State of Health Estimation Based on Dynamic Cell Behaviour in EVs Tata Motors, Limited

Format:

Book

Conference/Event

Author/Creator:

Nikam, Ashish, author.

Contributor:

Ambhore, Yogesh Gajanan

Hariyani, Ganesh

Sodha, Nirav

Tiwari, Awanish Shankar

Conference Name:

Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

In electric vehicle (EV) applications, accurate estimation of State of Health (SOH) of lithium ion battery pack is critical for ensuring its performance, reliability, operational safety and user confidence. SOH is a key parameter monitored by Battery Management System (BMS) to check the remaining usable life of the battery and to make informed decisions regarding charging, discharging, power delivery, and maintenance scheduling. In traditional SOH estimation techniques commonly rely on simplistic full-cycle charge-discharge data or single-parameter tracking (such as voltage or internal resistance) and other method like coulomb counting. Kalman filter, model based method such as equivalent circuit modelling, data driven models et cetera This methods not consider variable field conditions such as partial and full state of-charge usage condition, dynamic load profiles, and non-uniform aging. As a result, these methods can produce significant deviations in SOH estimation, potentially causing system-level shutdowns, inaccurate range estimation, and delayed detection of degradation or failure events and also customer not getting the confidence. This paper proposes an advanced methodology for SOH estimation based on a segmented voltage-window specific charge accumulation model. This method involves monitoring the capacity accumulated within four distinct voltage window such as 2.53.0 V (Condition A), 3.03.25 V (Condition B), 3.253.5 V (Condition C), and 3.53.65 V (Condition D) during either full or partial charge cycles. The maximum cell voltage was considered as key parameters to classify the voltage window and record the capacity accumulation corresponding to each voltage window. This accumulated capacity value was then compared with upper and lower limit of capacity accumulation which was generated from life cycle data with definite test parameters. The SOH estimation decision was made based on various conditions for both full and partial charging condition

Notes:

Vendor supplied data

Publisher Number:

2026-26-0183

Access Restriction:

Restricted for use by site license