Abstract
Accurate estimation of the state of charge (SOC) of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li)-ion batteries. First, an enhanced battery model is proposed to include the impacts due to different discharge rates and temperatures. An adaptive joint estimation of the battery SOC and battery internal resistance is then presented to enhance system robustness with battery aging. The SOC estimation algorithm has been developed and verified through experiments on different types of Li-ion batteries. The results indicate that the proposed method provides an accurate SOC estimation and is computationally efficient, making it suitable for embedded system implementation.
Highlights
Batteries have been widely used in many applications where electric energy storage is needed, such as renewable energy systems, telecommunication power supplies, electric power systems, military applications, and electric transportation systems [1,2,3,4,5]
The results indicate that both parameter vectors are effective and that a reasonable small change in the parameters, either caused by parameter estimation error or individual battery diversity, does not prevent the unscented KF (UKF) from obtaining a relatively accurate state of charge (SOC) estimation
This paper proposes an unscented Kalman filter algorithm for online battery SOC estimation when the battery is discharged
Summary
Batteries have been widely used in many applications where electric energy storage is needed, such as renewable energy systems, telecommunication power supplies, electric power systems, military applications, and electric transportation systems [1,2,3,4,5]. The model is structured as the ratio of two functions, one as a function of the temperature and the other of the charge/discharge rate This model structure represents data well, allows us to employ simple polynomials to represent each function, and has the flexibility of using one function under a fixed temperature or fixed current condition; (2) a recursive UKF-type algorithm for SOC estimation is developed under this new battery model with model validations. The models of [24] assume an unknown but fixed capacity without consideration of its dependence on temperature and charge/discharge current It uses a recursive least-squares-type algorithm with different convergence features. The proposed UKF-based SOC estimation method with the enhanced battery model in this paper adds new tools and algorithms to SOC estimation methodologies, and as a result can potentially enhance BMSs by addressing their core challenges.
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