period of time
and may be used to determine the driving behavior of a driver.
[0020] Based on the determined driving behavior, the driver is assigned to a
25 driver categorization. Thus, drivers with similar driving behavior may be assigned a
same driver categorization. Further, in an example, certain other factors such as drivers
credit information, driver’s history information, information regarding driving actions
may be accounted for driver categorization.
7
[0021] Thus, as described earlier, in an example of the present subject matter,
a possible engagement is included in the set of the engagements to be provided to the
driver, is based on engagement parameters, which may also include driver
categorization.
5 [0022] In an example of the present subject matter, the driver of the EV may
select an engagement from the set of engagements. The selection made by the driver
may be based on personal preferences of the driver. Further, once the selection of an
engagement from amongst the set of engagements is made by the driver, the destination
of the EV may be updated based on the destination of the selected engagement.
10 [0023] Thus, based on the selection of a possible engagement, the EV may be
utilized, during periods of underutilization.
[0024] The above and other features, aspects, and advantages of the subject
matter will be better explained with regard to the following description and
accompanying figures. It should be noted that the description and figures merely
15 illustrate the principles of the present subject matter along with examples described
herein and, should not be construed as a limitation to the present subject matter. It is
thus understood that various arrangements may be devised that, although not explicitly
described or shown herein, embody the principles of the present disclosure. Moreover,
all statements herein reciting principles, aspects, and examples thereof, are intended to
20 encompass equivalents thereof. Further, for the sake of simplicity, and without
limitation, the same numbers are used throughout the drawings to reference like
features and components.
[0025] Figure 1 illustrates a network environment 100 implemented for
utilization of an EV, in accordance with an embodiment of the present subject matter.
25 In an example of the present subject matter, the environment 100 includes multiple
EVs, such as EVs 102-1, …, 102-n. The EVs 102-1, …, 102-n may be
communicatively connected to an electric vehicle management system (EVMS) 104
through a network 106.
8
[0026] In an example, the EVs 102-1 , …, 102-n may be e-rickshaws, emotorbikes 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 , …, 102-n may also include hybrid vehicles that are powered not only by battery
5 packs but are also powered by oil. However, such hybrid vehicles may also require
engagements for utilization, from time to time, and may connect to the EVMS 104 for
effective obtaining possible engagements. For the ease of reference, the EVs , …, 102-
n 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
10 multiple such EVs may ply on roads and may be connected to the EVMS 104 through
the network 106.
[0027] In an example of the present subject matter, 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,
15 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.
20 [0028] 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
25 (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).
Depending on the technology, the network 106 includes various network entities, such
9
as gateways, routers; however, such details have been omitted for the sake of brevity
of the present description.
[0029] In an example of the present subject matter, the EVMS 104 may
communicate with different users 108-1, …, 108-n of the network and EVs 102, in
5 real-time, to determine the whereabouts of EVs, along with new engagements being
requested by users 108-1, …, 108-n. The users 108-1, …, 108-n may be connected to
the EVMS 104 through their respective communication devices 110-1, …, 110-n. For
the ease of reference, users 108-1, …, 108-n have been commonly referred to as users
108, hereinafter. Similarly, the communication devices 110-1, …, 110-n utilized by the
10 users have been referred to as communication devices 110, hereinafter.
[0030] In an example of the present subject matter, the users 108 may include
passengers willing to travel by the EVs 102 from one location to another. further, the
users 108 may also include customers/ sellers willing to receive/ ship products from
one location to another. Further, the communication devices 110 utilized by the users
15 108 may include, but not limited to, personal digital assistants (PDAs), smartphones,
laptops, desktops, pagers, messenger devices, tablets, and others.
[0031] The EVMS 104, based on the whereabouts of the EVs 102, such as their
current location, destination and other operational parameters, along with the requests
of the users 108, may determine possible engagements for the EVs 102. Further, the
20 EVMS 104 may determine a set of engagements from the possible engagements, to be
provided to the drivers of the EVS 102. As described earlier, the various engagement
factors based on which the EVMS 104 may determine the set of engagements for the
EVs may include, but not limited to, time of possible engagement, availability of the
EV for the possible engagement, pre-registered engagements of the EV, amount of
25 charge left in the EV, traffic congestion between the current location of the EV and the
destination of the possible engagement, ease of reaching to the destination of the
possible engagement, offers available at the possible engagement, fee applicable for
the possible engagement, and driver categorization.
10
[0032] The various engagement factors may be stored in a database 111,
communicatively coupled to the EVMS 104. The database 111 may either be
implemented within the EVMS 104 as an internal database or may be implemented as
outside EVMS 104 as an external database.
5 [0033] In an example of the present subject matter, the EVs 102 may charge
their battery packs through various charging stations (not shown), which may be
connected through the network 106, and may support different payment methods, such
as ChargePoint, Blink, Greenlots, eVgo, Aerovironment, Azra, SemaConnect, Circuit
Électrique, RéseauVer, Sun Country Highway, BHIM, Bharat QR and Unified
10 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 may also provide free energy to charge the battery
packs of the EVs 102.
[0034] The EVs 102 may include, apart from other things, a battery
15 management system (BMS) 112, one or more battery packs 114, at least one charging
connector 116, and a sensor assembly 118.
[0035] In an example, 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.
20 Therefore, the BMS 112 can be understood to act as a control terminal of the battery
packs 114. Among other functions, BMS 112 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
25 parameters of the battery packs 114, such as battery cell voltage, temperature, etc.
[0036] In another example of the present subject matter, the BMS 112 may also
communicate with the sensor assembly 118 to receive various operational parameters
of the EV 102. In an example, the BMS 112 may receive parameters, such as speed,
11
location, destination of the EV 102. Further, the sensor assembly 118 may include
multiple sensors to monitor and gather data corresponding to various parameters of the
EV 102.
[0037] In an example of the present subject matter, the battery packs 114 may
5 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
10 incorporated in the EVs 102 can be NCA, NMC, LMO, and LifePO4.
[0038] 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
15 example embodiment of the present subject matter, the charging connector 116 may
function in response to instructions given by a BMS 112.
[0039] The functioning of the BMS 112 and the EVMS 104 to communicate
whereabouts of the EVs 102 to provide effective utilization to the EVs 102 is further
described in reference to forthcoming figures.
20 [0040] Figure 2 illustrates a battery management system 112 for an EV,
according to an implementation of the present subject matter. In an example of the
present subject matter, the BMS 112 includes a communication engine 202, a control
engine 204 and a data store 206. As described earlier the BMS may act as a control to
the battery packs 114 of the EV 102 and may communicate with other elements of the
25 EV 102, such as sensor assembly 118 (not shown) and charging connector 116 (not
shown).
[0041] In an example, the communication engine 202 of the BMS 112 may
facilitate communication between the BMS 112 and the sensor assembly 118 and
12
charging connector 116. Further, the communication 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 communication engine
202 may use several methods of serial or parallel communication, for instance and not
5 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.
[0042] The communication engine 202 communicates internally with various
10 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. The
communication engine may also receive inputs from the driver of the EVs 102 relating
to their preferences and may communicate them to the EVMS 104. Based on the inputs
obtained from the sensor assembly 118 and other battery information communicated
15 by the communication engine 202 to the EVMS 104, the EVMS 104 may identify the
whereabouts of the EVs 102 along with driver preferences.
[0043] The control engine 204 may control the functioning of the battery packs
114 of the EV 102. For example, the control engine 204 may be responsible for
controlling the trigger signals to the charging connector which may determine charging
20 of the battery packs 114. In an example implementation, 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
25 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
13
204 ensures that the battery packs 114 charge at the specified rate by controlling the
charging connecter 116 accordingly.
[0044] In the present description, the engine(s) may be implemented as a
combination of hardware and programming (for example, programmable instructions)
5 to implement certain functionalities of the engine(s), such as transmitting signals. In
examples described herein, such combinations of hardware and programming may be
implemented in several different ways. For example, engine(s) may be implemented
by electronic circuitry.
[0045] In an example implementation, the data store 206 may be understood as
10 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 computerreadable medium including, for example, volatile memory (e.g., RAM), and/or nonvolatile memory (e.g., EEPROM, flash memory, etc.).
[0046] The data store 206 may store information, for example and not limited
15 to, inputs obtained from the sensor assembly 118 installed on the EVs 102, such as EV
geographic location, destination of the EV, speed of the EV, direction of travel of the
EV and current charge level information of the EV. The data store 206 may also store
instructions communicated to the BMS 112 by the EVMS 104, such as set of
engagements. Other examples of information that the data store 206 may store are:
20 customer rating in case of an electric rickshaw, battery maintenance data, drivers credit
information, driver’s history information, information regarding driving actions.
[0047] Figure 3 schematically depicts various components of an electric
vehicle management system 104, according to an example of the present subject matter.
[0048] The EVMS 104, among other things, includes processor(s) 302 and
25 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. Among
14
other capabilities, the processor(s) 302 is configured to fetch and execute computerreadable 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.).
5 [0049] The functions of the various elements shown in the Figures, including
any functional blocks labelled as “processor(s)”, may be provided through the use of
dedicated hardware as well as hardware capable of executing software. When provided
by a processor, 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
10 be shared. Moreover, 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
15 (RAM), non-volatile storage. Other hardware, conventional and/or custom, may also
be included.
[0050] 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
and the communication devices 110 of the users 108. In an example of the present
20 subject matter, 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 111 (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.
25 [0051] In an example of the present subject matter, 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 communicating devices 110. Further,
15
the analysis engine 312 may analyze different factors and data provided by the BMS
112 along with available engagements for the EVs 102.
[0052] In operation, the communication engine 314 of the EVMS 104 may
communicate with the BMS 112 of the EVs 102 to receive different data, such as
5 current engagement of the EVs 102, current location of the EVs 102, destination of the
EVs based on their current engagements, battery usage patterns of EVs, and driver
behavior of the EVs 102. In an example, the communication engine 314 may receive
whereabouts of the EVs 102 through the communication engine 202 of the BMS 112.
Further, the communication engine 314 of the EVMS 104 may also communicate with
10 the database 111 (not shown) to obtain information related to the EVs 102, that may
have been provided by the EVs 102 to the EVMS 104 over time.
[0053] In an example of the present subject matter, the communication engine
314 of the EVMS 104 may also communicate with different users 108 to obtain all
engagements either being undertaken by the users 108 or being requested by the users
15 108. As described earlier, the users 108 may utilize their communication devices 110
to communicate their engagements with the EVMS 104. In an example, the EVMS 104
may act as a nodal platform to gather engagement requests from the users 108 and
provide such requested engagements to the EVs 102 based on engagement parameters.
The engagement parameters may indicate the state of the EV 102 along with EVs past
20 plying statistics.
[0054] In an example implementation of the present subject matter, the analysis
engine 312 may analyze different parameters received from each EV, such as current
location of the EV, destination of the current engagement of the EV, along with all
available engagements received from different users to determine possible
25 engagements for the EV. The possible engagements identified for each EV may further
be refined to generate a set of engagements for the EV.
[0055] The analysis engine 312 may identify the set of engagements for the EV
based on engagement parameters, such as time of possible engagement of the EV,
16
availability of the EV for the possible engagement, pre-registered engagements of the
EV, amount of charge left in the EV, traffic congestion between the current location of
the EV and the destination of the possible engagement, ease of reaching to the
destination of the possible engagement, offers available at the possible engagement,
5 fee applicable for the possible engagement, and driver categorization.
[0056] In an example implementation, 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. In an example, the driving behavior may be usage of
a battery of the EV by the driver. For instance, when a driver drives the EV at very
10 high speed, a rate of battery drainage maybe high. The output of the battery may be
monitored to determine instances of over speeding. 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 battery usage to
15 determine the driving behavior. Similarly, information regarding any traffic violations
and accidents that the driver may have been involved in may also be accounted for
determination of driver behavior. Accordingly, a variety of parameters may be
monitored over a period of time and may be used to determine the driving behavior of
a driver.
20 [0057] Based on the determined driving behavior, the analysis engine 312 may
assign a driver, a driver categorization. Thus, drivers with similar driving behavior may
be assigned a same driver categorization. Further, in an example, certain other factors
such as drivers credit information, driver’s history information, information regarding
driving actions may be accounted for by the analysis engine 312 driver categorization.
25 [0058] Thus, as described earlier, in an example of the present subject matter,
a possible engagement is included in the set of the engagements to be provided to the
driver, is based on engagement parameters, which may also include driver
categorization.
17
[0059] The analysis engine 312 may utilize one or more of the engagement
parameters to determine the set of engagements for the EV. It would be noted that the
analysis engine 312 may utilize different weightage for the different engagement
parameters, that may be defined by the EVMS 104.
5 [0060] In an example of the present subject matter, the communication engine
314 of the EVMS 104 may provide the set of engagements to the driver of the EV. The
driver of the EV may choose an engagement from the set of engagements to increase
the utilization of the EV. In an example, the engines 308 may also include other engines
316 that supplement functions of the EVMS 104. The data 310 serves, amongst other
10 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, EV destination data 322, driver
categorization data 324, and other data 326 corresponding to the other engines 316.
[0061] In an example implementation of the present subject matter, the
15 communication engine 314 may further receive a selection from the driver of the EV
102, where the selection is indicative of an engagement from amongst the set of
engagements, chosen by the driver of the EV 102. Further, the analysis engine 312 may
further provide an updated route to the driver of the EV 102 based on the selection.
[0062] In an example, the analysis engine 312 of the EVMS 104 may provide
20 the updated route that may include a new destination for the EV (102). The new
destination may be either a pickup location of a user or a charging station allocated to
the EV (102).
[0063] It would be noted that the charging station allocated to the EV 102 may
control a charging schedule of the EV (102) by enabling the charging to be done only
25 at the time slot allotted to the EV (102).
[0064] Figure 4 illustrate a method 400 for utilization of EVs, in accordance
with an implementation of the present subject matter. Although the method 400 may
be implemented in a variety of EVs, for the ease of explanation, the present description
18
of the example method 400 of utilization of the EVs is provided in reference to erickshaws. Also, although the method 400 may be implemented in a variety of
computing devices, but for the ease of explanation, the present description of the
example method 400 for utilization of EVs is provided in reference to the above5 described BMS 112 and EVMS 104.
[0065] The order in which the method 400 is 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 method 400, or any alternative methods.
Furthermore, the method 400 may be implemented by processor(s) or computing
10 device(s) through any suitable hardware, non-transitory machine-readable instructions,
or combination thereof.
[0066] It may be understood that blocks of the method 400 may be performed
by programmed computing devices. The blocks of the method 400 may be executed
based on instructions stored in a non-transitory computer-readable medium, as will be
15 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.
[0067] Referring to Figure 4, at block 402 current location of an electric vehicle
(EV) is determined. In an example of the present subject matter, the current location of
20 the EV is determined based on known techniques, such as through use of Global
Positioning System (GPS), triangulation through mobile towers, assisted GPS (AGPS), and the like. Further, the current location of the EV may be provided to an EV
management system, such as the EVMS.
[0068] At block 404, destination of the EV is identified. The destination of the
25 EV may either be determined based on current engagement information of the EV or
may be provided by the driver of the EV. In an example, the EVMS 104 may identify
the destination of the EV based on its current engagement.
19
[0069] At block 406, possible engagements for EV between the current
location and the destination of the EV are checked. In an example of the present subject
matter, the EVMS 104 receives different engagement requests from different users and
based on the current location of the EV, the destination of the EV and other EV
5 parameters, the EVMS 104 may determine possible engagements for the EV. The
possible engagements between the current location of the EV and the destination of the
EV may include engagements to ferry passengers, deliver one or more products, or
provide certain services between EVs current location and the destination of the EV.
[0070] At block 408, driver of the EV is provided with a set of engagements
10 between the current location and the destination of the EV. The set of engagements are
determined by the EVMS 104 based on different engagement parameters, such as time
of possible engagement of the EV, availability of the EV for the possible engagement,
pre-registered engagements of the EV, amount of charge left in the EV, traffic
congestion between the current location of the EV and the destination of the possible
15 engagement, ease of reaching to the destination of the possible engagement, offers
available at the possible engagement, fee applicable for the possible engagement, and
driver categorization.
[0071] At block 410, a selection of an engagement from amongst the set of
engagements is received from the driver of the EV. Further, at block 412, the
20 destination of the EV is updated based on the selected engagement.
Although the subject matter has been described in considerable detail with reference to
certain examples and implementations thereof, other implementations are possible. As
such, the present disclosure should not be considered limited to the description of the
preferred examples and implementations contained therein.
20
I/We Claim:
1. A method comprising:
determining a current location and a destination of an electric vehicle
(EV), wherein the EV is plying from the current location to a predetermined
5 destination;
determining a plurality of possible engagements between the current
location and the destination of the EV, wherein each possible engagement from
amongst the plurality of possible engagements is indicative of a new fulfilment
for the EV;
10 selecting a set of engagements from amongst the plurality of engagements
for the EV based on predetermined selection criterion; and
providing the set of engagements to the EV.
2. The method as claimed in claim 1, wherein the selecting the set of engagements
15 is based on engagement parameters associated with the EV.
3. The method as claimed in claim 2, wherein the engagement parameters include
at least one of time of engagement, availability of another EV for the possible
engagement, pre-registered engagements of the EV, amount of charge left in the EV,
20 traffic congestion between the current location of the EV and the destination of the
possible engagement, ease of reaching to the destination of the possible engagement,
offers available at the possible engagement, fee applicable for the possible engagement
and driver categorization.
25 4. The method as claimed in claim 1 wherein the selecting the set of engagements
is based on categorization of a driver of the EV.
21
5. The method as claimed in claim 4, wherein the categorization of the driver of
the EV is based on at least one of driving pattern of the driver, charging pattern of the
EV by the driver, earlier engagements completed the driver, traffic violations and
accidents done by the driver, drivers credit information, and driver’s driving actions.
5
6. The method as claimed in claim 1 further comprising:
receiving a selection from the driver of the EV, wherein the selection is
indicative of an engagement from amongst the set of engagements, chosen by
the driver of the EV; and
10 providing an updated route to the driver of the EV based on the selection.
7. The method as claimed in claim 1, wherein the plurality of possible
engagements is at least one of a user ride fulfilment and a product delivery fulfilment.
15 8. An electric vehicle (EV) management system (104) comprising:
a processor (302);
a communication engine (314), coupled to the processor (302), to
receive, from an EV (102), a current location and a destination of the EV (102),
wherein the EV (102) is plying from the current location to a predetermined
20 destination; and
an analysis engine (312), coupled to the processor (302), to:
determine a plurality of possible engagements between the current
location and the destination of the EV (102), wherein each possible
engagement from amongst the plurality of possible engagements is
25 indicative of a new fulfilment for the EV (102); and
select a set of engagements from amongst the plurality of
engagements for the EV based on predetermined selection criterion;
wherein
22
the communication engine (314) is to provide the set of engagements to
the EV (102).
9. The EV management system (104) as claimed in claim 8, wherein the analysis
5 engine (312) is to select the set of engagements based on engagement parameters
associated with the EV (102).
10. The EV management system (104) as claimed in claim 8, wherein the
engagement parameters include at least one of time of engagement, availability of
10 another EV for the possible engagement, pre-registered engagements of the EV,
amount of charge left in the EV, traffic congestion between the current location of the
EV and the destination of the possible engagement, ease of reaching to the destination
of the possible engagement, offers available at the possible engagement, fee applicable
for the possible engagement and driver categorization.
15
11. The EV management system (104) as claimed in claim 8, wherein the analysis
engine (312) is to select the set of engagements based on categorization of a driver of
the EV (102), and wherein the categorization of the driver of the EV (102) is based on
at least one of driving pattern of the driver, charging pattern of the EV by the driver,
20 earlier engagements completed by the driver, traffic violations and accidents done by
the driver, drivers credit information, and driver’s driving actions.
12. The EV management system (104) as claimed in claim 8, wherein:
the communication engine (314) is further to receive a selection from
25 the driver of the EV (102), wherein the selection is indicative of an engagement
from amongst the set of engagements, chosen by the driver of the EV (102);
and
23
the analysis engine (312) is further to provide an updated route to the
driver of the EV (102) based on the selection.
13. The EV management system (104) as claimed in claim 12, wherein the updated
5 route comprises a new destination for the EV (102), and wherein the new destination
is one of a pickup location of a user and a charging station allocated to the EV (102).
14. The EV management system (104) as claimed in claim 13, wherein the charging
station allocated to the EV (102) controls a charging schedule of the EV (102) by
10 enabling the charging to be done only at the time slot allotted to the EV (102).
15. An electric vehicle (EV) (102) comprising:
at least one battery packs (114);
a battery management system (112), coupled to the at least one battery
15 packs (114), to determine at least one of driving pattern of the driver and a
charging pattern of the EV (102); and
a communication engine (202), coupled to the battery management
system (112), to communicate a current location and a destination of the EV
(102), wherein the EV (102) is plying from the current location to a
20 predetermined destination.
16. The EV (102) as claimed in claim 15, wherein the communication module (202)
is further to:
receive a set of engagements for the EV (102) from an EV management
25 system (104); and
provide a selection to EV management system (104), wherein the
24
selection is indicative of an engagement from amongst the set of engagements,
chosen by the driver of the EV (102).