Software-in-the-Loop Validation of Electric Vehicle Battery Systems Using AI-Augmented Digital Twins
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I1P133Keywords:
Digital Twin, Battery Software Validation, Electric Vehicle Batteries, AI-Augmented Modeling, Electro-Thermal Battery Modeling, Synthetic Scenario Generation, Software-In-The-Loop Validation, Safety-Critical SystemsAbstract
The increasing reliance on software-controlled battery management and protection functions has made software-in-the-loop (SiL) validation a critical challenge for electric vehicle battery systems. Conventional battery validation approaches, which depend heavily on physical testing and late-stage verification, are costly, limited in coverage, and poorly suited to exploring rare or compounded operating conditions. While digital twins and data-driven models have been widely studied for battery modeling and health estimation, their use as validation-centric instruments for safety-critical battery software remains limited. This paper presents a software-in-the-loop validation framework based on AI-augmented digital twins, integrating physics-based electrothermal battery models with machine learning based residual and risk modeling. The framework enables scalable validation of battery control logic under both measured and synthetically generated operating scenarios, with validation objectives focused on limit enforcement, thermal derating, and fault-handling robustness. The approach is demonstrated using publicly available national-laboratory battery datasets, showing that synthetic scenario execution significantly expands validation coverage compared to experimental testing alone and reveals software-mediated risk conditions that are difficult to detect through conventional methods. The results demonstrate that AI-augmented digital twins can transform battery validation into a continuous, lifecycle-oriented process, supporting shift-left verification and improved safety assurance in software-defined electric vehicles.
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