SOC Errors at Different Temperatures for Lithium-Ion Batteries

Experimental results for State of Charge (SOC) estimation errors in lithium-ion batteries under different temperature conditions. The 5.5 KB XLS file was authored by Huipin Lin and last updated on June 4, 2026. It contains results from experiments using the University of Maryland’s Dynamic Stress Test (DST), US06 test, and Federal Urban Driving Scheme (FUDS) datasets.

Use Cases

• Benchmarking SOC estimation algorithms based on reported MAE and RMSE error metrics.
• Evaluating the performance of hybrid models (e.g., TCN-UKF) against CNNs, LSTMs, and GRUs under different driving conditions.
• Analyzing the impact of temperature variations on battery state-of-charge estimation accuracy.

Strengths

• Includes specific error metrics: MAE values of 1.305%, 1.470%, and 1.015% under DST, US06, and FUDS conditions.
• Reports a comparative performance improvement, reducing SOC estimation error by approximately 7.6%–39.6% versus baseline models.
• Uses established, publicly referenced test datasets (DST, US06, FUDS) for validation.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (5.5 KB), indicating limited scope, likely containing summary results rather than raw time-series data.

Provenance

Source

Huipin Lin via figshare.

Collection Method

Experimental results from tests using University of Maryland’s DST, US06, and FUDS datasets.

Freshness

Last updated 2026-06-04 17:34:31; freshness should be verified.