Energy management method for an electric vehicle

• US 10,029,667 B2
• Filed: 12/07/2012
• Issued: 07/24/2018
• Est. Priority Date: 12/14/2011
• Status: Active Grant

• Current Assignee: Renault Sas (Renault)
• Original Assignee: Renault Sas (Renault)
• Inventors: Granato, Giovanni, Aouchiche, Kamal, Bonnans, Joseph Frederic, Zidani, Hasnaa, Rousseau, Gregory
• Primary Examiner(s): Holwerda, Stephen
• Application Number:
  US14/365,430
• Publication Number:
  US 20140350763A1
• Time in Patent Office: 2,055 Days
• Field of Search: 180 65265, 180 65275, 180 6528, 180 65285, 180 6529
• CPC Class Codes: B60K 6/20 the prime-movers consisting of electric motors and internal combustion engines, e.g. HEVs
• CPC Class Codes: B60W 10/06 including control of combustion engines
• CPC Class Codes: B60W 10/08 including control of electric propulsion units, e.g. motors or generators
• CPC Class Codes: B60W 20/00 Control systems specially adapted for hybrid vehicles hybrid vehicle design, B60K6/00; electric vehicles B60L
• CPC Class Codes: B60W 2552/05 Type of road, e.g. motorways, local streets, paved or unpaved roads
• CPC Class Codes: B60W 2552/10 Number of lanes
• CPC Class Codes: B60W 2552/15 Road slope, i.e. the inclination of a road segment in the longitudinal direction
• CPC Class Codes: B60W 2552/20 Road profile, i.e. the change in elevation or curvature of a plurality of continuous road segments
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/4042 Longitudinal speed
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 30/1882 characterised by the working point of the engine, e.g. by using engine output chart
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: Y02T 10/40 Engine management systems
• CPC Class Codes: Y02T 10/62 Hybrid vehicles
• CPC Class Codes: Y02T 10/84 Data processing systems or methods, management, administration

Abstract

A method managing energy consumption for an automobile including an electric battery and a heat engine, to select use phases of the engine along a route to minimize fuel consumption. The method includes: cutting a road network, for a route, into plural segments each defined by an input node and an output node; calculating, from a speed associated with a segment, a probability of a speed transition between a speed at an input node and at an output node of the segment, considering plural speeds at the input node and at the output node, executed gradually over all the route segments; applying a stochastic optimization algorithm considering all possible transition scenarios between each input node and each output node, and the probability associated therewith, and a fuel consumption model between two successive nodes, executed over all the route segments; and selecting use phases of the heat engine along the route.

Claims

1. A method for managing energy consumption for an electric automotive vehicle including an electric battery and a booster motor, the method making it possible to select phases of use of the booster motor along a predetermined route to minimize fuel consumption of the electric automotive vehicle, the method comprising:

cutting, using navigation circuitry of the electric automotive vehicle, a road network taken into consideration for the predetermined route into plural segments, including at least a first segment associated with a current position of the electric automatic vehicle and a last segment associated with a destination of the predetermined route, each said segment being delimited by a beginning node and an ending node;

computing, using circuitry of the electric automotive vehicle, for each said segment of the predetermined route, a probability of a speed transition between a speed at the beginning node and a speed at the ending node of the segment, based on a speed associated with the segment, and by considering plural speeds at the beginning node and plural speeds at the ending node;

applying, using the circuitry of the electric automotive vehicle, a stochastic optimization algorithm taking into account possible scenarios of transition between each said beginning node and respective ones of said ending nodes along the predetermined route, and their associated probability, and a model of fuel consumption between two successive nodes, and which is dependent on a speed of the electric automotive vehicle between the nodes and on a state of charge of the electric battery;

selecting, using the circuitry of the electric automotive vehicle, phases of use of the booster motor for use along the predetermined route based on said applying,

wherein the electric automotive vehicle is neither a hybrid automotive vehicle nor an engined automotive vehicle having an internal combustion engine as a sole power source, and

wherein said computing is performed sequentially, for each said segment of the predetermined route, and begins from the last segment associated with the destination of the predetermined route and tracks progressively toward the first segment associated with the current position of the electric automatic vehicle.

2. The method as claimed in claim 1, wherein said computing the speed transition probabilities is performed based on a travel history of the electric automotive vehicle along the predetermined route considered, and based on speeds recorded along the predetermined route.

3. The method as claimed in claim 1, further comprising recommencing one or more of said cutting, said computing, said applying and said selecting, said recommencing taking into account an actual state of charge of the electric battery at one of said beginning and ending nodes considered.

4. The method as claimed in claim 1, wherein said cutting the predetermined route into segments is carried out automatically by the navigation circuitry.

5. The method as claimed in claim 1, wherein said cutting the predetermined route into segments includes manually cutting a portion of the predetermined route into one or more of the segments.

6. The method as claimed in claim 1, wherein a determination of transition probabilities during said computing is refined by taking into account at least one parameter to be chosen from among a time of day, a state of automobile traffic, a type of road, or a number of lanes.

7. The method as claimed in claim 1, wherein one or more of said cutting, said computing, said applying, and said selecting take into consideration the following events:

the booster motor must be stopped when the booster motor does not yield any power;

it is necessary to comply with a time interval of a first predetermined amount of time between each stoppage and each switch-on of the booster motor; and

between a moment at which the booster motor is switched on and a moment at which the booster motor is active and yields power, there exists a latency time of a second predetermined amount of time.

8. The method as claimed in claim 1, further comprising outputting a signal to manage energy consumption for the electric automotive vehicle based on the selected phases of use of the booster motor along the predetermined route.