Vehicle thermal control system

1 . A system comprising:

one or more processors; and

non-transitory memory communicatively coupled to the one or more processors storing computer executable instructions that, when executed, cause the one or more processors to perform operations comprising:

receiving route data associated with a route for a vehicle to travel from a first location to a second location;

receiving environmental data representing an environment associated with the route data, wherein the environmental data comprises a first portion associated with conditions of the environment and a second portion associated with passenger preferences;

determining energy budget data, the energy budget data taking into account an estimated amount of energy needed to control a temperature of one or more components of the vehicle while navigating from the first location to the second location, wherein the energy budget data is determined based at least in part on:

the second portion of the environmental data, wherein the second portion includes passenger environment preference data comprising a passenger preference associated with at least one vent position, seat temperature, air speed, temperature control mode, seating location, or window tinting setting;

at least one of the route data or the first portion of the environmental data;

one or more estimated temperatures associated with areas of a cabin of the vehicle relative to a seat of the vehicle;

a machine-learned model comprising an adjustment to the machine-learned model, wherein:

the machine-learned model is configured to output:

one or more first outputs, the one or more first outputs being associated with a first energy amount needed to maintain a healthy performance of one or more vehicle components, and

one or more second outputs, the one or more second outputs being associated with a second energy amount needed to meet the passenger preferences; and

the adjustment is based at least in part on:

a difference between a previous estimated energy consumption and an actual previous energy consumption associated with controls used when navigating the vehicle from the first location to the second location,

one or more first altered parameters, the one or more first altered parameters associated with the one or more first outputs, and

one or more second altered parameters, the one or more second altered parameters associated with the one or more second outputs; and

controlling at least one of a propulsion system of the vehicle, a computing system of the vehicle, or a thermal control system of the vehicle based at least in part on the energy budget data.

2 . The system of claim 1 , wherein the route data comprises at least one of:

route distance;

route velocity data;

map data;

estimated route compute level;

current day of year;

current time of day; or

arrival time, and

wherein the environmental data includes at least one of:

ambient environmental conditions associated with the route data;

weather data;

solar load data;

ambient temperature data;

ambient air quality data;

internal environmental conditions associated with the vehicle;

internal temperature data;

internal zone temperature data;

internal air quality data;

passenger data;

passenger age;

passenger gender;

the passenger environment preference data; or

traffic data.

3 . The system of claim 1 , the operations further comprising:

receiving battery charge state data associated with a battery system of the vehicle;

determining, based on the battery charge state data, that a first amount of power associated with the energy budget data is greater than a second amount of power associated with the battery charge state data; and

altering at least one of a vehicle control or thermal control based at least in part on the first amount of power being greater than the second amount of power.

4 . The system of claim 1 , wherein controlling at least one of the propulsion system, the computing system, or the thermal control system comprises controlling the propulsion system, the operations further comprising one or more of:

determining, based on the energy budget data, at least one of a range of the vehicle, an updated route of the vehicle, or a velocity of the vehicle;

determining, based on the energy budget data, at least one of a number of sensors to use, a sensing range, or a level of compute to use; or

determining, based at least in part on the energy budget data, at least one of a blower control, a compressor control, a vent position control, a heating mode, or a cooling mode.

5 . The system of claim 1 , wherein the energy budget data is based at least in part on an estimated compute stress associated with the route data, wherein the estimated compute stress is based at least in part on a complexity associated with the first portion of the environmental data.

6 . The system of claim 1 , wherein:

the second portion of the environmental data comprises first passenger environment preference data associated with a first passenger and second passenger environment preference data associated with a second passenger; and

controlling at least one of the propulsion system, the computing system, or the thermal control system comprises a first control relative to a first area of the cabin associated with the first passenger and a second control relative to a second area of the cabin associated with the second passenger, wherein the first control is based at least in part on the first passenger environment preference data and the second control is based at least in part on the second passenger environment preference data.

7 . A method comprising:

receiving route data associated with a route for a vehicle to travel from a first location to a second location;

receiving environmental data representing an environment associated with the route data, wherein the environmental data comprises a first portion associated with conditions of the environment and a second portion associated with passenger preferences;

determining energy budget data, the energy budget data including an estimate of thermal management, wherein the energy budget data is determined based at least in part on:

the second portion of the environmental data, wherein the second portion includes passenger environment preference data comprising a passenger preference associated with at least one of vent position, seat temperature, air speed, temperature control mode, seating location, or window tinting setting;

at least one of the route data or the first portion of the environmental data;

one or more estimated temperatures associated with areas of a cabin of the vehicle;

a machine-learned model comprising an adjustment to the machine-learned model, wherein:

the machine-learned model is configured to output:

one or more first outputs, the one or more first outputs being associated with a first energy amount needed to maintain a healthy performance of one or more vehicle components, and

one or more second outputs, the one or more second outputs being associated with a second energy amount needed to meet the passenger preferences; and

the adjustment is based at least in part on:

a difference between a previous machine-learned model output associated with an estimated control and an actual previous control,

one or more first altered parameters, the one or more first altered parameters associated with the one or more first outputs, and

one or more second altered parameters, the one or more second altered parameters associated with the one or more second outputs; and

controlling at least one of a propulsion system of the vehicle, a computing system of the vehicle, or a thermal control system of the vehicle based at least in part on the energy budget data.

8 . The method of claim 7 , wherein determining the energy budget data comprises:

inputting at least one of the route data or the environmental data to the machine-learned model; and

receiving, from the machine-learned model, the energy budget data.

9 . The method of claim 7 , further comprising:

measuring an actual energy consumption used to navigate the vehicle from the first location to the second location; and

updating the machine-learned model based at least in part on the actual energy consumption used to navigate the vehicle from the first location to the second location.

10 . The method of claim 7 , wherein the route data is associated with transporting a passenger from the first location to the second location.

11 . The method of claim 7 , wherein the route data comprises at least one of:

route distance;

route velocity data;

map data;

estimated route compute level;

current day of year;

current time of day; or

arrival time.

12 . The method of claim 7 , wherein the environmental data includes at least one of:

ambient environmental conditions associated with the route data;

weather data;

solar load data;

ambient temperature data;

ambient air quality data;

internal environmental conditions associated with the vehicle;

internal temperature data;

internal zone temperature data;

internal air quality data;

passenger data;

passenger age;

passenger gender;

or

traffic data.

13 . The method of claim 7 , further comprising:

receiving battery charge state data associated with a battery system of the vehicle;

determining, based on the battery charge state data, that a first amount of power associated with the energy budget data is greater than a second amount of power associated with the battery charge state data; and

altering at least one of a vehicle control or thermal control based at least in part on the first amount of power being greater than the second amount of power.

14 . The method of claim 7 , further comprising:

receiving battery charge state data associated with a plurality of battery systems associated with a plurality of vehicles;

determining, based at least in part on the energy budget data and the battery charge state data, the vehicle of the plurality of vehicles to dispatch for the route; and

deploying the vehicle based on the route data.

15 . The method of claim 7 , wherein the second portion of the environmental data comprises first passenger environment preference data associated with a first passenger and second passenger environment preference data associated with a second passenger, the method further comprising:

determining the energy budget data based at least in part on the first passenger environment preference data and the second passenger environment preference data.

16 . The method of claim 7 , wherein controlling at least one of the propulsion system, the computing system, or the thermal control system comprises controlling the propulsion system, the method further comprising:

determining, based on the energy budget data, at least one of a range of the vehicle, an updated route of the vehicle, or a velocity of the vehicle.

17 . The method of claim 7 , wherein controlling at least one of the propulsion system, the computing system, or the thermal control system comprises controlling the computing system, the method further comprising:

determining, based on the energy budget data, at least one of a number of sensors to use, a sensing range, or a level of compute to use.

18 . The method of claim 7 , wherein controlling at least one of the propulsion system, the computing system, or the thermal control system comprises controlling the thermal control system, the method further comprising:

determining, based at least in part on the energy budget data, at least one of a blower control, a compressor control, a vent position control, a heating mode, or a cooling mode.

19 . The method of claim 7 , wherein determining the energy budget data comprises:

determining an estimated amount of energy needed to control the at least one of the propulsion system of the vehicle, the computing system of the vehicle, or the thermal control system of the vehicle from the first location to the second location.

20 . One or more non-transitory computer-readable media storing instructions executable by one or more processors, wherein the instructions, when executed, cause the one or more processors to perform operations comprising:

receiving route data associated with a route for a vehicle to travel from a first location to a second location;

receiving environmental data representing an environment associated with the route data, wherein the environmental data comprises a first portion associated with conditions of the environment and a second portion associated with passenger preferences, the passenger preferences comprising a first passenger preference data associated with a first passenger and a second passenger preference data associated with a second passenger;

determining energy budget data, wherein the energy budget data is determined based at least in part on:

the second portion of the environmental data, wherein the second portion includes, for each of the first passenger preference data and the second passenger preference data, passenger environment preference data comprising a passenger preference associated with at least one of vent position, seat temperature, air speed, temperature control mode, seating location, or window tinting setting;

at least one of the route data or the first portion of the environmental data;

one or more estimated temperatures associated with at least one of a breath level, chest level, or foot level, wherein the breath level, chest level, or foot level are areas of a cabin of the vehicle relative to a seat of the vehicle; and

a machine-learned model comprising an adjustment to the machine-learned model, wherein the adjustment is based at least in part on a difference between a previous estimated energy consumption and an actual previous energy consumption; and

controlling at least one of a propulsion system of the vehicle, a computing system of the vehicle, or a thermal control system of the vehicle based at least in part on the energy budget data, wherein controlling at least one of the propulsion system, the computing system, or the thermal control system comprises:

a first control relative to a first area of the cabin associated with the first passenger and based at least in part on the first passenger preference data,

a second control relative to a second area of the cabin associated with the second passenger and based at least in part on the second passenger preference data, and

the first control and the second control being performed along the route.