immediate charging: calculate adjustment values strategy

Currently our LDV immediate test does not use `adjustment_values` in the following function in `immediate.py`:

``` python
def adjust_bev(hourly_profile, adjustment_values): # noqa: N802
"""Adjusts the charging profiles by applying weighting factors based on
seasonal/monthly values

:param numpy.ndarray hourly_profile: normalized charging profiles
:param pandas.DataFrame adjustment_values: weighting factors for each
day of the year loaded from month_info_nhts.mat.
:return: (*numpy.ndarray*) -- the final adjusted charging profiles.
"""
adj_vals = adjustment_values.transpose()
profiles = hourly_profile.reshape((24, 365), order="F")

pr = profiles / sum(profiles)
adjusted = pr * adj_vals

return adjusted.T.flatten()
```

We need to define a strategy for calculating this parameter which incorporates urban and rural scaling?

Start with immediate.py and the adjust_bev function, then inspect the LDV immediate test that currently omits adjustment_values. Define how the parameter should be calculated, including urban and rural scaling, and document the expected adjusted charging profiles. Done means the strategy is agreed and the immediate test exercises the resulting adjustment values.

Written by the indexing model from the issue text.