Electric Vehicle Battery Management System - Course

Explore open circuit voltage and state of charge across chemistries, and how temperature, aging, hysteresis, and internal resistance shape voltage curves and power versus capacity.

Learn to read battery spec sheets, interpreting nominal voltage, capacity at 23 C, energy, impedance, charge rules, cycle life, and safety certification for EV design.

Learn a practical battery sizing method for electric vehicles: fix voltage, estimate kilowatt hours from range, and account for aging with usable energy, buffers, and design energy.

Explore how to size an electric vehicle battery using Excel, calculating cell data, kilowatt-hour requirements, and series/parallel configurations to design battery packs and modules.

Explore battery sizing by applying the calculation formula to design a two-wheeler pack that meets daily usage and a five-year warranty, with voltage and efficiency parameters.

Explore how lithium ion battery standards govern safe manufacturing and usage, with temperature and voltage defining safe regions. Respond immediately when the damage area is reached; call emergency services.

Explores how lithium battery standards cover design, performance, and safety, detailing electrical abuse protection, thermal and mechanical events, gas formation, and application-specific requirements for swappable, fast charging, and fixed systems.

The battery management system, the brain of any battery, uses sensor data to keep lithium ion packs safe and efficient, analyzing temperature and current to prevent faults and thermal runaway.

Explore the basic functions of a battery management system, including safety, performance, and diagnosis and prognosis, with emphasis on energy storage and aerospace applications like electric flight.

Explore how performance management collects temperature, voltage, and current data, stores and manipulates it, and how charging control, cell balancing, and state estimation optimize state of charge and health.

Explore core BMS algorithms for electric vehicles, including state of charge, state of health, state of power, and fault diagnostics, plus data-driven estimation and machine learning enhancements.

Explains initial soc estimation for electric vehicle battery management, detailing static and initial state, coulomb counting, terminal voltage, open circuit voltage, lookup tables, and MATLAB implementation.

Analyze cycle counting for battery state of health by using a cycle versus capacity chart, building a capacity loss lookup table, and computing charging and discharging duty.

Explain calculating battery state of health (SOH) cycles using cumulative cycles, depth of discharge, and capacity loss from a lookup table, with memory and degradation considerations.

Explore cell balancing in multi-cell packs and how passive and active methods equalize cell voltages despite variations, improving battery performance in a battery management system.

Compare passive and active cell balancing methods for series-connected battery packs, and explain why parallel balancing is avoided, with energy transfer via capacitors, transformers, or capacity balancing in storage applications.

Explore advanced cell balancing methods in electric vehicle battery management, from simple resistance balancing to DC converter and algorithm-driven active strategies that transfer energy among cells.

Understand the bms architecture with master and slave units, sensors in each module, can-wired data to a central master brain, and software-loaded control of the battery pack.

Explore how open-loop and closed-loop control use sensor feedback in a battery management system controlled by a vehicle control unit. Regulate current, temperature, and state of charge.

Explore development trends in BMS, emphasizing algorithm-driven efficiency, accuracy challenges with 10% error, and hardware and wireless innovations such as over-the-air calibration and master-slave communication.

Understand how an electric vehicle battery management system estimates initial state of charge and state of health by tracking cycle counts, capacity loss, and lookup-based capacity prediction.

Learn to design a MATLAB-based BMS, integrating sensor data with a master controller, implementing state logic and balancing, and generating deployable code for microcontrollers.

Explore how equivalent circuit cell models describe lithium-ion battery behavior, including diffusion delays and charging–discharging dynamics. Analyze how polarization and capacitors capture delayed responses in battery management systems.

Explore diffusion voltage, where removing current from a lithium-ion battery causes a delayed voltage recovery due to diffusion, not depletion.

Explore linear polarization and diffusion in lithium-ion batteries, show how RC parallel–series models capture these effects, discuss chemistry-specific capacitor counts, and plan MATLAB simulations.

Analyze diffusion-driven components in a battery management graph, linking current to an initial wattage drop and voltage change, and relate RC time constants to chemistry types like NMC or LTE.

Explore ocv testing and kalman-filter based battery modeling using an equivalent circuit with diffusion and polarization. Simulate current and voltage with Simulink and refine state estimation via Carmen filter calibration.

Explore machine learning for battery and BMS design, from rule-based models to data-driven methods, including supervised and unsupervised learning. Use neural networks to predict battery life from data.

Demonstrate building a Simulink battery model using memory blocks and discrete integrators, with Carmen filters for estimation, and discuss code generation for embedded controllers and battery types.

Explore global career scopes in battery engineering across mechanical, chemical, electrical, and civil domains, including battery design, operation, production, data science integration, and safety.