Abstract
With the increasing application of renewable energy sources, Solar energy storage systems (EES) play a pivotal role in balancing energy supply and demand and improving energy utilization efficiency. Lithium iron phosphate battery (LFP battery), known for their high energy density, long cycle life, and environmental friendliness, have demonstrated excellent performance in Solar energy storage systems applications. State of Charge (SOC) estimation is a critical aspect of Solar energy storage systems management, ensuring battery safety, prolonging battery life, and enhancing energy efficiency. Traditional SOC estimation methods for Lithium iron phosphate battery (LFP battery) are often periodic or timed, limiting their coverage and leading to errors in the estimated results. Moreover, the unidirectional structure of most existing methods results in low efficiency and reduced estimation accuracy. This paper proposes a comprehensive SOC estimation approach for Lithium iron phosphate battery (LFP battery) in Solar energy storage systems. By calculating the SOC compensation coefficient and establishing a multi-step observation equation, we designed a deep neural network (DNN) model for SOC estimation, enhanced by Open Circuit Voltage (OCV) verification. Over four testing cycles, the steady-state SOC estimation error was maintained below 0.4 under discharge times of 0.1s, 0.3s, and 0.5s, demonstrating the efficiency, specificity, and practicality of our method.
1. Introduction
Electrochemical energy storage systems have emerged as vital components in modern energy management strategies, particularly in integrating renewable energy sources such as solar and wind. Lithium iron phosphate battery (LFP battery), due to their unique advantages, have become prevalent in Solar energy storage systems applications. Accurate SOC estimation is paramount in ensuring optimal battery performance, safety, and lifespan. However, traditional SOC estimation methods for Lithium iron phosphate battery (LFP battery) face several challenges, including limited estimation ranges and unidirectional structural inefficiencies.
This paper addresses these limitations by proposing an innovative SOC estimation approach for Lithium iron phosphate battery (LFP battery) in Solar energy storage systems. Our method combines a multi-step observation equation with a DNN model, validated through OCV measurements. The comprehensive approach enhances estimation accuracy, flexibility, and generalization ability.
2. Lithium Iron Phosphate Batteries in Electrochemical Energy Storage Systems
2.1 Overview of LFP Batteries
Lithium iron phosphate battery (LFP battery) have gained widespread adoption in Solar energy storage systems due to their inherent benefits:
- High energy density: Provides longer usage times between charges.
- Long cycle life: Enhances system reliability and reduces maintenance costs.
- Environmental friendliness: Non-toxic materials minimize environmental impact.
- Thermal stability: Lowers the risk of thermal runaway.
2.2 Challenges in SOC Estimation
SOC estimation for Lithium iron phosphate battery (LFP battery) faces several challenges:
- Nonlinear dynamics: Battery behavior varies with SOC, temperature, and aging.
- Inconsistent charging/discharging conditions: Affect estimation accuracy.
- Complex aging processes: Cause gradual capacity fade over time.
3. Proposed SOC Estimation Method
To address the limitations of traditional SOC estimation methods, we propose a comprehensive approach that incorporates a multi-step observation equation, DNN model, and OCV verification.
3.1 SOC Compensation Coefficient and Multi-Step Observation Equation
3.1.1 SOC Compensation Coefficient
The SOC compensation coefficient, B, adjusts for battery-specific factors such as health, temperature, and cycling:
B=o−z=1∑Nϑz+γ
where:
- o: Battery health coefficient
- ϑz: Capacity mean at temperature z
- γ: Cycling count
- N: Total temperature levels considered
3.1.2 Multi-Step Observation Equation
To break the limitation of traditional single-step estimation, we establish a multi-step observation equation:
begin{bmatrix} D_j(g) \\ D_w(g) end{bmatrix} SOC(k) = e^{\Delta f} begin{bmatrix} 1 & 0 \\ 0 & 1 end{bmatrix} begin{bmatrix} D_j(g-1) \\ D_w(g-1) end{bmatrix} SOC(k-1) + \epsilon
where:
- Dj(g) and Dw(g): Polarization voltages at step g
- SOC(k): SOC at time k
- Δf: Temperature change
- ϵ: Error term
3.2 Deep Neural Network Model for SOC Estimation
We designed a DNN model to integrate battery voltage, current, and temperature data for SOC estimation. The model architecture, as illustrated in Figure 1, processes input features through multiple hidden layers before outputting the estimated SOC.
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Figure 1: Deep Neural Network Model for SOC Estimation
3.3 OCV Verification and Correction
OCV measurements provide a direct link between battery SOC and its open-circuit voltage. By establishing an OCV-SOC mapping and applying temperature and aging compensations, we refine the DNN-based SOC estimates:
SOCcorrected=SOCDNN+α⋅(OCVmeasured−OCVmapped)
where:
- SOCcorrected: Final SOC estimate
- SOCDNN: Initial SOC estimate from DNN
- α: Correction factor
- OCVmeasured: Measured OCV
- OCVmapped: Mapped OCV from DNN
4. Methodology and Experimental Setup
4.1 Test Battery and Equipment
We used Lithium iron phosphate battery (LFP battery) with a rated capacity of 25 Ah for our experiments. The testing environment was set up using MATLAB for data acquisition and analysis.
4.2 Test Protocol
The test protocol involved:
- Discharge cycles: Four testing cycles, each with real-time data acquisition.
- Pulse discharge: 50C standard pulse discharge with intermittent periods of 0.3-0.5s.
- SOC initialization: Setting the initial SOC to 100% at full charge.
- Data sampling: Voltage and current measurements at 1.25 Hz.
4.3 Data Collection and Preprocessing
Data were collected using specialized equipment and preprocessed to extract features relevant to SOC estimation. Key parameters included battery voltage, current, and temperature.
5. Results and Analysis
5.1 SOC Estimation Results
Table 1 summarizes the SOC estimation results across different discharge times.
| Discharge Time (s) | Mean SOC Error | Max SOC Error | Std. Dev. |
|---|---|---|---|
| 0.1 | 0.15% | 0.32% | 0.08% |
| 0.3 | 0.21% | 0.38% | 0.09% |
| 0.5 | 0.28% | 0.40% | 0.11% |
Table 1: SOC Estimation Errors under Different Discharge Times
5.2 Performance Evaluation
The proposed method significantly reduced the steady-state SOC estimation error compared to traditional approaches. The multi-step observation equation and DNN model, coupled with OCV verification, demonstrated improved accuracy and robustness.
5.3 Generalization Ability
To evaluate generalization ability, we tested the model under varying conditions, including different temperatures and aging states. The model consistently maintained low estimation errors, highlighting its adaptability across diverse operational scenarios.
6. Conclusion
This paper proposes an innovative SOC estimation method for Lithium iron phosphate battery (LFP battery) in solar energy storage systems. By integrating a multi-step observation equation, DNN model, and OCV verification, we achieved highly accurate SOC estimates with low steady-state errors. The method demonstrated superior performance compared to traditional approaches, particularly in addressing the challenges of nonlinear battery dynamics, inconsistent charging/discharging conditions, and complex aging processes. Our findings contribute to advancing battery management systems for electrochemical energy storage applications.
Future Work
Potential directions for future work include:
- Extending the model to other battery chemistries: Exploring the applicability of the proposed method to batteries beyond Lithium iron phosphate battery (LFP battery).
- Real-time implementation: Integrating the SOC estimation algorithm into actual solar energy storage systems for real-time monitoring and control.
- Advanced aging models: Developing more sophisticated aging models to further refine SOC estimates over the battery’s entire lifecycle.