====== Battery Management System ======

  * https://www.coursera.org/specializations/algorithms-for-battery-management-systems

===== Dual Battery System =====

Challenges - 
  * Different battery sizes requires different terminal current
  * Cross charging
  * Balancing
  * Peak power handling

Big Ideas -
  * VSYS voltage monitor comparetor should be inside PMIC or inside the GreenPak
  * Data based DoU characterization method - benefits
    * Direct comparable among different products/models
    * Standardized characterization procedure
    * Indisputable marketing DoU claim
    * Easy translation to what-if DoU
    * User oriented (vs engineering oriented) DoU communications

===== Modeling from Advanced BMS Work Shop AAC 2025 =====

  * Battery swells (some reversable, some permanent). It is found to be very beneficial to reduce aging by applying 5 psi pressure to physically limiting the swelling. Too much pressure can also be problematic. This information was provided by professor Anna G. Stefanopoulou from U. Michigan.
  * P2D physical model
  * PCA correlation analysis
  * Parallel R*C benefits
  * SOC physical model not temperature, c-rate dependent
  * Symbolic regression.
  * DFN (doyle fuller newman) model
  * Single particle model (simple model has limitations)
  * POD - reduced order model ROM
  * ESPM
  * Adaptive Ensemble Sparse Identification (AESI)
  * SPCI method
  * SYNDy approach
  * SPM -> ESPM
  * {{ :knowledge_base:professional:acc-plett-advanced_battery_management-perspectives_on_the_role_of_machine_learning.zip |}}

==== Raw Notes ====

  * PCA - Principal Component Analysis (PCA) and neural networks are powerful tools in data analysis, and they can be used together in various ways. PCA is often used as a preprocessing step for neural networks, particularly with high-dimensional data, to reduce dimensionality and improve model performance. Additionally, neural networks can be designed to perform PCA-like operations, effectively learning the principal components during training
  * Sys ID - surrogate AI model types and how to choose - simple feedforward, long-short (LSTM) memory model. Delayed input...
  * Model Conversion, Training methods - PyTorch, TensorFlow, MATLA
  * RNN vs LSTM vs GRU vs Transformers - https://www.geeksforgeeks.org/deep-learning/rnn-vs-lstm-vs-gru-vs-transformers/
  * Virtual sensor - How to identify if the import has correlation to output to reduce the order of the system - perturbation sensitivity analysis.
  * Imitation learning
  * NLP/QP solving for MPC is expensive
  * Neural state space model
  * Reinforcement learning (RL)
  * Pruning and projection (structure compression), quantization (data compression) to deploy for low memory low computing power applications.