The State of Health (SOH) estimation for automotive batteries is currently assessed with different techniques which may involve long testing procedure or require costly hardware to be implemented. This paper aims at contributing to this domain by exploiting the response of a lead-acid battery with respect to a short-term current profile using an Artificial Neural Network (ANN) classifier for SOH estimation. The method is applicable onboard the vehicle and no additional instrumentation is required on the retained vehicle. The design and validation of a SOH method with a short-term current profile using Artificial Intelligence (AI) in lead-acid batteries, which are commonly used in heavy-duty vehicles for cranking and cabin systems, are presented. The paper validates the considered approach with experimental data, which are representative of actual vehicle operations. In detail, the paper describes the retained hardware and software architectures and the design procedure related to the proposed SOH estimation technique based on AI. The retained lead-acid battery 12 V 225 Ah battery used in actual heavy-duty vehicles. The proposed AI-based algorithm relies on an ANN used for SOH classification, which is trained with a sufficient amount of collected data. Specifically, the proposed SOH classifier can process features extracted from buffers of input data that are recorded onboard. When considering the validation dataset only, the resulting SOH estimation algorithm can classify the battery SOH with an overall accuracy equal to 96.7%.

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