Vehicular Applications Research Articles

An Intelligent Harris Hawks Optimization Based Cluster Optimization Scheme for VANETs

In recent years, intelligent vehicles with cutting-edge vehicular applications have grown in popularity, enabling the growth of Vehicular Ad hoc Networks (VANETs). Vehicular Ad hoc Networks (VANETs) are a network of vehicles that share and analyze real-time data and require a well-organized and efficient data delivery method. The stability of clusters and dynamic topology change in VANETs are the major issues in finding an optimal route amongst the vehicles. The cooperative approach and surprise pounce chasing technique of Harris Hawks in nature serve as the main sources of inspiration for Harris Hawks Optimization. In this technique, several hawks work together to attack a victim from various angles to surprise it. Due to the unpredictable nature of situations and the prey’s fleeing movements, Harris Hawks can exhibit a variety of intelligent strategies. This study proposes a novel route clustering optimization technique that takes into account communication range, the number of nodes, velocity, orientations, and grid size. To create and evaluate ideal cluster head (CH), the proposed method is based on Harris Hawks Intelligent Optimization Algorithm for route Clustering (iCHHO) which finds optimal and reliable routes amongst the vehicles. Other state-of-the-art methods, such as the Grasshopper Optimization Algorithm (GOA), Gray Wolf Optimization (GWO), and Whale Optimization Algorithm (WOACNET), are utilized to evaluate and validate the proposed method. Our findings show that the developed method outperforms other current methods in terms of number of clusters, variable communication ranges, network size, and the number of vehicles. Furthermore, the statistical analysis concludes that the proposed method improves cluster optimization by 79% and increases cluster stability by an adjusted R -squared of 91.22.

Optimization of Planar Phased Arrays for Vehicles

Radar sensors are used for the detection of objects surrounding a vehicle to enable advanced driver assistance systems. Wide angle azimuth field-of-view (FOV) sensor having high angular resolution is beneficial, but presents challenges for low-cost implementation where element-to-element spacing may be larger than one-half wavelength. Irregular placement of array elements in a two-dimensional (2-D) array can achieve low-cost targets, but may generate large sidelobes if done without care. We introduce an optimization method to determine placement of array elements in a vehicular volumetric beam-scanning radar to minimize sidelobe power for typical automotive beam steering angles. Optimization with array constraints is realized using a gradient-based nonlinear programming technique. Optimization is accelerated by extending <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uv</i> -projection planes beyond the unit circle. For a grating lobe-free scan area based on vehicular applications, the azimuth scanning FOV is increased by 11° bidirectionally to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 54° compared to a reference triangular grid array with 24 elements. The validity of an antenna design generated using the optimization method is established by a three-dimensional (3-D) electromagnetic wave simulation of a representative optimized array configuration.

Incentivizing Semisupervised Vehicular Federated Learning: A Multidimensional Contract Approach With Bounded Rationality

To facilitate the implementation of deep learning-based vehicular applications, vehicular federated learning is introduced by integrating vehicular edge computing with the newly emerged federated learning technology. In vehicular federated learning, it is widely considered that the raw data collected by vehicles have complete ground-truth labels. This, however, is not realistic and inconsistent with the current applications. To deal with the above dilemma, a semisupervised vehicular federated learning (Semi-VFL) framework is proposed. In the framework, each vehicular client uses labeled data shared by an application provider, and its own unlabeled data to cooperatively update a global deep neural network model. Furthermore, the application provider combines the multidimensional contract theory with prospect theory (PT) to design an incentive mechanism to stimulate appropriate vehicular clients to participate in Semi-VFL. Multidimensional contract theory is used to deal with the information asymmetry scenario where the application provider is not aware of vehicular clients’ 3-D cost information, while PT is used to model the application provider’s risk-aware behavior and make the incentive mechanism more acceptable in practice. After that, a closed-form solution for the optimal contract items under PT is derived. We present the real-world experimental results to demonstrate that Semi-VFL achieves the advantages in both the test accuracy and convergence speed, in comparison with existing baseline schemes. Based on the experimental results, we further perform the simulations to verify that our incentive mechanism is efficient.

Fault-Tolerant Operation of Bidirectional ZSI-Fed Induction Motor Drive for Vehicular Applications

This paper presents an efficient and fast fault-tolerant control scheme for a bidirectional Z-source inverter (BiZSI)-fed induction-motor drive system for vehicular applications. The proposed strategy aims for the fault detection, localization and diagnosis of the proposed system during switch failures in the inverter module. Generally, power–semiconductor switch failures in inverter modules occur due to open- and short-circuit faults. An efficient modulation scheme is proposed and design specifications are thoroughly derived to obtain high voltage gains across the BiZSI network. A suitably fast detection and diagnosis scheme to isolate the faulty leg and resume the normal operation is discussed in this paper. The control scheme is provided such that the faulty leg is isolated and the motor phase is fed from a redundant leg to resume the operation. A feasible localization algorithm based on experimentally derived values and switching vectors is implemented. In addition, a fast fault diagnosis method based on current estimation and motor speed variation is designed and implemented. Moreover, the most important advantages of the proposed strategy include lower hardware requirements and less harmonic distortion in the output currents. Finally, the simulation and experimental results are presented to validate the feasibility of the theoretical analysis. An extensive performance evaluation of the proposed system with fault ride-through capabilities is performed to prove its suitability for vehicular applications. To validate its merits, the proposed strategy is compared with similar fault-tolerant schemes currently used in the industry.

Hybrid deep learning model based on Intelligent Microbat Routing (IMR) and Popularity Content Caching (PCC) for an effective caching and routing in vehicular edge networks

Vehicular networks are considered as one of the paramount components of intelligent transportation systems. With the help of new advanced technologies like Internet of things (IoT), and many wireless applications such as 5 G/6 G, these vehicular networks has changed its face of applications in terms of computations, communications and privacy. The arising vehicular applications such as crash awareness and emergency brake requires better communication, storage resources, and computation with severe execution prerequisites on response time and network bandwidth. But still, efficient data computation with an intelligent routing mechanism still remains to be darker side of the research. To achieve the improvisation in above parameters, this paper proposes the hybrid combination of the Intelligent Microbat Routing (IMR) and Deep learning based Popularity Content Caching (PCC) techniques which has been targeted for IoT based vehicular networks. This paper introduces the Reinforced Gated Recurrent Units (R-GRU) with High Speed Extreme Learning Machines for better prediction of contents’ popularity that aids for better caching. The proposed IMR-PCC architecture consists of four different tasks such as popularity prediction, cache placement, data retrieval and effective routing. The extensive experimentation is carried out to evaluate the IMR-PCC by implementing the dynamic network topology with multi factors, which directly impacts on the performance of the complete system. Nearly 300,000 contents were collected randomly and the complete algorithm was implemented in the OMNET++ interfaced with Python 3.8. Finally from the experimental results, the proposed IMR-PCC architecture proved its excellence in terms of accuracy (98.6%), precision (98.6%), recall (98.6%), F1-Score (98.8%), cache hit ratios (0.25), latency (0.3), Packet Delivery Ratio (PDR) (99%) than other existing methodologies such as Q-learning, BLSTME, MPCF, ICN. So the proposed IMR-PCC achieved the better Quality of Service (QoS) as well as executes cache performance which is highly suitable for the vehicular IoT networks.

Techno-Economic Analysis of Hydrogen Storage Technologies for Railway Engineering: A Review

According to the specific requirements of railway engineering, a techno-economic comparison for onboard hydrogen storage technologies is conducted to discuss their feasibility and potentials for hydrogen-powered hybrid trains. Physical storage methods, including compressed hydrogen (CH2), liquid hydrogen (LH2), and cryo-compressed hydrogen (CcH2), and material-based (chemical) storage methods, such as ammonia, liquid organic hydrogen carriages (LOHCs), and metal hydrides, are carefully discussed in terms of their operational conditions, energy capacity, and economic costs. CH2 technology is the most mature now but its storage density cannot reach the final target, which is the same problem for intermetallic compounds. In contrast, LH2, CcH2, and complex hydrides are attractive for their high storage density. Nevertheless, the harsh working conditions of complex hydrides hinder their vehicular application. Ammonia has advantages in energy capacity, utilisation efficiency and cost, especially being directly utilised by fuel cells. LOHCs are now considered as a potential candidate for hydrogen transport. Simplifying the dehydrogenation process is the important prerequisite for its vehicular employment. Recently, increasing novel hydrogen-powered trains based on different hydrogen storage routes are being tested and optimised across the world. It can be forecasted that hydrogen energy will be a significant booster to railway decarbonisation.

RSU-Aided Optimal Member Replacement Scheme with Improved Mobility Prediction for Vehicular Clouds in VANETs

The technique of vehicular clouds is considered an attractive approach in VANETs, because it provides a requester vehicle the ability to use resources of neighborhood vehicles (called cloud member vehicles) to construct a vehicular cloud to use next-generation vehicular applications during driving. Generally, member vehicles can move along different routes from the route of the requester vehicle in intersections and, as a result, leave the vehicular cloud. Then, the leaving member vehicle should be replaced by new member vehicles at intersections to reconstruct the vehicular cloud. However, identifying optimal replacement vehicles among many vehicles at intersections is a very difficult task involving minimizing the waste of resources of vehicles due to their irregular mobility. Thus, we propose an optimal member replacement scheme that finds optimal replacement vehicles through the improved mobility prediction of vehicles by borrowing the computational ability of RSUs on intersections. The proposed scheme first makes an improved mobility prediction model by combining both the trajectory prediction of vehicles using the Markov model and the location prediction of vehicles using the Gaussian distribution. Through the improved mobility prediction model, the proposed scheme then determines the leaving member vehicles and calculates their own leaving time. Next, the proposed scheme addresses the problem to find optimal replacement vehicles to minimize the waste resource and solves it through an integer linear programming. For the performance evaluation of the proposed scheme, we implement it in an NS-3 simulator, which includes the Manhattan mobility model, to reflect the mobility of vehicles on roads. Simulation results conducted in various environments verify that the proposed scheme achieves better performance than the existing scheme.

Dynamic modeling of an open cathode PEM fuel cell for automotive energy management applications

&lt;span lang="EN-US"&gt;Among the different clean energy conversion devices, fuel cells systems are largely employed due to their high-power densities and efficiencies. Several models are available in literature to characterize their functioning, both for stationary and dynamic behaviors. Vehicular application of a fuel cell requires a model that is accurate enough, and simple enough for quick computations for real-time reporting. To meet these constraints, the model of the different sources must be simple and efficient as they are used in the energy management strategy block to estimate the power references to be requested from the sources. The objective of this paper is then to develop a fuel cell model that is both efficient at representing the dynamic and static behavior of the fuel cell, and low consuming in terms of storage space and computation time. The aim is to have a suitable model for use in online simulations for vehicle energy management applications. The proposed model is compared to a representative dynamic model, and the results approve the performance of the model. An experimental test bench is performed to evaluate and validate the proposed model. The model shows good agreement with the experimental data.&lt;/span&gt;

A Real-Time NMPC Strategy for Electric Vehicle Stability Improvement Combining Torque Vectoring With Rear-Wheel Steering

On low-friction surfaces, vehicle stability is significantly influenced by nonlinear vehicle dynamics. To realize stability improvement, a real-time nonlinear model predictive control (NMPC) strategy that combines torque vectoring control (TVC) and rear-wheel steering (RWS) is proposed. A multitarget optimization objective function that considers system state tracking, stability, control constraints, and actuation energy consumption is established. Then, a fast-solving algorithm based on Pontryagin’s minimum principle (PMP) is proposed to reduce the heavy computational burden of NMPC and satisfy the real-time computation requirement of vehicular applications. The effectiveness of the proposed fast-solving algorithm is verified by comparing its closed-loop performance with interior point optimization (IPOPT). A series of hardware-in-the-loop (HIL) experiments with the real human driver is conducted to verify the effectiveness of the proposed fast-solving algorithm and combined control strategy. Furthermore, the HIL results show that the proposed combined control strategy can improve a vehicle’s handling stability on a low-friction surface and has a better performance and lower actuation energy consumption than the TVC-only method.

Design of hybrid SiC/Si based T‐type three‐level LLC$ LLC$ resonant converter with wide‐input range and low conduction loss for automotive auxiliary power module

The auxiliary power module (APM) in the electrical vehicle converts the high voltage of battery pack to the low voltage to supply the electric power for the internal loads, including electric power steering, electric turbo, headed windshield and cooling pumps. The increasing demands on charging time and mileage have made the battery packs with even higher voltage (e.g. 800 V) a promising option. However, it brings a new challenge for the APM, and either the 1.2-kV silicon carbide (SiC) device or the three-level (TL) topology are used conventionally. This work proposes a cost-effective hybrid SiC/Si-based T-type TL L L C $ LLC$ resonant converter for the vehicular APM application with a high step-down ratio. The 1.2-kV SiC and 650-V Si devices are used as the main and auxiliary switches, respectively. It fully utilizes the voltage rating of device, and also improves the efficiency compared with conventional TL topology. To improve the gain range and electromagnetic interference performance, a variable frequency and adjustable phase-shift modulation scheme is adopted. At the same time, the steady-state time-domain model is established to elaborate the operation principles, topology features, and boundary conditions for soft-switching. Furthermore, the precise solutions for switching frequency and phase-shift angle are figured out. Finally, the effectiveness of model is verified by the simulation as well as the experiment. A 1-kW prototype for the application in the APM is demonstrated with the input voltage of 640–840 V and a constant output of 48 V/ 21 A.

Multifusion schemes of INS/GNSS/GCPs/V-SLAM applied using data from smartphone sensors for land vehicular navigation applications

Contemporary smartphones contain embedded inertial measurement unit (IMU), global navigation satellite system (GNSS), camera, and other sensors that are capable of providing user position, velocity, and attitude. However, the actual navigation performance capabilities of smartphones are difficult to use because of the low-cost and disparate sensors employed, differing software technologies adopted by manufacturers, and considerable influence of environmental conditions. In this study, we proposed multifusion schemes that integrated sensor data from smartphone IMU, GNSS chipsets, cameras, and ground control points (GCPs), using an extended Kalman filter to enhance the system navigation performance. Different processes of scale recovery and refreshed-simultaneous localization and mapping (SLAM) based on GNSS and GCPs corresponding to outdoor and indoor environments were proposed to increase the accuracy and robustness of the integrated system. To verify the performance of the integrated system, field test data were collected in an urban area of Tainan City, Taiwan. The experimental results indicated improvements of 87.09% and 36.27% for the refreshed-SLAM and its integration system, respectively, compared with visual-SLAM one-scale recovery and conventional integrated schemes. The proposed integrated system that uses smartphone sensor data increased navigation accuracy in GNSS-challenged environments.

Artificial neural network optimized ultra wide band fractal antenna for vehicular communication applications

AbstractThe article proposes a microstrip patch antenna for vehicular communication applications under 5G communication frequency band. The dimensions of the antenna play an important role in the field of vehicular communication applications. The antenna dimensions are optimized by introducing back propagation algorithm. Overall dimension of the antenna is reduced by 16% as compare to the traditional formulations. Further fractal geometry is implemented on the patch and recessed ground as defected ground structure. FR4 epoxy is used to construct the substrate of the proposed antenna with the dimensions of 50 mm × 60 mm × 1.6 mm and the patch is designed as rectangular patch with H‐cut and I‐cut. The proposed antenna is simulated using Ansys high frequency structure simulator v15 and the frequency of operation is 27 to 33 GHz. The antenna parameters such as reflection coefficient, voltage standing wave ratio, gain, radiation, and current distribution are investigated. The antenna is designed in compact in shape such that it is suitable for vehicular communication applications.

Data-Driven Capacity Planning for Vehicular Fog Computing

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities [a.k.a fog nodes (FNs)]. In other words, VFC proposes to complement stationary FNs co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available FNs. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatiotemporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, and the mobility of FNs. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile FNs to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatiotemporal variation in both demand and supply. Using real-world traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile FNs and create more savings in operational costs in the long term.

Diff-Price: Differential Pricing Scheme for Provisioning Safety-as-a-Service in Vehicular IoT Applications

In this paper, we propose a differential pricing scheme, named as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Diff-Price</i> , to deliver customized safety services to the users at an optimal price and maximizing the profit of Safety Service Provider (SSP) using the Safety-as-a-Service (Safe-aaS) platform. None of the existing pricing schemes addresses the dynamic variation of price as the type of users and decision parameters requested by them vary. We assume road transportation as the implementation scenario of Safe-aaS. In order to deal with these issues, we formulate a pricing scheme, which renders different prices for the same decision parameters. The amount charged from the users are based on their type, decision parameters selected by them, and the duration for which they request the service. Additionally, the SSP allows suitable discounted price to the users, which is determined from their mode of payment and history of services availed. Further, to model the interaction between the users and SSPs, we design our problem as a multiple leader multiple follower non-cooperative Stackelberg game. We calculate the utility of SSPs and formulate an optimization problem to compute the optimal value of the basic price charged from the users. The exhaustive mathematical analysis and simulation results reveal that the proposed pricing scheme, Diff-Price improves the financial gain of the service providers as the number of users increases by 13.51%, 15.75%, and 14.173% respectively, compared to the adaptive incremental spot pricing scheme [1], differentiated cloud virtual-network as-a-service scheme [2], and smart road pricing system [3].

Learning Based Energy Efficient Task Offloading for Vehicular Collaborative Edge Computing

Extensive delay-sensitive and computation-intensive tasks are involved in emerging vehicular applications. These tasks can hardly be all processed by the resource constrained vehicle alone, nor fully offloaded to edge facilities (like road side units) due to their incomplete coverage. To this end, we refer to the new paradigm of vehicular collaborative edge computing (VCEC) and make the best use of vehicles’ idle and redundant resources for energy consumption reduction within the VCEC system. To realize this target, we are faced with several nontrivial challenges, including short-term decision making coupled with long-term queue delay constraints, information uncertainty, and task offloading conflicts. Accordingly, we apply Lyapunov optimization to decouple the original problem into three sub-problems and then tackle them one by one: the first sub-problem is resolved by Lagrange multiplier method; the second is handled by UCB learning-matching approach; the third is addressed by a carefully designed greedy method. Scenarios without volatility and real-world road topology with realistic vehicular traffics are utilized to evaluate the proposed solution. Results from extensive numerical simulations demonstrate that our solution can achieve superior performances compared with the benchmark methods, in terms of energy consumption, learning regret, task backlog, and end-to-end delay.

Life cycle assessment as a comparison tool for activated carbon preparations and biomethane storage for vehicular applications

Life cycle assessment as a comparison tool for activated carbon preparations and biomethane storage for vehicular applications

Edge intelligence in motion: Mobility-aware dynamic DNN inference service migration with downtime in mobile edge computing

Edge intelligence (EI) becomes a trend to push the deep learning frontiers to the network edge, so that deep neural networks (DNNs) applications can be well leveraged at resource-constrained mobile devices with benefits of edge computing. Due to the high user mobility among scattered edge servers in many scenarios such as internet of vehicular applications, dynamic service migration is desired to maintain a reliable and efficient quality of service (QoS). However, inevitable service downtime incurred by service migration would largely degrade the real-time performance of delay-sensitive DNN inference services. To address this issue, we advocate a user-centric management for dynamic DNN inference service migration with flexible multi-exit mechanism, aiming at maximizing overall user utility (e.g., DNN model inference accuracy) with various service downtime. We first leverage dynamic programming to propose an optimal offline migration and exit point selection strategy (OMEPS) algorithm when complete future information of user behaviors is available. Amenable to a more practical application domain without complete future information, we incorporate the OMEPS algorithm into a model predictive control (MPC) framework, then construct a mobility-aware service migration and DNN exit point selection (MOMEPS) algorithm, which improves the long-term user utility within limited predictive future information. However, heavy computation overheads of MOMEPS algorithm impose burdens on mobile devices, thus we further advocate a cost-efficient algorithm, named smart-MOMEPS, which introduces a smart migration judgement based on Neural Networks to control the implementation of (MOMEPS) algorithm by wisely estimating whether the DNN service should be migrated or not. Extensive trace-driven simulation results demonstrate the superior performance of our smart-MOMEPS algorithm for achieving significant overall utility improvements with low computation overheads compared with other online algorithms.

Modeling and Decentralized Predictive Control of Ejector Circulation-Based PEM Fuel Cell Anode System for Vehicular Application

Modeling and Decentralized Predictive Control of Ejector Circulation-Based PEM Fuel Cell Anode System for Vehicular Application

Computational Resources Allocation and Vehicular Application Offloading in VEC Networks

With the advances in wireless communications and the Internet of Things (IoT), various vehicular applications such as image-aided navigation and autonomous driving are emerging. These vehicular applications require a significant number of computation resources and a lower processing delay. However, these resource-limited and power-constrained vehicles may not meet the requirements of processing these vehicular applications. By offloading these vehicular applications to the edge cloud, vehicular edge computing (VEC) is deemed a novel paradigm for improving vehicular performance. However, how to optimize the allocation of computation resources of both vehicles and VEC servers to reduce the energy and delay is a challenging issue when deploying the VEC systems. In this article, we try to address this issue and propose a vehicular application offloading and computational resources allocation strategy. We formulate an optimization problem and present an efficient offloading scheme for vehicular applications. Extensive simulation results are offered to analyze the performances of the proposed scheme. In comparison with the benchmark schemes, the proposed scheme can outperform them in terms of computation cost.

Supercapacitor State of Health Estimation for Vehicular Applications

The transportation electrification requires an energy storage system that can absorb and deliver high amounts of energy during short periods of time, such us acceleration and breaking phases. One of the solutions applied is the using of electrical double layer capacitors (ELDC) or supercapacitors, due to their higher power density. However, the performance and the safety of these components depend on their state of health (SoH). Therefore, the health monitoring of supercpacitor cells is necessary to assure their integration and their safety in vehicular application. The aging of supercapacitors is usually correlated with the decreasing of their capacitance end the increasing of their internal resistance. Thus, the monitoring of these two parameters is necessary for the diagnosis of the state of heath. The measuring of these two parameters cannot be performed directly during the working conditions of supercapacitors in electrical vehicles. Therefore, an online estimation approach of these parameters is needed to enable the diagnosis of supercapacitors.This paper proposes a model-based method for supercapacitors state of health estimation. The approach uses the high gain observer to estimate the parameters of the equivalent RC network. The high gain observer, is one of the observers used for the system identification, it has shown its accuracy and robustness for dealing with nonlinear systems. The equivalent RC network used for modeling the system, simulates the energy and the electrical behavior of supercapacitors, it also presents a best tradeoff between accuracy and complexity. This approach enables the estimation of supercapacitors capacitance and internal resistance from current and voltage measurements.In order to test the proposed method, an experimental test was realized to validate this method. During this test two supercapacitors cells were cycled, in a climate chamber at high temperature T=45°C, in order to accelerate their aging. After each number of cycles, the supercapacitors cells are discharged using WLTC current profile (World harmonized Light-duty vehicles Test Cycle) presented in figure 1. Then, the capacitance and the internal resistance were measured to calculate their state of health.The WLTC current profiles is a dynamic current profile composed of charge and discharge current which allow to test the performance and the diagnosis method in conditions close to real electrical vehicles conditions.Since the aging of supercpacitor is correlated with the evolution of their capacitance and their internal resistance, then the state of health of these components is defined based on these two parameters using these two equations: and are the resistance and the capacitance measured at the beginning of life, and and and are and the resistance the capacitance measured at the end of each state of health k.After each number of cycles, the parameters of the RC equivalent circuit model were estimated, using the high gain observer, from the voltage and current measured during WLTC current profile. In order to compare results, the Root mean square error (RMSE) is calculated between the measured and the estimated SoHr and SoHc.The results presented in figure 2 and figure 3 and table I, show that the high gain observer presents a good accuracy for the state of health estimation, with an RMSE less than 0.77% for the SoHc and less than 1.58% for SoHr estimation. These results, indicate to robustness and the accuracy of this approach to estimate the supercpacitor parameters from a dynamic current profile such as WLTC, which make the used approach a good candidate for on board diagnosis in vehicular applications. The future work of this study is to implement this algorithm in microprocessor in order to test its performance in a real time.References Chaoui, A. El Mejdoubi, A. Oukaour, and H. Gualous: "Online System Identification for Lifetime Diagnostic of Supercapacitors with Guaranteed Stability", IEEE Transactions on Control Systems Technology, Volume: 24, Issue: 6, pages 2094-2102, November 2016.Li and K. Wang, "The Literature Review on Control Methods of SOH and SOC for Supercapacitors," 2019 4th International Conference on Control, Robotics and Cybernetics (CRC), 2019, pp. 17-21, doi: 10.1109/CRC.2019.00013.Saha, P. Saha and M. Khanra, "Performance Comparison of Nonlinear State Estimators for State-of-Charge Estimation of Supercapacitor," 2021 IEEE Second International Conference on Control, Measurement and Instrumentation (CMI), 2021, pp. 105-109, doi: 10.1109/CMI50323.2021.9362850.El Mejdoubi, H. Chaoui, H. Gualous and J. Sabor, "Online Parameter Identification for Supercapacitor State-of-Health Diagnosis for Vehicular Applications," in IEEE Transactions on Power Electronics, vol. 32, no. 12, pp. 9355-9363, Dec. 2017, doi: 10.1109/TPEL.2017.2655578. Figure 1