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An electrochemical Parameter Estimation (PE) study of lithium-ion batteries for different materials is presented. The PE methodology is developed in Part I of the study and the challenges on the different materials for the positive electrode including LiCoO 2 , LiMn 2 O 4 and LiFePO 4 are examined in Part II. The most influential electrochemical parameters of the Li-ion battery are estimated by means of an inverse method. The inverse method rests on five elements: the input parameters, a direct model, the reference data, an objective function and an optimizer. Eight electrochemical variables are considered as the target of the PE study. A simplified version of Pseudo-two-Dimensional (P2D) model is developed for the direct model. The P2D model predictions coupled to a random noise function are employed to generate the reference data. The data include the cell potential values with respect to the battery capacity at low and high discharge rates. The least-squared function and Genetic Algorithm are employed as the objective function and its optimizer, respectively. The best time domain for the estimation of each parameter is calculated by using a sensitivity analysis performed for different discharge curves. Results show that the methodology remains accurate and stable at both low and high discharge rates. The simultaneous high power and energy density of Lithium-ion (Li-ion) batteries have made it the preferred device for storing electricity. As a result, Li-ion batteries are increasingly used in various applications including electronics and the automotive industry. Regardless of the shape and of the battery pack arrangement, the internal structure of the battery usually comprises four main components: Two electrodes, positive and negative, an electrolyte, and a separator. These components are made of materials that have been gradually modified over the years so as to improve the efficiency, the safety and the performance of the batteries and to reduce their cost. 1-3A Battery Management System (BMS) is crucial for monitoring the operation of the battery pack. The BMS must interact with all the elements of the system in order to control it and to protect the Li-ion cells. The intelligence of the BMS is based on a mathematical model that simulates and predicts the different operating conditions of the Li-ion battery pack. In high tech and automotive industries, the BMS usually relies on empirical-based models. These models are simple and provide fast response. They cannot however predict the performance of the battery as it ages. Moreover, they are only applicable to a specific cell, i.e., they cannot be transposed to other battery packs without recalibration. [4][5][6] Electrochemical-based models of Li-ion batteries, on the other hand, overcome these shortcomings. These models rest on chemical/electrochemical kinetics and transport equations. These Li-ion battery models are more complicated and CPU time-consuming than empirical based models. They are, on the other hand, more versatile and they provide reliable a...

This paper is the second part of a two part study on parameter estimation of Li-ion batteries. The methodology was developed in Part I. In Part II, the methodology is tested for LiCoO 2 , LiMn 2 O 4 and LiFePO 4 positive electrode materials. An inverse method combined to a simplified version of the Pseudo-two-Dimensional (P2D) model is used to identify the solid diffusion coefficients (D s,n and D s,p ), the intercalation/deintercalation reaction-rate constants (K n and K p ), the initial SOC (SOC n,0 and SOC p,0 ), and the electroactive surface areas (S n and S p ) of Li-ion batteries. Experimental cell potentials for both low and high discharge rates provide the reference data for minimizing the objective function in the best time interval. For all cases simulated, the numerical predictions show excellent agreement with the experimental data. Lithium-ion (Li-ion) batteries are increasingly employed for energy storage. Their working voltage and energy density are higher than those of similar energy storage technologies. Their service life is longer. They exhibit high energy-to-weight ratios and low selfdischarge. As a result, they have become the preferred energy storage devices in the electronics and the automotive industries.Mathematical modeling of Li-ion batteries is an essential engineering tool for their design and operation. Two different approaches are usually adopted to predict their behavior. These approaches may be divided, broadly speaking, into empirical models and electrochemical models.Empirical models are the simplest mathematical models. They are relatively easy to implement and they provide fast responses. This is why they are mostly suited for control systems used in the high tech industry and in the automotive industry. The scope of applications of empirical models is however narrow. Empirical models ignore the physical phenomena that take place in the cell. Consequently, they cannot predict the life and the capacity fading of the battery. Furthermore, they are only valid for the battery for which they have been developed. [1][2][3] Electrochemical models provide, on the other hand, reliable responses of the battery under a wide range of operating conditions and for different applications. They account for the chemical/ electrochemical kinetics and the transport phenomena. Electrochemical models are unequivocally superior to empirical models. But they are also more complex and require longer computation times.Among the electrochemical models, the Pseudo-two-Dimensional (P2D) model stands out. The P2D model rests on the porous electrode theory, the concentrated solution theory and the use of appropriate kinetics equations.4-6 A simplified and computationally efficient version of the P2D model is the Single Particle Model (SPM). In the SPM, it is assumed that the current distribution along the thickness of the porous electrode remains uniform and that the electrolyte properties are constant. 5,7Both empirical and electrochemical models need to be calibrated in order to simulate faithfully the ...

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