Digital twin-driven estimation of state of charge for Li-ion battery

Under the net-zero carbon transition, lithium-ion batteries (LIB) plays a critical role in supporting the connection of more renewable power generation, increasing grid resiliency and creating more flexible energy systems. However, poor useful life and relatively high cost of batteries result in barriers that hinder the wider adoption of battery technologies e.g., renewable resources storage. Furthermore, the useful life of a battery is significantly affected by the materials composition, system design and operating conditions, hence, made the control and management of battery systems more challenging. Digitalisation and artificial intelligence (AI) offer an opportunity to establish a battery digital twin that has great potentials to improve the situational awareness of battery management systems and enable the optimal operation of battery storage units. An accurate estimation of the state of charge (SOC) can indicate the battery's status, provide valuable information for maintenance and maximise its useful life. In this paper, a digital twin-driven framework based on a hybrid model that connects LSTM (long short-term memory) and EKF (extended Kalman filter) has been proposed to estimate the SOC of a li-ion battery. LSTM provides more accurate initial SOC estimations and impedance model data to EKF. According to experimental results, the developed battery digital twin is considered less dependent on the initial SOC conditions and is deemed more robust compared to traditional means with a lower RMSE (root mean squared error)

An Analysis of the Requirement for Energy Management Systems in India for Electric Vehicles

Conventional fuels used in combustion engines are the main sources of carbon dioxide emissions, which affect the environment. If energy is available from renewable sources compared to conventional sources, electric vehicles (EVs) offer efficient and cost-effective solutions to the above issue. However, EVs employ batteries for energy storage, which presents a number of issues. For example, overheating produced by chemical reactions during the charging and discharging process in high temperatures can result in the battery's fatal destruction. Hence, an effective energy management system (EMS) is in need of the technology required for the accomplishment of EVs in the long term. Monitoring and optimizing electricity use is the aim of energy management, which aims to cut costs and emissions without interfering with operations. When lifetime CO2 emissions are taken into consideration, EVs will be far more environmentally friendly than regular fuel vehicles because of the incorporation of sustainable power. Distributed solar energy will help reduce the distribution and transmission losses, which will further lower the lifetime CO2 emissions and operating costs of EVs and hasten their commercial viability. This paper presents a review of energy management challenges and their necessity. EV energy management is very important as it helps to minimize EV charging costs

Digital twins for condition and fleet monitoring of aircraft: towards more-intelligent electrified aviation systems

The convergence of Information Technology (IT), Operational Technology (OT), and Educational Technology (ET) has led to the emergence of the fourth industrial revolution. As a result, a new concept has emerged known as Digital Twins (DT), which is defined as "a virtual representation of various objects or systems that receive data from physical objects/systems to make changes and corrections”. In the aviation industry, numerous attempts have been made to utilize DT in the design, manufacturing, and condition monitoring of aircraft fleets. Among these research efforts, real-time, accurate, fast, and predictive condition monitoring methods play a crucial role in ensuring the safe and efficient performance of aircraft. Using DT for condition and fleet monitoring not only enhances the reliability and safety of aircraft but also reduces operational and maintenance costs. In this paper, the conducted studies on the applications of DT systems for condition monitoring of aircraft units and the aerospace sector are discussed and reviewed. The aim of this review paper is to analyse the current developments of DT systems in the aviation industry as well as explain the remaining challenges of DT systems. Then Finally, future trends of DT systems along with aircraft are presented. Among reviewed papers, most of them have used computational fluid dynamics, finite element methods, and artificial intelligence techniques for developing DT models for aircraft. At the same time, most of these analyses are dedicated to the failure and crack detection body of aircraft as well as engine fault detection. Life prediction is another popular application for using DT in aircraft units that could help the engineers predict the maintenance required for different parts of the aircraft. Finally, the application of DT in marine, power systems, and space programs has been also reviewed and the lessons learned from them have been translated to the aviation sector