Parameter identification of Li-Po batteries in electric vehicles: A comparative study

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-of-health. 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.

I Introduction

Electric and Plug-in Hybrid Electric Vehicles (EVs/PHEVs) are one of the most promising technologies to achieve a more sustainable utilisation of energy resources from both economic and environmental points of view. In conjunction with renewable resources, they provide a viable solution to dramatically reduce greenhouse gas emissions. A crucial component of EVs and PHEVs is the energy storage system, which has a huge impact on their cost and performance. On the one hand, Li-ion battery technology, and particularly the Li-Polymer (Li-Po) variant, provides specific and volumetric energy and power densities, charge/discharge efficiency, and lifetime suitable for the implementation of the energy storage system in EVs and PHEVs. On the other hand, a Li-ion cell is very sensitive to overcharge and deep discharge, as well as to be operated outside its safe temperature range. Should these conditions happen, the cell may be permanently damaged. Consequently, an electronic system, called Battery Management System (BMS), is usually employed to protect the battery by measuring the voltage, the temperature, and the current of each cell of the battery. These data feed the algorithms executed by the BMS to evaluate the residual energy stored in the battery and the available power to the load. This information is of paramount importance for an effective management of the battery in the vehicle. It also optimises the battery lifetime, which should be comparable to the life of the vehicle [1].