Fuzzy Logic SOC Estimation for Lithium-Ion Battery Safety
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Solution Overview
Problem
Lithium-ion rechargeable batteries in electric vehicles face challenges in safely estimating State of Charge (SOC) due to potential overcharging, which can lead to temperature increases, gas decomposition, and spontaneous ignition, necessitating accurate SOC estimation methods.
Innovation Solution
A SOC estimation method integrating fuzzy theory, improved Coulomb detection, and open circuit voltage (OCV) measurement to estimate both SOC and State of Health (SOH) in a battery management system, using a battery control unit with interface circuits for voltage and temperature measurement, and processing circuits that apply fuzzy control and time domain dynamic equations to monitor and calculate SOC values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If Li ion rechargeable battery is used for high energy storage, then energy density is improved, but safety deteriorates due to overcharging risks
Solution Approach 1:
The patent applies preliminary action by continuously monitoring battery parameters (voltage, current, temperature) and estimating SOC in advance before overcharging occurs. The fuzzy logic controller and time domain dynamic monitor predict battery state and issue warnings or control charging before dangerous conditions develop, preventing safety issues before they arise.
Solution Approach 2:
The patent implements feedback through continuous measurement of battery parameters and closed-loop SOC estimation. The system measures voltage, current, and temperature, feeds this data to the fuzzy logic controller and time domain dynamic monitor, and uses the estimated SOC to control charging/discharging processes, creating a self-regulating system that maintains safety while maximizing energy storage.
2Device complexity
If conventional SOC estimation methods are used, then simplicity is maintained, but measurement precision deteriorates due to instability under varying conditions
Solution Approach 1:
The patent merges multiple SOC estimation methods (Coulomb counting, OCV measurement, and fuzzy logic control) into a unified system. The Coulomb counting provides baseline SOC, OCV measurement corrects for voltage deviations, and fuzzy logic handling stabilizes the estimation under varying conditions, achieving high precision without excessive complexity through intelligent integration of complementary methods.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the fuzzy logic control parameters and weighting factors based on battery operating conditions (charge/discharge state, temperature, current rate). This allows the system to adapt to varying conditions and maintain high measurement precision across different operating scenarios without requiring completely different estimation methods for each condition.
3Measurement precision
If fuzzy control and time domain dynamic monitoring are implemented, then SOC estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies self-service by designing the fuzzy logic controller and time domain dynamic monitor to automatically adapt and optimize their own parameters based on real-time battery behavior. The system learns from historical data and adjusts its internal parameters without external intervention, reducing the need for complex manual tuning and configuration while maintaining high estimation accuracy.
Solution Approach 2:
The patent implements dynamics by making the estimation algorithm adaptive and flexible rather than static. The fuzzy logic control rules and time domain parameters are dynamically adjusted based on battery state, temperature, and operating conditions, allowing the system to maintain high accuracy across varying conditions without requiring a completely different hardware architecture for each scenario.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method effectively estimates SOC and SOH, ensuring safe operation by preventing overcharging and maintaining battery health, thereby enhancing the reliability and safety of lithium-ion batteries in electric vehicles.
Implementation Method 1
an improved Coulomb detection and an OCV (open circuit voltage) to estimate SOC and SOH (state of health)
Data Source
AI summary
A SOC (state of charge) estimation method for a rechargeable battery includes: measuring a battery parameter of the rechargeable battery; judging whether the battery parameter of the rechargeable battery is stable; if the battery parameter of the rechargeable battery is not stable yet, estimating an open circuit voltage of the rechargeable battery by a fuzzy control and expanding an established experiment data of the rechargeable battery into a 3D function by the fuzzy control; and calculating a time domain dynamic equation and converting into a SOC function, substituting the SOC function into the fuzzy control to estimate an SOC estimation value, wherein the time domain dynamic equation performing a time domain dynamic monitor.