Design of a Hardware-Efficient SOC and SOH Estimator for Electric Vehicle Batteries

  1. Vidyasaraswathi H. N
  2. Lakshmi C. R.
  3. Kruthika D. S.
  4. Madhavi C.

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Abstract

The increasing reliance on lithium-ion batteries in Electric Vehicles (EVs) and energy storage systems demands accurate, real-time estimation of State of Charge (SOC) and State of Health (SOH). Conventional digital estimation methods such as Kalman Filters and machine-learning models provide high accuracy but require complex computation, expensive microcontrollers, and high power consumption. This project presents a hardware-efficient, analog-centric estimation system that performs both SOC and SOH analysis using low-power operational amplifier circuits. A Hybrid Coulomb Counting technique, combined with voltage-based correction, enables driftfree SOC tracking, while a pulse-based internal resistance measurement accurately detects battery degradation for SOH estimation. Cadence Virtuoso simulations validate the design's ability to distinguish healthy and degraded batteries through measurable voltage sag characteristics. The proposed approach significantly reduces system complexity, cost, and computational load, making it suitable for compact, real-time Battery Management Systems (BMS) in low-cost EVs and stationary energy storage applications.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22309061.

    Does the introduction explain the objective of the research presented in the preprint? Partly The intro concisely lists the limitations of present methods used and but instead of listing their contributions, they mostly just vaguely go over it.
    Are the methods well-suited for this research? Somewhat appropriate Even though the methods used are well-suited and standard, they lack description. They do not completely explain how the volatge correction is applied, given that terminal voltage needs to relaxed before ocv vs soc calibration, no mention of how the rest works. In general, description is lacking and confusing.
    Are the conclusions supported by the data? Somewhat unsupported The conclusion is a bit over claimed. They only did cadence simulations of an otherwise complex system with batteries and actual sensors. The claims are a bit far reached and under supported.
    Are the data presentations, including visualizations, well-suited to represent the data? Somewhat inappropriate or unclear The figures are a bit low quality, making them hard to read and understand.
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Neither clearly nor unclearly They have some future works listed in general but they do not mention their next steps.
    Is the preprint likely to advance academic knowledge? Moderately likely
    Would it benefit from language editing? Yes The writing needs refining, specifically the claims need proper explaination and support.
    Would you recommend this preprint to others? Yes, but it needs to be improved
    Is it ready for attention from an editor, publisher or broader audience? No, it needs a major revision

    Competing interests

    The author declares that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The author declares that they did not use generative AI to come up with new ideas for their review.

  2. Version published to 10.36227/techrxiv.177092213.38234529/v1