eyeprolog

EV range worlds

Will the battery last the trip, and under which assumptions?

Four electric-vehicle trips are judged under four modelling worlds, from a naive per-kilometre estimate to physics plus a safety reserve. The answer is not one verdict but a pattern: which trips are safe in which worlds, and why the verdict changes.

Run it in the playground · Source

The question

A range estimate depends on its model. Driving fast, cold weather, and heavy payloads all raise consumption, and a careful planner adds a reserve on top. Instead of picking one model, the example evaluates every trip in four worlds:

w1: base consumption only;
w2: base adjusted for speed;
w0: speed, temperature, and payload factors;
w3: the w0 estimate plus a 30% safety reserve.

The data

% trip_data(Trip, DistanceKm, SpeedKmh, TemperatureC, PayloadKg, BatteryKWh, BaseKWhPerKm).
trip_data(city_errand, 40, 45, 20, 100, 30, 0.18).
trip_data(winter_highway, 260, 115, -5, 400, 60, 0.20).
trip_data(heavy_delivery, 180, 80, 15, 700, 55, 0.22).
trip_data(cold_commute, 120, 90, -8, 100, 35, 0.19).

speed_factor(T, 1.20) :- trip_data(T, _, S, _, _, _, _), (S > 100).
speed_factor(T, 1.00) :- trip_data(T, _, S, _, _, _, _), (S =< 100).

Each factor is a small rule over the trip’s own data, so adding a trip means adding one fact.

The worlds

required_energy(T, w0, E) :-
  base_energy(T, Base),
  speed_factor(T, Sf),
  temperature_factor(T, Tf),
  payload_factor(T, Pf),
  (A is Base * Sf),
  (B is A * Tf),
  (E is B * Pf).

required_energy(T, w3, E) :-
  required_energy(T, w0, W0),
  (E is W0 * 1.30).

risky_in_world(T, W) :-
  trip_data(T, _, _, _, _, Battery, _),
  required_energy(T, W, Required),
  (Required > Battery).

One required_energy/3 clause per world; the safe/risky test is shared.

The answer

eyeprolog examples/ev-range-worlds.pl

riskyInWorld(winter_highway, w2).
riskyInWorld(winter_highway, w0).
riskyInWorld(winter_highway, w3).
riskyInWorld(heavy_delivery, w3).
reason(winter_highway, "cold fast payload trip exceeds battery in physics-aware worlds").
reason(heavy_delivery, "safety buffer turns a physics-safe delivery into a cautious risk").
status(ev_range_worlds, expected_world_pattern).

The full output has 19 answers, including 12 safeInWorld/2 facts. The city errand and the cold commute are safe everywhere. The winter highway trip is safe only under the naive model, and the heavy delivery fails only once the reserve is added.

The reason

step(risky_in_world(winter_highway, w0),
     rule(22),
     ['T' = winter_highway, 'W' = w0, 'Battery' = 60, 'Required' = 77.5008],
     [trip_data(winter_highway, 260, 115, -5, 400, 60, 0.2),
      required_energy(winter_highway, w0, 77.5008),
      77.5008 > 60]).

The proof shows the numbers: the physics-aware world needs about 77.5 kWh, and the battery holds 60. The comparison itself is a recorded built-in step.

The check

condition('C1', resolution, ok, 71).
condition('C5', re_decision, ok, 41).
condition('C7', relevance, ok, 131).
verdict(checked).

The checker replays 71 rule steps and recomputes all 41 arithmetic and comparison steps itself. Every number in the proof is re-derived, so nothing is taken on trust.

Try it

Raise the winter highway battery from 60 to 80 kWh. In which worlds does the trip become safe?
Add a fifth world with a 10% reserve instead of 30%. Does the heavy delivery survive it?
Add a trip of your own and predict its pattern across the four worlds before running it.

Learn more in The Art of EyeProlog, Chapters 14 and 29.

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