The battery is a fundamental component of electric vehicles, which represent a step forward towards sustainable mobility. Lithium chemistry is now acknowledged as the technology of choice for energy storage in electric vehicles. However, several research points are still open. They include the best choice of the cell materials and the development of electronic circuits and algorithms for a more effective battery utilization. This paper initially reviews the most interesting modeling approaches for predicting the battery performance and discusses the demanding requirements and standards that apply to ICs and systems for battery management. Then, a general and flexible architecture for battery management implementation and the main techniques for state-of-charge estimation and charge balancing are reported. Finally, we describe the design and implementation of an innovative BMS, which incorporates an almost fully-integrated active charge equalizer

A simple but effective analysis to calculate the performances achievable by a balancing circuit for series-connected lithium-ion batteries (i.e., the time required to equalise the battery and the energy lost during this process) is described in this paper. Starting from the simple passive technique, in which extra energy is dissipated on a shunt resistor, active techniques, aiming at an efficient energy transfer between battery cells, are investigated. The basic idea is to consider the balancing circuit as a DC/DC converter capable of transferring energy between its input and output with a certain efficiency and speed. As the input and output of the converter can be either a single cell or the entire battery pack, four main active topologies are identified: cell to cell, cell to pack, pack to cell and cell to/from pack (i.e., the combination of the cell to pack and pack to cell topologies when the converter is bidirectional). The different topologies are compared by means of statistical simulations. They clearly show that the cell to cell topology is the quickest and most efficient one. Moreover, the pack to cell topology is the least effective one and surprisingly dissipates more energy than the passive technique, if the converter efficiency is below 50 %.

No abstract

An effective management of the onboard energy storage system is a key point for the development of electric vehicles. This requires the accurate estimation of the battery state over time and in a wide range of operating conditions. The battery state is usually expressed as state-of-charge and state-ofhealth. Its estimation demands an accurate model to represent the static and dynamic behaviour of the battery. Developing such a model requires the online identification of the battery parameters. This paper aims at comparing the performance of two popular system identification techniques, i.e., the Extended Kalman Filter and the classic Least Squares method. A significant contribution of this work is the definition of a benchmark which is representative of the real use of the battery in an electric vehicle. Simulation results show the peculiarities of both methods and their effectiveness.

An accurate model of the elementary accumulation device is fundamental for sizing and controlling the battery pack to be used in electric and hybrid vehicles. Indeed, the implementation of such a model within the Battery Management System makes it possible to evaluate the status and the behavior of the battery pack in every condition and to apply a correct control strategy. This work shows the characterization and modeling of a commercial Lithium-Polymer cell, which properly considers thermal effects on cell behavior. The specific designed thermostatic chamber is described and the experimental results are presented and compared to those simulated with the developed model.