Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

Abstract

In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium-ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circuit voltage, the SOC and the available capacity in the scope of whole lifespan, is constructed to describe the capacity attenuation, and the battery available capacity is identified by a genetic algorithm (GA), together with the parameters related to SOC. The square root cubature Kalman filter (SRCKF) is employed to estimate the SOC with the consideration of capacity degradation. The experimental results demonstrate the effectiveness and feasibility of the co-estimation scheme.Energies 2020, 13, 1410 2 of 15 initial values, and the latter is not suitable for online estimation, as it usually costs long shelving time to acquire the OCV value. With the development of computation technologies and machine learning, a variety of artificial intelligence-based, data-driven methods, such as neural networks [6] and support vector machines [7], are proposed for SOC estimation by establishing black-box models. Data-driven methods feature a strong nonlinear mapping capability with high accuracy; however, these approaches show high complexity, and require a considerable amount of training data. Alternatively, model-based methods have been widely investigated and applied for SOC estimation, thanks to the capabilities of online application, high precision and the independence of initial values. Conventional modeling manners mainly include electrochemical models and the equivalent circuit models (ECMs). Compared with complicated electrochemical models, ECM is commonly used to describe the electrical behavior of batteries, and subsequently to estimate the SOC due to its simplification and preferable precision. Yanwen Li et al. proposed a multi-model probability fusion algorithm to describe the battery's electrical characteristics, and subsequently estimate the SOC [8]. In model-based methods, the combination of the battery model and the intelligent filtering algorithm is a hotspot in SOC estimation research. The frequently used filtering algorithms include Kalman filtering (KF) [9], the H-infinity filter (HIF) [10], particle filter (PF) [11], and their various extensions. In particular, the extended KF (EKF) is widely employed to execute SOC estimation using a first-order Taylor expansion on the basis of the battery's nonlinear model [12]. Nonetheless, the second and higher order expansion is usually neglected, thus leading to slow a convergence rate, and even divergence. The unscented KF (UKF) is exploited to estimate battery SOC, based on the recursive unscented transformation to approximate the nonlinear observation without Taylor polynomial expansions [13]. The UKF shows better estimation precision and robustness than the EKF in strong, nonlinear systems [14]. On the basis of the radial-spherical cubature criterion, the cubature Kalman filter (CKF) leverages a set of volume points to appro...

A novel streamlined particle‐unscented Kalman filtering method for the available energy prediction of lithium‐ion batteries considering the time‐varying temperature‐current influence

“…When the environment around the carrier changes, its statistical characteristics change greatly together with the observation noise of the carrier. Meanwhile, the prediction accuracy and stability are reduced greatly and the error covariance is updated, as shown in Equation (12).…”

“…To realize the accurate available energy prediction, observing strategies are introduced into the iterative calculation process, such as Adaptive Dual Kalman Filtering, Thermoelectric Coupling Modeling, and Open-Circuit Voltage (OCV). [9][10][11][12] In the calculation process, the effective energy state prediction can be realized by combining the experimental data. By conducting the State of Balance prediction for lithium-ion battery packs, the onboard dynamic balancing adjustment of the battery pack is carried out to improve its power supply performance.…”

“…To realize the accurate available energy prediction, observing strategies are introduced into the iterative calculation process, such as Adaptive Dual Kalman Filtering, Thermoelectric Coupling Modeling, and Open‐Circuit Voltage (OCV) 9‐12 . In the calculation process, the effective energy state prediction can be realized by combining the experimental data.…”

Capacity Prediction and Validation of Lithium-Ion Batteries Based on Long Short-Term Memory Recurrent Neural Network

“…Based on effective electrical models such as equivalent circuit model (ECM), a number of advanced filter algorithms (such as Kalman filter (KF) and PF) can then be adopted to conduct the joint estimation of battery status and model parameters [13]. In [14], a second-order resistancecapacitance (RC) ECM is established, and the square root cubature KF is employed to estimate the SOC. Meanwhile, the capacity, as one of the key parameters of model, is identified by the genetic algorithm (GA).…”

Improved Backward Smoothing—Square Root Cubature Kalman Filtering and Variable Forgetting Factor—Recursive Least Square Modeling Methods for the High-Precision State of Charge Estimation of Lithium-Ion Batteries

“…16,17 At present, a lot of researchers have made numerous studies on the SOC estimation of Li-ion batteries and suggested various approaches to estimate SOC. [18][19][20] A model-based SOC estimation approaches are most widely employed, including extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). 21,22 EKF has large calculations and weak stability due to its Jacobian matrix of nonlinear model, which constrains its application scope.…”