Akram Eddahech

Remaining useful life prediction of lithium batteries in calendar ageing for automotive applications

Abstract In this paper, a method for calendar ageing quantification of power batteries taking into account the State of Charge (SOC) and temperature ( T ) effects is presented. The main goal is the determination of the battery Remaining Useful Life (RUL). We focused on a single parameter identified from Electrochemical Impedance Spectroscopy (EIS) tests performed according to a specific protocol regarding thermal and electrical kinetics. This parameter refers to the impedance real-part at a defined frequency (0.1xa0Hz). It is used to identify the battery RUL. The choice of this frequency is based on the analysis of the changes of the real-part plots with ageing. From these plots, we noticed that the greatest influence of the ageing is observed in the [0.1xa0Hz, 1xa0Hz] frequency range.

Real-time SOC and SOH estimation for EV Li-ion cell using online parameters identification

In this paper, we present a real-time adaptive estimation of the state of charge (SOC) and the voltage of a high-energy-density lithium-ion cell used in electric vehicle (EV). We developed a simple and linear-recursive battery SOC and voltage models that proved their efficiency regarding the comparison between simulation results and real data from power cycling with ECE 15 European driving cycle tests. Based on the on-line estimation of battery model parameters, a recursive least squared algorithm (RLS) with a time-variant forgetting factor is used to describe the battery dynamic behavior which can vary within each experiment. The estimations have shown very good performances, the maximum and the mean relative modeling error do not exceed 1% for both of the estimators.

Ultracapacitor performance determination using dynamic model parameter identification

We present a real-time adaptive process for ultracapacitor performance estimation using a dynamic model describing the device behavior. We develop a simple and linear-recursive model that proved its efficiency regarding the comparison between simulation results and real data from power cycling tests. Model shows very good performances since the maximum relative modeling error do not exceed 3%. Based on a recursive least squared algorithm with a time-variant forgetting factor, the on-line estimation of the dynamic ultracapacitor-model parameters mainly the internal resistance served as a state of health indicator.

Lithium-ion battery heat generation investigation based on calorimetric entropy measurements

Battery global heat generation is related to both joule heating and entropy change. Basically, joule heat is due to internal resistance reaction to current flow. However, electrochemical reactions that occur during charge and discharge affect battery entropy and thus affect heat generation. In this paper, we focus on the thermal behavior of 12 Ah high-power lithium-ion cell during charge-discharge at different C rates. We conduct a series of tests on an accelerated rate calorimeter (ARC). The cell heat capacity (Cp) is identified and global heat generated by the battery is quantified. We measure cell entropy using both the potentiometric and the calorimetric methods. The part of reversible reaction in the cell thermal behavior during charge-discharge tests is identified. Experimental results are presented and discussed.

Thermal cycling impacts on supercapacitor performances during calendar ageing

In this paper, we focus on supercapacitors thermal cycling ageing at constant voltage. Obtained results are compared with those issued from calendar ageing at constant voltage and constant temperature. Experimental measurements from periodical characterization tests during calendar ageing up to 7500 h and results of characterization for thermal cycling test up to 2500 h are presented. A model for the supercapacitor performances prediction, in terms of capacitance and resistance evolution with ageing, is proposed and validated. Finally, the impact of the temperature cycling is highlighted and quantified.