bug: fix scaling discrepancy between smart and immediate charging

**Daily VMT Edits**
- [x] Move the `daily_values` multiplier out of the `get_total_hdv_daily_vmt` function (currently modifying the sample data's `annual_vmt`) via this equation
`daily_vmt_total = daily_values * annual_vmt`
and into the `smart_charging` function to modify the `bev_vmt` scalar via these equations
Line 274: `outputelectricload * daily_values[day_iter] / (daily_vmt_total[day_iter] * 1000) * bev_vmt`
Line 221: `cost += tripload * daily_values[day_iter] / 1000 / daily_vmt_total[day_iter] * bev_vmt`

- [x] The same edit above holds for both the LDV and HDV process (Lines 295 and 244 in the LDV code)

- [x] Change `get_total_daily_vmt` to return a vector with each day's total VMT, as the name of the function suggests

- [x] Merge the `get_total_hdv_daily_vmt` and `get_total_daily_vmt` into one function. This will require adding something to the HDV dataset signifying only one type of day exists in this data, whereas the LDV data has weekdays and weekends separated. Perhaps this could be generalized to have any number of "types of days" instead of assuming only weekdays vs weekends. This also should be generalized to be a vector of any length, not hard coded as 365 as it currently is.

- [x] We should double-check that the function `generate_daily_weighting` works for the HDV dataset, since that does not have the `weekday` and `weekend` differentiation. In the HDV case, we still want the monthly variation along with the `rural` and `urban` variation

**Vehicle Filtering Edits**
- [x] Remove the vehicle range filtering from the `get_total_hdv_daily_vmt' function

- [x] Instead, modify the data sent to the `immediate` and `smart` functions by removing vehicles that drive more than that input range (e.g. 100 miles) is able to support (see next bullet for one potential implementation)

- [ ] Add a function (or perhaps repurpose the `calculate_charging` function) that for each vehicle, assumes a full battery at the start of the day, with the starting state of charge a calculation based on the battery range (e.g. 100mile, 200mile, etc) and vehicle's fuel efficiency (`kwhmi`). Then, calculate the state of charge for each vehicle over the course of their trips in the sample data, including the increases to the state of charge when dwell times allow for charging. If at any point the SOC goes below zero (and with a cap maxing the SOC to never exceed 100%), then that vehicle should be filtered out for this particular set of inputs.

**Other Edits discovered**
- [ ] The lines currently in 48-50 of the `smart_charging_HDV.py` file are extraneous and should be removed
if veh_type.lower() == "ldv":
newdata = data_helper.load_hdv_data("lhdv", filepath)
# hdv_cat = 1