Li-ion Batteries For Electric Vehicles Research Articles

Comparative study of thermodynamic & kinetic parameters measuring techniques in lithium-ion batteries

Quantitative analysis of the aging process of lithium-ion batteries by using electrochemical thermodynamic and kinetic parameters such as electrochemical potential, Li stoichiometry, and Li inventory loss is a key research topic in the development of Li-ion battery for electric vehicles and smart grids. It is generally known the above parameters can be acquired through the analysis of Electromotive Force (EMF) or Open-Circuit Voltage (OCV) curves. In this work, we proposed and applied five EMF measurement techniques to obtain the EMF-SoC relationships in LFP/Gr single-layer laminated pouch cells at different temperatures and States of Health (SoH), and comprehensively examined them from the viewpoints of eight evaluation dimensions. A Python program, named Degradation Modes Analysis (DMA), is used to diagnose the thermodynamic degradation modes of the battery automatically. Furthermore, electrochemical kinetic parameters were also extracted along with the depolarization process. For faster and more accurate aging diagnosis, we recommend an optimal blend of short relaxation time GITT (Short-Rest-GITT) and extrapolated EMF (Extrap-EMF) to reach the most precise EMF measurements and gain the most comprehensive information about battery thermodynamics and kinetics at the same time.

Effective incorporation of cobalt near surface region for cobalt-free cathodes in lithium-ion batteries

Cobalt-free cathode materials, particularly Ni-rich layered oxide cathode materials, are ideal for electric vehicle Li-ion batteries, offering high energy density and cost-effectiveness. However, high Ni content in high-temperature synthesis leads to issues like increased Li/Ni cation mixing and reduced rate capability, mainly due to the absence of Co. Therefore, this study investigated the utilization of a small amount of Co instead of completely removing it to enhance electrochemical performance. A simple pre-calcination process was used to induce Co substitution beneath the Ni-rich layered oxide cathode materials surface. A coating process was also performed for comparison. The Li/Ni cation mixing in the Co-substituted sample during high-temperature calcination was 2.43%, demonstrating a superior value compared to the Co-coating process. Rate capability tests showed superior performance for Co-substituted sample (164.2 mAh g−1) at 2 C compared to coated sample (151.0 mAh g−1). Moreover, Co substitution improved not only the cycle stability but also the high-temperature stability at an elevated state of charge.This study focuses on the surface modification of cobalt-free cathode, providing direct insight into the effects of Co substitution and typical Co coating and suggests an optimal process that uses small amounts of Co.

Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles

The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods’ performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies.

A novel hybrid deep learning model for accurate state of charge estimation of Li-Ion batteries for electric vehicles under high and low temperature

This paper presents a novel architecture, termed Fusion-Fission Optimisation (FuFi) based Convolutional Neural Network with Bi-Long Short Term Memory Network (FuFi-CNN-Bi-LSTM), to enhance state of charge (SoC) estimation performance. The proposed FuFi-CNN-Bi-LSTM model leverages the power of both Convolutional Neural Networks (CNN) and Bi-Long Short Term Memory Networks (Bi-LSTM) while utilizing FuFi optimization to effectively tune the hyperparameters of the network. This optimization technique facilitates efficient SoC estimation by finding the optimal configuration of the model. A comparative analysis is conducted against FuFi Algorithm-based models, including FuFi-CNN-LSTM, FuFi-Bi-LSTM, FuFi-LSTM, and FuFi-CNN. The comparison involves assessing performance on SoC estimation tasks and identifying the strengths and limitations of models. Furthermore, the proposed FuFi-CNN-Bi-LSTM model undergoes rigorous testing on various drive cycle tests, including HPPC, HWFET, UDDS, and US06, at different temperatures ranging from -20 to 25 degrees Celsius. The model’s robustness and reliability are assessed under different real-world operating conditions using well-established evaluation indexes, including Relative Error (RE),Mean Absolute Error (MAE), R Square (R2), and Granger Causality Test. The results demonstrate that the proposed FuFi-CNN-Bi-LSTM model achieves efficient SoC estimation performance across a wide range of temperatures at higher and lower ranges. This finding signifies the model’s efficacy in accurately estimating SoC in various operating conditions.

Thermal management of Li-ion batteries in electric vehicles by nanofluid-filled loop heat pipes

An analytical model is developed to determine the thermal performance of a Loop Heat Pipe filled (LHP) with copper oxide–water and alumina–water nanofluids for battery thermal management in electric vehicles. The thermal performances of the LHP are predicted for different heat loads and nanoparticle concentrations. It is demonstrated that for fast charging operation corresponding to a heat load of 150 W, the LHP ensures evaporator temperatures of less than 60 °C for a heat sink temperature of 40 °C. The heat transport capacity of the LHP is enhanced and the evaporator temperature is deceased by augmenting the nanoparticle concentration. The water–CuO nanofluid-filled LHP performs better than the water–Al2O3 nanofluid-filled one. The addition of the nanoparticles increases the LHP total pressure drop and the driving capillary pressure. The capillary limit of the water–CuO nanofluid-filled LHP is hardly affected by CuO nanoparticle concentration until 6% beyond which the capillary limit starts decreasing. For the water–Al2O3 nanofluid-filled LHP, the capillary limit decreases when Al2O3 nanoparticle concentration increases. Beyond 6% Al2O3 nanoparticle concentration, the capillary limit of the Al2O3-filled LHP becomes lower than the water-filled one.

Numerical investigation on the thermal management of Li-ion batteries for electric vehicles considering the cooling media with phase change for the auxiliary use

This study investigates the thermal management of a battery module of an electric vehicle for the recovery of waste heat. In the evaluation of this waste heat, a two-phase flow system is used to provide a more effective heat transfer. The battery system was formed of 6 serial connected prismatic Lithium-Ion cells. It was aimed to design a 134.55 kW electric vehicle battery system. For this aim, 360 battery modules were used. The maximum amount of waste heat obtained from these modules was recorded as 6.192 kW. Since it is aimed to use this waste heat in the auxiliary cycles such as air conditioning and extra electricity production, R600a was used as the cooling media. A parametric study was conducted for different discharging rates (C) and depths of discharging (DoD) through computational fluid dynamics (CFD) for performing the thermal analysis of the battery module. The heat transfer coefficient was determined ranging between 1.10 and 5.49 kWm−2 K−1. The cooled module temperature was recorded between 290.93 K and 317.31 K, whereas it was recorded as 332.57 K for the non-cooled system. For secondary purposes, the refrigerant mass rate ranging between 0.2760 kgs−1 and 0.6559 kgs−1 was obtained in a temperature range of 283 K and 313 K.

Direct Lithium Extraction Using Intercalation Materials.

Worldwide lithium demand has surged in recent years due to increased production of Li-ion batteries for electric vehicles and stationary storage. Li supply and production will need to increase such that the transition towards increased electrification in the energy sector does not become cost prohibitive. Many countries have taken policy steps such as listing Li as a critical mineral. Current commercial Li mining is mostly from dedicated mine sources, including ores, clays, and brines. The conventional ways to extract Li+ from those resources are through chemical processing and includes steps of calcination, leaching, precipitation, and purification. The environmental and economic sustainability of conventional Li processing has recently received increased scrutiny. Routes such as direct Li+ extraction may provide advantages relative to conventional Li+ extraction technologies, and one possible route to direct Li+ extraction includes leveraging intercalation materials. Intercalation material processing has recently demonstrated high selectivity towards Li+ as opposed to other cations. Reviews and reports of direct Li+ extraction with intercalation materials are limited, even as this technology has started to show promise in smaller-scale demonstrations. This paper will review selective Li+ extraction via intercalation materials, including both electrochemical and chemical methods to drive Li+ in and out, and efforts to characterize the Li+ insertion/deinsertion processes.

A Data-Driven Digital Twin of Electric Vehicle Li-Ion Battery State-of-Charge Estimation Enabled by Driving Behavior Application Programming Interfaces

Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs’ SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study’s results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications.

Lithium-ion battery thermal management for electric vehicles using phase change material: A review

Lithium-ion (Li-ion) batteries in electric vehicles (EVs) present a promising solution to energy and environmental challenges. These batteries offer numerous advantages, including high energy density, endurance, minimum self-discharge, and long life, accelerating their adoption in EVs. High temperatures can lead to thermal runaways, causing safety hazards such as short circuits and explosions. Conversely, low temperatures can trigger the formation of lithium dendrites, resulting in failures and operational issues. To address these concerns, phase change materials (PCM) are being explored to store and release thermal energy without significant temperature changes. This review paper presents an overview of PCM for battery thermal management systems. It examines and compares thermal management strategies employed for Li-ion batteries, highlighting their merits, drawbacks, and cost-effectiveness. Different types of heating and cooling mechanism are summarized. Furthermore, the study discusses potential future developments in the field to enhance the thermal management of Li-ion batteries in EVs.

Lithium-ion battery state-of-charge estimation based on long-short-term memory neural network and square-root cubature Kalman filter

Due to the widespread use of Li-ion batteries in electric vehicles, battery management systems for monitoring the status and ensuring the safe operation of Li-ion batteries have been extensively studied. Online monitoring of the state of charge (SOC) is crucial for lithium-ion batteries, but achieving precise SOC estimation is a difficult task due to battery dynamics and the influence of factors such as current, temperature, and operating conditions on SOC variability. This paper introduces a novel approach that combines a Long Short-Term Memory (LSTM) network with a square-root cubature Kalman filter (SRCKF) to address this challenge. To tackle the issue at hand, the proposed methodology employs a two-step approach. Initially, the LSTM network is utilized to capture the complex relationship between the state of charge (SOC) and the measured variables, including current, voltage, and temperature. Subsequently, the output of the LSTM network is subjected to smoothing using the SRCKF technique, leading to precise and consistent SOC estimation. A notable advantage of this method is its ability to simplify the arduous task of parameter tuning for the LSTM network, eliminating the need for constructing a battery model. The experimental results demonstrate that the maximum error in estimating the state of charge (SOC) using this particular method is constrained within the 5% threshold. Compared with using a separate LSTM method at different temperatures, by using the combination method, the root mean square error and maximum error of SOC estimation are greatly reduced.

Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles

State of charge (SoC) estimation is critical for the safe and efficient operation of electric vehicles (EVs). This work proposes a hybrid multi-layer deep neural network (HMDNN)-based approach for SoC estimation in EVs. This HMDNN uses Mountain Gazelle Optimizer (MGO) as a training algorithm for the deep neural network. Our method leverages the intrinsic relationship between the SoC and the voltage/current measurements of the EV battery to accurately estimate the SoC in real time. We evaluate our approach on a large dataset of real-world EV charging data and demonstrate its effectiveness in comparison to traditional SoC estimation methods. Four diverse Li-ion battery datasets of electric vehicles are employed which are the dynamic stress test (DST), Beijing dynamic stress test (BJDST), federal urban driving schedule (FUDS), and highway driving schedule (US06) with different temperatures of 0oC,25oC,45oC. The comparison is made with Mayfly Optimization Algorithm based DNN, Particle Swarm Optimization based DNN and Back-Propagation based DNN. The evaluation indices used are normalized mean square error (NMSE), root mean square error (RMSE), mean absolute error (MAE), and relative error (RE). The proposed algorithm achieves 0.1% NMSE and 0.3% RMSE on average on all datasets, which validates the effective performance of the proposed model. The results show that the proposed neural network-based approach can achieve higher accuracy and faster convergence than existing methods. This can enable more efficient EV operation and improved battery life.

Online Soft Short-Circuit Diagnosis of Electric Vehicle Li-Ion Batteries Based on Constant Voltage Charging Current

Online Soft Short-Circuit Diagnosis of Electric Vehicle Li-Ion Batteries Based on Constant Voltage Charging Current

Electrification of Transport: Advantages and Prospects

Electrification has become the main mode of new energy transportation. This paper compared electric vehicles with internal combustion and hydrogen fuel cell vehicles. Less environmental pollution, higher efficiency of power system, and lower cost were the most favourable attributes of electric vehicles. In addition, comparing Li-ion and other batteries, the greatest energy density, the lowest self-discharge rate, and the longest cycle life made Li-ion the most suitable choice for electric vehicles. However, the Li-ion battery in electric vehicles still has some limitations. The 400 km driving range limits electric vehicles in urban and developed areas. It also made traditional fossil fuel vehicles the best choice for people with long-range travel needs. Under low temperatures, the capacity decline and polarization effects of Li-ion batteries made electric vehicles unable to meet the needs of use in extreme environments. A higher energy density and a wider range of working temperatures are needed to advance further in transportation.

Investigating re-parametrization of electrochemical model-based battery management using real-world driving data

Li-ion batteries in electric vehicles must be utilized more efficiently to lower their economic and environmental cost. To achieve this increase in efficiency, it is of large interest to develop more thorough battery management that can predict internal states in online settings and update usage and control accordingly. Electrochemical models are an important tool in achieving this, and their implementation in battery management systems is the topic of ongoing research. However, electrochemical battery management relies on accurate parametrization and thus requires re-parametrization as a battery ages. We therefore studied viability of re-parametrization for electrochemical model-based battery management. To this end, we performed global sensitivity analysis on selected Doyle-Fuller-Newman model parameters using on-board current measurements. Representative driving data was collected from several types of heavy-duty vehicles. This elucidated which model parameters should be updated periodically to conserve model accuracy and which parameters are sensitive enough to be estimated from the on-board data. Additionally, we studied how parameter uncertainty affects estimation of internal states and highlight how model-based state estimation relying on a beginning-of-life parametrization degrades as electrochemical parameters change with aging.

Nanotechnology used in Li-ion Battery for Electric Vehicles

In recent years, environmental issues have been given increasing attention, and electric vehicles have replaced conventional combustion engined vehicles as a critical focus for development. In order to win the market, electric vehicles (EVs) must have sufficiently good performance, such as a longer mileage range, long cycling life, and time-saving charging, and the performance of the battery is one of the essential factors determining the performance of the EVs. In order to make more environmentally friendly electric vehicles available, the performance of lithium-ion batteries, which are the mainstream EV batteries, must be improved, and nanotechnology can facilitate this process. This report summarizes four promising materials for the cathodes and anodes of lithium batteries for electric vehicles and how nanotechnology can improve them. For LiMn2O4 (LMO), LiNi1-x-yMnxCoyO2 (NMC), and LiNiCoAlO2 (NCA) as cathode materials and Li4Ti5O12 (LTO) as anode material, nano-doping, nano-structuring, and surface coating are effective techniques to enhance the stability and rate capacity, and cycle performance of the battery. In the case of LMC, nanotechnology can help solve the Jahn-Teller distortions and stabilize the structure; for NMC, nanotechnology can suppress ion dissolution and chemical reactivity. For NCA, it can strengthen conductivity and capacity. And finally, for LTO, nanotechnology can faster charge/discharge rate. This paper looks forward to the maturation of these nanotechnologies and their practical application, and the identification of more nanotechnologies that can enhance the performance of Li-ion batteries to help make electric vehicles more usable and contribute to environmental development.

Characterization and modelling of Al and Cu busbar during charging and discharging of Li-ion battery for electric vehicles

• The simulated temperature of the Al-Cu busbar attached to the Li-ion pouch cell is validated with the experimental result. • The temperature generation in the Li-ion cell is influenced by the electrical contact resistance of the weld joint. • The joint resistance of a busbar is mainly dependent on intermetallic formation and grain size at the weld joint of a busbar. The electrical components such as bimetallic busbar joints of the lithium-ion (Li-ion) batteries should be able to withstand high voltages during charge and discharge processes. The busbar is an essential component that transmits high power to electrify the vehicle. The present study describes the sustainability of friction stir welded (FSW) busbar at different C-rates by simulating a Li-ion battery attached to a busbar, then correlating the heat generation of simulation results with an experimental result at 1, 1.5, and 2C-rates. The change in process parameters of FSW samples varies with electrical conductivity at the weld interface. The variation in electrical conductivity with different busbars is due to the formation of various intermetallic and changes in the grain size of the Al and Cu joints. However, the busbar with Cu-rich intermetallic exhibits smaller electrical resistivity. The specific electrical contact resistance of a busbar is obtained from simulation by validating the heat generated during constant time charge-discharge cycles. The temperature rises due to contact resistance in the Al-Cu busbar which can lead to thermal runaway and, eventually, short circuits in the Li-ion battery pack. Based on previous simulation parameters, the Li-ion cells are simulated at 5 and 10C-rates to understand thermal runaway behaviour.

Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review

Electric Vehicles (EVs) have transformed the automotive industry and are becoming a more reliable and consistent mode of public transportation. The development of a pollutionfree environment and improved ecological surroundings is being significantly assisted by battery-powered vehicles. Lithium-ion (Li-ion) batteries are the most widely used type of batteries in EVs because of their superior performance as compared to their counterparts. The core of EVs is their battery management systems (BMS), which can unarguably improve a battery’s performance, operation, safety, and lifespan. Li-ion battery state estimation is one of the most important parts of the implementation of BMS, as it serves an important role in safe and reliable battery operation. Recently, researchers are working on the development of digital twin models to automate and optimize the BMS state estimation process by utilizing machine learning (ML) algorithms and cloud computing. The objective of this study is to review, characterize, and compare various ML-based approaches for the state estimation of different Li-ion battery states. Firstly, this study describes and characterizes several Li-ion battery state estimation approaches proposed in recent years. Secondly, the battery state estimation of electric vehicles is discussed. In addition, the challenges and prospects of Li-ion battery state estimation are put forward.

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

With the widespread use of Lithium-ion (Li-ion) batteries in Electric Vehicles (EVs), Hybrid EVs and Renewable Energy Systems (RESs), much attention has been given to Battery Management System (BMSs). By monitoring the terminal voltage, current and temperature, BMS can evaluate the status of the Li-ion batteries and manage the operation of cells in a battery pack, which is fundamental for the high efficiency operation of EVs and smart grids. Battery capacity estimation is one of the key functions in the BMS, and battery capacity indicates the maximum storage capability of a battery which is essential for the battery State-of-Charge (SOC) estimation and lifespan management. This paper mainly focusses on a review of capacity estimation methods for BMS in EVs and RES and provides practical and feasible advice for capacity estimation with onboard BMSs. In this work, the mechanisms of Li-ion batteries capacity degradation are analyzed first, and then the recent processes for capacity estimation in BMSs are reviewed, including the direct measurement method, analysis-based method, SOC-based method and data-driven method. After a comprehensive review and comparison, the future prospective of onboard capacity estimation is also discussed. This paper aims to help design and choose a suitable capacity estimation method for BMS application, which can benefit the lifespan management of Li-ion batteries in EVs and RESs.

The Effect of Pressure during Sintering on the Interface between Oxide Solid Electrolyte and Cathode in Solid State Lithium Batteries

Conventional Li-ion batteries (LIBs), which have been widely used as main power sources for various electronic devices, are facing huge challenges with the explosive growth of electric vehicles (EVs) market. The safety problems of LIBs in EVs causing fire incident mainly result from the use of liquid electrolytes which provide fuel for combustion. Therefore, solid state lithium batteries using a solid electrolyte are widely accepted as promising candidates for next generation energy storage devices with superior safety performances. However, solid state batteries have limitations that originate from solid-solid interfaces between electrode and solid electrolyte, hindering practical development of solid state batteries. Especially, compared to sulfide based solid electrolyte, oxide based solid electrolyte is rigid, resulting in difficulty in forming intimate contact between solid electrolyte and electrode materials. In this study, using lithium vanadium phosphate (Li3V2(PO4)3, LVP) and lithium aluminum germanium phosphate (Li1.5Al0.5Ge1.5(PO4)3, LAGP) as cathode material and oxide solid electrolyte model system, we demonstrate the effect of pressure during sintering on the contact between LVP and LAGP, and concomitant cell performance. In addition, we found that the crystallinity of solid electrolyte and the content of carbon conducting agent critically affect the contact. Without any complicated interfacial modification, we successfully made decent cathode-solid electrolyte interface with simple method for superior solid state lithium batteries.

Charging Optimization for Li-Ion Battery in Electric Vehicles: A Review

Battery electric vehicles (BEVs) are advocated due to their environmental benign characteristic. However, the long charging time and the degradation caused by fast charging impede their further popularization. Extensive research has been carried out to optimize the charging process, such as minimizing charging time and aging, of lithium-ion batteries (LIBs). Motivated by this, a comprehensive review of existing charging optimization (ChgOp) techniques is provided in this article. First, the operation and models for LIBs are explained. Then, unexpected side effects, especially for the aging mechanism (AM) of LIB associated with unregulated fast charging, are scrutinized. This provides a solid theoretical foundation and forms the optimization problem. Following this endeavor, the general framework with critical concerns for ChgOp system design is overviewed. Within this horizon, the state-of-the-art ChgOp techniques, clustered into open- and close-loop categories, are reviewed systematically with their respective merits and shortcomings discussed. Finally, the development of an emerging charging control protocol with both real-time affordability and degradation consciousness is further discussed as an open outlook.