Online System Identification for Lifetime Diagnostic of Supercapacitors with Guaranteed Stability

In this paper, an online system identification method is introduced for lifetime diagnostic of supercapacitors. The online strategy uses a Lyapunov-based adaptation law to estimate online the supercapacitor's parameters. Therefore, the adaptive observer's stability is guaranteed by Lyapunov's direct method. State-of-health (SoH) estimation is crucial since aging introduces degradation in supercapacitors' performance, which might eventually lead to their failure. SoH is usually measured by offline time or frequency domain characterization techniques such as spectroscopy. However, these methods require interruption of the system's operation, and hence, they are not suitable for real-time applications such as electric vehicles. Unlike other online estimation strategies, only voltage and current measurements are required. Simulation and experimental results along with comparison against two different methods highlight the effectiveness of the proposed approach in estimating the SoH

Experimental investigation of calendar aging of lithium-ion batteries for vehicular applications

The main challenge with lithium-ion batteries in vehicular applications is aging. It is known that the battery aging is sensitive to various factors such as current, temperature and depth of discharge. These elements have a considerable impact on the loss of the battery's capacity, as well as on the increase of the internal resistance. In this article, we present an analysis of the aging of lithium-ion batteries in order to predict their failures. The comprehension of aging can retroact on the operating conditions in order to improve reliability. Thus, the work carried out involves the experimental analysis of a LiFePO4 battery's calendar aging under different discharge currents and temperatures

Online supercapacitors diagnosis for electric buses technologies

This paper presents online diagnosis model based on the adaptive observer for supercapacitors' State-of-Health, which is an important feature since aging introduces degradation in supercapacitors' performance. Unlike Electrochemical Impedance Spectroscopy, that requires the interruption of the system, this paper presents online diagnosis technique for supercapacitors using the adaptive observer as well-known tool for theirs particularities and performances to study nonlinear parameters estimation. The main objective of this paper is the online State-of-Health diagnosis based on the supercapacitors aging indicators estimation. The effectiveness of the proposed online observers is shown through experimental results, which are compared and analyzed

Remaining Useful Life Prognosis of Supercapacitors Under Temperature and Voltage Aging Conditions

This paper presents a remaining useful life (RUL) prognosis model for supercapacitors considering the aging conditions. The proposed model uses the particle filter to predict the posterior values of the aging indicators, i.e., capacitance and resistance. Unlike other prognosis methods, the proposed model predicts the RUL considering the aging conditions such as temperature and voltage. In order to validate the proposed method, experiments have been carried out under different aging conditions. Results highlight the effectiveness of the approach in predicting capacitance and resistance, and the RUL for different initial conditions

Supercapacitors health prognosis for vehicular applications

This paper presents a health prognosis model for supercapacitors considering the aging conditions. The proposed model uses the particle filter to predict the posterior values of the aging indicators, i.e., capacitance and resistance. Unlike other prognosis methods, the proposed model predicts the RUL considering the aging conditions such as temperature and voltage. In order to validate the proposed method, experiments have been carried out under different aging conditions. Results highlight the effectiveness of the approach in predicting capacitance and resistance, and the RUL for different initial conditions

Experimental investigation of aging calendar parameters for supercapacitors

Supercapacitors have received an increasing interest from the power electronics community due to their high power density and compact size. These advantages make them good candidates for high-performance applications, such as electric/hybrid vehicles. In transportation application, the calendar phase presents an important part in the supercapcacitor life cycle. Understanding the aging calendar behavior allows us to manage well the aging parameters, as the operating temperature and the bias voltage. In order to achieve this goal, an investigation has been conducted on supercapacitor aging calendar conditions. Various tests are carried-out on 12 supercapacitors under 3 bias voltages (2,8V, 2,9V, and 3V) and 4 temperatures (55°C, 60°C, 65°C, and 70°C) until the limit of aging is reached for each supercapacitor. The supercapacitors' resistances and capacitances evolution are studied to establish the aging rate law with respect to the aging parameters variation

State of charge estimation of LiFePO4 batteries with temperature variations using neural networks

This paper presents a neural network oriented approach for online estimation of the state of charge (SOC) for lithium-ion batteries. Unlike other estimation strategies, this proposed technique requires no knowledge of any battery parameter and no mathematical model of the battery rather i

Lithium-ion Batteries Health Prognosis Considering Aging Conditions

The prognosis and health management of lithium-ion batteries are extremely important issues for operating performance as well as the cost of energy storage systems in vehicular applications. This is achieved through the estimation of the State-of-Health (SOH) and the prediction of Remaining Useful Life (RUL). This paper presents a lithium battery prognosis model considering the battery aging conditions. The proposed model is developed based on the Rao-Blackwellization particle filter, which is able to estimate the posterior values of the aging indicators, i.e., capacity and resistance, and to predict the RUL. The particularity of the proposed model is that it considers the batteries aging conditions of batteries as inputs of the prognosis model. In order to validate the proposed method, experiments have been carried out under different aging conditions for two types of lithium-ion batteries. The proposed model performances have been evaluated. A comparison against the particle filter prognosis model is presented. Results highlight the effectiveness of the proposed technique to predict the remaining useful life for different cases: initial conditions, types of lithium-ion batteries, and aging conditions. The remaining useful life prediction using the proposed prognosis model presents a maximum relative error of 6.64%, which is low compared to 14.3