Joint estimation of SOC and SOH for lithium-ion batteries based on FOAMIUHF-UKF model
Leveraging advanced AI to derive actionable intelligence from cutting-edge research.
Executive Impact & AI Opportunity
This study proposes an innovative FOAMIUHF-UKF model for the accurate and rapid co-estimation of State of Charge (SOC) and State of Health (SOH) in lithium-ion batteries. Addressing challenges like model inaccuracy and noise robustness, the model integrates a fractional-order second-order equivalent circuit model (FOM), optimized by the sparrow search algorithm (SSA), with an adaptive multi-innovations unscented H-infinity filter. Experimental results under various dynamic conditions (DST, HWFET, Japan working) and aging levels demonstrate high accuracy (max MAE 0.53%, RMSE 0.61% for SOC) and robustness, outperforming existing methods. This approach offers a reliable solution for battery state estimation in complex operational and aging scenarios, crucial for electric vehicle safety and lifespan.
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Max SOC MAE
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Max SOC RMSE
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Capacity Decay Tested
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Battery Management Systems
This category focuses on technologies and algorithms designed to optimize and monitor the performance, safety, and lifespan of battery packs. Key areas include accurate state estimation (SOC, SOH), thermal management, and fault diagnosis for various battery chemistries, particularly lithium-ion.
Key Concepts:
- State of Charge (SOC) Estimation
- State of Health (SOH) Estimation
- Equivalent Circuit Models (ECMs)
- Fractional-Order Models (FOMs)
- Kalman Filtering Techniques
- Adaptive Filtering
- Multi-Innovation Algorithms
- Unscented H-Infinity Filter (UHF)
- Swarm Intelligence Optimization
Enterprise Process Flow
Fractional-Order Model (FOM) Establishment
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Sparrow Search Algorithm (SSA) for Parameter Identification
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Adaptive Multi-Innovations Unscented H-Infinity Filter (FOAMIUHF)
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Unscented Kalman Filter (UKF) for SOH & Capacity Updates
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Joint SOC/SOH Estimation Output
0.53%
Maximum Mean Absolute Error (MAE) for SOC estimation under various dynamic conditions (DST, HWFET, Japan working conditions).
| Feature | Current State | AI-Enhanced State |
|---|---|---|
| FOAMIUHF-UKF |
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| FOMIUKF-UKF |
|
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| FOMIUKF |
|
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Aging Robustness Case: SOC Estimation at 72.2% Capacity
The FOAMIUHF-UKF algorithm demonstrated remarkable robustness even when battery capacity degraded significantly to 72.2% of its rated capacity (nearing end-of-life). Under these extreme aging conditions, the maximum SOC estimation errors (MAE and RMSE) remained below 1%, showcasing the algorithm's adaptability and effectiveness in maintaining accuracy over the battery's lifespan. Key Result: SOC estimation errors remained below 1% at 72.2% capacity.
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Your AI Implementation Roadmap
A phased approach ensures successful integration and measurable results for your enterprise.
Phase 1: Model & Parameter Baseline
Establish a fractional-order equivalent circuit model and identify initial parameters using the Sparrow Search Algorithm (SSA). This phase focuses on foundational model accuracy.
Phase 2: Adaptive Filter Integration
Integrate the adaptive multi-innovations unscented H-infinity filter (FOAMIUHF) to enhance noise suppression and improve SOC estimation dynamics. Tune fading and weighting factors.
Phase 3: Joint SOC/SOH Framework Development
Implement the Unscented Kalman Filter (UKF) for SOH prediction and dynamic battery capacity updates, establishing the joint SOC/SOH estimation framework. Define macro- and micro-time steps.
Phase 4: Validation & Optimization
Conduct extensive experimental validation under diverse dynamic conditions (DST, HWFET, Japan working) and various initial SOC/aging levels. Refine algorithm parameters for optimal performance and robustness.
Phase 5: Deployment & Monitoring
Deploy the validated FOAMIUHF-UKF model into battery management systems (BMS) for real-time, accurate SOC and SOH estimation, enabling proactive maintenance and improved EV operation.
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