On Electrical and Hybrid Vehicles (EVs, HEVs), energy is stored in accumulators, mainly electro-chemical batteries. A reliable and cost effective management of energy storage system is a key point for the development of such devices, their durability and for vehicle performance optimization. This requires the accurate estimation of the battery state over time and in a wide range of operating conditions. The battery state is usually expressed as State Of Charge (SOC) and State Of Health (SOH). Their estimations requires an accurate model to represent the static and dynamic behaviors of the battery. This paper presents a model adaptive Unscented Kalman Filter (UKF) method to estimate online SOC of Li-ion batteries. The proposed approach uses a Recursive Least Squares method to update the UKF model parameters during a discharge period. The effectiveness of the method has been verified based on real data acquired from five LiFePO4 battery packs installed on a working EV.

Online Identification of Thevenin Equivalent Circuit Model Parameters and Estimation State of Charge of Lithium-Ion Batteries

Lutzemberger, Giovanni
2018

Abstract

On Electrical and Hybrid Vehicles (EVs, HEVs), energy is stored in accumulators, mainly electro-chemical batteries. A reliable and cost effective management of energy storage system is a key point for the development of such devices, their durability and for vehicle performance optimization. This requires the accurate estimation of the battery state over time and in a wide range of operating conditions. The battery state is usually expressed as State Of Charge (SOC) and State Of Health (SOH). Their estimations requires an accurate model to represent the static and dynamic behaviors of the battery. This paper presents a model adaptive Unscented Kalman Filter (UKF) method to estimate online SOC of Li-ion batteries. The proposed approach uses a Recursive Least Squares method to update the UKF model parameters during a discharge period. The effectiveness of the method has been verified based on real data acquired from five LiFePO4 battery packs installed on a working EV.
9781538651858
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/937343
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