Search Results

Battery temperature variations have a strong effect on both battery aging and battery performance. Significant temperature variations will lead to different battery behaviors. This influences the performance of the Hybrid Electric Vehicle (HEV) energy management strategies. This paper investigates how variations in battery temperature will affect Lithium-ion battery aging and fuel economy of a HEV. The investigated energy management strategy used in this paper is the Equivalent Consumption Minimization Strategy (ECMS) which is a well-known energy management strategy for HEVs. The studied vehicle is a Honda Civic Hybrid and the studied battery, a BLS LiFePO4 3.2Volts 100Ah Electric Vehicle battery cell. Vehicle simulations were done with a validated vehicle model using multiple combinations of highway and city drive cycles. The battery temperature variation is studied with regards to outside air temperature.

Lithium-ion batteries (LIBs) have been widely used as the energy storage system in plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) due to their high power and energy density and long cycle life compared to other chemistries. However, LIBs are sensitive to operating conditions, including temperature, current demand and surface pressure of the cell. One very well understood phenomenon of lithium-ion battery is the reduction in charge capacity over time due to cycling and storage commonly known as capacity fade. Considering the need for predicting the behavior of an aged cell and the need for estimating battery useful life for warranty purpose, it is crucial to predict the capacity fade with reasonable accuracy. To accommodate this need, a novel cell level empirical aging model is built based on storage tests and cycle tests. The storage test captures the calendar aging of the lithium-ion cell while the cycle test estimates the cycle aging of the cell.

This paper studies the hardware-in-the-loop (HiL) design of a power-split hybrid electric vehicle (HEV) for the research of HEV lithiumion battery aging. In this paper, an electrochemical model of a lithium-ion battery pack with the characteristics of battery aging is built and integrated into the vehicle model of Autonomie® software from Argonne National Laboratory. The vehicle model, together with the electrochemical battery model, is designed to run in a dSPACE real-time simulator while the powertrain power distribution is managed by a dSPACE MicroAutoBoxII hardware controller. The control interface is designed using dSPACE ControlDesk to monitor the real-time simulation results. The HiL simulation results with the performance of vehicle dynamics and the thermal aging of the battery are presented and analyzed.

This paper studies the nonlinear model predictive control for a power-split Hybrid Electric Vehicle (HEV) power management system to improve the fuel economy. In this paper, a physics-based battery model is built and integrated with a base HEV model from Autonomie®, a powertrain and vehicle model architecture and development software from Argonne National Laboratory. The original equivalent circuit battery model from the software has been replaced by a single particle electrochemical lithium ion battery model. A predictive model that predicts the driver’s power request, the battery state of charge (SOC) and the engine fuel consumption is studied and used for the nonlinear model predictive controller (NMPC). A dedicated NMPC algorithm and its solver are developed and validated with the integrated HEV model. The performance of the NMPC algorithm is compared with that of a rule-based controller.

Battery State of Charge (SOC) constraints are used to prevent the battery in Hybrid Electric Vehicles (HEVs) from over-charging or over-discharging. These constraints strongly influence the power-split of the HEV. This paper presents results on how Battery State of Charge (SOC) constraints effects Lithium ion battery aging and fuel economy when using the Equivalent Consumption Minimization Strategy (ECMS). The vehicle studied is the Honda Civic Hybrid. The battery used is A123 Systems’ ANR26650 battery cell. Vehicle simulation uses multiple combinations of highway and city drive cycles. For each combination of drive cycles, nine SOC constraints ranges are used. Battery aging is evaluated using a semi-empirical model combined with the accumulated Ah-throughput method which uses, as an input, the battery SOC trajectory from the vehicle simulations. The simulation results provide insight into how SOC constraints effect fuel economy as well as battery aging.

This paper investigates the aging performance of the lithium ion cobalt oxide battery pack of a single shaft parallel hybrid electric vehicle (HEV) under different ambient temperatures. Varying ambient temperature of HEVs results in different battery temperature and then leads to different aging performance of the battery pack. Battery aging is reflected in the increasing of battery internal resistance and the decreasing of battery capacity. In this paper, a single shaft parallel hybrid electric vehicle model is built by integrating Automotive Simulation Model (ASM) from dSPACE and AutoLion-ST battery model from ECPower to realize the co-simulation of HEV powertrain in the common MATLAB/Simulink platform. The battery model is a physics-based and thermally-coupled battery (TCB) model, which enables the investigation of battery capacity degradation and aging. Standard driving cycle with differing ambient temperatures is tested using developed HEV model.

A large amount of heat is generated in electric vehicle battery packs during high rate charging, resulting in the need for effective cooling methods. In this paper, a prototype liquid cooled large format Lithium-ion battery module is modeled and tested. Experiments are conducted on the module, which includes 31Ah NMC/Graphite pouch battery cells sandwiched by a foam thermal pad and heat sinks on both sides. The module is instrumented with twenty T-type thermocouples to measure thermal characteristics including the cell and foam surface temperature, heat flux distribution, and the heat generation from batteries under up to 5C rate ultra-fast charging. Constant power loss tests are also performed in which battery loss can be directly measured.

Accurate battery state of charge (SOC) estimation is essential for safe and reliable performance of electric vehicles (EVs). Lithium-ion batteries, commonly used for EV applications, have strong time-varying and non-linear behaviour, making SOC estimation challenging. In this paper, a processor in the loop (PIL) platform is used to assess the execution time and memory use of different SOC estimation algorithms. Four different SOC estimation algorithms are presented and benchmarked, including an extended Kalman filter (EKF), EKF with recursive least squares filter (EKF-RLS) feedforward neural network (FNN), and a recurrent neural network with long short-term memory (LSTM). The algorithms are deployed to two different NXP S32Kx microprocessors and executed in real-time to assess the algorithms' computational load. The algorithms are benchmarked in terms of accuracy, execution time, flash memory, and random access memory (RAM) use.

Thermal management of lithium-Ion battery (LIB) has become very critical issue in recent years. One of the challenges for the design and packaging of the battery is to maintain the battery temperature within acceptable ranges and also reduce temperature gradients within the battery cells. Controlling the battery temperature is essential for the battery performance and the long-term battery life. Increased difference between battery cell temperatures can lead to non-uniform charging and non-uniform ageing of battery cells. The purpose of this paper is to investigate available technologies using heat pipes as a means of improving battery thermal management. Several studies have been conducted regarding the effect of heat pipes on battery temperature. However, in this paper we present a comprehensive study of heat pipes effects through transient analysis of a complete vehicle thermal model.

Electrified powertrains will play a growing role in meeting global fuel consumption and CO2 requirements. In support of this, FCA US has developed its first dedicated hybrid transmission (the eFlite® transmission), used in the Chrysler Pacifica Hybrid. The Chrysler Pacifica is the industry’s first electrified minivan. [2] The new eFlite hybrid transmission architecture optimizes performance, fuel economy, mass, packaging and NVH. The transmission is an electrically variable FWD transaxle with an input split configuration and incorporates two electric motors, both capable of driving in EV mode. The lubrication and cooling system makes use of two pumps, one electrically operated and one mechanically driven. The Chrysler Pacifica has a 16kWh lithium ion battery and a 3.6-liter Pentastar® engine which offers total system power of 260 hp with 84 MPGe, 33 miles of all electric range and 566 miles total driving range. [2] This paper’s focus is on the eFlite transmission.

To achieve an accurate estimate of losses in a battery it is necessary to consider the reversible entropic losses, which may constitute over 20% of the peak total loss. In this work, a procedure for experimentally determining the entropic heating coefficient of a lithium-ion battery cell is developed. The entropic heating coefficient is the rate of change of the cell’s open-circuit voltage (OCV) with respect to temperature; it is a function of state-of-charge (SOC) and temperature and is often expressed in mV/K. The reversible losses inside the cell are a function of the current, the temperature, and the entropic heating coefficient, which itself is dependent on the cell chemistry. The total cell losses are the sum of the reversible and irreversible losses, where the irreversible losses consist of ohmic losses in the electrodes, ion transport losses, and other irreversible chemical reactions.

Recently, the automotive industry has experienced rapid growth in powertrain electrification, with more and more battery electric vehicles (BEV) and hybrid electric vehicles being launched. Lithium-ion batteries play an important role due to their high energy capacity and power density, however they experience high heat generation in their operation, and if not properly cooled it can lead to serious safety issues as well as lower performance and durability. In that way, good prediction of a battery behavior is crucial for successful design and management. This paper presents a 1D electrochemical model development of a 144 Ah prismatic rolled cell using the GT-Autolion software with a pseudo 2D approach. The model correlation is done at cell level comparing model results and test data of cell open circuit voltage at different temperatures and voltage and temperature profile under different C-rates and ambient temperatures.

Lithium-ion battery (LIB) terminal voltage models are investigated using two modelling approaches. The first model is a third-order Thevenin equivalent circuit model (ECM), which consists of an open-circuit voltage in series with a nonlinear resistance and three parallel RC pairs. The parameters of the ECM are obtained by fitting the model to hybrid pulse power characterization (HPPC) test data. The parametrization of the ECM is performed through quadratic-based programming. The second is a novel modelling approach based on long short-term memory (LSTM) recurrent neural networks to estimate the battery terminal voltage. The LSTM is trained on multiple vehicle drive cycles at six different temperatures, including −20°C, without the necessity of battery characterization tests. The performance of both models is evaluated with four automotive drive cycles at each temperature. The results show that both models achieve acceptable performance at all temperatures.

As more electric vehicles (BEV, HEV, PHEV, etc.) are adopted in the upcoming decades, it is becoming increasingly important to conduct vehicle-level thermal simulations under different drive-cycle conditions while incorporating the various subsystem thermal losses. Thermal management of the various heat sources in the vehicle is essential both in terms of ensuring passenger safety as well as maintaining all the subsystems within their corresponding safe temperature limits. It is also imperative that these thermal simulations include energy consumption prediction, while considering the effect of battery degradation both in terms of increased thermal losses as well as reduction in the vehicle’s range. For this purpose, a three-dimensional transient thermal analysis framework was coupled with an electrochemical P2D-based battery model and a vehicle dynamics model to test different scenarios and their effect on a hybrid vehicle’s range and the lithium-ion battery life.

Battery state-of-charge (SOC) is critical information for the vehicle energy management system and must be accurately estimated to ensure reliable and affordable electrified vehicles (xEV). However, due to the nonlinear temperature, health, and SOC dependent behaviour of Li-ion batteries, SOC estimation is still a significant automotive engineering challenge. Traditional approaches to this problem, such as electrochemical models, usually require precise parameters and knowledge from the battery composition as well as its physical response. In contrast, neural networks are a data-driven approach that requires minimal knowledge of the battery or its nonlinear behaviour. The objective of this work is to present the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The method includes a description of data acquisition, data preparation, development of an FNN, FNN tuning, and robust validation of the FNN to sensor noise.

Due to the recent progress in electrification, lithium-ion batteries have been widely used for electric and hybrid vehicles. Lithium-ion batteries exhibit high energy density and high-power density which are critical for vehicle development with high driving range enhanced performance. However, high battery temperature can negatively impact the battery life, performance, and energy delivery. In this paper, we developed and applied an analytical algorithm to estimate battery life-based vehicle level testing. A set of vehicle level tests were selected to represent customer duty cycles. Thermal degradation models are applied to estimate battery capacity loss during driving and park conditions. Due to the sensitivity of Lithium-Ion batteries to heat, the effect of high ambient temperatures throughout the year is considered as well. The analysis provides an estimate of the capacity loss due to calendar and cyclic effects throughout the battery life.