A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

Abstract

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

“…Moreover, the actual capacities and OCV curves of the test LIBs were determined by the standard capacity and hybrid pulse power characterization (HPPC) experiments. These experimental procedures are standard processes, which can be found in References [34,35].…”

A Novel Screening Method Based on a Partially Discharging Curve Using a Genetic Algorithm and Back-Propagation Model for the Cascade Utilization of Retired Lithium-Ion Batteries

“…Usually, the internal resistance of a cell is obtained using the pulse method [24,25]. As shown in Figure 1, the internal resistance can be obtained as follows:…”

A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles

“…However, it has obvious limitations: it suffers from an initial condition problem and the accumulation of sensor offset due to the integrator. Artificial neural networks (ANNs) and fuzzy algorithms are also model-less methods that implement intelligent algorithms [14][15][16]. However, these data-driven methods have inherent problems, such as a long training time and a large number of data sets.…”