Experimental Investigation of Fast−Charging Effect on Aging of Electric Vehicle Li−Ion Batteries

A huge increase in fast−charging stations will be necessary for the transition to EVs. Nevertheless, charging a battery pack at a higher C−rate impacts its state of health, accelerating its degradation. The present paper proposes a different and innovative approach that considers the daily routine of an EV Li−ion battery based on a standard driving cycle, including charging phases when the depth of discharge is 90%. Through dynamic modeling of the EV battery system, the state of charge evolution is determined for different charging C−rates, considering both real discharging and charging current profiles. Finally, by applying a suitable post−processing procedure, aging test features are defined, each being related to a specific EV battery working mode, including charging at a particular C−rate, considering the global battery operation during its lifespan. It is demonstrated that, according to the implemented procedure, fast−charging cycles at 50 kW reduce battery lifespan by about 17% with respect to charge in a 22 kW three−phase AC column, in parity with the discharge rate. Thus, this work can provide a deep insight into the expected massive penetration of electric vehicles, providing an estimate of battery useful life based on charging conditions

Review of Parameter Determination for Thermal Modeling of Lithium Ion Batteries

This paper reviews different methods for determination of thermal parameters of lithium ion batteries. Lithium ion batteries are extensively employed for various applications owing to their low memory effect, high specific energy, and power density. One of the problems in the expansion of hybrid and electric vehicle technology is the management and control of operation temperatures and heat generation. Successful battery thermal management designs can lead to better reliability and performance of hybrid and electric vehicles. Thermal cycling and temperature gradients could have a considerable impact on the lifetime of lithium ion battery cells. Thermal management is critical in electric vehicles (EVs) and good thermal battery models are necessary to design proper heating and cooling systems. Consequently, it is necessary to determine thermal parameters of a single cell, such as internal resistance, specific heat capacity, entropic heat coefficient, and thermal conductivity in order to design suitable thermal management system

Model migration neural network for predicting battery aging trajectories

Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction

Infrared imaging investigation of temperature fluctuation and spatial distribution for a large laminated lithium ion power battery

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The present study investigates the thermal behaviors of a naturally cooled NCM-type LIB (LiNi1−x−yCoxMnyO2 as cathode) from an experimental and systematic approach. The temperature distribution was acquired for different discharge rates and Depth of Discharge (DOD) by the infrared imaging (IR) technology. Two new factors, the temperature variance ( ) and local overheating index (LOH index), were proposed to assess the temperature fluctuation and distribution. Results showed that the heat generation rate was higher on the cathode side than that on the anode side due to the different resistivity of current collectors. For a low-power discharge, the eventual stable high-temperature zone occurred in the center of the battery, while with a high-power discharge, the upper part of the battery was the high temperature region from the very beginning of discharge. It was found that the temperature variance ( ) and local overheating index (LOH index) were capable of holistically exhibiting the temperature non-uniformity both on numerical fluctuation and spatial distribution with varying discharge rates and DOD. With increasing the discharge rate and DOD, temperature distribution showed an increasingly non-uniform trend, especially at the initial and final stage of high-power discharge, the heat accumulation and concentration area increased rapidly

Electric Vehicles Lithium-Polymer Ion Battery Dynamic Behaviour Charging Identification and Modelling Scheme

Lithium-ion batteries are considered the substantial electrical storage element for electric vehicles (EVs). The battery model is the basis of battery monitoring, efficient charging, and safety management. Non-linear modelling is the key to representing the battery and its dynamic internal parameters and performance. This paper proposes a smart scheme to model the lithium-polymer ion battery while monitoring its present charging current and terminal voltage at various ambient conditions (temperature and relative humidity). Firstly, the suggested framework investigated the impact of temperature and relative humidity on the charging process using the constant current-constant voltage (CC-CV) charging protocol. This will be followed by monitoring the battery at the surrounding operating temperature and relative humidity. Hence, efficient non-linear modelling of the EV battery dynamic behaviour using the Hammerstein-Wiener (H-W) model is implemented. The H-W model is considered a black box model that can represent the battery without any mathematical equivalent circuit model which reduces the computation complexity. Finally, the model beholds the boundaries of the charging process that not affecting on the lifetime of the battery. Several dynamic models are applied and tested experimentally to ensure the effectiveness of the proposed scheme under various ambient conditions where the temperature is fixed at 40°C and the relative humidity (RH) at 35%, 52%, and 70%. The best fit using the H-W model reached 91.83% to describe the dynamic behaviour of the battery with a maximum percentage of error 0.1V which is in good agreement with the literature survey. Besides, the model has been scaled up to represent a real EV and expressed the significance of the proposed H-W model

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

Charging Pattern Optimization for Lithium-Ion Batteries with An Electrothermal-Aging Model

This paper applies advanced battery modeling and multi-objective constrained nonlinear optimization techniques to derive suitable charging patterns for lithium-ion batteries. Three important yet competing charging objectives, including battery health, charging time, and energy conversion efficiency, are taken into account simultaneously. These optimization objectives are first subject to a high-fidelity battery model that is synthesized from recently developed individual electrical, thermal, and aging models. The coupling relationship and multiple timescales among different model dynamics are identified. Furthermore, constraints are considered explicitly on the current, voltage, state-of-charge, and temperature. Such a complex charging problem is solved by using an ensemble multi-objective biogeography-based optimization (EM-BBO) approach. As a result, two charging patterns, namely the constant current-constant voltage (CC-CV) and multistage constant current-constant voltage (MCC-CV), are optimized to balance various combinations of charging objectives. Different trade-offs and sensitive elements are compared and analyzed based on the Pareto frontiers. Illustrative results demonstrate that the proposed strategy can effectively offer feasible health-conscious charging with desirable trade-offs among charging speed and energy conversion efficiency under different demand priorities