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Data from LiFePO4 Battery:

  • capacity: 280Ah
  • charge upper limit voltage: 3.65V
  • discharge lower threshold voltage: 2.5V
  • rated voltage: 3.2V
  • current rate range: 0~1C
  • rated current rate: 0.2C

data format: steps of 1 second

Criteria:

  1. Accuracy: Maximum Absolute Error (MaxAE) measures the largest deviation between estimated and actual SoC values, assessing worst-case scenarios.
  2. Robustness: Evaluates the algorithm's stability and accuracy across different conditions.
  3. Efficiency: Assesses execution time, memory usage, and consistency across a uniform testing environment.
  4. Transient Convergence: Measures the algorithm’s ability to quickly correct incorrect initial SoC values.
  5. Documentation: Evaluates clarity, organization, and code quality.

Need good/high/best efficiency (good for embedded use) -> avoid neural networks, use system that can easily be made into an FPGA or ASIC, integratable into a BMS
We'll use an empirical model (along with an electrical model ?) -> provided by the challenge

  1. (off-line) OCV-SOC relationship is predetermined -> stored in lookup table or fitted by a math func
  2. (off-line) Get model parameters
  3. (on-line) Filtering to better estimation

Wei, J., Dong, G., & Chen, Z. (2017). On-board adaptive model for state of charge estimation of lithium-ion batteries based on Kalman filter with proportional integral-based error adjustment. Journal of Power Sources, 365, 308-319.

  • Ampere hours integral (AHI) -> needs open circuit voltage (OCV)
  • Kalman filter (regular, (adaptive?) extended, (adaptive?) unscented, sigma-point, cubature ?) + regulator/observer (P, PI, PD or PID ?) for error correction/adjustment -> exteded KF because non-linear

Wang, W., & Mu, J. (2019). State of charge estimation for lithium-ion battery in electric vehicle based on Kalman filter considering model error. Ieee Access, 7, 29223-29235.

Assume that the open circuit voltage is error-free.

Ahmed, M. S., Raihan, S. A., & Balasingam, B. (2020). A scaling approach for improved state of charge representation in rechargeable batteries. Applied energy, 267, 114880.

El Maliki, A., Benlafkih, A., Anoune, K., & Hadjoudja, A. (2024). Reduce state of charge estimation errors with an extended Kalman filter algorithm. International Journal of Electrical and Computer Engineering (IJECE), 14(1), 57-65. doi:http://doi.org/10.11591/ijece.v14i1.pp57-65

Provided OCV-SOC relationship (100 datapoints x2: charge and discharge):
OCV-SOC_curve

HPPC (Hybrid pulse power characterization) charging and discharging tests ?