• PRAVEENA A
• N.Dhanush
• A.Dinesh Kumar
• D.Hemanandhini
• J.Kiruthika

Accurate estimation of Li-Ion batteries life cycle is important to the battery management system (BMS). Compared to direct measurement methods and model-based methods, data-driven methods are drawing much attention for online SOH estimation due to their simple structure, flexibility for online application, and independence on battery model. Data-driven methods first extract a strong health indicator (HI) to measure the SOH and then build a machine learning model to map the relationship between them. Based on data analytics on the battery ageing data, this project proposes to use a partial charge and discharge current sequence. Then it is then fed into KNN algorithm, which has fast learning speed and good generalization property. By selecting charging and discharging capacity, the impact on the estimation accuracy is comprehensively evaluated. The proposed method is tested with an open dataset and the results verify the effectiveness of the proposed method. This project provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications.

Battery Management, Lithium Ion

"DESIGN AND IMPLEMENTATION OF BATTERY MANAGEMENT SYSTEM FOR ELECTRIC VEHICLES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.v, April-2024, Available :http://www.jetir.org/papers/JETIR2404934.pdf

"DESIGN AND IMPLEMENTATION OF BATTERY MANAGEMENT SYSTEM FOR ELECTRIC VEHICLES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppv, April-2024, Available at : http://www.jetir.org/papers/JETIR2404934.pdf