Method for predicting battery life

Patent metadata

Field	Value
Publication number	US-2019176639-A1
Application number	US-201715837489-A
Country	US
Kind code	A1
Filing date	Dec 11, 2017
Priority date	Dec 11, 2017
Publication date	Jun 13, 2019
Grant date	—

Abstract

Methods and systems are provided for reliably providing a prognosis of the life-expectancy of a vehicle battery. A state of degradation of the battery is predicted based on a rate of convergence of a metric, that is derived from a sensed vehicle operating parameter, towards a defined threshold, determined based on past history of the metric. The predicted state of degradation is then converted into an estimate of time or distance remaining before the component needs to serviced, and displayed to the vehicle operator. Vehicle control and communication strategies may be defined with respect to the predicted state of degradation.

First claim

1 . A method for a vehicle, comprising:
predicting a state of degradation of a vehicle battery based on a rate of convergence of a plurality of battery metrics, derived from sensed vehicle operating parameters, towards corresponding thresholds, the thresholds determined based on past driving history data including the past history of each of the plurality of battery metrics; and converting the predicted state of degradation into a remaining time or duration before battery end of life for display to a vehicle operator.

2 . The method of claim 1 , wherein the plurality of battery metrics include a battery resistance and a battery capacity, and the sensed vehicle operating parameters includes one or more of a battery current, a battery voltage, and a battery terminal temperature.

3 . The method of claim 2 , wherein the predicting includes predicting a higher state of degradation as the rate of convergence of any one of the plurality of battery metrics increases.

4 . The method of claim 1 , further comprising updating the corresponding thresholds based on vehicle performance following the converting.

5 . The method of claim 1 , further comprising:
comparing the predicted state of degradation to an end of life threshold and one or more intermediate thresholds in between a current state of degradation and the end of the life threshold; and limiting one or more vehicle function as based on the comparing.

6 . The method of claim 5 , wherein the vehicle is an autonomous vehicle and wherein the limiting includes:
when the predicted state of degradation is lower than each of the one or more intermediate thresholds and the end of life threshold, operating the vehicle without limiting any autonomous vehicle functionality; when the predicted state of degradation is higher than a first of the one or more intermediate thresholds, operating the vehicle with a first degree of limiting of autonomous vehicle functionality;
when the predicted state of degradation is higher than a second of the one or more intermediate thresholds, larger than the first of the one or more intermediate thresholds, operating the vehicle with a second degree of limiting of autonomous vehicle functionality, higher than the first degree; and
when the predicted state of degradation is higher than each of the one or more intermediate thresholds and the end of life threshold, operating the vehicle without autonomous vehicle functionality.

7 . The method of claim 1 , further comprising, estimating a value of the determined metric as a function of a most recent estimate of the determined metric retrieved from the past history of the determined metric, and a distance traveled by the vehicle since the most recent estimate of the determined metric.

8 . The method of claim 1 , further comprising, estimating a value of the determined metric as a function of an initial estimate of the determined metric, retrieved from the past history of the determined metric at a time of installation of the component in the vehicle.

9 . The method of claim 1 , further comprising, converting the predicted state of degradation into a remaining number of engine start events for display to the vehicle operator based on the past driving history data and predicted future driving.

10 . The method of claim 1 , wherein the thresholds are determined off-board the vehicle while the rate of convergence is determined on-board the vehicle.

11 . The method of claim 6 , wherein the vehicle is one of a plurality of vehicles of a fleet, the method further comprising:
estimating the plurality of battery metrics for each vehicle of the fleet over at least a threshold duration; and predicting the state of degradation of the vehicle battery responsive to the estimating.

12 . The method of claim 11 , further comprising, updating each of the end of life threshold and the one or more intermediate thresholds of the vehicle responsive to performance of each vehicle of the fleet following the predicting.

13 . A method for predicting battery health for a vehicle, comprising:
monitoring at least one battery health parameter in real-time using one or more onboard battery monitoring sensors; determining a threshold of the monitored battery health parameter based on information gathered from vehicle communication network and vehicle operating conditions; defining a battery end-of life prediction algorithm based on a speed of convergence of the monitored battery health parameter to the determined threshold; and estimating an end of life of the battery based on the prediction algorithm.

14 . The method of claim 13 , further comprising, limiting one or more autonomous functions or safety-related functions requiring electrical power of the vehicle based on the estimating, a degree of the limiting based on the estimated end of life relative to an end of life threshold.

15 . The method of claim 13 , further comprising, limiting one or more non-essential electrical power requiring systems of the vehicle based on the estimating, a degree of the limiting based on the estimated end of life relative to an end of life threshold, the one or more non-essential electrical power requiring systems of the vehicle including an electrically-actuated anti-roll control system.

16 . The method of claim 13 , wherein the vehicle is one of a plurality of vehicles in a vehicle fleet, and wherein determining the threshold includes determining the thresholds based on battery end-of-life information gathered from each of the plurality of vehicles of the fleet and received via the vehicle communication network.

17 . A vehicle system, comprising:
a battery; one or more sensors coupled to the battery; a motor driven using electrical power drawn from the battery; an engine; a network communicatively coupling the vehicle system to one or more additional vehicles of a fleet; a display; and a controller with computer readable instructions for:
predicting a duration remaining until an end of life of the battery based on a rate of convergence of a plurality of sensed battery parameters towards corresponding parameter thresholds, the thresholds determined based on battery history for the vehicle system and each of the one or more additional vehicles of the fleet;
comparing the predicted duration to a threshold end of life; and
limiting one or more functions of the vehicle based on the comparing.

18 . The system of claim 17 , wherein the controller includes further instructions for:
when the predicted duration is higher than the threshold end of life, displaying the predicted duration to a vehicle operator on the display and limiting an autonomous functionality of the vehicle.

19 . The system of claim 17 , wherein limiting one or more functions of the vehicle based on the comparing includes limiting one or more of a powered steering assist, climate control, and entertainment system operation.

20 . The system of claim 17 , further comprising a battery life predicting system communicatively coupled to the controller and the one or more sensors, wherein the controller includes further instructions for updating the threshold end of life based on a performance of the battery life prediction system that monitors each of the one or more additional vehicles of the fleet following the predicting, the threshold end of life lowered responsive to a drop in the prediction system performance.

Assignees

• Ford Global Tech Llc

Inventors

• Kumar Pankaj
• Makki Imad Hassan
• Landolsi Fakhreddine
• Eifert Mark
• Malekpour Sheida
• Linden David W

Classifications

• B60L2260/50

  by future state prediction · CPC title

• B60L50/16

  with provision for separate direct mechanical propulsion · CPC title

• B60L58/20

  having different nominal voltages · CPC title

• B60L3/12

  Recording operating variables {; Monitoring of operating variables} · CPC title

• B60L3/0046

  relating to electric energy storage systems, e.g. batteries or capacitors · CPC title