Battery Power Limit Derating Using Fuzzy Logic Inputs
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Solution Overview
Problem
Conventional methods for estimating the power limit of a battery are prone to overestimation due to inaccuracies in algorithms and cell models, leading to unsafe operating conditions like undervoltage, especially in electric vehicles where computational resources are limited.
Innovation Solution
A method using fuzzy logic to determine a derating factor for the power limit, based on acquired inputs such as state of charge, voltage difference, and temperature, which corrects the estimated power limit to prevent overestimation and ensure safe operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional algorithms and cell models are used to estimate power limit, then the estimation process is simple and computationally efficient, but the estimation accuracy deteriorates leading to overestimation of power limit
Solution Approach 1:
The patent introduces a fuzzy logic system as an intermediary between the conventional power limit estimation and the final power limit determination. The fuzzy logic controller takes multiple inputs (state of charge, state of power, voltage difference, temperature) and processes them through fuzzy inference to produce a derating factor that corrects the overestimation. This intermediary layer improves accuracy without requiring complete redesign of the underlying estimation algorithms.
Solution Approach 2:
The patent changes the parameter representation by introducing fuzzy sets and membership functions for battery parameters (state of charge, state of power, voltage difference, temperature). Instead of using precise numerical values directly, the system transforms these parameters into fuzzy linguistic variables (e.g., low, medium, high) and processes them through fuzzy logic rules, enabling more nuanced decision-making that improves estimation accuracy.
2Measurement precision
If the algorithm complexity is increased to improve estimation accuracy, then the power limit estimation accuracy improves, but the computational requirements increase which is impractical for embedded systems with limited resources
Solution Approach 1:
The fuzzy logic system serves as a computationally efficient intermediary that processes battery parameters through predefined membership functions and inference rules. This approach avoids the need for complex iterative optimization algorithms or machine learning models that would consume excessive computational resources, while still achieving improved estimation accuracy through systematic handling of uncertainties.
Solution Approach 2:
The patent uses simplified fuzzy logic rules and predefined membership functions that can be implemented with minimal computational resources. Rather than employing complex, resource-intensive algorithms, the system uses straightforward fuzzy inference mechanisms that are computationally inexpensive and well-suited for embedded battery management systems with limited processing power and memory.
3Productivity
If power limits are overestimated to maximize system performance, then the system can operate at higher power levels, but the battery may enter unsafe operating conditions such as undervoltage
Solution Approach 1:
The patent applies preliminary anti-action by using fuzzy logic to predict and prevent potential undervoltage conditions before they occur. The fuzzy inference system continuously evaluates battery parameters and adjusts the power limit downward (derating) when conditions suggest that the estimated power limit may be unsafe. This proactive approach prevents the battery from entering unsafe operating conditions while still maximizing performance within safe boundaries.
Solution Approach 2:
The system implements feedback by continuously monitoring battery parameters (state of charge, state of power, voltage difference, temperature) and using this information to dynamically adjust the power limit through the fuzzy logic controller. The derating factor is continuously updated based on current battery conditions, creating a closed-loop control system that adapts to changing conditions and maintains safety while optimizing performance.
Data Source
AI summary
A method for determining a derated power limit of a battery, the method comprising: estimating a power limit of the battery; acquiring a plurality of inputs from the battery;determining a derating factor for the power limit by applying a fuzzy logic to the plurality of inputs; and determining the derated power limit by multiplying the derating factor to the power limit.