This article introduces an efficient modeling approach based on the Wiener structure to reinforce the capacity of classical equivalent circuit models (ECMs) in capturing the nonlinearities of lithium-ion (Li-ion) batteries. The proposed block-oriented modeling architecture is composed of a simple linear ECM followed by a static output nonlinearity block, which helps achieving a superior nonlinear mapping property while maintaining the real-time efficiency. The observability of the established battery model is analytically proven. This article also introduces an efficient parameter estimator based on extended-kernel iterative recursive least squares algorithm for real-time estimation of the parameters of the proposed Wiener model. The proposed approach is applied for state-of-charge (SoC) estimation of 3.4-Ah 3.6-V nickel-manganese-cobalt-based Li-ion cells using the extended Kalman filter (EKF). The results show about 1.5% improvement in SoC estimation accuracy compared with the EKF algorithm based on the second-order ECM. A series of real-time tests are also carried out to demonstrate the computational efficiency of the proposed method.

The state of charge (SOC) and state of health (SOH) are two crucial indicators needed for a proper and safe operation of the battery. Coulomb counting is one of the most adopted and straightforward methods to calculate the SOC. Although it can be implemented for all kinds of applications, its accuracy is strongly dependent on the operation conditions. In this work, the behavior of the batteries at different current and temperature conditions is analyzed in order to adjust the charge measurement according to the battery efficiency at the specific operating conditions. The open-circuit voltage (OCV) is used to reset the SOC estimation and prevent the error accumulation. Furthermore, the SOH is estimated by evaluating the accumulated charge between two different SOC using a recursive least squares (RLS) method. The SOC and SOH estimations are verified through an extensive test in which the battery is subjected to a dynamic load profile at different temperatures.

With the increasing adoption of electric vehicles (EVs) by the general public, a lot of research is being conducted in Li-ion battery-related topics, where state-of-health (SoH) estimation has a prominent role. Accurate knowledge of this parameter is essential for efficient and safe EV operation. In this work, machine-learning techniques are applied to estimate this parameter in EV applications and in diverse scenarios. After thoroughly analysing cell ageing in different storage conditions, a novel approach based on impedance data is developed for SoH estimation. A fully-connected feed-forward neural network (FC-FNN) is employed to estimate the battery’s maximum available capacity from a small set of impedance measurements. The method was tested for estimation in long-term scenarios and for diverse degradation procedures with data from real EV batteries. High accuracy was obtained in all situations, with a mean absolute error as low as 0.9%. Thus, the proposed algorithm constitutes a powerful and viable solution for fast and accurate SoH estimation in real-world battery management systems.

Idling periods are a major part of the Lithium-ion battery operation. Due to parasitic reactions, the battery capacity is decreasing and self-discharge occurs over time. Thus, in order to predict the battery lifetime and optimize its operation, it is required to capture this behavior. In this study, two different storage periods of 2 and 6 months were investigated and used to develop and validate models dedicated to reversible and irreversible capacity loss. It has been observed that while for the shorter storage period, the self-discharge rate does not change significantly, for the longer storage period it decreased during aging. Moreover, the degradation rates vary significantly for various time scales at low temperature, while at medium and high temperatures they are matching closely for 2- and 6-months periodic storage.

The increasing growth of the second-hand electric vehicle market demands reliable methods for evaluating the state of health of deployed electric vehicle batteries. Among these methods, incremental capacity analysis is a commonly used technique for state of health evaluation via the quantification of degradation modes. While the optimal conditions for its application typically involve low currents and a controlled temperature, this cannot be easily applied to deployed batteries. It is therefore essential to understand the impact of varying charging rates and temperatures on the accuracy of the analysis. In this study, the characteristics and behavior of incremental capacity features for seven electric vehicle batteries tested under different calendar aging conditions were investigated. The results show that accurate state of health estimations under different test conditions could be obtained using specific electrochemical features.