EV Battery Capacity Estimation With Adaptive Sample Sizing
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Solution Overview
Problem
Existing methods for estimating the capacity of an electric vehicle's traction battery are inefficient, often requiring large sample sizes that increase storage requirements and calculation complexity, while smaller sample sizes result in inaccurate capacity estimation.
Innovation Solution
A method to determine an appropriate sample size for a moving average filter based on battery decay assumptions, using equations that consider uncertainty in state of charge and ampere-hour integration to balance accuracy and hardware requirements, thereby estimating the true capacity of the traction battery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large sample sizes are used for capacity estimation, then measurement precision is improved, but device complexity and storage requirements increase
Solution Approach 1:
The patent applies dynamics by making the sample size adaptive rather than fixed. The controller dynamically adjusts the number of previous instantaneous capacity values to include in the moving average calculation based on the current state of charge level. When state of charge is high, a larger sample size is used for more accurate estimation; when state of charge is low, a smaller sample size reduces computational burden. This dynamic adjustment resolves the contradiction between maintaining high measurement precision and reducing device complexity.
Solution Approach 2:
The patent changes the parameter of sample size based on the state of charge condition. By varying this critical parameter according to operational conditions, the system achieves optimal balance between accuracy and complexity for different battery states, directly addressing the technical contradiction.
2Device complexity
If smaller sample sizes are used for capacity estimation, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system dynamically adjusts sample size based on state of charge, ensuring that measurement precision is maintained at adequate levels while reducing complexity when full precision is not critical. This conditional approach allows the system to optimize the trade-off between these two parameters.
Solution Approach 2:
The patent applies partial action by using only the necessary number of sample points required for adequate estimation at each state of charge level, rather than always using the maximum sample size. This provides sufficient measurement precision without the excessive computational burden of always using large samples.
Data Source
AI summary
An automotive power control system alters a maximum discharge power of a traction battery according to an estimated capacity of the traction battery. The estimated capacity depends on a set of previous instantaneous capacity values of the traction battery and a current instantaneous capacity value of the traction battery. A total number of the previous and current instantaneous capacity values depends on a state of charge of the traction battery at two different instants in time.