Prediction of battery power requirements for electric vehicles

• US 9,493,089 B2
• Filed: 03/24/2015
• Issued: 11/15/2016
• 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
• Primary Examiner(s): Badii, Behrang
• Application Number:
  US14/666,369
• Publication Number:
  US 20150266390A1
• Time in Patent Office: 602 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 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.

2. The method of claim 1 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.

3. The method of claim 1 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.

4. The method of claim 1 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.

5. The method of claim 1 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.

6. The method of claim 1 further comprises determining slope for the given road segment from a topographical map.

7. The method of claim 1 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.

8. The method of claim 1 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.