In recent years, Artificial Intelligence has been widely used for determining the current state of Li-ion batteries used for Electric Vehicle applications. It is crucial to have an accurate battery state estimation to prevent over-charging/over-discharging of Li-ion batteries, which would contribute to increased lifetime, thus reducing the running costs of Electric Vehicles. This review paper focuses on some of the commonly proposed Artificial Intelligence data-driven based State of Charge and State of Health estimation that has not been covered in much detail previously. The recent works indexed under Web of Science that used Support Vector Machines, Gradient Boosting and Artificial Neural Networks (with a deep insight on the use of recurrent architectures) for battery state estimation are reviewed. A handful of recent works that implemented their proposed battery state estimation algorithm on a hardware prototype is also discussed, along with the current challenges faced in implementation. Since various input features have been suggested for State of Charge and State of Health estimation in the recent literature, a detailed analysis is presented in this paper. Key observations with research gaps are made from the reviewed literature, with identification of major challenges. Future research paths are deduced, with the goal of increasing the feasibility of implementing Artificial Intelligence-based battery state estimation in Electric Vehicles. 

Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review

This article studies the hierarchical sliding-mode surface (HSMS)-based adaptive optimal control problem for a class of switched continuous-time (CT) nonlinear systems with unknown perturbation under an actor&#x2013;critic (AC) neural networks (NNs) architecture. First, a novel perturbation observer with a nested parameter adaptive law is designed to estimate the unknown perturbation. Then, by constructing an especial cost function related to HSMS, the original control issue is further converted into the problem of finding a series of optimal control policies. The solution to the HJB equation is identified by the HSMS-based AC NNs, where the actor and critic updating laws are developed to implement the reinforcement learning (RL) strategy simultaneously. The critic update law is designed via the gradient descent approach and the principle of standardization, such that the persistence of excitation (PE) condition is no longer needed. Based on the Lyapunov stability theory, all the signals of the closed-loop switched nonlinear systems are strictly proved to be bounded in the sense of uniformly ultimate boundedness (UUB). Finally, the simulation results are presented to verify the validity of the proposed adaptive optimal control scheme. 

Hierarchical Sliding-Mode Surface-Based Adaptive Actor–Critic Optimal Control for Switched Nonlinear Systems With Unknown Perturbation

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy. 

/pdf/a-comprehensive-survey-on-cooperative-intersection-2v6paaak.pdf

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Battery sensor data collection and transmission are essential for battery management systems (BMS). Since inaccurate battery data brought on by sensor faults, communication issues, or even cyber-attacks can impose serious harm on BMS and adversely impact the overall dependability of BMS-based applications, such as electric vehicles, it is critical to assess the durability of battery sensor and communication data in BMS. Sensor data are necessary for a BMS to perform every operation. Effective sensor fault detection is crucial for the sustainability and security of electric vehicle battery systems. This research suggests a system for battery data, especially lithium ion batteries, that allows deep learning-based detection and the classification of faulty battery sensor and transmission information. Initially, we collected the sensor data, and preprocessing was carried out using z-score normalization. The features were extracted using sparse principal component analysis (SPCA), and enhanced marine predators algorithm (EMPA) was used for feature selection. The BMS’s safety and dependability may be enhanced by the suggested incipient bat-optimized deep residual network (IB-DRN)-based false battery data identification and classification system. Simulations using MATLAB (2021a), along with statistics, machine learning, and a deep learning toolbox, along with experimental research, were used to show and assess how well the suggested strategy performs. It is shown to be superior to traditional approaches.

/pdf/a-smart-battery-management-system-for-electric-vehicles-q2zy7iuh.pdf

A Smart Battery Management System for Electric Vehicles Using Deep Learning-Based Sensor Fault Detection

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

https://www.mdpi.com/1424-8220/21/11/3783/pdf

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

State-of-charge (SOC) prediction is an important part of the battery management system (BMS) in electric vehicles. Since external factors (voltage, current, temperature, arrangement of the battery, etc.) impact SOC prediction differently, the SOC is difficult to model. In this article, we apply principal component analysis (PCA) to analyze the contribution of various external factors and propose a new SOC prediction method based on an improved support vector machine for regression (SVR) with data classification and training set size optimization. Three groups of simulation experiments with different inputs based on the original SVR algorithm are conducted in the software ADVISOR, and the simulation results show that the input of three features of the battery (current, voltage and temperature) can satisfy the SOC prediction accuracy. The improved SVR algorithm is then applied to the simulation experiment of the three input features. The proposed method is demonstrated to be faster and more accurate than the original SVR algorithm through a comparison of the simulation results.

/pdf/state-of-charge-prediction-of-battery-management-system-3ks924of53.pdf

State-of-Charge Prediction of Battery Management System Based on Principal Component Analysis and Improved Support Vector Machine for Regression

For the parallel parking problem in narrow space, this paper proposes a trajectory tracking control method with a novel trajectory planning layer for autonomous parallel parking based on a numerical optimization algorithm and model predictive control. In the trajectory planning layer, the vehicle kinematics model suitable for the low-velocity parking scene is established. Considering the vehicle physical constraints, boundary condition constraints, and obstacle avoidance constraints during the parking process, the parking trajectory planning task is described as an optimal control problem, further transformed into a nonlinear programming problem by Gauss pseudo-spectral method. Taking the shortest parking completion time as the optimization objective function, the parking trajectories of the large, medium and small parking spaces are obtained, respectively. A parking trajectory tracking controller based on the model predictive control algorithm is designed in the trajectory tracking control layer. The linear error model is used as the prediction model, and the quadratic programming is adopted as the rolling optimization algorithm in the tracking controller. The velocity and front-wheel swing angle are obtained as control signals for parking trajectory tracking. Through CarSim and Simulink’s co-simulation, the feasibility and effectiveness of the proposed parallel parking trajectory planning and tracking control method are verified. The co-simulation results show that the maximum tracking errors of horizontal and longitudinal positions are less than 0.15m. The maximum tracking errors of heading angle are less than 2° under three different parking spaces. Real vehicle tests are carried out to verify the effectiveness of the proposed hierarchical control method. The test results show that the vehicle can park in the parking space safely, quickly and accurately when the actual parking space is detected. The proposed method can plan the parking trajectory with the constraints and the shortest time and control the vehicle to complete the parking operation accurately along the planned trajectory.

/pdf/hierarchical-control-of-trajectory-planning-and-trajectory-53pkj12n8w.pdf

Hierarchical Control of Trajectory Planning and Trajectory Tracking for Autonomous Parallel Parking

In the last decade, research studies on parking planning mainly focused on path planning rather than trajectory planning. The results of trajectory planning are more instructive for a practical parking process. Therefore, this paper proposes a trajectory planning method in which the optimal autonomous valet parking (AVP) trajectory is obtained by solving an optimal control problem. Additionally, a vehicle kinematics model is established with the consideration of dynamic obstacle avoidance and terminal constraints. Then the parking trajectory planning problem is modeled as an optimal control problem, while the parking time and driving distance are set as the cost function. The homotopic method is used for the expansion of obstacle boundaries, and the Gauss pseudospectral method (GPM) is utilized to discretize this optimal control problem into a nonlinear programming (NLP) problem. In order to solve this NLP problem, sequential quadratic programming is applied. Considering that the GPM is insensitive to the initial guess, an online calculation method of vertical parking trajectory is proposed. In this approach, the offline vertical parking trajectory, which is calculated and stored in advance, is taken as the initial guess of the online calculation. The selection of an appropriate initial guess is based on the actual starting position of parking. A small parking lot is selected as the verification scenario of the AVP. In the validation of the algorithm, the parking trajectory planning is divided into two phases, which are simulated and analyzed. Simulation results show that the proposed algorithm is efficient in solving a parking trajectory planning problem. The online calculation time of the vertical parking trajectory is less than 2 s, which meets the real-time requirement.

https://www.mdpi.com/1424-8220/20/22/6435/pdf

A Trajectory Planning Method for Autonomous Valet Parking via Solving an Optimal Control Problem.

This study presents an optimal control strategy for connected and automated vehicles (CAVs) at unsignalized intersections. A control framework with task-decomposition is designed for the near-intersection area. Firstly, the continuous traffic flow is discretized with a proposed intersection area-division scheme. Then, vehicles in different lanes are regrouped based on longitudinal positions to establish multivehicle cooperative optimal control problems of limited vehicle number. The expected optimal trajectory in the merging zone is obtained through offline simulation, which simplifies the constraints of the optimal control problem and improves the real-time performance of the online trajectory solving. Finally, an ideal capacity model is proposed based on the transit time window of vehicle, so that the capacity of a single lane is increased to 2400veh/h. The simulation results demonstrate that the proposed strategy ensures the driving safety at the unsignalized intersection, and this strategy efficiently reduces planning time for vehicle trajectories.

Optimal Control of Connected and Automated Vehicles at Unsignalized Intersections: Discrete and Regroup

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Papers

State-of-Charge Prediction of Battery Management System Based on Principal Component Analysis and Improved Support Vector Machine for Regression

Hierarchical Control of Trajectory Planning and Trajectory Tracking for Autonomous Parallel Parking

A Trajectory Planning Method for Autonomous Valet Parking via Solving an Optimal Control Problem.

Optimal Control of Connected and Automated Vehicles at Unsignalized Intersections: Discrete and Regroup