State of Charge Estimation for Li-Ion Batteries Based on an Unscented H-Infinity Filter
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ORIGINAL ARTICLE
State of Charge Estimation for Li‑Ion Batteries Based on an Unscented H‑Infinity Filter Yuanyuan Liu1 · Tiantian Cai1 · Jingbiao Liu1 · Mingyu Gao1 · Zhiwei He1 Received: 28 September 2019 / Revised: 29 June 2020 / Accepted: 15 September 2020 © The Korean Institute of Electrical Engineers 2020
Abstract The state of charge (SOC) of lithium-ion batteries reflects their remaining capacity. Accurate estimation of SOC helps battery safety and is beneficial to the efficient management of batteries. The charging and discharging processes of lithium-Ion batteries are very complicated, and it is difficult to obtain accurate SOC estimation results. Therefore, it is important to study improved algorithms for SOC estimation for this nonlinear non-Gaussian battery system. In this paper, we propose an unscented H-infinity filter (UHF) based SOC estimation method, which combines the advantages of both the unscented Kalman filter (UKF) and the H-infinity filter (HF). The UKF propagates the sigma points through the nonlinear system and does not need the first-order linear approximation of the system equation, while the HF can suppress the non-Gaussian noise in the system to the greatest extent. The proposed UHF based SOC estimation algorithm is verified and evaluated in the battery management system, and further optimized in practical problems. Experimental results show that the proposed UHF based algorithm can perform accurate SOC estimation for lithium-ion batteries, and is superior to the UKF based SOC estimation. Keywords Index terms—unscented kalman filterer · H-infinity filter · State of charge
1 Introduction ITHIUM-ION batteries are widely used as energy storage batteries and power batteries because of their high specific energy, high load capacity, low self-discharge rate and no memory. However, lithium-ion batteries should not be overcharged or over discharged, otherwise it will cause irreversible battery capacity loss. Therefore, the effective management of the battery is the basis to ensure the safety of lithium-ion battery [1–5]. Although the battery management system (BMS) has entered the practical application stage, the accuracy of state of charge (SOC) estimation still needs to be improved. Battery SOC is an important index to measure the remaining capacity of battery, which refers to the ratio of the remaining capacity of battery to its nominal capacity [6]. However, SOC cannot be obtained by direct measurement. Generally, SOC is obtained by measuring the current, voltage and temperature of the battery, and then
* Zhiwei He [email protected] 1
Hangzhou Dianzi University, Hangzhou, China
estimated according to a preset algorithm using a specific model [7, 8]. In engineering practice, many algorithms such as open circuit voltage, Coulomb integral [9], neural network [10] and the Kalman filter [11, 12] based methods have been proposed for SOC estimation, and the model-based Kalman filter and its extension algorithm such as the EKF and the UKF [13–15] are commonly used algorithms for SOC estima
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