The automobile industry is currently undergoing a paradigm change from conventional, diesel, and gasoline-powered vehicles to hybrid and electric vehicles of the second generation. Lithium-ion (Li-ion) batteries have sparked the automotive industry's interest for quite some time. One of the most crucial components of an electric car is the battery management system (BMS). Since the battery pack is an electric vehicle's most significant and expensiv. The automobile industry is currently undergoing a paradigm change from conventional, diesel, and gasoline-powered vehicles to hybrid and electric vehicles of the second generation. Lithium-ion (Li-ion) batteries have sparked the automotive industry's interest for quite some time. One of the most crucial components of an electric car is the battery management system (BMS). Since the battery pack is an electric vehicle's most significant and expensive component, it must be carefully monitored and controlled. The precise measurement and calculation of the many states of a Li-ion battery's cells, such as the State of Health (SOH) and State of Charge (SOC) is a difficult procedure as they cannot be monitored directly. This paper examines various methodologies and approaches for estimating the SOC and SOH of Li-ion batteries using Artificial Intelligent methods. Six machine learning algorithms are intensively utilized to investigate the Li-ion battery state estimation. The employed methods are linear, random forest, gradient boost, light gradient boosting (light-GBM), extreme gradient boosting (XGB), and support vector machine (SVM) regressors. In comparison to all other models employed in this study, the discharge prediction made using random forest exhibits significantly greater performance at a low loss of accuracy. For instance, with the highest R2-score of 0.999, the random forest regressor achieves only 0.0035, 0.0013, and 0.0097 for mean and median absolute error, and root means squared erro. Artificial IntelligenceBattery Management SystemLithium-ion BatteriesNeural NetworkState of ChargeState of HealthBecause of overexploitation in several sectors, particularly transportation and energy, worldwide stocks of fossil fuels are rapidly depleting. Overexploitation of fossil fuels produces massive volumes of CO2 and other Green House Gas Emissions (GHGE), which has had a significant impact on the environment and contributed to climate change. The GHGE can be decreased by up to 40% with the use of renewable energy and the electrification of the transportation sector. Due to the irregular nature of renewable energy sources such as wave, wind, tidal, and solar, an energy storage system (ESS) is used to make the supply to the customer more reliable,,,,,,. (SEE Table 1.).Table 1. List of Abbreviations and Symbols.The Electric Vehicle (EV) as shown in Fig. 1 is thought to be the answer to reducing the hazardous pollution emissions from automobiles. Additionally, because electric vehicles can be utilized as energy storage systems to store energy from renewable energy sources, they can engage actively with the electrical grid. This is known as vehicle-to-grid (V2G) interaction. In recent years, many chemistries of energy storage systems (ESSs) have been approved for use in transportation. Li-ion batteries, nickel–cadmium batteries, and lead acid batteries are the most commonly used batteries in EV. Currently, battery modeling for SOC determination is routinely created using a variety of equivalent circuit (RC network) models, each with its own set of material properties and accuracy criteria. The generic model, on the other hand, is based on the assumption that the internal resistance remains constant during charge and discharge cycles. Therefore, the correctness of this model is debatable. The battery deterioration model based on capacity fading was simulated and created while taking the SOH estimate into account. These model parameters were mostly determined by the physical properties of the individual anode and cathode. However, in a dynamic setting, external factors such as ambient temperature and discharge current load will cause these stationary models to be erroneous.In this investigation, a fourth-order electrochemical model was used due to its ability to correctly record the battery's complicated terminal voltage behavior. As indicated in Fig. 2, ten characteristics must be determined, including the open circuit voltage source (Eoc), series resistance (R0), and the resistance and capacitance of each RC network (Rm, Cm), where m = 1:4.At time t, the terminal voltage (Vterm) may be computed as follows:(1)Vterm(n)=EocSOCn-itR0-i1(t)R1-i2(t)R2-i3(t)R3-i4(t)R4A Battery Management System (BMS) is a software and hardware system that regulates the battery for effective functioning. A BMS is made up of various functional units, such as a cell voltage balance, fuel gauge monitor, cut-off field effect transistor, a cell voltage monitor, a state machine, temperature monitors, and a real-time clock. There are several varieties of BMS-integrated chips on the market. The functional pieces are organized differently for different systems; they might range from a simple analog front end with a microcontroller capable of balancing and monitoring to a stand-alone fully integrated solution capable of running autonomously.The BMS in EVs may incorporate a variety of actuators, controls, and sensors. BMSs are responsible for safeguarding batteries, operating batteries within acceptable parameters of voltage, current, and temperature, and accurately monitoring battery parameters. In terms of hardware structure, three basic types of topologies have been used: modular architectures, centralized, and distributed. Richter and Meissner presented a layer structure for monitoring and managing the status of a battery. According to Gold, BMSs can be classified based on their different functionalities. These concepts could be used to create a broad framework with basic functionality. Various sensors located within the battery pack collect data at the monitoring layer. The B.
