The automotive energy storage market is currently dominated by the existing Li-ion technologies that are likely to continue in the future. Thus, the on-road electric (and hybrid) vehicles running on the Li-ion battery systems require critical diagnosis considering crucial battery aging. This work aims to provide a guideline for pack-level lifetime model development that could facilitate battery maintenance, ensuring a safe and reliable operational lifespan. The first of the twofold approach is a cell-level empirical lifetime model that is developed from a lab-level aging dataset of commercial LTO cells. The model is validated with an exhaustive sub-urban realistic driving cycle yielding a root-mean-square error of 0.45. The model is then extended to a 144S1P modular architecture for pack-level simulation. The second step provides the pack electro-thermal simulation results that are upscaled from a cell-level and validated 1D electrical model coupled with a 3D thermal model. The combined simulation framework is online applicable and considers the relevant aspects into account in predicting the battery system’s lifetime that results in over 350,000 km of suburban driving. This robust tool is a collaborative research outcome from two Horizon2020 EU projects—GHOST and Vision xEV, showcasing outstanding cell-level battery modeling accuracies.

Jute is a cheap, eco-friendly, widely available material well-known for its cooling properties. In electric vehicles (EVs), dissipating a huge amount of the heat generated from lithium-ion batteries with an efficient, light, and low-power consumption battery thermal management system (BTMS) is required. In our previous study, jute fibers were proposed and investigated as a novel medium to enhance the cooling efficiency of air-based battery thermal management systems. In this paper, as the first attempt, jute was integrated with a phase change material (PCM) passive cooling system, and the thermal performance of a 50 Ah prismatic battery was studied. Temperature evolution, uniformity, and cooling efficiency were investigated. A comparison between the thermal behavior of the air-based BTMS and PCM-assisted cooling system was performed. The results indicated that adding jute to the BTMS increased the cooling efficiency and especially decreased the temperature development. Furthermore, the temperature difference (ΔT) efficiency was enhanced by 60% when integrating jute with PCM, and temperature uniformity improved by 3% when integrating jute with air-based BTMS. This article compared the integration of jute with active cooling and passive cooling; thus, it shed light on the importance of jute as a novel, eco-friendly, lightweight, cheap, available, and nontoxic material added to two strategies of BTMS. The setup was physically made and experimentally studied for the purpose of BTMS optimization.

Power Hardware-In-the-loop (HIL) testing is increasing in the automotive industry for testing subsystems for new vehicles. Usually, the test is located in a unique place (stand-alone (local) HIL). The key-points for a vehicle manufacturer and subsystems suppliers are time and confidentiality. Using the cloud is a way to satisfy the both. Cloud model sharing is developed to spare time and cloud-based real-time simulation limits the access to the models to only certain variables. In this paper, a cloud-based power HIL testing is proposed using a cloud of vehicle models. The Energetic Macroscopic Representation (EMR) formalism is used to organize all the models. It facilitates the interconnections between the simulated and power tested subsystems. Two new batteries are experimentally tested, one for an electric vehicle, the other one for a plug-in hybrid vehicle. The same cloud is used and the power test facilities are located in two different locations. This shows the flexibility of the method.