Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures.

Batteries are extensively used to power electric vehicles (EVs), hybrid electric vehicles (HEVs), and for many other power demanding applications, where the modeling of a battery plays a vital role in their efficiency, safety, and reliability. In this paper, initially different types of batteries used in the EVs and HEVs are investigated, according to the latest battery management systems (BMS). Li-Ion batteries are a popular source of EVs and HEVs because of their long-life span, high energy and power density, and good charging and discharging performance. However, there remain some issues associated with the deployment of Li-ion batteries, such as complex electrochemistry, degradation, and inaccurate battery health estimation. The latest techniques for the estimation of the battery state of health (SOH) are reviewed in a comparative table. The table furthermore helps in concluding the best estimation technique so far with respect to the models/algorithms, estimation errors, pros, and cons as well as the cost of the system. The electrification of vehicles has been a global trend including Asia and Pakistan. Subsequently, the paper explores implications of the increasing use of the electrified vehicles on the economy, environment and the energy efficiency.

Researchers are focused toward the development of electric vehicles and hybrid electric vehicles for the preservation of the ecosystem. Energy storage, conversely, became essential with these changes in vehicular technology. This opens new research fronts and introduces complex problems that are to be investigated regarding the primary storage element. Battery modeling offers improved efficiency, safety, and reliability. Lithium-ion batteries are a popular source of energy in many applications, especially for electric vehicles because of their high performance and high energy density. However, there exist some inherent issues like complex electrochemical reactions that make accurate monitoring a challenging task. This article performs a comparative study of the existing battery state of health (SOH) monitoring and capacity estimation techniques using the NASA dataset for lithium-ion batteries. Charging cycle datasets for voltage, current, and temperature are used for feature identification and extraction. After the modeling of extracted features, the SOH and the capacity of four batteries are estimated using three data-driven techniques. Including convolutional neural network, feed-forward neural network, and long short-term memory (LSTM), and their results are compared. The comparative analysis highlights an outstanding performance and high accuracy of the LSTM-based machine learning technique because of the inherited long-term memory of the LSTM. The study, therefore, recommends the use of LSTM to researchers for battery health monitoring and capacity estimation with the highest possible accuracy.

Electric Vehicles (EVs) have gained substantial attention in the recent years, since they are an efficient, sustainable and zero-carbon emitting means of transportation as compared to the conventional fossil-fuel powered vehicles. As EVs are becoming popular, the use of Lithium-ion (Li-Ion) batteries is exponentially increasing due to its good charge/discharge performance, high energy and current density and optimum power support. For safe operation of battery, precise estimation of the State of Charge (SOC) is necessary. SOC determines the residual charge accumulated in the battery and how further it can operate under specific conditions. This paper uses the Thevenin-equivalent circuit theory to model the transient behaviour of the Li-Ion battery and the SOC is evaluated using Coulomb counting and Extended Kalman Filter (EKF) methods. First, the battery is mathematically modelled and then the estimation is done via Coulomb counting and EKF in MATLAB/Simulink. A comparison of these two methods indicate that the SOC evaluation of the battery using EKF is more precise than Coulomb counting. The results show that the error is reduced by 1% when implemented via EKF.

Micro-grid is sub-unit of a smart grid consisting of a complex power electronics, energy storage systems and several generating sources distributed throughout the network. Real-time based simulations of Micro-grid (MG) demands large computational resources. This research presents MG system that utilizes a field programmable gate array (FPGA, NI PXIe-7976R) module where real-time simulations, communication, and analysis is performed to determine the behavior of the MG system. Also, the data acquisition using NI CompactDAQ cards with the integration of the LabView program is used to determine MG output signals as a power quality analyzer. The three-phase output signals are then modified to emulate real-time MG scenario using Regatron (TC ACS). Further, the AC variable loads such as resistive, capacitive and inductive loads are incorporated to investigate the behavior of MG outputs. Overall, the MG system design and hardware implementation, the transmission line model and the algorithms in software environment as LabView and OPAL-RT, the results and its verification are discussed.