SYSTEM AND METHOD FOR ESTIMATING ELECTRIC VEHICLE CHARGE NEEDS AMONG A POPULATION IN A REGION

1 . A method for estimating electric vehicle (EV) charge needs among a population in a particular region, comprising:

obtaining region-specific information associated with the particular region;

wherein the region-specific information includes a number of EV drivers in the particular region;

generating needs prediction data that estimates the EV charge needs among the population by applying the region-specific information to a mobility simulation of electrical vehicle drivers in the particular region;

wherein the mobility simulation comprises a plurality of probability distribution functions;

based on the needs prediction data, generating, on a display device of a computing device, a display that suggests a plurality of locations at which to place EV charging stations within the particular region to satisfy the estimated EV charge needs indicated in the needs prediction data;

wherein the method is performed by one or more computing devices.

2 . The method of claim 1 further comprising determining:

a first percentage of EV drivers in the particular region that qualify as essential drivers; and

a second percentage of EV drivers in the particular region that qualify as opportunistic drivers; and

wherein the needs prediction data is based, at least in part, on the first percentage and the second percentage.

3 . The method of claim 2 wherein:

the needs prediction data includes information about:

a number of charging stations needed in the particular region;

a location of one or more charging stations needed in the particular region; and

a type of one or more charging stations needed in the particular region.

4 . The method of claim 3 wherein the type of each charging station needed in the particular region is determined based, at least in part, on the first percentage and the second percentage.

5 . The method of claim 2 wherein the first percentage and the second percentage are determined based, at least in part, on:

distributions of average distances EV drivers travel in the particular region;

ranges of EVs that are being driven in the particular region;

access to charging stations in the particular region;

charging speeds of charging stations in the particular region; and

duration of charging events in the particular region.

6 . The method of claim 1 wherein:

the prediction is for a particular time in the future; and

the method further comprises predicting the number of EV drivers that will be in the particular region at the particular time based on a current number of EV users in the particular region and an EV adoption model.

7 . The method of claim 6 wherein the EV adoption model takes into account at least:

relative cost of owning EVs versus internal combustion engine (ICE) vehicles;

sensitivity of drivers in the particular region to cost differences between EVs and ICE vehicles; and

charging infrastructure available in the particular region.

8 . The method of claim 1 wherein:

the region-specific information includes home charging access information and work charging access information; and

the mobility simulation generates the needs prediction data based, at least in part, on the home charging access information and work charging access information.

9 . The method of claim 1 wherein:

the region-specific information includes charging speeds of one or more EV charging stations within the particular region; and

the mobility simulation generates the needs prediction data based, at least in part, on charging speeds of EV charging stations within the particular region.

10 . The method of claim 1 further comprising:

displaying user interface controls for selecting a set of one or more optimization factors from a plurality of optimization factors supported by the mobility simulation;

receiving user input that selects a particular set of one or more optimization factors;

wherein the mobility simulation generates the needs prediction data based on the particular set of one or more optimization factors.

11 . The method of claim 10 wherein the plurality of optimization factors include:

satisfying demand for EV charging;

reducing demand on grid that results from EV charging; and

maximizing revenue lift.

12 . One or more computing devices configured to:

obtain region-specific information associated with the particular region;

wherein the region-specific information includes a number of EV drivers in the particular region;

generate needs prediction data that indicates the estimated EV charge needs among the population by applying the region-specific information to a mobility simulation of electrical vehicle drivers in the particular region;

wherein the mobility simulation comprises a plurality of probability distribution functions;

based on the needs prediction data, generate, on a display device of a computing device, a display that suggests a plurality of locations at which to place EV charging stations within the particular region to satisfy the estimated EV charge needs indicated in the needs prediction data.

13 . The one or more computing devices of claim 12 , further configured to:

determine a first percentage of EV drivers in the particular region that qualify as essential drivers; and

determine a second percentage of EV drivers in the particular region that qualify as opportunistic drivers; and

wherein the needs prediction data is based, at least in part, on the first percentage and the second percentage.

14 . The one or more computing devices of claim 13 , wherein the needs prediction data includes information about:

a number of charging stations needed in the particular region;

a location of one or more charging stations needed in the particular region; and

a type of one or more charging stations needed in the particular region.

15 . The one or more computing devices of claim 12 , wherein the prediction is for a particular time in the future, and wherein the one or more computing devices are further configured to:

predict the number of EV drivers that will be in the particular region at the particular time based on a current number of EV users in the particular region and an EV adoption model.

16 . The one or more computing devices of claim 12 , further configured to:

display user interface controls for selecting a set of one or more optimization factors from a plurality of optimization factors supported by the mobility simulation;

receive user input that selects a particular set of one or more optimization factors;

wherein the mobility simulation generates the needs prediction data based on the particular set of one or more optimization factors.

17 . A non-transitory computer readable medium comprising instructions executable by a processor to:

obtain region-specific information associated with the particular region;

wherein the region-specific information includes a number of EV drivers in the particular region;

generate needs prediction data that indicates estimated EV charge needs among the population by applying the region-specific information to a mobility simulation of electrical vehicle drivers in the particular region;

wherein the mobility simulation comprises a plurality of probability distribution functions;

based on the needs prediction data, generate, on a display device of a computing device, a display that suggests a plurality of locations at which to place EV charging stations within the particular region to satisfy the estimated EV charge needs indicated in the needs prediction data.

18 . The non-transitory computer readable medium of claim 17 , wherein the instructions, when executed by the processor, further:

determine a first percentage of EV drivers in the particular region that qualify as essential drivers; and

determine a second percentage of EV drivers in the particular region that qualify as opportunistic drivers; and

wherein the needs prediction data is based, at least in part, on the first percentage and the second percentage.

19 . The non-transitory computer readable medium of claim 18 , wherein the needs prediction data includes information about:

a number of charging stations needed in the region;

a location of one or more charging stations needed in the region; and

a type of one or more charging stations needed in the region.

20 . The non-transitory computer readable medium of claim 17 , wherein the prediction is for a particular time in the future, and wherein the instructions, when executed by the processor, further:

predict the number of EV drivers that will be in the particular region at the particular time based on a current number of EV users in the particular region and an EV adoption model.