Course Content

The battery state of charge (SOC) is an important parameter of the battery capacity state. Accurate estimation of SOC is one of the key problems in a battery management system. As Li-ion cell behaves dynamically, characteristics depend on function of temperature, discharge charge cycles, charge & discharge pattern, ageing, utilised SOC and more. The coulomb counting technique with correction (after rest periods) using an OCV-SOC correlation curve is not practical for cells exhibiting hysteresis since the battery cell takes a long time to reach a steady-state OCV after a current pulse. Current SOC- estimation models are unable to take care of all of these complications. 

 

A more robust algorithm is needed to estimate the instantaneous total charge available. The EKF technique, an adaptive estimator, has emerged as one of the practical solutions to enhance the accuracy of SOC determination, but is complicated and needs heavy computing resources on-board the vehicle. Available embedded technology is capable to process complex computations. This is the direction ahead to industry, EKF based estimations are predominantly used for real time applications. 

 

This course will help you understand & build the MBD based approach for SOC based on EKF approach. By the end of course you will have the solid exposure to estimation approaches, limitations, calculations, cell behaviour, cell characteristics, end to end functional model for SOC estimations, comparison of simulation data and real time test data.

 

Course content

  • Introduction
  • BMS Functionalities
  • SOC Estimation Strategies

Coulomb Counting Methodology

Current Integration Methodology

Voltage Lookup Based Estimation Methodology

Kalman Filter Based Estimation

Example Calculations

 

  • Introduction to Kalman Filters

Basic Introduction to UKF, EKF

Understanding the EKF flow process

Predict and Update Steps

State Equations

Discretization of the State Equations

 

  • Governing State Equations for the SOC Estimation

State Space Representation of the Equations

Discretization of the State Space Equations

 

  • Development of Matlab Code for SOC Estimation

SOC Estimation for Constant Current Discharge/ Charge

SOC Estimation for Dynamic Profiles

 

7. Model Based SOC Estimation

Predict Step Modelling

Update Step Modelling

Thevenin Circuit Modelling

 

8. Post Processing 

9. Results and Discussion

SOC Estimations Plots

Voltage Variation w.r.t Discharge Capacity

Voltage Variation across R0 and RC Circuit

Estimation Errors


10. Model prediction validation w.r.t tested data

11.  Future Scope of Study

Course Curriculum

    1. Course overview

    2. How to access the course

    3. Software needed

    4. Course access duration

    5. Piracy & infringement warning

    1. Introduction to SOC and SOC Estimation Strategies

    2. Introduction to Kalman Filters

    3. Equivalent Circuit Modeling

    1. RC Parameter Equations

    2. Derivation of State Equations

    3. Estimation of RC Parameters

    4. Matlab Code Explanation

    1. Data files for Simulink

    2. Profiles

    3. Matlab Code Development

    4. Plots Visualisation

    5. Model Development in Simulink

    1. Model Development Continuation in Simulink Part II

    2. Model Development Continuation Part III

    3. Errors Identification Discussions

    1. Error Identification and Results Discussion

About this course

  • 33 lessons
  • 16 hours of video content

Course is created with real world tests & experiments done by Venu Gopal