2026-10-13T16:47:59Z https://data.mendeley.com/oai

oai:data.mendeley.com/ff3jh6hjkc.4 2026-09-01T12:55:30Z

Gan, Lin Code and source data for "UC-PPO: Universal Cell-wise Proximal Policy Optimization for Passive Cell Balancing in Battery Packs" Mendeley Data This dataset contains Python code, trained reinforcement-learning models, evaluation outputs, and source data supporting the manuscript "UC-PPO for Passive Cell Balancing in Battery Packs with Variable Series Configurations." The archive covers comparisons with no-balancing, rule-based BMS, and pack-specific deep-reinforcement-learning baselines; direct deployment of the same 8S-trained UC-PPO policy to different series configurations; computational-cost evaluation; action-related reward ablation; input-feature masking and mutual-information analysis; integrated-gradients and permutation-sensitivity analysis; voltage-rank retraining ablation; and standardized large-pack baseline comparisons. A figure-to-file map, checksums, environment information, and reproduction notes are included. Energy Engineering Artificial Intelligence Data Analysis Ma, Donglin Zhao, Hong Liu, An Lu, Yuer Lin, Hai Li, Huaqiong Shuai, Jianwei Zhong, Jinjin Dataset doi:10.17632/ff3jh6hjkc.4 oai:data.mendeley.com/ff3jh6hjkc.4 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/ff3jh6hjkc 2026-09-01T12:55:30Z