The battery management system in an electric vehicle must be reliable and durable to forecast the state of charge. Considering that battery degradation is generally nonlinear, state of charge (SOC) estimation with lower degradation can be challenging. Lithium-ion batteries are highly dependent on the knowledge of aging, which is usually costly or not available online. In this paper, we suggest the state of charge estimation of lithium-ion battery systems by using an extreme gradient boosting algorithm for electric vehicles application, which acquires the nonlinear relationship model can with offline training. The extreme gradient boosting algorithm is the tree on based learning, which effectively performs and speeds. Voltage-time data used as an input of this system from the partial constant current phase; the proposed algorithm improves the accuracy of predicting the relevant. Additionally, no initial state of charge is required in our proposed method; thus, estimating the state of charge can consider each battery state.

The use of a Model Predictive Controller (MPC) in an urban traffic network allows for controlling the infrastructure of a traffic network and errors in its operations. In this research, a novel, stable predictive controller for urban traffic is proposed and state-space dynamics are used to estimate the number of vehicles at an isolated intersection and the length of its queue. This is a novel control strategy based on the type of traffic light and on the duration of the green-light phase and aims to achieve an optimal balance at intersections. This balance should be adaptable to the unchanging behavior of time and to the randomness of traffic situations. The proposed method reduces traffic volumes and the number of crashes involving cars by controlling traffic on an urban road using model predictive control. A single intersection in Tehran, the capital city of Iran, was considered in our study to control traffic signal timing, and model predictive control was used to reduce traffic. A model of traffic systems was extracted at the intersection, and the state-space parameters of the intersection were designed using the model predictive controller to control traffic signals based on the length of the vehicle queue and on the number of inbound and outbound vehicles, which were used as inputs. This process demonstrates that this method is able to reduce traffic volumes at each leg of an intersection and to optimize flow in a road network compared to the fixed-time method.

Electric Vehicles (EVs) reliance on batteries, which currently have lower energy and power densities than liquid fuels and are prone to aging and performance degradation over time, restricts their mainstream adoption. With applications like electric vehicles and grid-scale energy storage, effective management of lithium-ion batteries is a vital enabler for a low-carbon future. Monitoring the battery's condition of health and charge over the lifetime of an EV is, therefore, a highly pertinent issue. Battery Management Systems (BMS) are used during the operation of EVs to monitor, estimate and control battery states to ensure that batteries can function effectively and safely. Additionally, the materials composition, system design, and operating circumstances substantially impact a battery's usable life, making it more challenging to govern and maintain battery systems. This work proposes the structure of a battery digital twin-based battery for the electronic vehicle, which has the potential to enhance BMS situational awareness greatly and enable the optimal functioning of battery storage units. Digitalization and Artificial Intelligence (AI) present an opportunity and offer a Digital Twin (DT) of the EV battery as a solution to the difficulty of onboard computation for the incremental State Of Health (SOH) and State Of Charge (SOC) based on Extreme Gradient Boost (XGBoost) and Extended Kalman Filter (EKF) to predict the state estimate. The battery's condition has been determined by using the EKF, which can provide vital information for maintenance. The battery's usable life can be extended with an accurate estimate of the SOC to continue then a learning-based prediction approach to gauge the battery's health state is suggested in order to increase battery life. A SOC model is frequently retrained to depict the effects of aging, and a SOH model is often performed to foretell the reduction in the highest battery capacity. According to a result, DT models are useful for managing batteries, and full life cycle statistics are important for planning the battery's upgrade path.

Traffic congestion is a significant issue in many countries today. The suggested method is a novel control method based on multiple intersections considering the kind of traffic light and the duration of the green phase to determine the optimal balance at intersections by using fuzzy logic control, for which the balance should be adaptable to the unchanging behavior of time. It should reduce traffic volume in transport, average waits for each vehicle, and collisions between cars by controlling this balance in response to the typical behavior of time and randomness in traffic conditions. The proposed method is investigated at intersections using a sampling multi-agent system to set traffic light timings appropriately. The program is provided with many intersections, each of which is an independent entity exchanging information with the others. The stability per entity is proven separately. Simulation results show that Takagi–Sugeno (TS) fuzzy modeling performs better than Takagi–Sugeno (TS) fixed-time scheduling in decreasing the length of queueing times for vehicles.

The instability and variable lifetime are the benefits of high efficiency and low-cost issues in lithium-ion batteries.An accurate equipment’s remaining useful life prediction is essential for successful requirement-based maintenance to improve dependability and lower total maintenance costs. However, it is challenging to assess a battery’s working capacity, and specific prediction methods are unable to represent the uncertainty. A scientific evaluation and prediction of a lithium-ion battery’s state of health (SOH), mainly its remaining useful life (RUL), is crucial to ensuring the battery’s safety and dependability over its entire life cycle and preventing as many catastrophic accidents as feasible. Many strategies have been developed to determine the prediction of the RUL and SOH of lithium-ion batteries, including particle filters (PFs). This paper develops a novel PF-based technique for lithium-ion battery RUL estimation, combining a Kalman filter (KF) with a PF to analyze battery operating data. The PF method is used as the core, and extreme gradient boosting (XGBoost) is used as the observation RUL battery prediction. Due to the powerful nonlinear fitting capabilities, XGBoost is used to map the connection between the retrieved features and the RUL. The life cycle testing aims to gather precise and trustworthy data for RUL prediction. RUL prediction results demonstrate the improved accuracy of our suggested strategy compared to that of other methods. The experiment findings show that the suggested technique can increase the accuracy of RUL prediction when applied to a lithium-ion battery’s cycle life data set. The results demonstrate the benefit of the presented method in achieving a more accurate remaining useful life prediction.