Battery prognosis method and system

Patent metadata

Field	Value
Publication number	US-2025180650-A1
Application number	US-202318525954-A
Country	US
Kind code	A1
Filing date	Dec 1, 2023
Priority date	Dec 1, 2023
Publication date	Jun 5, 2025
Grant date	—

Abstract

An electric vehicle includes an electric motor to propel the electric vehicle and a battery adapted to provide electrical energy to the electric motor for propelling the electric vehicle. The electric vehicle also includes one or more controllers collectively programmed with the following instructions: measure battery data of the battery; use the battery data to create a model of storage capacity of the battery versus distance driven by the electric vehicle; assess a reliability of the model; if the model is assessed as sufficiently reliable, then perform prognosis on the battery using the model; and if the model is assessed as not sufficiently reliable, then adopt a substitute model of storage capacity of the battery versus distance driven by the electric vehicle, the substitute model based on battery data of one or more other vehicles, and perform prognosis on the battery using the substitute model.

First claim

What is claimed is:

1 . An electric vehicle comprising:
an electric motor to propel the electric vehicle; a battery adapted to provide electrical energy to the electric motor for propelling the electric vehicle; and one or more controllers collectively programmed with the following instructions:
measure battery data of the battery;
use the battery data to create a model of storage capacity of the battery versus distance driven by the electric vehicle;
assess a reliability of the model;
if the model is assessed as sufficiently reliable, then perform prognosis on the battery using the model; and
if the model is assessed as not sufficiently reliable, then adopt a substitute model of storage capacity of the battery versus distance driven by the electric vehicle, the substitute model based on battery data of one or more other vehicles, and perform prognosis on the battery using the substitute model.

2 . The electric vehicle of claim 1 , further comprising selecting the one or more other vehicles based on the one or more other vehicles having operating characteristics similar to those of the electric vehicle.

3 . The electric vehicle of claim 2 , wherein the operating characteristics include average distance driven per day.

4 . The electric vehicle of claim 2 , wherein the operating characteristics include average ambient temperature.

5 . The electric vehicle of claim 1 , wherein the model is assessed as not sufficiently reliable if the battery data has an insufficient number of data measurements.

6 . The electric vehicle of claim 1 , wherein the model is assessed as not sufficiently reliable if the model demonstrates increasing storage capacity versus distance driven or if the model demonstrates noise.

7 . The electric vehicle of claim 1 , wherein the prognosis includes comparing projected storage capacities among constituent portions of the battery.

8 . The electric vehicle of claim 1 , wherein the prognosis includes comparing projected storage capacity loss among constituent portions of the battery against a threshold.

9 . The electric vehicle of claim 1 , wherein the prognosis includes using the model or the substitute model in combination with internal resistance measurements of the battery.

10 . An electric vehicle comprising:
an electric motor to propel the electric vehicle; a battery adapted to provide electrical energy to the electric motor for propelling the electric vehicle; and one or more controllers collectively programmed with the following instructions:
measure battery data of the battery;
use the battery data to create a model of storage capacity of the battery versus time in service of the battery in the electric vehicle;
assess a reliability of the model;
if the model is assessed as sufficiently reliable, then perform prognosis on the battery using the model; and
if the model is assessed as not sufficiently reliable, then adopt a substitute model of storage capacity of the battery versus time in service of the battery in the electric vehicle, the substitute model based on battery data of one or more other vehicles, and perform the prognosis on the battery using the substitute model.

11 . The electric vehicle of claim 10 , further comprising selecting the one or more other vehicles based on the one or more other vehicles having operating characteristics similar to those of the electric vehicle.

12 . The electric vehicle of claim 11 , wherein the operating characteristics include average distance driven per day.

13 . The electric vehicle of claim 11 , wherein the operating characteristics include average ambient temperature.

14 . The electric vehicle of claim 10 , wherein the model is assessed as not sufficiently reliable if the battery data has an insufficient number of data measurements.

15 . The electric vehicle of claim 10 , wherein the model is assessed as not sufficiently reliable if the model demonstrates increasing storage capacity versus time in service of the battery in the electric vehicle or if the model demonstrates noise.

16 . The electric vehicle of claim 10 , wherein the prognosis includes:
comparing storage capacities among constituent portions of the battery; and identifying the battery as faulty based on the comparing.

17 . The electric vehicle of claim 10 , wherein the prognosis includes:
comparing storage capacity loss among constituent portions of the battery; and identifying the battery or constituent portions thereof as faulty based on the comparing.

18 . The electric vehicle of claim 10 , wherein the prognosis includes using the model or the substitute model in combination with internal resistance measurements of the battery.

19 . A method for prognosis of a battery of an electric vehicle, the method comprising:
through one or more controllers, measuring battery data for the battery; through one or more controllers, using the battery data to create a model of storage capacity of the battery versus distance driven by the electric vehicle or versus time in service of the battery in the electric vehicle; selecting one or more other vehicles based on the one or more other vehicles having operating characteristics similar to operating characteristics of the electric vehicle; creating a substitute model of storage capacity of the battery versus distance driven by the electric vehicle or versus time in service of the battery in the electric vehicle based on battery data of the one or more other vehicles; using the model or the substitute model in combination with internal resistance measurements of the battery for prognosis of the battery; and identifying one or more failure root causes of degradation of the battery predicted by the prognosis.

20 . The method of claim 19 , wherein the prognosis includes:
comparing storage capacities or storage capacity loss among constituent portions of the battery; and identifying the battery as faulty based on the comparing.

Assignees

• Gm Global Tech Operations Llc

Inventors

• Sankavaram Chaitanya
• Diallo Ammar
• Moulliet Ryan B

Classifications

• B60L2260/54

  Energy consumption estimation · CPC title

• B60L3/12

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

• B60L58/12 Primary

  responding to state of charge [SoC] · CPC title

• B60L2260/44

  by parameter estimation · CPC title

• B60L2240/70

  Interactions with external data bases, e.g. traffic centres · CPC title