Abstract
This paper presents a new method for estimating the capacity of a lithium ion battery cell in the presence of a reference cell - the parameters of which are well characterized - in series with it. The method assumes that both cells are cycled using the same current trajectory starting from the same state of charge (e.g. fully charged). Voltage measurements for both cells as well as current measurements for the series string constitute the input to a nonlinear least squares minimization problem. The goal of this problem is to estimate the capacity of the cell given the difference between its voltage and that of the reference cell. We refer to this as the differential estimation problem, and use Monte Carlo simulation to compare it to the more traditional approach of estimating the capacity of each cell in a battery string independently using its current/voltage measurements. Two key conclusions emerge from this simulation. Compared to traditional estimation, differential estimation results in capacity estimates whose variance is (i) twice as sensitive to voltage measurement noise but (ii) significantly less sensitive to current measurement noise. This makes differential estimation more appealing for battery packs with high current measurement noise and low voltage measurement noise.
| Original language | English (US) |
|---|---|
| Title of host publication | Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Systems; Biomedical/Bioengineering Applications; Building Energy Systems; Condition Based Monitoring; Control Design for Drilling Automation; Control of Ground Vehicles, Manipulators, Mechatronic Systems; Controls for Manufacturing; Distributed Control; Dynamic Modeling for Vehicle Systems; Dynamics and Control of Mobile and Locomotion Robots; Electrochemical Energy Systems |
| Publisher | American Society of Mechanical Engineers |
| ISBN (Electronic) | 9780791846186 |
| DOIs | |
| State | Published - 2014 |
| Event | ASME 2014 Dynamic Systems and Control Conference, DSCC 2014 - San Antonio, United States Duration: Oct 22 2014 → Oct 24 2014 |
Publication series
| Name | ASME 2014 Dynamic Systems and Control Conference, DSCC 2014 |
|---|---|
| Volume | 1 |
Other
| Other | ASME 2014 Dynamic Systems and Control Conference, DSCC 2014 |
|---|---|
| Country/Territory | United States |
| City | San Antonio |
| Period | 10/22/14 → 10/24/14 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Mechanical Engineering
- Industrial and Manufacturing Engineering
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