State of charge battery monitoring
US-2016114696-A1 · Apr 28, 2016 · US
US10569660B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-10569660-B2 |
| Application number | US-201615054686-A |
| Country | US |
| Kind code | B2 |
| Filing date | Feb 26, 2016 |
| Priority date | Feb 26, 2016 |
| Publication date | Feb 25, 2020 |
| Grant date | Feb 25, 2020 |
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A vehicle includes a battery, and a controller programmed to charge and discharge the battery based on a health indicator output by a model that describes changes in internal resistance of the battery over time identified from (i) a plurality of different representative battery usage aggressiveness drive cycles and (ii) changes in internal resistance of the battery that are derived from a state of charge, temperature, and current associated with the battery.
Opening claim text (preview).
What is claimed is: 1. A vehicle comprising: a battery; a memory; and a controller operable to, determine a reference internal resistance of the battery by correlating a battery state of charge (SOC), a battery temperature, and a battery current to an impedance map stored in the memory; determine a present internal resistance of the battery based on the sum of a battery calendar fade and a battery cycle fade, wherein the battery calendar fade and the battery cycle fade are determined using the battery SOC, the battery temperature, the battery current, and a battery terminal voltage; determine a battery resistance change based on the difference between the reference internal resistance and the present internal resistance; and charge and discharge the battery based on a battery health indicator, wherein the battery health indicator is determined by applying an inverse battery life model using the battery, resistance change and one or more battery usage aggressiveness drive cycles. 2. The vehicle of claim 1 , wherein the health indicator is indicative of a percentage of life remaining of the battery, a usage tune remaining until end-of-life of the battery, or a usage time to-date of the battery. 3. The vehicle of claim 2 , wherein the inverse battery life model defines the end-of-life. 4. The vehicle of claim 2 , wherein the percentage of life remaining and the usage time remaining are defined using the usage time to-date, and wherein the usage time to-date is defined from correlating the proportion to the curve. 5. The vehicle of claim 1 , wherein the curve includes a best-fit curve defined using a regression analysis. 6. A method comprising: determining a reference internal resistance of a battery by correlating a battery state of charge (SOC), a battery temperature, and a battery current to an impedance map; determining a present internal resistance of the battery based on the sum of a battery calendar fade and a battery cycle fade, wherein the battery calendar fade and the battery cycle fade are determined using the battery SOC, the battery temperature, the battery current, and a battery terminal voltage; determining a battery resistance change based on the difference between the reference internal resistance and the present internal resistance; and charging and discharging the battery based on a battery health indicator, wherein the battery health indicator is determined by applying an inverse battery life model using the battery resistance change and one or more battery usage aggressiveness drive cycles. 7. The method of claim 6 , wherein the health indicator is indicative of a percentage of life remaining of the battery, a usage time remaining until end-of-life of the battery, or a usage time to-date of the battery. 8. The method of claim 7 , wherein the inverse battery life model defines the end-of-life of the battery. 9. The method of claim 7 , wherein the percentage of life remaining and the usage time remaining are defined using the usage time to-date, and wherein the usage time to-date is defined from correlating the proportion to the inverse battery life model. 10. The method of claim 6 , wherein the inverse battery life model includes a best-fit curve defined using a regression analysis.
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