Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate ( $\text{LiFePO}_4$ ) and lithium titanate ( $\text{LTO}$ ) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs’ battery management system (BMS).

State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks

Application of electrochemical impedance spectroscopy to commercial Li-ion cells: a review

Because of their characteristics, which have been continuously improved during the last years, Lithium-ion batteries have been proposed as an alternative viable solution to present fast-reacting conventional generating units to deliver the primary frequency regulation service. However, even though there are worldwide demonstration projects, where energy storage systems based on Lithium-ion batteries are evaluated for such applications, the field experience is still very limited. In consequence, at present, there are no very clear requirements on how the Lithium-ion battery energy storage systems should be operated, while providing frequency regulation service and how the system has to reestablish its state of charge (SOC) once the frequency event has passed. Therefore, this paper aims to investigate the effect on the lifetime of the Lithium-ion batteries energy storage system of various strategies for reestablishing the batteries’ SOC after the primary frequency regulation is successfully delivered.

Operation of a Grid-Connected Lithium-Ion Battery Energy Storage System for Primary Frequency Regulation: A Battery Lifetime Perspective

Sulfur exhibits a high theoretical capacity of 1675 mA h g–1 via a distinct conversion reaction, which is different from the insertion reactions in commercial lithium-ion batteries In consideration of its conversion-reaction battery chemistry, a custom design for electrode materials could establish the way for attaining high-loading capability while simultaneously maintaining high electrochemical utilization and stability In this study, this process is undertaken by introducing carbon cotton as an attractive electrode-containment material for enhancing the dynamic and static stabilities of lithium–sulfur (Li–S) batteries The carbon cotton possessing a hierarchical macro-/microporous architecture exhibits a high surface area of 805 m2 g–1 and high microporosity with a micropore area of 557 m2 g–1 The macroporous channels allow the carbon cotton to load and stabilize a high amount of active material The abundant microporous reaction sites spread throughout the carbon cotton facilitate the redox chemist

A Carbon-Cotton Cathode with Ultrahigh-Loading Capability for Statically and Dynamically Stable Lithium–Sulfur Batteries

The development of lifetime estimation models for Lithium-ion battery cells, which are working under highly variable mission profiles characteristic for wind power plant applications, requires a lot of expenditures and time resources. Therefore, batteries have to be tested under accelerated lifetime ageing conditions. This paper presents a three-stage methodology used for accelerated lifetime testing of Lithiumion batteries. The results obtained at the end of the accelerated ageing process can be used for the parameterization of a performance-degradation lifetime model. In the proposed methodology both calendar and cycling lifetime tests are considered since both components are influencing the lifetime of Lithium-ion batteries. The methodology proposes also a lifetime model verification stage, where Lithium-ion battery cells are tested at normal operating conditions using an application specific mission profile.

Accelerated lifetime testing methodology for lifetime estimation of Lithium-ion batteries used in augmented wind power plants

Lithium-ion (Li-ion) batteries are complex energy storage devices with their performance behavior highly dependent on the operating conditions (i.e., temperature, load current, and state-of-charge (SOC)). Thus, in order to evaluate their techno-economic viability for a certain application, detailed information about Li-ion battery performance behavior becomes necessary. This paper proposes a comprehensive seven-step methodology for laboratory characterization of Li-ion batteries, in which the battery’s performance parameters (i.e., capacity, open-circuit voltage (OCV), and impedance) are determined and their dependence on the operating conditions are obtained. Furthermore, this paper proposes a novel hybrid procedure for parameterizing the batteries’ equivalent electrical circuit (EEC), which is used to emulate the batteries’ dynamic behavior. Based on this novel parameterization procedure, the performance model of the studied Li-ion battery is developed and its accuracy is successfully verified (maximum error lower than 5% and a mean error below 8.5 mV) for various load profiles (including a real application profile), thus validating the proposed seven-step characterization methodology.

https://www.mdpi.com/2313-0105/2/4/37/pdf

Generalized Characterization Methodology for Performance Modelling of Lithium-Ion Batteries

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Investigation of the Self-Discharge Behavior of Lithium-Sulfur Batteries

A self-discharge model of Lithium-Sulfur batteries based on direct shuttle current measurement

Electrothermal impedance spectroscopy as a cost efficient method for determining thermal parameters of lithium ion batteries: Prospects, measurement methods and the state of knowledge

Daniel Loan Stroe

Papers

Accelerated lifetime testing methodology for lifetime estimation of Lithium-ion batteries used in augmented wind power plants

Generalized Characterization Methodology for Performance Modelling of Lithium-Ion Batteries

Investigation of the Self-Discharge Behavior of Lithium-Sulfur Batteries

A self-discharge model of Lithium-Sulfur batteries based on direct shuttle current measurement

Electrothermal impedance spectroscopy as a cost efficient method for determining thermal parameters of lithium ion batteries: Prospects, measurement methods and the state of knowledge