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Composing Optimized Embedded Software Architectures for Physics-Based EKF-MPC Smart Sensor for Li-Ion Battery Cell

Anne K Madsen1, Darshika G Perera1

  • 1Department of Electrical and Computer Engineering, University of Colorado Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, CO 80918, USA.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

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This research introduces an efficient embedded software architecture for battery management systems (BMS) using physics-based models (PBM), extended Kalman filters (EKF), and model predictive control (MPC). The novel design optimizes battery performance and lifespan on resource-constrained devices.

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Materials Science

Background:

  • Efficient battery technology is crucial for clean energy adoption, particularly in electric vehicles.
  • Battery management systems (BMS) are essential for maximizing battery performance and lifespan.
  • Current BMS methods face challenges with computational complexity on embedded devices.

Purpose of the Study:

  • To develop a novel embedded software architecture for BMS that overcomes computational limitations.
  • To enable the use of physics-based models (PBM), extended Kalman filters (EKF), and model predictive control (MPC) on resource-constrained embedded systems.
  • To enhance battery performance, safety, and lifespan for smart applications.

Main Methods:

  • Implementation of a physics-based model (PBM) for battery operation.
Keywords:
battery cell managementembedded software architectureembedded systemsmodel predictive controlphysics-based modelsmart sensors

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  • Utilization of an extended Kalman filter (EKF) as a virtual observer for unobservable battery states.
  • Application of model predictive control (MPC) for optimizing BMS functionality.
  • Development of an efficient embedded software architecture tailored for resource-constrained microprocessors.

    • Main Results:

    • The proposed architecture successfully integrates PBM, EKF, and MPC on a 32-bit embedded microprocessor (100 MHz, 128 KB memory).
    • Achieved an average execution time of 4.8 ms, demonstrating computational efficiency.
    • Successfully addressed the computational complexity challenges of PBM for MPC and EKF.

    Conclusions:

    • The novel embedded software architecture enables advanced BMS functionalities on resource-constrained devices.
    • This breakthrough can significantly prolong the useful life and improve the performance of batteries, especially Li-ion.
    • The adaptable control process is well-suited for smart systems and portable applications, extending beyond the automotive industry.