Research on Health Assessment and Estimation Methods for Lithium Power Batteries

Health assessment and estimation methods for lithium power batteries represent one of the core technologies in Battery Management Systems (BMS), directly impacting battery lifespan prediction, charging strategy optimization, and system safety. Health evaluation typically relies on key parameters such as capacity degradation and internal resistance changes, while estimation methods combine data-driven models with physical models to enhance accuracy and robustness.

Health Evaluation Indicators Capacity fade rate: The ratio of actual usable capacity to initial capacity serves as the most intuitive State of Health (SOH) indicator. In code implementation, this can be calculated using capacity calibration algorithms through Coulomb counting or voltage-capacity curve analysis. Internal resistance growth: The increasing trend of internal resistance during cycling reflects battery aging. Measurement typically involves electrochemical impedance spectroscopy (EIS) or DC pulse tests, with algorithms like recursive least squares for parameter identification. Charge-discharge efficiency: Declining efficiency indicates intensified side reactions within the battery, leading to energy loss. This can be monitored through real-time efficiency calculations comparing input and output energy during cycles.

Estimation Methods Model-based approaches: Utilize equivalent circuit models or electrochemical models combined with algorithms like Kalman filtering for real-time joint estimation of SOC and SOH. Implementation often involves state-space modeling and recursive prediction-correction algorithms. Data-driven methods: Employ machine learning techniques (such as Support Vector Machines and Neural Networks) to analyze historical data and establish health prediction models. Code implementation typically includes feature extraction from cycling data and supervised learning with cross-validation. Hybrid methods: Combine advantages of model-based and data-driven approaches, for instance by integrating physical model parameters with data fitting results to improve adaptability. This may involve Bayesian fusion techniques or adaptive weighting algorithms in implementation.

Applications and Challenges Health estimation is crucial for electric vehicles and energy storage systems, but practical applications must consider external factors like temperature and cycle count. Future research could explore multi-source data fusion and high-precision online monitoring technologies to enhance estimation reliability. Potential code implementations may involve sensor fusion algorithms and real-time adaptive filtering for dynamic parameter adjustment.