Design and On-Field Validation of an Embedded System for Monitoring Second-Life Electric Vehicle Lithium-Ion Batteries

Abstract

In the last few years, the growing demand for electric vehicles (EVs) in the transportation sector has contributed to the increased use of electric rechargeable batteries. At present, lithium-ion (Li-ion) batteries are the most commonly used in electric vehicles. Although once their storage capacity has dropped to below 80–70% it is no longer possible to use these batteries in EVs, it is feasible to use them in second-life applications as stationary energy storage systems. The purpose of this study is to present an embedded system that allows a Nissan® LEAF Li-ion battery to communicate with an Ingecon® Sun Storage 1Play inverter, for control and monitoring purposes. The prototype was developed using an Arduino® microcontroller and a graphical user interface (GUI) on LabVIEW®. The experimental tests have allowed us to determine the feasibility of using Li-ion battery packs (BPs) coming from the automotive sector with an inverter with no need for a prior disassembly and rebuilding process. Furthermore, this research presents a programming and hardware methodology for the development of the embedded systems focused on second-life electric vehicle Li-ion batteries. One second-life battery pack coming from a Nissan® Leaf and aged under real driving conditions was integrated into a residential microgrid serving as an energy storage system (ESS).

A Review of the Technical Challenges and Solutions in Maximising the Potential Use of Second Life Batteries from Electric Vehicles

“…The battery-management system is the central controller of the second-life battery packs installed in the stationary energy-storage system [119]. In each battery module, there some sensors installed to measure the cells' temperature and voltage and to estimate the current applied under various operating conditions [120]. The battery-management system will use the measured sensor data from each module to estimate the state of charge and state of health of the battery cells, modules, and packs during operation using characterized ECM and SOH estimation algorithms that are programmed into the microcontroller board (Figure 23) [120].…”

“…In each battery module, there some sensors installed to measure the cells' temperature and voltage and to estimate the current applied under various operating conditions [120]. The battery-management system will use the measured sensor data from each module to estimate the state of charge and state of health of the battery cells, modules, and packs during operation using characterized ECM and SOH estimation algorithms that are programmed into the microcontroller board (Figure 23) [120]. The state-of-health estimation algorithm accuracy of the second-life battery packs plays a key role as it enables the BMS to calculate the probability of reaching the thermal runaway point in each battery module inside the pack [121].…”

Second Life of Lithium-Ion Batteries of Electric Vehicles: A Short Review and Perspectives

“…Energies 2023, 16, 953 5 of 14 A three-stage assessment method was introduced by Castillo-Martínez et al [22]. The first step is the visual inspection, which reveals cells or modules with apparent physical damage, which can be immediately rejected.…”

IoT Network for International Trade Cold Chain Logistics Tracking Based on Kalman Algorithm

“…However, according to the current state of technological development, most operating systems on the market are equipped with large-capacity storage devices, and large-capacity storage devices can promote the increase in computing speed to a certain extent. Therefore, when comparing embedded devices with other operating systems on the market, the processing speed of the embedded operating systems is much lower than other large operating systems [ 15 , 16 ]. In the following description in this article, nonembedded general-purpose computer devices are collectively referred to as general-purpose devices.…”