Chitosan, which is a derivative of chitin, is particularly popular due to its biodegradable and renewable nature. However, the properties of chitosan can be inconsistent due to the extraction process and its natural origin, which poses a challenge to its use in composite materials as a matrix. The properties of chitosan can be tuned by controlling the degree of deacetylation (the extent to which acetyl groups are removed from chitin to form chitosan) and molecular weight. This paper presents a detailed study on the extraction and characterization of chitosan from shrimp shells. The structural thermal and mechanical characterization were studied using several techniques: Fourier-transform infrared spectroscopy, X-ray diffraction, thermogravimetric analysis and differential scanning calorimetry. The intrinsic viscosity and deacetylation degree were determined using various methods. The results showed an average degree of deacetylation of 77%. The chitosan films exhibited a high tensile strength of 43.9 MPa and an elongation at break of 3.14%. The thermal analysis revealed that the films had a glass transition temperature of 88 °C and a maximum thermal degradation temperature of 320 °C. The findings of this research could contribute to the development of chitosan-based materials with improved properties, leading to its wider adoption in the future for composite matrix application. The simple and efficient method used for the extraction and purification of chitosan from shrimp shells makes it a cost-effective and eco-friendly alternative to synthetic polymers.

This paper investigates energy harvesting performances of porous piezoelectric polymer films to collect electrical energy from vibrations and power various sensors. The influence of void content on the elastic matrix, dielectric, electrical, and mechanical properties of porous piezoelectric polymer films produced from available commercial poly(ethylene-co-vinyl acetate) using an industrially applicable melt-state extrusion method (EVA) were examined and discussed. Electrical and mechanical characterization showed an increase in the harvested current and a decrease in Young’s modulus with the increasing ratio of voids. Thermal analysis revealed a decrease in piezoelectric constant of the porous materials. The authors present a mathematical model that is able to predict harvested current as a function of matrix characteristics, mechanical excitation and porosity percentage. The output current is directly proportional to the porosity percentage. The harvested power significantly increases with increasing strain or porosity, achieving a power value up to 0.23, 1.55, and 3.87 mW/m³ for three EVA compositions: EVA 0%, EVA 37% and EVA 65%, respectively. In conclusion, porous piezoelectric EVA films has great potential from an energy density viewpoint and could represent interesting candidates for energy harvesting applications. Our work contributes to the development of smart materials, with potential uses as innovative harvester systems of energy generated by different vibration sources such as roads, machines and oceans.

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.

The on-board energy storage system plays a key role in electric vehicles since it directly affects their performance and autonomy. The lithium-ion battery offers satisfactory characteristics that make electric vehicles competitive with conventional ones. This article focuses on modeling and estimating the parameters of the lithium-ion battery cell when used in different electric vehicle drive cycles and styles. The model consists of an equivalent electrical circuit based on a second-order Thevenin model. To identify the parameters of the model, two algorithms were tested: Trust-Region-Reflective and Levenberg-Marquardt. To account for the dynamic behavior of the battery cell in an electric vehicle, this identification is based on measurement data that represents the actual use of the battery in different conditions and driving styles. Finally, the model is validated by comparing simulation results to measurements using the mean square error (MSE) as model performance criteria for the driving cycles (UDDS, LA-92, US06, neural network (NN), and HWFET). The results demonstrate interesting performance mostly for the driving cycles (UDDS and LA-92). This confirms that the model developed is the best solution to be integrated in a battery management system of an electric vehicle.

“Parameter Identification of a Lithium-Ion Battery Model Using Levenberg-Marquardt Algorithm”