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Assignments
Overview
This specialized course offers a detailed understanding of energy storage technologies and Battery Management Systems (BMS) for electric vehicles (EVs). Participants will explore various battery chemistries and their characteristics, learn about battery modeling and simulation, and implement State of Charge (SOC) and State of Health (SOH) estimation techniques using advanced methods like Coulomb counting and Kalman filtering. The course also covers BMS design and safety considerations to ensure optimal battery performance and longevity.
Objectives
By the end of this course, leaner will be able to:
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Understand the various types of battery chemistries and their applications in EVs.
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Analyze and interpret battery performance parameters.
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Model and simulate battery behavior under different conditions.
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Implement SOC and SOH estimation algorithms for battery monitoring.
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Design and optimize Battery Management Systems (BMS).
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Evaluate battery safety and implement thermal management solutions.
Prerequisites
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Control System Fundamentals
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Automotive Systems Knowledge
Course Outline
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Role of energy storage systems in electric vehicles.
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Overview of energy storage technologies: batteries, fuel cells, ultracapacitors.
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Battery fundamentals: electrochemistry, cell types, and configurations.
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Comparative analysis of energy storage options for EV applications.
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Detailed analysis of various battery chemistries:
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Lithium-ion (Li-ion): LFP, NMC, NCA, LTO
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Nickel-metal hydride (NiMH)
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Lead-acid
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Solid-state batteries
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Battery characteristics: voltage, capacity, energy density, power density, cycle life, safety.
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Choosing the right battery chemistry for specific EV applications
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Understanding equivalent circuit models for battery simulation.
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Parameter identification and model validation.
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Simulating battery behavior across different load profiles and temperatures.
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Introduction to battery simulation tools.
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Importance of SOC estimation in BMS.
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Coulomb counting method and error calibration.
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Kalman filter-based SOC estimation for improved accuracy.
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Implementing SOC estimation algorithms.
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Understanding SOH estimation and its importance.
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Factors influencing battery degradation and aging.
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Methods for estimating SOH: impedance spectroscopy, capacity fade analysis.
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Analyzing real-world battery data for SOH estimation.
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Overview of BMS architecture and functionalities:
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Cell balancing
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Temperature monitoring
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Charge and discharge control
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Safety mechanisms
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BMS hardware and software components.
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Understanding communication protocols like CAN.
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Designing and implementing BMS algorithms for enhanced battery performance.
