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In this paper, initial results of Li-ion battery performance characterization through field tests are presented. A fully electrified Ford Escape that is equipped by three Li-ion battery packs (LiFeMnPO4) including an overall 20 modules in series is employed. The vehicle is in daily operation and data of driving including the powertrain and drive cycles as well as the charging data are being transferred through CAN bus to a data logger installed in the vehicle. A model of the vehicle is developed in the Powertrain System Analysis Toolkit (PSAT) software based on the available technical specification of the vehicle components. In this model, a simple resistive element in series with a voltage source represents the battery. Battery open circuit voltage (OCV) and internal resistance in charge and discharge mode are estimated as a function of the state of charge (SOC) from the collected test data.

Despite significant progress toward application of Li-ion batteries in electric vehicles, there are still major concerns about the range of electric vehicles and battery life. Depending on the climate of the region where the vehicle is in use, auxiliary loads could also play a significant role on the battery performance and durability. In this paper, the effect of air conditioning (AC) load on the electric range and Li-ion battery life is evaluated. For this purpose, a thermodynamic model for the vehicle cabin is developed and integrated to a battery model. The thermodynamic model takes the ambient conditions, solar load, and the vehicle drive cycle as inputs and calculates the instantaneous cabin temperature and humidity. The battery model, which represents a Li-on battery pack installed on a fully electrified Ford Escape 2009, consists of a voltage source in series with a lump resistance, a thermal sub-model, and a degradation sub-model to predict the battery capacity fade.

Due to rising fuel prices and environmental concerns, Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) have been gaining market share as fuel-efficient, environmentally friendly alternatives. Lithium-ion batteries are commonly used in EV and HEV applications because of their high power and energy densities. During controls development of HEVs and EVs, hardware-in-the-loop simulations involving real-time battery models are commonly used to simulate a battery response in place of a real battery. One physics-based model which solves in real-time is the reduced-order battery model developed by Dao et al. [1], which is based on the isothermal model by Newman [2] incorporating concentrated solution theory and porous electrode theory [3]. The battery models must be accurate for effective control; however, if the battery parameters are unknown or change due to degradation, a method for estimating the battery parameters to update the model is required.

A major challenge in the development of the next generation electric and hybrid electric vehicle (EV and HEV) technology is the control and management of heat generation and operating temperatures. Vehicle performance, reliability and ultimately consumer market adoption are integrally dependent on successful battery thermal management designs. In addition to this, crucial to thermal modeling is accurate thermo-physical property input. Therefore, to design a thermal management system and for thermal modeling, a designer must study the thermal characteristics of batteries. This work presents a purely experimental thermal characterization of thermo-physical properties of a lithium-ion battery utilizing a promising electrode material, LiFePO4, in a prismatic pouch configuration. In this research, the thermal resistance and corresponding thermal conductivity of prismatic battery materials is evaluated.

Modern electrified vehicles rely on drivers to manually adjust control parameters to modify the vehicle's powertrain, such as regenerative braking strength selection or drive mode selection. However, this reliance on infrequent driver input may lead to a mismatch between the selected powertrain control modifiers and the true driving environment. It is therefore advantageous for an electric vehicle's powertrain controller to make online identifications of the current driving conditions. This paper proposes an online driving condition identification scheme that labels drive cycle intervals collected in real-time based on a clustering model, with the objective of informing adaptive powertrain control strategies. HDBSCAN and K-means clustering models are fitted to a data set of drive cycle intervals representing a full range of characteristic driving conditions.

The recent increase in oil prices and environmental concerns have attracted various research efforts on hybrid electric vehicles (HEVs) which provide promising alternatives to conventional engine-powered vehicles with better fuel economy and fewer emissions. To speed up the design and prototyping processes of new HEVs, a method that automatically generates mathematics equations governing the vehicle system response in an optimized symbolic form is desirable. To achieve this goal, we employed MapleSimTM, a new physical modeling tool developed by Maplesoft Inc., to develop the multi-domain model of a series-HEV, utilizing the symbolic computing algorithms of Maple software package to generate an optimized set of governing equations. The HEV model consists of a mean-value internal combustion engine (ICE), a chemistry-based Ni-MH battery pack, and a multibody vehicle model. Simulations are then used to demonstrate the performance of the developed HEV system.

An electric vehicle model has been developed with four direct-drive in-wheel motors. A high-level vehicle stability controller is proposed, which uses the principles of fuzzy logic to determine the corrective yaw moment required to minimize the vehicle sideslip and yaw rate errors. A genetic algorithm has been used to optimize the parameters of the fuzzy controller. The performance of the controller is evaluated as the vehicle is driven through a double-lane-change maneuver. Preliminary results indicate that the proposed control system has the ability to improve the performance of the vehicle considerably.

Battery packs of electric vehicles are typically composed of lithium-ion batteries with aluminum and copper acting as cell terminals. These terminals are joined together in series by means of connector tabs to produce sufficient power and energy output. Such critical electrical and structural cell terminal connections involve several challenges when joining thin, highly reflective and dissimilar materials with widely differing thermo-mechanical properties. This may involve potential deformation during the joining process and the formation of brittle intermetallic compounds that reduce conductivity and deteriorate mechanical properties. Among various joining techniques, laser welding has demonstrated significant advantages, including the capability to produce joints with low electrical contact resistance and high mechanical strength, along with high precision required for delicate materials like aluminum and copper.