The growing share of renewable energies in power production and the rise of the market share of battery electric vehicles increase the demand for battery technologies. In both fields, a predictable operation requires knowledge of the internal battery state, especially its state of charge (SoC). Since a direct measurement of the SoC is not possible, Kalman filter-based estimation methods are widely used. In this work, a step-by-step guide for the implementation and tuning of an extended Kalman filter (EKF) is presented. The structured approach of this paper reduces efforts compared with empirical filter tuning and can be adapted to various battery models, systems, and cell types. This work can act as a tutorial describing all steps to get a working SoC estimator based on an extended Kalman filter.

Battery degradation is critical to the cost-effectiveness and usability of battery-powered products. Aging studies help to better understand and model degradation and to optimize the operating strategy. Nevertheless, there are only a few comprehensive and freely available aging datasets for these applications. To our knowledge, the dataset¹ presented in the following is one of the largest published to date. It contains over 3 billion data points from 228 commercial NMC/C+SiO lithium-ion cells aged for more than a year under a wide range of operating conditions. We investigate calendar and cyclic aging and also apply different driving cycles to cells. The dataset¹ includes result data (such as the remaining usable capacity or impedance measured in check-ups) and raw data (i.e., measurement logs with two-second resolution). The data can be used in a wide range of applications, for example, to model battery degradation, gain insight into lithium plating, optimize operating strategies, or test battery impedance or state estimation algorithms using machine learning or Kalman filtering.

The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, energy management systems are central as they coordinate energy flows and optimize them toward economic, technical, ecological, and social objectives. While numerous scientific publications study the infrastructure, optimization, and implementation of residential energy management systems, only little research exists on industrial energy management systems. However, results are not easily transferable due to differences in complexity, dependency, and load curves. Therefore, we present a systematic literature review on state-of-the-art research for residential and industrial energy management systems to identify trends, challenges, and future research directions. More specifically, we analyze the energy system infrastructure, discuss data-driven monitoring and analysis, and review the decision-making process considering different objectives, scheduling algorithms, and implementations. Thus, based on our insights, we provide numerous recommendations for future research in residential and industrial energy management systems.

Since the evolution of the electric and hybrid vehicle, the analysis of batteries’ characteristics and influence on driving range has become essential. This fact advocates the necessity of accurate simulation modeling for batteries. Different models for the Li-ion battery cell are reviewed in this paper and a group of the highly dynamic models is selected for comparison. A new open circuit voltage (OCV) model is proposed. The new model can simulate the OCV curves of lithium iron magnesium phosphate (LiFeMgPO₄) battery type at different temperatures. It also considers both charging and discharging cases. The most remarkable features from different models, in addition to the proposed OCV model, are integrated in a single hybrid electrical model. A lumped thermal model is implemented to simulate the temperature development in the battery cell. The synthesized electro-thermal battery cell model is extended to model a battery pack of an actual electric vehicle. Experimental tests on the battery, as well as drive tests on the vehicle are performed. The proposed model demonstrates a higher modeling accuracy, for the battery pack voltage, than the constituent models under extreme maneuver drive tests.