Battery Management System Course

Lesson 1 • Battery Pack Architecture Concepts

Introduces series and parallel cell configurations, module design, and pack-level trade-offs. Sets the structural context for BMS hardware integration.

Lesson 2 • Battery Cell Chemistries Overview

Compares lithium-ion, NiMH, lead-acid, and solid-state chemistries by energy density and cycle life. Guides chemistry selection for target applications.

Lesson 3 • Cell Degradation and Failure Modes

Examines lithium plating, SEI growth, electrolyte decomposition, and mechanical fatigue. Provides the degradation context that BMS algorithms must address.

Lesson 4 • Electrochemical Energy Storage Basics

Covers oxidation-reduction reactions, ion transport, and electrode potentials. Establishes the electrochemical vocabulary used throughout the course.

Lesson 1 • Protection Circuits and Switching Devices

Explains MOSFETs, contactors, fuses, and pre-charge circuits for overcurrent and overvoltage protection. These components enforce the safety limits defined by BMS firmware.

Lesson 2 • BMS Microcontroller and ASIC Selection

Compares dedicated BMS ASICs against general-purpose microcontrollers for processing and cost trade-offs. Selection criteria include channel count, accuracy, and communication interfaces.

Lesson 3 • Voltage and Current Sensing Circuits

Details resistive shunts, Hall-effect sensors, and differential amplifiers for accurate measurement. Accuracy directly affects SOC and SOH estimation quality.

Lesson 4 • Temperature Sensing and Management

Covers NTC/PTC thermistors, thermocouples, and placement strategies for thermal monitoring. Proper sensing placement prevents thermal runaway detection delays.

Lesson 5 • BMS Functional Block Overview

Maps sensing, protection, balancing, and communication blocks within a BMS. Clarifies how each block contributes to safe pack operation.

Lesson 1 • Estimation Validation and Error Analysis

Defines RMSE, MAE, and bias metrics for evaluating estimator accuracy against reference data. Validation under dynamic load profiles ensures real-world reliability.

Lesson 2 • Battery Modeling for Estimation

Introduces equivalent circuit models (ECM) and electrochemical models as estimation foundations. Model accuracy determines the upper bound of estimator performance.

Lesson 3 • State of Health Estimation Techniques

Quantifies capacity fade and resistance rise as SOH indicators using incremental capacity analysis. SOH feeds remaining useful life predictions and replacement decisions.

Lesson 4 • Observer and Filter-Based Algorithms

Implements Luenberger observers, particle filters, and adaptive filters for robust state estimation. Filter tuning balances noise rejection against estimation lag.

Lesson 1 • Balancing Performance Evaluation

Measures balancing time, energy loss, and residual imbalance to assess topology effectiveness. Results guide topology selection for cost-sensitive and efficiency-critical designs.

Lesson 2 • Passive Balancing Topologies

Covers resistive dissipation balancing using fixed and switched resistors. Passive methods are simple but waste energy as heat during balancing.

Lesson 3 • Need for Cell Balancing

Explains how cell-to-cell variation in capacity and self-discharge causes pack imbalance over time. Imbalance reduces usable capacity and accelerates degradation.

Lesson 4 • Active Balancing Topologies

Examines capacitor-based, inductor-based, and transformer-based energy transfer circuits. Active balancing improves efficiency but increases hardware complexity and cost.

Lesson 1 • Thermal Modeling and Simulation

Builds lumped-parameter and finite-element thermal models to predict temperature distribution. Simulation results validate cooling design before physical prototyping.

Lesson 2 • Thermal Runaway Detection and Mitigation

Identifies thermal runaway triggers, propagation paths, and BMS-level detection thresholds. Mitigation strategies include venting, isolation, and active cooling response.

Lesson 3 • Air Cooling System Design

Designs forced-air cooling channels, fan selection, and airflow path optimization for battery packs. Air cooling suits low-to-medium power applications with cost constraints.

Lesson 4 • Heat Generation in Battery Cells

Quantifies Joule heating, entropic heat, and side-reaction heat as functions of current and SOC. Accurate heat source modeling is the basis for thermal system sizing.

Lesson 1 • Wireless BMS Communication

Evaluates Bluetooth, Zigbee, and proprietary RF links for wireless cell monitoring in large packs. Wireless links eliminate wiring harness weight but introduce latency and security concerns.

Lesson 2 • SMBus and I2C for Cell Monitoring

Uses SMBus and I2C to interface BMS ASICs with host microcontrollers for cell data retrieval. These protocols suit short-distance, low-speed intra-board communication.

Lesson 3 • System Integration and HIL Testing

Integrates BMS firmware with hardware-in-the-loop simulators to validate communication and control. HIL testing reduces risk before deployment in real battery systems.

Lesson 4 • CAN Bus Implementation for BMS

Configures CAN frames, message IDs, and timing for BMS-to-vehicle controller communication. CAN is the dominant protocol in automotive and industrial BMS applications.

Lesson 1 • Overtemperature and Short-Circuit Protection

Implements temperature-based derating, shutdown logic, and short-circuit detection for pack safety. Fast short-circuit response is critical to preventing thermal runaway initiation.

Lesson 2 • Compliance with Battery Safety Standards

Maps BMS design requirements to functional safety, transportation, and abuse testing standards. Compliance documentation is required for product certification and market access.

Lesson 3 • Functional Safety Principles for BMS

Introduces hazard analysis, risk assessment, and safety integrity levels applicable to BMS. Safety goals drive the entire protection architecture and firmware design.

Lesson 4 • Fault Diagnosis and Fault-Tolerant Design

Applies FMEA and FTA to identify BMS failure modes and design redundant safety paths. Fault-tolerant architectures maintain safe operation during partial system failures.

Lesson 1 • Machine Learning for Battery Diagnostics

Trains classification and anomaly detection models on charge-discharge data to identify fault signatures. ML-based diagnostics detect subtle degradation patterns invisible to rule-based logic.

Lesson 2 • Cloud-Connected BMS and Fleet Analytics

Streams BMS data to cloud platforms for fleet-level health monitoring and algorithm updates. Cloud connectivity enables continuous improvement of on-device estimation models.

Lesson 3 • Power and Energy Management Optimization

Uses dynamic programming and rule-based strategies to allocate power across cells and modules. Optimization reduces peak stress and improves overall pack efficiency.

Lesson 4 • Optimal Charging Algorithm Design

Formulates multi-stage and model predictive charging strategies that minimize aging while meeting time constraints. Optimal charging extends cycle life without sacrificing charge speed.