A New Approach for Estimation of Lithium-Ion Battery State of Charge and Health Using Mixed H_∞/H₂ Control With Sliding Mode Observer

Abstract

For efficient battery management that ensures lifetime and dependability in applications like electric vehicles, an accurate real-time assessment of the State of Charge (SOC) and State of Health (SOH) of lithium-ion (Li-ion) batteries is essential. To overcome the difficulties presented by aging, unmodeled dynamics, and temperature fluctuations, this study attempts to create a reliable estimation method that improves the precision and robustness of SoC and SoH assessments. To maximize transient responsiveness and guarantee estimator convergence to the actual battery state, the suggested system combines a H_∞/H₂ controller with pole placement, which is built using Linear Matrix Inequality (LMI) techniques. Furthermore, this controller is complemented by a sliding mode estimator to assess SoH, which is a novel combination in battery state estimating techniques. By optimizing the disturbance matrix structure and taking into account changes in internal resistances, capacitances, and actual capacity, the H_∞/H₂ controller is designed to reduce disturbances caused by things like age and temperature fluctuations. To evaluate SoH, the sliding mode estimator makes use of state variables from the H_∞/H₂ controller. The approach is validated under real-world circumstances, including driving schedules like UDDS, US06, and HWFET, using numerical simulations that consider variations in battery internal properties. The accuracy and dependability of SOC and SOH assessments are significantly improved by the combined estimation technique. By lowering estimating errors, the controller improves resilience to disruptions. The resilience of the approach is shown by simulations conducted under a range of driving circumstances, suggesting that battery management systems might use it in practice.

Keywords

battery management systems / H∞/H2 pole placement control / lithium-ion battery / sliding mode observer / state of charge estimation / State of Health estimation

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2025 The Author(s). Battery Energy published by Xijing University and John Wiley & Sons Australia, Ltd.