A Review of SOH Estimation Methods in Lithium-ion Batteries for Electric Vehicle Applications

A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery

Accurate estimation of lithium-ion battery state-of-health (SOH) is important for the safe operation of electric vehicles; however, in practical applications, the accuracy of SOH estimation is affected by uncertainty factors, including human operation, working conditions, etc. To accurately estimate the battery SOH, a hybrid neural network based on the dilated convolutional neural network and the bidirectional gated recurrent unit, namely dilated CNN-BiGRU, is proposed in this paper. The proposed data-driven method uses the voltage distribution and capacity changes in the extracted battery discharge curve to learn the serial data time dependence and correlation. This method can obtain more accurate temporal and spatial features of the original battery data, resulting higher accuracy and robustness. The effectiveness of dilated CNN-BiGRU for SOH estimation is verified on two publicly lithium-ion battery datasets, the NASA Battery Aging Dataset and Oxford Battery Degradation Dataset. The experimental results reveal that the proposed model outperforms the compared data-driven methods, e.g., CNN-series and RNN-series. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are limited to within 1.9% and 3.3%, respectively, on the NASA Battery Aging Dataset.

Towards Safer and Smarter Design for Lithium-Ion-Battery-Powered Electric Vehicles: A Comprehensive Review on Control Strategy Architecture of Battery Management System

As the battery provides the entire propulsion power in electric vehicles (EVs), the utmost importance should be ascribed to the battery management system (BMS) which controls all the activities associated with the battery. This review article seeks to provide readers with an overview of prominent BMS subsystems and their influence on vehicle performance, along with their architectures. Moreover, it collates many recent research activities and critically reviews various control strategies and execution topologies implied in different aspects of BMSs, including battery modeling, states estimation, cell-balancing, and thermal management. The internal architecture of a BMS, along with the architectures of the control modules, is examined to demonstrate the working of an entire BMS control module. Moreover, a critical review of different battery models, control approaches for state estimation, cell-balancing, and thermal management is presented in terms of their salient features and merits and demerits allowing readers to analyze and understand them. The review also throws light on modern technologies implied in BMS, such as IoT (Internet of Things) and cloud-based BMS, to address issues of battery safety. Towards the end of the review, some challenges associated with the design and development of efficient BMSs for E-mobility applications are discussed and the article concludes with recommendations to tackle these challenges.

Degradation of Lithium-Ion Batteries in an Electric Transport Complex

The article provides an overview and comparative analysis of various types of batteries, including the most modern type—lithium-ion batteries. Currently, lithium-ion batteries (LIB) are widely used in electrical complexes and systems, including as a traction battery for electric vehicles. Increasing the service life of the storage devices used today is an important scientific and technical problem due to their rapid wear and tear and high cost. This article discusses the main approaches and methods for researching the LIB resource. First of all, a detailed analysis of the causes of degradation was carried out and the processes occurring in lithium-ion batteries during charging, discharging, resting and difficult operating conditions were established. Then, the main factors influencing the service life are determined: charging and discharging currents, self-discharge current, temperature, number of cycles, discharge depth, operating range of charge level, etc. when simulating a real motion process. The work considers the battery management systems (BMS) that take into account and compensate for the influence of the factors considered. In the conclusion, the positive and negative characteristics of the presented methods of scientific research of the residual life of LIB are given and recommendations are given for the choice of practical solutions to engineers and designers of batteries. The work also analyzed various operating cycles of electric transport, including heavy forced modes, extreme operating modes (when the amount of discharge and discharge of batteries is greater than the nominal value) and their effect on the degradation of lithium-ion batteries.

A Study on State of Charge and State of Health Estimation in Consideration of Lithium-Ion Battery Aging

Due to rapid development of industries around the world, more and more consumption of fossil fuels was unavoidable, resulting in serious environmental problems. The many pollutant emissions—a major contributor to global warming and weather pattern change—have been at the center of concern. In order to solve this issue, research and development of electric vehicles and energy storage systems made great progress and successfully introduced products in the market. Nevertheless, accurate measurement of the state of charge (SOC) and state of health (SOH) of the Li-ion battery, the most popular electric energy storage device, has not yet been fully understood due to the nature of battery aging. In this study, ideas to estimate the capacity and ultimately SOC and SOH of Li-ion batteries are discussed. With these ideas, we expect not only to accommodate the issues with battery aging but also to implement an algorithm for an on-board battery management system. The key idea is to chase and monitor internal resistance continuously in a fast and reliable manner in real time. With further investigation of the key idea, we also fully expect to come up with a reliable SOC and SOH measurement scheme in the near future.

Grid-Scale BESS for Ancillary Services Provision: SoC Restoration Strategies

The future power system, characterized by lower inertia, reduced programmability and more distributed architecture, will depend on prompt and reliable control systems. Quick ancillary services provided by battery energy storage systems (BESS) could be a resource in order to deliver fast and precise response to frequency events. Degrees of freedom in the design of innovative products traded on ancillary services markets give the asset manager room for developing state-of-charge (SoC) restoration mechanisms. These are necessary to effectively exploit BESS as key resources for electricity balancing. This study compares the main SoC restoration strategies. It aims to define which ones are suitable for guaranteeing the reliability of the provision and the return on the investment. A robust regulatory framework analysis describes the degrees of freedom guaranteed by the main experiences around Europe. In this paper, a BESS model with variable efficiency is used to compare the provision of Frequency Containment Reserve (FCR) with different SoC restoration strategies exploiting one or more degrees of freedom. Here, we show that the degrees of freedom are key to the reliability of provision. Among most diffused mechanisms, dead-band strategies secure the desired consistency, but require large energy flows for SoC management. Thus, BESS life and economics decrease. The strategies based on minimum available energy guarantee assured reliability while being fair with BESS life and operation costs.

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value.

Autonomous Battery Swapping System and Methodologies of Electric Vehicles

The transportation industry contributes a significant amount of carbon emissions and pollutants to the environment globally. The adoption of electric vehicles (EVs) has a significant potential to not only reduce carbon emissions, but also to provide needed energy storage to contribute to the adoption of distributed renewable generation. This paper focuses on a design model and methodology for increasing EV adoption through automated swapping of battery packs at battery sharing stations (BShS) as a part of a battery sharing network (BShN), which would become integral to the smart grid. Current battery swapping methodologies are reviewed and a new practical approach is proposed considering both the technical and socio-economic impacts. The proposed BShS/BShN provides novel solutions to some of the most preeminent challenges that EV adoption faces today such as range anxiety, grid reliability, and cost. Challenges and advancements specific to this solution are also discussed.