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Technical Paper

Advanced Management System for Lithium-ion Batteries in Hybrid Inverters Optimized for Photovoltaic Systems Connected to the Grid

One main feature of the power demand profile is it varies time to time and its price changes accordingly. During the peak the less cost-effective and flexible power supplies must complement the base-load power plants in order to supply the power demand. Conversely, during the off-peak period when less electricity is consumed, those costly power plants can be stopped. This is a scenario which Energy Storage System (ESS) and photovoltaic (PV) generation plants could add flexibility and cost reduction to the customers and utilities. These aspects are only achieved due to the ESS, which enables the optimal use of energy produced by the photovoltaic modules through load management and discharge of the battery in the most convenient times.
Technical Paper

Design of an energy storage system with blended of Li-ion batteries for pure electric vehicle of high performance

The state of art in Li-ion cells has one technologic gap to find in the same cell all requirements of performance to a vehicle that need high impulse and high autonomy combined. Certain Li-ion cells have characteristics of high energy density that exhibit high voltage and high rate capability, but have poor cycling and low power capacity. Alternately, other types of cells have high power density exhibit good thermal stability, good cycling, and high rate regime operation characteristics but have low rate capability and low voltage. Blending different cathode Li-ion cells in the same Energy Storage System is a new approach to design the better batteries for Pure Electric Vehicle of High Performance. This paper is intended to develop an Energy Storage System that take the advantage of the unique properties of each electric type of Li-ion cell and optimize its performance with respect to the automotive operating requirements.
Technical Paper

Study of machine learning algorithms to state of health estimation of iron phosphate lithium-ion battery used in fully electric vehicles

State of Health (SOH) is an important parameter in Battery Management Systems (BMS) because it avoids the failure of a battery that could lead to reduced performance, operational impairment and even catastrophic failure, especially in electric vehicles. However a reliable battery state estimation management system in electric vehicles greatly depends on the validity and generalizability of battery models. This paper presents a generic data-driven approach for lithium-ion battery health management that eliminates the dependency of battery physical models for SOH estimation. In this work, iron phosphate Lithium-ion batteries were used. They were repeatedly submitted to charge-discharge cycles based on standard IEC and ISO profiles. The tension, current, charge, cell temperature and ambient temperature were constantly monitored in this period, and one big data set was created and stored.
Technical Paper

Comparing techniques used to estimate the state of charge of lithium-ion batteries for electric vehicles

Electric vehicles (EVs) are becoming popular in the industry as well as in automotive and aerospace systems. Currently, new technologies have been developed for Battery Management Systems (BMS), and consequently, they have improved energy efficiency and consumption. Among of many challenges, the State of Charge (SoC) of battery has become a key role in the BMS for lithium-ion batteries. Accurate State of Charge estimation also enables more optimized battery pack design for the electric vehicle. Many researchers have been developing new algorithms, technologies and practices to estimate the SoC of lithium-ion batteries. This paper presents a comparison of techniques used to estimate the State of Charge of lithium-ion batteries in EVs application. It is based on a review of literature, discussion and comparison of advanced techniques used to estimate SoC in lithium-ion batteries. This comparison goes through experimental data, modeling, and simulation.
Technical Paper

Electric Vehicles Batteries Modeling Analysis Based on a Multiple Layered Perceptron Identification Approach

A reliable battery state estimation management system in electric vehicles greatly depends on the validity and generalizability of battery models. This paper presents a Li-ion and Lead Acid batteries neural model. This model does not consider battery details, bringing universality, which is suitable for parameters estimation of all battery kinds. The final model proposes describe the dynamic contributions due to open-circuit voltage, polarization time constants, electrochemical hysteresis, effects of temperature, state of charge and state of health.