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# System prepended metadata

title: Algorithm Kickoff 13.04.2021

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# Algorithm Kickoff 13.04.2021
## Goals & Desired Outcomes
- Commit to MVP scope ->necessary data requirements 
-- start with passenger vehicles
--- Later:  Improve data sets to expand EV models we can recommend

Tasks:
### MVP Substitution Calculation
- Build data set based on WSW data
-- Reach out for car type
-- Define % space to account for conditions that we do not know in MVP
-- Penny to put together initial findings from WSW data set

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- get all trips
- aggregate by day
- sum distances traveled
- get the maximum daily distance traveled for each vehicle
- plot into a histogram
- we define hardcoded bands for the histogram e.g., 0-50km easily replaced by vehicles x, y, and z; 50-100km replaceable by vehicles a, b, c; ... 200km+ no good replacements yet
- recommendation is based on distance that an EV can drive with a single charge group by vehicle class
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- Define data to be collected via app during MVP/pilot
- Consider options for paying for data from  

- Review initial model idea(s) -> necessary research and implementation tasks
- Data architecture -> next steps

If time allows:

- Roadmap for introducing new data points to substitution
- How will we measure the accuracy of what we've built? / How are we structuring our learning?

Later:
- Apply learnings to other formulas/algorithms: CO2 Emissions and EV Replacement Recommendations
- 

## Notes

- Goals of MVP, "with some degree of confidence":
  1. Give to fleet managers and CXO's the % of their fleeet that can be replaced to EVs.
  2. Recommend vehicles matching the requirements for replacement.
- WSW gave us trip data. Alphabet Denmark may share this as well; it's a larger fleet and company, so it may require more time to negotiate and really take place.
- Ranges differ between vehicle types.
- We want all the cars with the exception of large trucks. Alphabet: small passenger cars, SUVs, small transporters...
- When talking about transporation cars, we need to take many more different variables into account: loading weight, space of the trunk
- Datasets for transporation vehicles are not easily available. If we want to persue this, we need to choose between:
  1. Research better for datasets containing these vehicles.
  2. Create a dataset ourselves.
  3. Manually list ~3 models and recommend from this hardcoded list and not from a long dataset.
- Alphabet vehicles: https://www.alphabet.com/files/alphabet_alphaelectric_fahrzeuguebersicht.pdf
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