Lithium-ion batteries are fuelling the advancing renewable-energy based world. At the core of transformational developments in battery design, modelling and management is data. In this work, the datasets associated with lithium batteries in the public domain are summarised. We review the data by mode of experimental testing, giving particular attention to test variables and data provided. Alongside highlighted tools and platforms, over 30 datasets are reviewed.

Lithium-ion battery data and where to find it

Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.

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A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

The purpose of this paper is to review the recent literature regarding the effects of low temperatures on Lithium ion (Li-ion) batteries for electric vehicle, plug-in hybrid electric vehicle, and hybrid electric vehicle applications. Special consideration is given to nine major effects that directly or indirectly impact the battery use at low temperatures and it is presented that they are correlated to each other. The main discussions in this paper are capacity loss, power loss, life degradation, safety hazard, unbalanced capacity, charging difficulty, thermal management system complexity, battery model and state estimation method complexity, and incremental cost. The effects correlations and possible solutions are explained to provide a detailed, yet broader understanding of the Li-ion batteries low-temperature operating scenarios.

xEV Li-Ion Battery Low-Temperature Effects—Review

Electric vehicles (EVs) are a promising technology to reduce emissions, but its development enormously depends on the technology used in batteries. Nowadays, batteries based on lithium-ion (Li-Ion) seems to be the most suitable for traction, especially nickel-manganese-cobalt (NMC) and nickel-cobalt-aluminum (NCA). An appropriate model of these batteries is fundamental for the simulation of several processes inside an EV, such as the state of charge (SoC) estimation, capacity and power fade analysis, lifetime calculus, or for developing control and optimization strategies. There are different models in the current literature, among which the electric equivalent circuits stand out, being the most appropriate model when performing real-time simulations. However, impedance models for battery diagnosis are considered very attractive. In this context, this paper compares and contrasts the different electrical equivalent circuit models, impedance models, and runtime models for battery-based EV applications, addressing their characteristics, advantages, disadvantages, and usual applications in the field of electromobility. In this sense, this paper serves as a reference for the scientific community focused on the development of control and optimization strategies in the field of electric vehicles, since it facilitates the choice of the model that best suits the needs required.

Analysis of the Current Electric Battery Models for Electric Vehicle Simulation

In this paper, a review on road friction virtual sensing approaches is provided. In particular, this work attempts to address whether the road grip potential can be estimated accurately under regular driving conditions in which the vehicle responses remain within low longitudinal and lateral excitation levels. This review covers in detail the most relevant effect-based estimation methods; these are methods in which the road friction characteristics are inferred from the tyre responses: tyre slip, tyre vibration, and tyre noise. Slip-based approaches (longitudinal dynamics, lateral dynamics, and tyre self-alignment moment) are covered in the first part of the review, while low frequency and high frequency vibration-based works are presented in the following sections. Finally, a brief summary containing the main advantages and drawbacks derived from each estimation method and the future envisaged research lines are presented in the last sections of the paper.

Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

To examine different battery modeling approaches, three equivalent circuit battery model types and two battery model parametrization methods are investigated in this paper. A simple model, consisting an open circuit voltage and a series resistance, is compared with two enhanced approaches. The first enhanced approach includes a Warburg Impedance to capture diffusion and the second adds two parallel RC circuit pairs to capture double layer capacitance and charge transfer resistance effects. The model parameters are determined with either time or frequency domain test data and performance for both parameterization methods are compared. Finally, model accuracy is experimentally evaluated for a matrix of drive cycles and temperature values.

Li-ion battery model performance for automotive drive cycles with current pulse and EIS parameterization

Linear antenna arrays consisting of dipole antennas find wide applications in wireless communication systems. Typically, arrangements with uniform spacing between the array elements are applied to gain highly directive radiation patterns. In the present paper an antenna array arrangement with non-uniformly spacing between the array elements is synthesized in the multi-objective sense. The optimization relies on the firefly algorithm and the PEEC method.

PEEC-based multi-objective synthesis of non-uniformly spaced linear antenna arrays

The near-field communication (NFC) technique finds wide applications in many aspects of modern life, e.g., contactless payment systems or authentication. Regardless of the application, typically, the requirements on the antenna structure are manifold. Frequently, limitations in the available space make the antenna design quite challenging. Beside geometrical limitations, also the matching of the antenna structure to the analog front end of the NFC transponder IC has to be taken into account. Furthermore, the NFC antenna together with the connected IC has to fulfill specific standardized values. In this paper, the optimization of an NFC antenna structure including the needed matching circuit in the multi-objective sense is proposed. Due to its simplicity and the consequently reduced computational effort, the partial-element electric circuit (PEEC) method is applied to carry out the needed field computation in the so-called NFC operating volume. Furthermore, the PEEC method offers the possibility of simply connecting lumped components to the antenna structure within the numerical analyses, which enables the optimization of the matching circuit parallel to the optimization of the antenna structure. The optimization relies on a stochastic optimization strategy, namely, the firefly algorithm (FFA). In this paper, an extended version of the general FFA is applied.

Multi-Objective Synthesis of NFC-Transponder Systems Based on PEEC Method

Many real world optimization problems have to be treated as multi-objective optimization problems. The Firefly Algorithm (FFA), a stochastic optimization method mimics the behavior of fireflies, which use a kind of flashing light to communicate with other members of their species. FFA is implicitly able to detect good local solutions on its way to the best solution. This disposition is successfully boosted by identifying clusters of fireflies which gather around promising local solutions. Subsequently, the update rules used for finding the new positions of the fireflies are applied among members of the particular clusters only. This extended FFA will be used to solve the well known Rastrigin test function and an electromagnetic field problems, the optimal design of a magneto-rheologic clutch, respectively.

Extended firefly algorithm for multimodal optimization

Experimental validation of a non-linear first-order tyre dynamics approach

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