The EV model object defined by `{evprof}`

is generated
with function `get_ev_model()`

. This function returns an
object of class `evmodel`

. This object prints a summary of
its components. The package provides an example of `evmodel`

created in the California
study case article, using the charging sessions data provided by ACN.

`evprof::california_ev_model`

```
## EV sessions model of class "evmodel", created on 2024-01-29
## Timezone of the model: America/Los_Angeles
## The Gaussian Mixture Models of EV user profiles are built in:
## - Connection Models: logarithmic scale
## - Energy Models: logarithmic scale
##
## Model composed by 2 time-cycles:
## 1. Workday:
## Months = 1-12, Week days = 1-2
## User profiles = Visit, Worktime
## 2. Weekend:
## Months = 1-12, Week days = 6-7
## User profiles = Visit
```

The `evmodel`

object has two components:

`metadata`

: creation date, data time zone, if the scale of connection/energy models is natural or logarithmic, …`models`

: tibble containing the different time-cycles models and information. The columns of this tibble are:`time_cycle`

: character, given name to the time-cycle`months`

: integer vector, corresponding months of the time-cycle`wdays`

: integer vector, corresponding days of the time-cycle (week starting on day 1)`user_profiles`

: tibble with every user profile GMM models. The columns of this tibble are:`profile`

: character vector, given name to the user profile`ratio`

: numeric, share of daily sessions corresponding to this profile`connection_models`

: tibble with the connection bi-variate GMM`energy_models`

: tibble with the energy uni-variate GMM

The model itself is composed by multiple Gaussian models (GMM). The
`connection_models`

are Gaussian models of two variables
(connection start time and connection duration) and the
`energy_models`

are built with a single variable (energy).
Thus, the statistic features of `connection_models`

are a
centroid (\(\mu\)), a covariance matrix
(\(\Sigma\)) and the weight or ratio of
the corresponding model. Besides, the statistic features of
`energy_models`

are a mean (\(\mu\)), a standard deviation (\(\sigma\)) and the weight or ratio of the
corresponding model.

Let’s take a look to these statistical features of the Worktime user profile for Working days in the California model:

`california_ev_model$models`

```
## # A tibble: 2 × 4
## time_cycle months wdays user_profiles
## <chr> <list> <list> <list>
## 1 Workday <int [12]> <int [5]> <tibble [2 × 4]>
## 2 Weekend <int [12]> <int [2]> <tibble [1 × 4]>
```

```
workday_model <- california_ev_model$models$user_profiles[[1]]
workday_model
```

```
## # A tibble: 2 × 4
## profile ratio connection_models energy_models
## <chr> <dbl> <list> <list>
## 1 Visit 0.460 <tibble [3 × 3]> <tibble [1 × 3]>
## 2 Worktime 0.540 <tibble [3 × 3]> <tibble [1 × 3]>
```

`worktime_model <- workday_model[2, ]`

The connection model is a mixture of 3 bi-variate Gaussian Models:

`worktime_model$connection_models`

```
## [[1]]
## # A tibble: 3 × 3
## mu sigma ratio
## <list> <list> <dbl>
## 1 <dbl [2]> <dbl [2 × 2]> 0.305
## 2 <dbl [2]> <dbl [2 × 2]> 0.428
## 3 <dbl [2]> <dbl [2 × 2]> 0.267
```

On the other hand, the energy models can be based on the charging
rate (`Power`

variable) of the sessions. It has been observed
that the energy demand increases together with the charging power (big
vehicles have larger batteries and can charge at higher rates). Thus,
function `get_energy_models`

has the logical parameter
`by_power`

to fit the Energy Gaussian Models for the
different groups of charging powers separately. In the case of
California study case, we set `by_power=FALSE`

, that’s why we
got the `Unknown`

in the `energy_models`

tibble
with a `ratio`

of 1:

`worktime_model$energy_models[[1]]`

```
## # A tibble: 1 × 3
## charging_rate ratio energy_models
## <chr> <int> <list>
## 1 Unknown 1 <tibble [8 × 3]>
```

Thus, the energy model of all Worktime sessions is a mixture of 9 Gaussian models:

`worktime_model$energy_models[[1]]$energy_models[[1]]`

```
## # A tibble: 8 × 3
## mu sigma ratio
## <dbl> <dbl> <dbl>
## 1 1.34 0.129 0.0204
## 2 1.78 0.129 0.164
## 3 2.11 0.129 0.167
## 4 2.48 0.129 0.158
## 5 2.63 0.129 0.179
## 6 3.01 0.129 0.0969
## 7 3.35 0.129 0.0941
## 8 3.65 0.129 0.120
```