# US 12,601,604 B2 · Energy prediction device

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 12,601,604 B2
- Document ID: US12601604B2
- Status: Granted
- Kind: B2
- Application number: 18/651,257
- Filed: 2024-04-30
- Published: 2024-11-14
- Granted: 2026-04-14
- Earliest claimed: 2023-05-11
- Assignees: Denso Corporation
- Applicants: Denso Corporation
- Inventors: Yuki Kishita; Hiroyuki Nanjo; Noriaki Ikemoto
- Web page: https://patentgrep.com/patent/US12601604B2

## CPC

- G01C 21/3469: {Fuel consumption; Energy use; Emission aspects}
- B60L 15/2045: {for optimising the use of energy}
- G01C 21/3446: {Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags or using precalculated routes}
- G01C 21/3461: {Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types or segments such as motorways, toll roads or ferries}
- G01C 21/3484: {Personalized, e.g. from learned user behaviour or user-defined profiles}
- G01C 21/3492: {employing speed data or traffic data, e.g. real-time or historical (traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968)}
- B60L 2260/54: Energy consumption estimation
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- B60L: PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES (arrangements or mounting of electrical propulsion units or of plural diverse prime-movers for mutual or common propulsion in vehicles B60K1/00, B60K6/20; arrangements or mounting of electrical gearing in vehicles B60K17/12, B60K17/14; preventing wheel slip by reducing power in rail vehicles B61C15/08; dynamo-electric machines H02K; control or regulation of electric motors H02P); SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES (electric coupling devices combined with mechanical couplings of vehicles B60D1/64; electric heating for vehicles B60H1/00); ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL (control or regulation of electric motors H02P); MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES

## Abstract

An energy prediction device includes a route setting unit configured to divide a travel route traveled by a vehicle and set route sections into which the travel route is divided and a model construction unit configured to construct a consumption prediction model for predicting an energy consumed by the vehicle for each route section.

## Claims

### Claim 1

An energy prediction device comprising:
a route setting unit configured to divide a travel route traveled by a vehicle and sets route sections into which the travel route is divided; and
a model construction unit configured to construct a consumption prediction model for predicting an energy consumed by the vehicle for each route section, and
an information acquisition unit configured to acquire condition information about the energy consumed when the vehicle travels the travel route, wherein
the route setting unit is configured to divide the travel route and set route sections into which the travel route is divided, based on proximity of the condition information.

### Claim 2 (depends on 1)

The energy prediction device according to claim 1, wherein
the route setting unit is configured to set a plurality of preliminary route sections by preliminarily dividing the travel route, determine, for each pair of adjacent preliminary route sections, whether a merge condition is met based on the proximity of condition information between the pair of adjacent preliminary route sections, and set the route sections through a process of merging a respective pair of preliminary adjacent route sections that meet the merge condition.

### Claim 3 (depends on 1)

The energy prediction device according to claim 1, wherein
the condition information includes at least one of driving condition information for identifying a driving condition in which the vehicle travels the travel route, energy condition information for identifying energy required for the vehicle to travel the travel route, and road condition information for identifying a road condition that is a condition of the travel route that affects the energy required for the vehicle to travel the travel route, and
the route setting unit is configured to set the route sections based on the proximity of the at least one of the driving condition information, the energy condition information, and the road condition information.

### Claim 4

An energy prediction device comprising:
a route setting unit configured to divide a travel route traveled by a vehicle and sets route sections into which the travel route is divided; and
a model construction unit configured to construct a consumption prediction model for predicting an energy consumed by the vehicle for each route section,
the model construction unit is configured to construct the consumption prediction model as a learning model with explanatory variables and an objective variable,
the explanatory variables include at least one of weather information, a road condition, time information, and driver information, and
the objective variable includes at least one of a metric for each vehicle speed pattern and a metric for energy consumed.

### Claim 5 (depends on 1)

The energy prediction device according to claim 1, further comprising an energy prediction unit configured to predict the energy consumed for each route section using the consumption prediction model constructed for each route section and predicts the energy consumed on the travel route.

### Claim 6 (depends on 4)

The energy prediction device according to claim 4, further comprising an energy prediction unit configured to, in a case where the objective variable is the metric for each vehicle speed pattern, predict for each route section the energy consumed based on the vehicle speed pattern that is created for the route section based on the metric for each vehicle speed pattern, and predict the energy consumed on the travel route.

### Claim 7

An energy prediction method comprising:
dividing a travel route traveled by a vehicle and sets route sections into which the travel route is divided; and
constructing a consumption prediction model for predicting an energy consumed by the vehicle for each route section, and
acquiring condition information about the energy consumed when the vehicle travels the travel route, wherein
the travel route is divided and the route sections are set based on the proximity of condition information.

### Claim 8

A non-transitory computer readable medium having stored thereon instructions executable by a computer to cause the computer to perform an energy prediction method, comprising:
dividing a travel route traveled by a vehicle and sets route sections into which the travel route is divided; and
constructing a consumption prediction model for predicting an energy consumed by the vehicle for each route section, and
acquiring condition information about the energy consumed when the vehicle travels the travel route, wherein
the travel route is divided and the route sections are set based on the proximity of condition information.

### Claim 9 (depends on 4)

The energy prediction device according to claim 4, further comprising an energy prediction unit configured to predict the energy consumed for each route section using the consumption prediction model constructed for each route section and predicts the energy consumed on the travel route.

### Claim 10

An energy prediction method comprising:
dividing a travel route traveled by a vehicle and sets route sections into which the travel route is divided; and
constructing a consumption prediction model for predicting an energy consumed by the vehicle for each route section, wherein
the consumption prediction model is constructed as a learning model with explanatory variables and an objective variable,
the explanatory variables include at least one of weather information, a road condition, time information, and driver information, and
the objective variable includes at least one of a metric for each vehicle speed pattern and a metric for energy consumed.

### Claim 11

A non-transitory computer readable storage medium having stored thereon instructions executable by a computer to cause the computer to perform an energy prediction method, comprising:
dividing a travel route traveled by a vehicle and sets route sections into which the travel route is divided; and
constructing a consumption prediction model for predicting an energy consumed by the vehicle for each route section, wherein
the consumption prediction model is constructed as a learning model with explanatory variables and an objective variable,
the explanatory variables include at least one of weather information, a road condition, time information, and driver information, and
the objective variable includes at least one of a metric for each vehicle speed pattern and a metric for energy consumed.