IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 95 10.51583/IJLTEMAS.2026.150700091 Management Core Temperature Estimation and Thermal Management of Lithium-Ion Batteries Using a Nonlinear Adaptive Extended Kalman Filter Madhav Mahajan Kalpesh Department of Electrical Engineering, SSBT's College of Engineering and Technology, Bambhori, Jalgaon, Maharashtra 425001, India India Manohar Shembekar Suhas Department of Electrical Engineering, SSBT's College of Engineering and Technology, Bambhori, Jalgaon, Maharashtra 425001, India India Mangalsing Deshmukh Vijay Department of Electrical Engineering, SSBT's College of Engineering and Technology, Bambhori, Jalgaon, Maharashtra 425001, India India 15 7 1128 1150 29 07 2026 03 08 2026 14 08 2026 lithium-ion battery; core temperature estimation; extended Kalman filter; adaptive filtering; battery thermal management Abstract

The core temperature of a cylindrical lithium-ion cell cannot be measured directly, yet it governs both safety and ageing. This paper presents a nonlinear adaptive extended Kalman filter (AEKF) that estimates core temperature from a single surface thermistor, together with a battery thermal management strategy driven by the resulting estimate. Three features distinguish the formulation from earlier two-node observers. First, heat generation is evaluated at the estimated core temperature rather than treated as an exogenous input; because internal resistance follows an Arrhenius law, this makes the process model genuinely nonlinear in the state and requires an explicit Jacobian, derived here in closed form. The state-feedback term reaches 25.4 % of the dominant conduction term at peak current, and substituting the measured surface temperature into the heat-generation expression incurs an error of up to 1.35 W. Second, the lumped two-node parameters are identified against a radially resolved finite-volume reference model using an excitation containing coolant-flow steps; omitting those steps yields a surface capacitance an order of magnitude too small and an observer that mispredicts the core whenever the pump switches. Third, innovation-based covariance adaptation is made safe for closed-loop use by a Student-t test on the innovation mean that distinguishes sensor degradation from transient model error, and by bounded directional process-noise inflation applied only to the measured state. Validation is by simulation only. Against a structurally different reference plant with deliberate parameter mismatch and a mid-run doubling of thermistor noise, the proposed filter attains a core-temperature RMSE of 0.348 degrees Celsius, against 0.359 for a fixed-gain EKF, 0.429 for a Sage-Husa AEKF and 0.447 for the linear formulation in which heat generation is an input. Under twenty Monte-Carlo realisations with random parameter error the proposed filter and the fixed-gain EKF are statistically indistinguishable. In closed loop, estimate-driven cooling holds the same peak core temperature as a conventional surface-triggered controller while consuming 47.5 % less pump energy.

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