Battery Management System Reference Design

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ID 683279
Date 4/02/2016
Public
Document Table of Contents
Document Table of Contents x
1. Battery Management System Reference Design
1. Battery Management System Reference Design x
1.1. BMS Reference Design Features 1.2. BMS Reference Design Getting Started 1.3. BMS Reference Design Functional Description 1.4. BMS Reference Design FPGA Resource Usage 1.5. BMS Reference Design Benchmarking 1.6. Acknowledgements for the BMS Reference Design
1.2. BMS Reference Design Getting Started x
1.2.1. BMS Reference Design Software Requirements 1.2.2. BMS Reference Design Hardware Requirements 1.2.3. Downloading and Installing the BMS Reference Design 1.2.4. Setting Up the MAX 10 Development Board 1.2.5. Compiling the FPGA Hardware Design for the BMS Reference Design 1.2.6. Compiling the Nios Software for the BMS Reference Design 1.2.7. Programming the BMS Reference Design Hardware onto the Device 1.2.8. Downloading the BMS Reference Design Nios II Software to the Device 1.2.9. MATLAB Simulink Top-Level Design for the BMS Reference Design 1.2.10. Running the BMS Reference Design in a System-in-the-Loop Simulation
1.3. BMS Reference Design Functional Description x
1.3.1. BMS Reference Design Car Model 1.3.2. BMS Reference Design Battery Model 1.3.3. BMS Reference Design DEKF Technique 1.3.4. BMS Reference Design Hardware Implementation
1.3.4. BMS Reference Design Hardware Implementation x
1.3.4.1. BMS Reference Design Matrix Processor 1.3.4.2. BMS Reference Design uCode Controller Interface 1.3.4.3. BMS Reference Design DEKF IP (Design C)
1. Battery Management System Reference Design

1.3.3. BMS Reference Design DEKF Technique

In the DEKF technique, the reference design simultaneously executes two cooperating Kalman filters for nonlinear systems: one for the state and the other for the parameters estimation.

Dual estimation, rather than joint estimation, with only one Kalman filter reduces the state matrix dimensions and may improve the estimation robustness. Equation 2 describes the parameter evolution that with the measurement equation 4 builds the first EKF. Equation 3 represents the state evolution that combines to the measurement equation to form the second EKF.

Figure 13. Equation 2
Figure 14. Equation 3
Figure 15. Equation 4
Figure 16. Measurement Equation

The measurement equation is the same for both filters. In the above equations:

  • k is the discrete time
  • p is parameters vector
  • x = [SOC; VRC1] is the battery state vector
  • χ­, ξ and ψ are the parameters, the state and measurement noise, with zero mean and covariance matrix ­ Σχ­, Σξ and Σψ, respectively.
Figure 17. Circuit Equations
Figure 18. DEFK Matrix Equations
Figure 19. Initialization Equation
Figure 20. Prediction Equations
Figure 21. Correction Equation
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