A Comprehensive Analysis on the Advances in Battery Modeling, Intelligent Management Systems, and Digital Twin Integration

The ever rising need for electrical energy in the world has led to an exemplar change in the power sector. This also creates a big impact on global warming. The world is now shifting towards the sources of renewable energy. Because of the erratic availability of the renewable energy sources, the demand for reliable electrical energy storage devices are increasing exponentially. The conventional way of storing the electrical energy is batteries. There is an increase in the usage of the batteries as many equipment in modern days are mobile and rely on the energy stored within them. This increased rate of battery usage also brings in the improved performance of the batteries with less faults. Thus, the accurate battery modeling is an utmost priority for optimal and improved battery performance. The battery modeling comes with an additional responsibility of optimal maintenance of the parameters involved in the battery like, state of charge (SOC), state of health (SOH), remaining useful life (RUL). Here comes in the battery management system (BMS) that monitors and checks the control parameters involved in the battery for improved performance of the battery. When the BMS is interfaced with the evolving technologies like big data, artificial intelligence, internet of things, it is intelligent BMS which can monitor the control parameters and can also predict the SOC, SOH and RUL of the battery, promising an effective performance of the battery. Digital twin (DT) is an upcoming technology that replicates the physical system in the virtual mode, which adds to the safety of the battery and user. This paper involves in reviewing the popular methods of battery modeling, summarizing the better option. The paper also explains the intelligent BMS with DT and how this evolving technology can improve the efficacy of the batteries.