PREDICTION OF BATTERY POWER REQUIREMENTS FOR ELECTRIC VEHICLES

• US 20150266390A1
• Filed: 03/24/2015
• Published: 09/24/2015
• Est. Priority Date: 03/24/2014
• Status: Active Grant

• Current Assignee: Board of Regents of the University of Michigan (University of Michigan)
• Original Assignee: Board of Regents of the University of Michigan (University of Michigan)
• Inventors: Shin, Kang G., Kim, Eugene
• Granted Patent: US 9,493,089 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60L 15/2045 for optimising the use of energy
• CPC Class Codes: B60L 2240/12 Speed
• CPC Class Codes: B60L 2240/14 Acceleration
• CPC Class Codes: B60L 2240/642 Slope of road
• CPC Class Codes: B60L 2260/44 by parameter estimation
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• CPC Class Codes: B60L 3/12 Recording operating variables ; Monitoring of operating variables
• CPC Class Codes: B60L 58/10 for monitoring or controlling batteries
• CPC Class Codes: H01M 10/48 Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte constructional details of current conducting connections for detecting conditions inside cells or batteries, e.g. details of voltage sensing terminals, H01M50/569
• CPC Class Codes: H01M 2220/20 Batteries in motive systems, e.g. vehicle, ship, plane
• CPC Class Codes: H02J 7/00 Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
• CPC Class Codes: Y02E 60/10 Energy storage using batteries
• CPC Class Codes: Y02T 10/64 Electric machine technologies in electromobility
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/72 Electric energy management in electromobility
• CPC Class Codes: Y02T 90/16 Information or communication technologies improving the operation of electric vehicles

Abstract

An efficient way of predicting the power requirements of electric vehicles is proposed based on a history of vehicle power consumption, speed and acceleration as well as road information. By using this information and the operator'"'"'s driving pattern, a model extracts the vehicles history of speed and acceleration, which in turn enables the prediction of the vehicle'"'"'s future power requirements. That is, the power requirement prediction is achieved by combining a real-time power requirement model and the estimation of vehicle'"'"'s acceleration and speed.

Claims

1. A computer-implemented method for predicting battery power requirements along a route traversed by a vehicle, comprising:

defining a power requirement model for battery cells powering the vehicle, where the power requirement model has one or more coefficients and outputs a power requirement for the battery cells as a function of velocity of the vehicle and acceleration of the vehicle;

segmenting road along the route into a plurality of road segments;

determining velocity and acceleration for the vehicle along a given road segment of the plurality of road segments;

predicting power requirement for the battery cells of the vehicle while the vehicle is traversing the given segment, where the power requirement is predicted using the determined velocity of the vehicle along the given road segment, the determined acceleration of the vehicle along the given road segment and the power requirement model; and

configuring the battery cells in accordance with the predicted power requirement while the vehicle traverses the given road segment.

2. The method of claim 1 further comprises segmenting road along the route in accordance with slope of the road and speed limit along the road.

3. The method of claim 1 further comprises determining slope for the given road segment, where the power requirement model is further defined as

P_total(a,V,θ

)=c₁·

a·

V+c₂·

V³+c₃·

sin θ

·

V+c₄·

V

where V is velocity of the vehicle, a is acceleration of the vehicle, θ

is road slope, and c₁, c₂, c₃ and c₄ are coefficients.

4. The method of claim 1 further comprises capturing measures of velocity of the vehicle, acceleration of the vehicle and power requirements for the battery cells as the vehicle traverses the route;

and estimating coefficients of the power requirement model from the captured measures of velocity, acceleration and power requirements using a linear regression method.

5. The method of claim 1 further comprises determining acceleration of the vehicle in a given road segment based on relative speed between the vehicle and another vehicle traversing the route, speed limit for given road segments.

6. The method of claim 1 wherein configuring the battery cells further comprises allocating battery cells to an electric load of the electric vehicle based in part on the predicted power requirements of the electric vehicle while traversing the given segment.

7. A computer-implemented method for managing power consumption of battery cells in a vehicle as it traverses a route, the route being divided into a plurality of road segments, comprising:

defining, by a battery manager, a power requirement model for battery cells powering the vehicle, where the power requirement model having one or more coefficients and outputs a power requirement for the battery cells as a function of velocity of the vehicle, acceleration of the vehicle and slope of road;

identifying, by the battery manager, a given road segment from the plurality of road segments;

estimating, by the battery manager, coefficients for the power requirement model using a linear regression method;

determining, by the battery manager, slope for the given road segment;

predicting, by the battery manager, acceleration of the vehicle in the given road segment;

calculating, by the battery manager, velocity of the vehicle in the given road segment;

predicting, by the battery manager, power requirement for the battery cells in the given road segment, where the power requirement is predicted using the estimated coefficients, the slope for the given road segment, the predicted acceleration, the calculated velocity and the power requirement model; and

configuring, by the battery manager, the battery cells in accordance with the predicted power requirement while the vehicle traverses the given road segment, where the battery manager is implemented by a computer processor.

8. The method of claim 7 further comprises monitoring the vehicle as it traverses the route and, upon entering the given road segment, configuring the battery cells in accordance with the predicted power requirement.

9. The method of claim 7 further comprises monitoring the vehicle as it traverses the route and, upon entering the given road segment, predicting power requirement for the battery cells in a next road segment.

10. The method of claim 7 wherein identifying a given road segment further comprises segmenting road along the route in accordance with slope of the road and speed limit along the road.

11. The method of claim 7 wherein the power requirement model is further defined as

P_total(a,V,θ

)=c₁·

a·

V+c₂·

V³+c₃·

sin θ

·

V+c₄·

V

where V is velocity of the vehicle, a is acceleration of the vehicle, θ

is road slope, and c₁, c₂, c₃ and c₄ are coefficients.

12. The method of claim 7 further comprises determining slope for the given road segment from a topographical map.

13. The method of claim 7 further comprises predicting acceleration of the vehicle in a given road segment based on relative speed between the vehicle and another vehicle traversing the route, speed limit for given road segments.

14. The method of claim 7 wherein configuring the battery cells further comprises allocating battery cells to an electric load of the vehicle based in part on the predicted power requirement for the battery cells.

15. A battery management system for use in an electric vehicle, comprising:

a vehicle monitor configured to monitor the vehicle as it traverses a route and operates to segment the road along the route into a plurality of road segments;

a power requirements model that predicts power requirements for battery cells powering the vehicle and resides in a data store, where the power requirement model defines one or more coefficients and outputs a power requirement for the battery cells as a function of velocity of the vehicle, acceleration of the vehicle and slope of road;

a power predictor configured to retrieve slope for a given road segment and operates to determine velocity of the vehicle in the given road segment and the acceleration of the vehicle in the given road segment, wherein the power predictor accesses the power requirements model and further operates to predict power requirement for the battery cells in the given road segment using the slope for the given road segment, the acceleration in the given road segment, the velocity in the given road segment and the power requirement model; and

a battery configurator interfaced with the power predictor and the battery cells and operates to allocate battery cells to an electric load of the vehicle based in part on the predicted power requirements of the vehicle, where vehicle monitor, the power predictor and the battery configurator are implemented by a computer processor residing in the vehicle.

16. The battery management system of claim 15 wherein the vehicle monitor tracks the vehicle in relation to the plurality of road segments and the power predictor predicts the power requirement for the battery cells upon entering the given road segment.

17. The battery management system of claim 15 wherein the vehicle monitor tracks the vehicle in relation to the plurality of road segments and the battery configurator allocates battery cells to an electric load for the given road segment upon entering the given road segment.

18. The battery management system of claim 15 wherein the power requirement model is further defined as

P_total(a,V,θ

)=c₁·

a·

V+c₂·

V³+c₃·

sin θ

·

V+c₄·

V

where V is velocity of the vehicle, a is acceleration of the vehicle, θ

is road slope, and c₁, c₂, c₃ and c₄ are coefficients.