An electric circuit model for a lithium-ion battery cell based on automotive drive cycles measurements

The on-board energy storage system plays a key role in electric vehicles since it directly affects their performance and autonomy. The lithium-ion battery offers satisfactory characteristics that make electric vehicles competitive with conventional ones. This article focuses on modeling and estimating the parameters of the lithium-ion battery cell when used in different electric vehicle drive cycles and styles. The model consists of an equivalent electrical circuit based on a second-order Thevenin model. To identify the parameters of the model, two algorithms were tested: Trust-Region-Reflective and Levenberg-Marquardt. To account for the dynamic behavior of the battery cell in an electric vehicle, this identification is based on measurement data that represents the actual use of the battery in different conditions and driving styles. Finally, the model is validated by comparing simulation results to measurements using the mean square error (MSE) as model performance criteria for the driving cycles (UDDS, LA-92, US06, neural network (NN), and HWFET). The results demonstrate interesting performance mostly for the driving cycles (UDDS and LA-92). This confirms that the model developed is the best solution to be integrated in a battery management system of an electric vehicle

General parameter identification procedure and comparative study of Li-Ion battery models

Accurate and robust battery models are required for the proper design and operation of battery-powered systems. However, the parametric identification of these models requires extensive and sophisticated methods to achieve enough accuracy. This article shows a general and straightforward procedure, based on Simulink and Simscape of Matlab, to build and parameterize Li-ion battery models. The model parameters are identified with the Optimization Toolbox of Matlab, by means of an iterative process to minimize the sum of the squared errors. In addition, this procedure is applied to a selection of five different models available in the literature for electric vehicle applications, obtaining a comparative study between them. Also, the performance of each battery model is evaluated through two current profiles from two driven profiles known as the Urban Driving Cycle (ECE-15 or UDC) and the Hybrid Pulse Power Characterization (HPPC). The experimental results obtained from a Li-ion polymer battery have been compared with the data provided by the models, confirming the effectiveness of the proposed procedure, and also, the application field of each model as a function of the required accuracy.This work was supported by the Ministry of Economy and Competitiveness and FEDER funds through the research project “Storage and Energy Management for Hybrid Electric Vehicles based on Fuel Cell, Battery, and Supercapacitors”—ELECTRICAR-AG-(DPI2014- 53685-C2-1-R)

Modeling and On-Road Testing of an Electric Two-Wheeler towards Range Prediction and BMS Integration

The automotive sector is currently shifting its focus from traditional fossil fuels to electrification. The deployment of a Battery Management System (BMS) unit is the key point to oversee the battery state of the electric vehicle (EV) to ensure safety and performances. The development and assessment of electric vehicle models in turn lays the groundwork of the BMS design as it provides a quick and cheap solution to test battery optimal control logics in a Software-in-the-Loop environment. Despite the various contribution to the literature in battery and vehicle modeling, electric scooters are mostly disregarded together with a reliable estimation of their performance and electric range. The present paper hence aims at filling the gap of knowledge through the development of a numerical model for considering a two-wheeler. The latter model relies on the conservation energy based-longitudinal dynamic approach and is coupled to a Li-Ion Battery second-order RC equivalent circuit model for the electric range prediction. More specifically, the presented work assesses the performance and electric range of a two-wheeler pure electric scooter in a real-world driving cycle. The e-powertrain system embeds an Electrical Energy Storage System (EESS) Li-Ion Battery pack. On-road tests were initially conducted to retrieve the main model parameters and to perform its validation. A global battery-to-wheels efficiency was also calibrated to account for the percentual amount of available net power for the vehicle onset. The model proved to properly match the experimental data in terms of total distance traveled over a validation driving mission

Comparative analysis of battery electric vehicle thermal management systems under long-range drive cycles

Due to increasing regulation on emissions and shifting consumer preferences, the wide adoption of battery electric vehicles (BEV) hinges on research and development of technologies that can extend system range. This can be accomplished either by increasing the battery size or via more efficient operation of the electrical and thermal systems. This study endeavours to accomplish the latter through comparative investigation of BEV integrated thermal management system (ITMS) performance across a range of ambient conditions (-20 °C to 40 °C), cabin setpoints (18 °C to 24 °C), and six different ITMS architectures. A dynamic ITMS modelling framework for a long-range electric vehicle is established with comprehensive sub models for the operation of the drive train, power electronics, battery, vapor compression cycle components, and cabin conditioning in a comprehensive transient thermal system modelling environment. A baseline thermal management system is studied using this modelling framework, as well as four common thermal management systems found in literature. This study is novel for its combination of comprehensive BEV characterization, broad parametric analysis, and the long range BEV that is studied. Additionally, a novel low-temperature waste heat recovery (LT WHR) system is proposed and has shown achieve up to a 15% range increase at low temperatures compared to the baseline system, through the reduction of the necessary cabin ventilation loading. While this system shows performance improvements, the regular WHR system offers the greatest benefit, a 13.5% increase in cold climate range, for long-range BEV drive cycles in terms of system range and transient response without the need for additional thermal system equipment

Flexible simulation of an electric vehicle to estimate the impact of thermal comfort on the energy consumption

The energy consumption of electric vehicles depends on the traction energy but also on the thermal comfort energy. Some studies lead to the estimation of this energy consumption from real measurements on different driving and climatic conditions. However, those results rely on a large number of vehicle tests, which is time consuming. Moreover, the impacts of the different subsystems cannot be differentiated in such global studies. A flexible simulation tool can help to analyze the impact of the different parts of a vehicle. This paper proposes a multi-physical parametrized model of an electric vehicle including the traction and comfort subsystems. A flexible model of a Renault Zoe is developed thanks to the energetic macroscopic representation. This model is validated by experimental tests of the real vehicle. Then, the impact of the HVAC (heating, ventilation, and air conditioning) subsystem is studied for different driving cycles and climatic conditions. In very cold conditions, the use of the HVAC subsystem represents an increase of up to 248% of the total energy consumption, compared to summer conditions