Description:FIELD OF THE INVENTION
The present invention is related to range estimation of electric vehicles, in particularly to a system and method for estimating a cruising range of an electric vehicle using battery gauge parameters.

BACKGROUND OF THE INVENTION

The following description of related art is intended to provide background information pertaining to the field of the present disclosure. This section may include certain aspects of the art that may be related to various aspects of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.

At present, developments are increasingly made towards electric vehicles associated with low energy consumption and low emissions. An electric vehicle can operate by driving motors using electricity charged in a battery. One of the most common problems faced by customers/users of the existing electric vehicle is range anxiety. Range anxiety refers to fear of running out of power on a travel, without being able to find a charging station on time to replenish a battery of the electric vehicle. Thus, accurate estimation of a cruising range of the electric vehicle is necessary to enable a driver/user to properly plan long trips in accordance with a location of distance, charging stations, and so on.

In some examples, a range estimator may be implemented in the electric vehicle to estimate the cruising range of the electric vehicle. The range estimator may estimate the cruising range based on mechanical quantities associated with the electric vehicle. Examples of the mechanical quantities may include vehicle weight, air resistance, rolling resistance, speed, inclination, motor torque, climate control setting, vehicle infotainment system settings, and so on. All of these mechanical quantities by extension, effect an amount of electrical power that is being drawn from a battery pack of the electric vehicle. Thus, even a small change in any one of the mechanical quantities can significantly affect an overall cruising range of the electric vehicle.
Further, considering each and every one of the above-mentioned mechanical quantities in estimating the cruising range of the electric vehicle is not possible due to hardware limitations. For example, a large number of sensors may require to accurately measure the mechanical quantities, which adds further complexity to the entire range estimation setup. Such complexity affects performance and accuracy of the range estimator. In addition, the additional hardware requirements especially sensors lead to an increase in cost of the range estimator, which leads to an increase in overall cost of the electric vehicle.

SUMMARY OF THE INVENTION
In view of the foregoing, an embodiment herein provides a method for estimating a cruising range of an electric vehicle. The method includes i) obtaining battery gauge parameters being affecting the cruising range of the electric vehicle; ii) deriving an inference indicating a mapping of a combination of the battery gauge parameters to one of a plurality of cruising ranges of the electric vehicle; and iii) estimating the cruising range of the electric vehicle based on the derived inference.

In an embodiment, the battery gauge parameters comprise an instantaneous power, a rate of change of power, and an energy available in a battery pack of the electric vehicle.

In yet another embodiment, the step of deriving the inference includes i) classifying each of the battery gauge parameters into at least one category; ii) mapping each of the battery gauge parameters onto an input set based on the associated at least one category, wherein the input set of each battery gauge parameter comprises one or more degree of memberships for the battery gauge parameter; and iii) deriving the inference based on the input set of each battery gauge parameter and a correlation between the battery gauge parameters and the cruising range of the electric vehicle.

In yet another embodiment, the step of deriving the inference includes i) evaluating the input set of each battery gauge parameter with respect to the input sets of other battery gauge parameters in accordance with the correlation between the battery gauge parameters and the cruising range of the electric vehicle; and ii) generating an output set based on the evaluation, wherein the output set indicates one or more degree of memberships associated with one of the plurality of cruising ranges of the electric vehicle.

In yet another embodiment, the step of estimating the cruising range of the electric vehicle includes i) determining at least one category associated with the cruising range of the electric vehicle based on the output set; and ii) estimating the cruising range of the electric vehicle based on the output set and the determined at least one category associated with the cruising range of the electric vehicle.

In another aspect, a range estimator for estimating a cruising range of an electric vehicle is provided. The range estimator comprises a controlling circuitry being adapted for obtaining battery gauge parameters being affecting the cruising range of the electric vehicle. The controlling circuitry is adapted for deriving an inference indicating a mapping of a combination of the battery gauge parameters to one of a plurality of cruising ranges of the electric vehicle. The controlling circuitry is adapted for estimating the cruising range of the electric vehicle based on the derived inference.

In another embodiment, the battery gauge parameters comprise an instantaneous power, a rate of change of power, and an energy available in a battery pack of the electric vehicle.

In yet another embodiment, the controlling circuitry is adapted for deriving the inference by classifying each of the battery gauge parameters into at least one category; mapping each of the battery gauge parameters onto an input set based on the at least one category, wherein the input set of each battery gauge parameter comprises one or more degree of memberships for the battery gauge parameter; and deriving the inference based on the input set of each battery gauge parameter and a correlation between the battery gauge parameters and the cruising range of the electric vehicle.

In yet another embodiment, the controlling circuitry is adapted for deriving the inference by evaluating the input set of each battery gauge parameter with respect to the input sets of other battery gauge parameters in accordance with the correlation between the battery gauge parameters and the cruising range of the electric vehicle; and generating an output set based on the evaluation, wherein the output set indicates one or more degree of memberships associated with one of the plurality of cruising ranges of the electric vehicle.

In yet another embodiment, the controlling circuitry is adapted for determining at least one category associated with the cruising range of the electric vehicle based on the output set; and estimating the cruising range of the electric vehicle based on the output set and the determined at least one category associated with the cruising range of the electric vehicle.

These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS
The invention will now be described in relation to the accompanying drawings in which

Figure 1 discloses an example implementation for estimating a cruising range of an electric vehicle in accordance with an embodiment herein;

Figure 2 is a flowchart illustrating method steps of a method performed for estimating a cruising range of an electric vehicle in accordance with an embodiment herein;

Figures 3 is a schematic block diagram illustrating an example range estimator in accordance with an embodiment herein;

Figure 4 is a schematic diagram illustrating estimation of a cruising range of an electric vehicle in accordance with an embodiment herein;

Figure 5 is an example flowchart illustrating method steps of a method performed for estimating a cruising range of an electric vehicle using a fuzzy logic and defuzzification logic in accordance with an embodiment herein;

Figure 6A discloses exemplary membership function plots of input parameters, wherein the input parameters include battery gauge parameters, in accordance with an embodiment herein; and

Figure 6B discloses an exemplary membership function plot of an output, wherein the output includes a cruising range of the electric vehicle, in accordance with an embodiment herein.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

The present invention provides an improved method and arrangement for estimating a cruising range of an electric vehicle with only fewer battery gauge parameters, instead of measuring a plurality of mechanical quantities. The battery gauge parameters consists of an instantaneous power, a rate of change of power, and an energy available in a battery pack of the electric vehicle. Estimation of the cruising range of the electric vehicle using fewer battery gauge parameters reduces an amount of sensing hardware requirements and complexity involved in estimation. Thereby, reducing total processing overhead involved in estimating the cruising range of the electric vehicle, which further improves performance and reduces cost.

Figure 1 discloses an example implementation for estimating a cruising range of an electric vehicle 100 in accordance with an embodiment herein. The electric vehicle 100 referred herein corresponds to a battery based operating vehicle, having an electric motor. In some examples, the electric vehicle 100 may include, but are not limited to, a two-wheeler, a four-wheeler, and so on. For instance, the electric vehicle 100 may include, an electric bike, an electric car, or the like.
The electric vehicle 100 comprises a range estimator 102 and a battery pack 104. In practice, the electric vehicle 100 may further include any additional elements to operate. In different embodiments, the electric vehicle 100 may comprise one or more of: a charging port, a Direct Current (DC)/DC converter, an electric traction motor, an onboard charger, a power electronic controller, a transmission unit, and so on.

The battery pack 104 may be adapted to power up the electric motor and other accessories of the electric vehicle 100. The range estimator 102 referred herein may include a device, a controller, an on-board controller, or the like. The range estimator 102 is adapted for estimating a cruising range of the electric vehicle 100. Embodiments herein use the terms such as “cruising range”, “driving range”, “distance to travel (DTE)”, “endurance mileage”, “range”, and so on, interchangeably to refer to a remaining distance that can be covered by the electric vehicle 100 at a given instance of time.

One of the problems faced by a user/driver of the electric vehicle 100 is range anxiety. Range anxiety refers to fear of running out of power on a travel, without being able to find a charging station on time to replenish the battery of the electric vehicle 100. Thus, accurate estimation of the cruising range of the electric vehicle 100 is necessary to enable the user to properly plan long trips in accordance with a location of distance, charging stations, and so on.

In some exemplary solutions, the range estimator 102 may estimate the cruising range of the electric vehicle 100 using mechanical quantities such as, but are not limited to, vehicle weight, air resistance, rolling resistance, speed, inclination, motor torque, climate control setting, vehicle infotainment system settings, and so on. However, a small change in any one of the mechanical quantities can significantly affect an overall cruising range of the electric vehicle. Further, a large number of sensors may be required to accurately measure the mechanical quantities, which adds further complexity to the entire range estimation setup affecting performance and accuracy of the range estimator.

Therefore, according to some embodiments of the present disclosure, the range estimator 102 implements a method for improving an estimation of a cruising range of the electric vehicle 100.

The range estimator 102 is adapted to obtain battery gauge parameters being affecting the cruising range of the electric vehicle 100. In some embodiments, the battery gauge parameters may include, but are not limited to, an instantaneous power, a rate of change of power, and an energy available in the battery pack 104 of the electric vehicle 100. In embodiments herein, the energy available in the battery pack 104 may also be referred to as a State of Charge (SoC).

In some examples, the battery gauge parameters (may also be referred to as input quantities, input parameters, crisp measurement values, crisp input or the like) may be varied over a given range of values depending upon, for example, vehicle parameters, driver behavior, and so on. Examples of the vehicle parameters may include, but are not limited to, vehicle weight, speed, inclination, motor torque, vehicle infotainment system settings, and so on.

Upon obtaining the battery gauge parameters, the range estimator 102 is adapted to derive an inference indicating a mapping of a combination of the battery gauge parameters to one of a plurality of cruising ranges of the electric vehicle 100. The range estimator 102 is adapted to estimate the cruising range of the electric vehicle 100 based on the derived inference.

In some embodiments, the range estimator 102 may derive the inference using a fuzzy logic/fuzzy inference logic and estimate the cruising range of the electric vehicle 100 by applying a defuzzification logic on results of the inference. In some examples, the fuzzy logic may be an approach to computing based on “degrees of truth” rather than a Boolean logic (i.e., usual “true” or “false” logic). In some examples, the defuzzification logic may be an approach for converting results of the inference into a single crisp output value. In accordance with embodiments herein, the crisp output value may be the cruising range of the electric vehicle 100.

For deriving the inference, the range estimator 102 classifies each of the battery gauge parameter into at least one category based on values of the battery gauge parameters (i.e., measured values). In embodiments herein, the category may also be referred to as a membership function. In some examples, the category with respect to the battery gauge parameters may include “low”, “medium”, and “high”. It should be noted that a number and names of the category may vary, for example, the category may also include “very low”, “very high”, or the like including “low”, “medium”, and “high”.

In some embodiments, classification of the each of the battery gauge parameters may associate each battery gauge parameter with one or more categories/membership functions. For example, the instantaneous power may be associated with 90% of the category/membership function “low”, 10% of the membership function “medium”, and 0% of the membership function “high”, the rate of change of power may be associated with 0% of the membership function “low”, 55% of the membership function “medium”, and 45% of the membership function “high”, and the energy available in the battery pack 104/SoC may be associated with 0% of the membership function “low”, 10% of the membership function “medium”, and 90% of the membership function “high”.

Upon classification of the battery gauge parameters into the at least one category, the range estimator 102 maps each of the battery gauge parameters onto an input set based on the associated at least one category. In embodiments herein, the input set may also be referred to as a fuzzy set, fuzzy input variables, fuzzy linguistic variables, or the like. The input set of each battery gauge parameter comprises one or more degree of memberships for the battery gauge parameter. In some examples, the degree of membership ranges from 0 to 1. For example, if the instantaneous power is associated with 90% of the category/membership function “low”, 10% of the membership function “medium”, and 0% of the membership function “high”, then the input set corresponding to the instantaneous power may include the degree of memberships as [0.9, 0.1, 0].

After mapping each of the battery gauge parameters onto the input set, the range estimator 102 derives the inference based on the input set of each battery gauge parameter, and a set of rules. Embodiments herein use the terms “set of rules”, “correlation”, “fuzzy rules”, and so on, interchangeably to refer to a correlation between the battery gauge parameters and the cruising range of the electric vehicle 100. Specifically, the set of rules/correlation may indicate a category associated with the cruising range of the electric vehicle 100 for a combination of each category/membership associated with the battery gauge parameters. For example, the set of rules/correlation may indicate that “if energy is low AND power is low AND rate of change of power is low THEN the cruising range is low”. A table depicting the set of rules/correlation is explained in later parts of the description.

In accordance with embodiments herein, deriving the inference comprises generating an output set with respect to the input sets of all the battery gauge parameters. The output set may also be referred to as a fuzzy output set, fuzzy output variables, or the like. The output set comprises one or more degree of memberships associated with one of the plurality of cruising ranges of the electric vehicle 100. The degree of membership associated with the cruising range of the electric vehicle 100 may range from 0 to 1. In some examples, the range estimator 102 may derive the inference/generate the output set using fuzzy inference systems such as, but are not limited to, Mamdani system, Takagi-Sugeno system, and so on.

In some embodiments, for generating the output set, the range estimator 102 evaluates the input set of each battery gauge parameters with the input sets of other battery gauge parameters in accordance with the set of rules. In some examples, evaluating the input sets in accordance with the set of rules comprises deriving a logical AND relationship between the one or more degree of memberships of each battery gauge parameters and the respective one or more degree of memberships of other battery gauge parameters. The range estimator 102 generates the output set based on the evaluation. The output set indicates the one or more degree of memberships associated with one of the plurality of cruising ranges of the electric vehicle 100.

Consider an example scenario, wherein the input sets corresponding to the instantaneous power, the rate of change of power, and the energy available in the battery pack 104 may be [0, 0.5, 0.5], [0, 0.4, 0.6], and [0.1, 0.9, 0] respectively. In such a scenario, the range estimator 102 generates the output set as [0, 0.4, 0] by linking the input sets using the logical AND operation. The output set [0. 0.4, 0] indicates the degree of memberships associated with one of the plurality of cruising ranges of the electric vehicle 100.

Once the output set/inference is generated, the range estimator 102 estimates the cruising range of the electric vehicle 100 based on the inference.

For estimating the cruising range, the range estimator 102 determines at least one category associated with the cruising range of the electric vehicle 100 from the output set/inference. In some examples, the category/membership function associated with the cruising range of the electric vehicle 100 may include “low”, “medium”, and “high”. It should be noted that a number and names of the category may vary.

Upon determining the at least one category associated with the cruising range of the electric vehicle 100, the range estimator 102 estimates the single crisp output value based on the output set (indicating the one or more degree of memberships associated with the cruising range) and the at least one category/membership function associated with the cruising range of the electric vehicle 100. The single crisp output value indicates the remaining cruising range of the electric vehicle 100 at a given instance of time.

In some examples, the range estimator 102 may use one of defuzzification methods such as, but are not limited to, a bisector method, a center of area method/center of gravity method, or the like to estimate the single crisp output value indicating the cruising range of the electric vehicle 100. For instance, in accordance with the defuzzification method, the range estimator 102 may apply a Gaussian expression on the output set and the at least one category/membership function associated with the cruising range of the electric vehicle 100 to estimate the cruising range. In an example herein, the Gaussian expression may be represented as exp (-0.5 (x-c)/s), wherein ‘x’ represents the output set, ‘c’ represents any constant, and ‘s’ is a standard deviation of the output set.

The range estimator 102 may further be adapted to display the estimated cruising range of the electric vehicle 100 to the user/driver. Thereby, solving a problem of range anxiety.

Working of Invention: While operating the electric vehicle 100 by the user, the range estimator 102 obtains the fewer battery gauge parameters including the instantaneous power, the rate of change of power, and the available energy in the battery pack 104 of the electric vehicle 100. The range estimator 102 estimates the cruising range of the electric vehicle 100 by applying the fuzzy inference logic and the defuzzification logic on the measured battery gauge parameters. The range estimator 102 further displays the estimated range of the electric vehicle 100 to the user.

Figure 2 is a flowchart illustrating method steps of a method 200 performed for estimating the cruising range of the electric vehicle 100. The method 200 is being performed by the range estimator 102.

At step 202, the method 200 comprises obtaining battery gauge parameters being affecting the cruising range of the electric vehicle 100. In some embodiments, the battery gauge parameters may include an instantaneous power, a rate of change of power, and an energy available in the battery pack 104 of the electric vehicle 100/SoC.

At step 204, the method 200 comprises deriving an inference indicating a mapping of a combination of the battery gauge parameters to one of a plurality of cruising ranges of the electric vehicle 100.

The step 204 of deriving the inference may comprise classifying each of the battery gauge parameters into at least one category. Examples of the category with respect to the battery gauge parameter may include, but are not limited to, “low”, “medium”, and “high”. The method may comprise mapping each of the battery gauge parameters onto an input set based on the associated at least one category. The input set of the battery gauge parameter comprises one or more degree of memberships for the battery gauge parameter. In some examples, the degree of membership ranges from 0 to 1. Upon mapping, the method may comprise deriving the inference based on the input set of each battery gauge parameter and a correlation between the battery gauge parameters and the cruising range of the electric vehicle 100.

In some embodiments, the step of deriving the inference may comprise evaluating the input set of each battery gauge parameter with respect to the input sets of other battery gauge parameters in accordance with the correlation between the battery gauge parameters and the cruising range of the electric vehicle 100. The method may further comprise generating an output set based on the evaluation. The output set indicates one or more degree of memberships associated with one of the plurality of cruising ranges of the electric vehicle 100. In some examples, the degree of membership associated with one of the plurality of cruising ranges of the electric vehicle 100 ranges from 0 to 1.

At step 206, the method 200 comprises estimating the cruising range of the electric vehicle 100, based on the derived inference.

The step 206 of estimating the cruising range of the electric vehicle 100 may comprise determining at least one category associated with the cruising range of the electric vehicle 100 based on the output set. Examples of the category associated with the cruising range of the electric vehicle 100 may include, but are not limited to, “low”, “medium”, and “high”. The method may further comprise estimating the cruising range of the electric vehicle 100 based on the output set and the determined at least one category associated with the cruising range of the electric vehicle.

Figure 3 is an example schematic diagram showing the range estimator 102. The range estimator 102 is capable of estimating the cruising range of the electric vehicle 100 and may be configured to cause performance of the method 200 for estimating the cruising range of the electric vehicle 100.

According to at least some embodiments of the present invention, the range estimator 102 in Figure 3 comprises one or more modules. These modules may e.g. be a memory 302, a processor 304, a controlling circuitry 306, a transceiver 308, a display 310, a measuring module 312, and a fuzzy and defuzzification module 314. The controlling circuitry 306, may in some embodiments be adapted to control the above mentioned modules.

The memory 302, the processor 304, the transceiver 308, the display 310, the measuring module 312, and the fuzzy and defuzzification module 314 as well as the controlling circuitry 306, may be operatively connected to each other.

The controlling circuitry 306 may be adapted to estimate the cruising range of the electric vehicle 100, as described above in conjunction with the method 200 and Figure 2.

The measuring module 312 may be adapted to obtain the battery gauge parameters including an instantaneous power, a rate of change of power, and an energy available in the battery pack 104 of the electric vehicle 100.

The fuzzy and defuzzification module 314 may be adapted to estimate the cruising range of the electric vehicle by applying a fuzzy inference logic and a defuzzification logic on the obtained battery gauge parameters.

The processor 304 may be adapted to initiate estimation of the cruising range of the electric vehicle 100, once the electric vehicle 100 starts operating.

The display 310 may be adapted to display the estimated cruising range of the electric vehicle 100 to a user/driver operating the electric vehicle 100.

The transceiver 308 may be adapted to receive the fuzzy inference logic and the defuzzification logic from at least one external database.

The memory 302 may store the battery gauge parameters, the fuzzy inference logic and the defuzzification logic, and the estimated cruising range of the electric vehicle 100.

Figure 4 is a schematic diagram illustrating estimation of the cruising range of the electric vehicle 100.

As depicted in Figure 4, the range estimator 102 measures an instantaneous power in Watt (W), a rate of change of power in Watt/second (W/s), and a remaining energy in the battery pack 104 in kilo Watt hour (KWh). The range estimator 102 estimates the cruising range of the electric vehicle 100 using such measured parameters.

Figure 5 is an example flowchart illustrating method steps of a method performed for estimating the cruising range of the electric vehicle 100 using a fuzzy logic and a defuzzification logic in accordance with an embodiment herein.

At step 501, the range estimator 102 obtains/measures instantaneous values of power, remaining energy in the battery pack 104 of the electric vehicle 100, and a rate of change of power.

At step 502, the range estimator 102 converts each of the measured parameter into an input set/fuzzy set using at least one category (also be referred to as at least one fuzzy linguistic variable/membership function) associated with each measured parameter. At step 503, the range estimator 102 develops an inference based on a set of rules that correlate inputs with an output. Herein, the inputs correspond to the measured parameters and the output correspond to one of the plurality of cruising ranges of the electric vehicle 100.

At step 504, the range estimator 102 develops an output set/fuzzy output set based on the inference. At step 505, the range estimator 102 applies a defuzzification method/logic on the developed fuzzy output set to obtain a defuzzified output set. The defuzzified output set indicates the estimated cruising range of the electric vehicle 100. At step 506, the range estimator 102 displays the estimated cruising range to a user operating the electric vehicle 100. Thereby, the user may not face a problem of range anxiety while planning long trips in accordance with location of distance, charging station, or the like.

Consider an example scenario, wherein a user starts operating the electric vehicle 100. In such a scenario, the range estimator 102 obtains input parameters such as, an available energy in the battery pack 104/SoC, an instantaneous power, and a rate of change of power (dP/dT). The range estimator 102 determines one or more categories/membership functions associated with each of the input parameters based on its value. In some examples, the membership functions may include “low”, “medium”, and “high”. Upon determining the one or more membership functions associated with each of the input parameters, the range estimator 102 maps each of the input parameters onto an input set/fuzzy set based on the associated one or more membership functions. The fuzzy set comprises one or more degree of memberships. The degree of membership function may vary from 0 to 1. Examples membership function plots of the input parameters, the SoC, the power, and the rate of change of power are depicted in Figure 6A. The example membership function plot of the input parameter indicates the one or more degree of memberships and the one or more membership functions for the given value of the input parameter.

Upon mapping each of the input parameters onto the one or more degree of memberships, the range estimator 102 generates the output set by deriving the inference based on a set of rules that correlate the input parameters with the cruising range of the electric vehicle 100/output. Exemplary set of rules is depicted in below table:

S. No SoC Power dP/dT Range
1 Low Low Low Low
2 Low Low Medium Low
3 Low Low High Low
4 Low Medium Low Low
5 Low Medium Medium Low
6 Low Medium High Low
7 Low High Low Low
8 Low High Medium Low
9 Low High High Low
10 Medium Low Low Medium
11 Medium Low Medium Medium
12 Medium Low High Low
13 Medium Medium Low Low
14 Medium Medium Medium Low
15 Medium Medium High Low
16 Medium High Low Low
17 Medium High Medium Low
18 Medium High High Low
19 High Low Low High
20 High Low Medium High
21 High Low High Medium
22 High Medium Low High
23 High Medium Medium Medium
24 High Medium High Medium
25 High High Low Medium
26 High High Medium Medium
27 High High High Medium
Table: Set of rules

The output set generated by deriving the inference indicates one or more degree of memberships associated with one of the plurality of cruising ranges of the electric vehicle 100. The degree of membership associated with one of the plurality of cruising ranges of the electric vehicle 100 may vary from 0 to 1. Thus, by deriving the inference/output set, the range estimator 102 determines the one or more degree of memberships associated with one of the plurality of cruising ranges of the electric vehicle 100 for the measured input parameters.

Upon generating the output set, the range estimator 102 determines the one or more categories/membership functions associated with the output set. In some examples, the membership functions associated with the output set may include “low”, “medium”, and “high”. The range estimator 102 estimates the single crisp output value indicating the cruising range of the electric vehicle 100 based on the one or more degree of memberships and the one or more member functions associated with the one cruising range of the electric vehicle 100. An example membership function plot of the cruising range of the electric vehicle 100 is depicted in Figure 6B. The example membership function plot of the cruising range indicates the remaining cruising range of the electric vehicle 100 for the given one or more degree of memberships and the one or more member functions associated with cruising range.

Various embodiments of the range estimator 102 and methods disclosed herein provides technical advantage over the conventional systems. As the claimed invention enables users to have an accurate picture of the available range for their electric vehicles, the users may not face any problem of range anxiety.

The present invention enables estimation of the cruising range by considering only fewer battery gauge parameters consisting of the instantaneous power, the rate of change of power, and the energy available in the battery pack 104 of the electric vehicle 100. Estimation of the cruising range of the electric vehicle 100 using fewer battery gauge parameters reduces an amount of sensing hardware requirements and complexity involved in estimation. Thereby, reducing total processing overhead involved in estimating the cruising range of the electric vehicle 100, which further improves performance and reduces cost.

The present invention further enables estimation of the cruising range with improved accuracy, as all the battery gauge parameters that affect the range of the electric vehicle 100 in any manner are taken into consideration.

The present invention further eliminates a need to model a behavior of the electric vehicle 100 and a driver for estimating the cruising range of the electric vehicle 100. This makes estimation of the cruising range of the electric vehicle 100 highly flexible and can be deployed in any electric vehicle of any size/capacity/type with minimal adjustments.

Although the present invention has been described in considerable detail with reference to certain preferred embodiments and examples thereof, other embodiments and equivalents are possible. Even though numerous characteristics and advantages of the present invention have been set forth in the foregoing description, together with functional and procedural details, the disclosure is illustrative only, and changes may be made in detail, especially in terms of the procedural steps within the principles of the invention to the full extent indicated by the broad general meaning of the terms. Thus, various modifications are possible of the presently disclosed system and process without deviating from the intended scope of the present invention. , Claims:WE CLAIM:
1. A method (200) for estimating a cruising range of an electric vehicle (100), the method (200) comprising:
obtaining (202), by a range estimator (102), battery gauge parameters being affecting the cruising range of the electric vehicle (100);
deriving (204), by the range estimator (102), an inference indicating a mapping of a combination of the battery gauge parameters to one of a plurality of cruising ranges of the electric vehicle (100); and
estimating (206), by the range estimator (102), the cruising range of the electric vehicle (100) based on the derived inference.

2. The method (200) according to claim 1, wherein the battery gauge parameters comprise an instantaneous power, a rate of change of power, and an energy available in a battery pack (104) of the electric vehicle (100).

3. The method (200) according to claim 1, wherein deriving, by the range estimator (102), the inference comprises:
classifying each of the battery gauge parameters into at least one category;
mapping each of the battery gauge parameters onto an input set based on the at least one category, wherein the input set of each battery gauge parameter comprises one or more degree of memberships for the battery gauge parameter; and
deriving the inference based on the input set of each battery gauge parameter and a correlation between the battery gauge parameters and the cruising range of the electric vehicle (100).

4. The method (200) according to claim 3, wherein deriving the inference comprises:
evaluating the input set of each battery gauge parameter with respect to the input sets of other battery gauge parameters in accordance with the correlation between the battery gauge parameters and the cruising range of the electric vehicle (100); and
generating an output set based on the evaluation, wherein the output set indicates one or more degree of memberships associated with one of the plurality of cruising ranges of the electric vehicle (100).

5. The method (200) according to claim 1, wherein estimating (206), by the range estimator (102), the cruising range of the electric vehicle (100) comprises:
determining at least one category associated with the cruising range of the electric vehicle (100) based on the output set; and
estimating the cruising range of the electric vehicle (100) based on the output set and the determined at least one category associated with the cruising range of the electric vehicle (100).

6. A range estimator (102) for estimating a cruising range of an electric vehicle (100), the range estimator (102) comprising a controlling circuitry (306) being adapted for:
obtaining battery gauge parameters being affecting the cruising range of the electric vehicle (100);
deriving an inference indicating a mapping of a combination of the battery gauge parameters to one of a plurality of cruising ranges of the electric vehicle (100); and
estimating the cruising range of the electric vehicle (100) based on the derived inference.

7. The range estimator (102) according to claim 6, wherein the battery gauge parameters comprise an instantaneous power, a rate of change of power, and an energy available in a battery pack (104) of the electric vehicle (100).

8. The range estimator (102) according to claim 6, wherein the controlling circuitry (306) is adapted for deriving the inference by:
classifying each of the battery gauge parameters into at least one category;
mapping each of the battery gauge parameters onto an input set based on the at least one category, wherein the input set of each battery gauge parameter comprises one or more degree of memberships for the battery gauge parameter; and
deriving the inference based on the input set of each battery gauge parameter and a correlation between the battery gauge parameters and the cruising range of the electric vehicle (100).

9. The range estimator (102) according to claim 8, wherein the controlling circuitry (306) is adapted for deriving the inference by:
evaluating the input set of each battery gauge parameter with respect to the input sets of other battery gauge parameters in accordance with the correlation between the battery gauge parameters and the cruising range of the electric vehicle (100); and
generating an output set based on the evaluation, wherein the output set indicates one or more degree of memberships associated with one of the plurality of cruising ranges of the electric vehicle (100).

10. The range estimator (102) according to claim 6, wherein the controlling circuitry (306) is adapted for estimating the cruising range of the electric vehicle (100) by:
determining at least one category associated with the cruising range of the electric vehicle (100) based on the output set; and
estimating the cruising range of the electric vehicle (100) based on the output set and the determined at least one category associated with the cruising range of the electric vehicle (100).