Electric Vehicle Battery Pack Research Articles

Design Methodology and Circuit Analysis of Wireless Power Transfer Systems Applied to Electric Vehicles Wireless Chargers

In road transportation, the market for electric vehicles (EVs) is considered a potential solution for addressing issues related to gas emissions and noise pollution. Due to the limited driving range of the EV battery pack, the charging process must be fast and safe for EV drivers. Wireless charging technology for EVs has gained attention in recent years, and in this research, the authors explore the analysis and design of a resonant magnetic wireless system for charging electric vehicles. The authors propose a design methodology for a serial–serial (SS) wireless system, which outlines how to determine the appropriate pad dimensions for transferring power to the EV battery. The design approach is crucial to attaining the best possible coupling performance and efficiency. Additionally, the magnetic design of the pad is validated using Ansys Maxwell software, and the proposed design is co-simulated using Ansys Simplorer to analyze the performance of the system. Simulation results demonstrate that the proposed model can transfer over 3.7 kW of power with an efficiency of over 90.02%. The paper also discusses the bifurcation phenomenon at the resonance condition to ensure maximum efficiency.

Safety assessment of thermal runaway behavior of lithium-ion cells with actual installed state

To quantitatively evaluate the overcharge-to-thermal runaway (OC-to-TR) behaviors of lithium-ion (Li-ion) cells in practice, here reported are overcharge tests on Li-ion pouch cells with NCM811 cathode under actual restricted condition. Such a restriction results from two splints during the overcharging process to simulate their actual installed state in the battery pack of electric vehicles (EVs). From the monitoring of the cell voltage, temperature, and expansion force, a unique feature that is discovered in this work is that the OC-to-TR process is strongly correlated to the evolution of expansion force, which can further be divided into five stages with distinctly different features. Moreover, this unique correlation depends critically on the current rate (C-rates) and preload. It is concluded that the Li-ion cells were more hazardous at higher C-rates; when a larger preload was applied to the cell, it exhibited better tolerance to overcharge, but the TR reactions became more hazardous (i.e., higher temperature and more severe TR reactions). These findings allow us to propose a safety evaluation method to evaluate the TR risks and TR hazards for Li-ion cells under different restricted conditions. In addition, a safety management method was proposed based on the evolution of several key parameters. The research results can provide some useful information to the safety design of Li-ion pouch cells and the enhancement of the safety function of battery management system.

Battery thermal runaway propagation time delay strategy using phase change material integrated with pyro block lining: Dual functionality battery thermal design

In the view of the thermal runaway scenario phase change material are not capable to withstand thermal propagation phenomenon, hence it is important to provide a novel structure which can delay thermal runaway trigger point and not allows generated heat to propagate at faster rate from one cell to another. In this work, a series of numerical investigations are performed from cell to pack level configuration under heater assisted triggering method by providing a constant heat supply of 500 W until electrochemical kinetics of cell gets trigger. This setup is used to investigate, time delay and thermal propagation phenomenon by integrating submerged layer of phase change material. Conclusive evidence from investigation suggests that by integrating a submerged layer of phase change material, the thermal runaway trigger point can be effectively delayed. However, because phase change material is highly flammable in nature, heat propagates from one cell to another in a very short period, causing the entire pack to go into catastrophic thermal failure within short span of 10s. To delay this thermal propagation phenomenon and compliance GB 38031 and GB 38032 “national safety regulation of battery pack for Electric Bus and Electric Vehicle” that suggest once cell is trigger, neighbouring cell should not run in thermal runaway for at least 300s, we have optimized battery pack structural design by introducing low thermal conductive pyro liner at the wall of PCM. Results showed, this optimize design represents dual functionality of delaying thermal runaway trigger point of cell along with slowing down thermal propagation for at least 800s.

Fuzzy logic approach for failure analysis of Li-ion battery pack in electric vehicles

Vehicle electrification is one of the changes in the modern-day car enterprise trend. The battery pack is the most vital and precarious part of a battery-powered electric vehicle, which necessitates accurate and reliable designs to ensure acceptable safety. To this end, one of the tried and tested methods that help identify problems and make products more reliable is Failure Modes and Effect Analysis (FMEA). This paper presents a Fuzzy FMEA for risk assessment of an immersion-cooled battery pack (ICBP) in electric vehicles. As a new technology, immersion cooling can facilitate high-rate fast charging and a longer battery life cycle for lithium-ion batteries. Different failure modes and relevant causes and effects are investigated in this vein. Each failure's severity, occurrence, and detection rank are extracted and displayed as a standard FMEA table. The monitoring results of all stages of design, manufacture, and initial tests indicate that the most important causes of failure in an ICBP are the sealing method, BMS function, cell temperature control, structure, and assembly of mechanical components. To some extent, it was attempted to reduce the occurrence of failures by recommending actions to reduce the occurrence of those failures. Consequently, a significant improvement was achieved, and the production and installation of problematic parts were managed efficiently and effectively with the help of an FMEA control plan table.

An Early Soft Internal Short-Circuit Fault Diagnosis Method for Lithium-Ion Battery Packs in Electric Vehicles

The early detection of soft internal short-circuit faults in lithium-ion battery packs is critical to ensuring the safe and reliable operation of electric vehicles. This article proposes a fault diagnosis method that can achieve the detection and assessment of soft internal short-circuit faults for lithium-ion battery packs. Specifically, based on the incremental capacity curve, fault features are extracted from the data, making them easier to identify than small voltage differences. Then, the local outlier factor method is proposed to detect the early soft internal short-circuit fault by calculating the local outlier factor value of each cell within the battery pack. Furthermore, soft short-circuit simulations of a series-connected battery pack under different conditions and various short-circuit resistance values are conducted to generate an internal short-circuit fault data set. Finally, the validity of the proposed fault diagnosis method is verified using simulation and real-world vehicle data. The results show that the proposed method can effectively identify the short-circuit fault of the battery at the early stage, accurately locate the faulty cells in the battery pack, and describe the severity of the fault.

Capacity evaluation and degradation analysis of lithium-ion battery packs for on-road electric vehicles

Accurately calculating the capacity of battery packs is of great significance to battery fault diagnosis, health evaluation, residual value assessment, and predictive maintenance in electric vehicles (EVs). However, many existing methods are not suitable for real vehicle conditions. In this paper, a capacity calculating method specialized for EVs is proposed. This method uses an open circuit voltage (OCV) correction strategy to guarantee the credibility of the battery state of charge (SOC). Its accuracy is validated by full charging carried out on 5 testing vehicles and the mean absolute error is 2.6 Ah. Data from 707 on-road electric vehicles are collected and the capacities of their battery packs are calculated through the proposed method. Taking the mileage and service life as variables, two degradation models of battery capacity are established with mean absolute errors equal to 3.138 Ah and 3.137 Ah. According to the degradation model, the battery's average service life is 71.8 months, and the average service mileage is 121,700 km. Moreover, correlations between capacity degradation and user behaviors are analyzed, and the results indicate that charging the battery with the start point at a SOC between 30 % and 40 % can effectively relieve the degradation.

Frequency Folding for LLC Resonant Converters in EV Charging Applications

To realize a wide voltage variation of electric vehicles (EV) battery packs, the conventional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converter needs to operate over a wide frequency range, which generally complicates the magnetics design and decreases the efficiency when the operating frequency is far away from the resonant frequency. Responding to the challenge, a frequency folding technique is proposed and explained with an exemplary new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converter. The proposed converter utilizes both the inductive region and the capacitive region of the conventional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converter through a one-shot operation of the relay during the whole charging process. As the battery voltage increases, the converter switches from the capacitive region to the inductive region. Throughout the charging process, the operating frequency is effectively reduced, and zero-voltage switching is maintained, resulting in a relatively higher overall efficiency. A detailed description on the design procedure of the proposed converter is described. A scaled-down 400-W prototype with 200 V input and 120 V–210 V output range verified the advantages of the proposed concept and measured efficiency of 98% at the maximum output power.

Connection fault diagnosis for lithium-ion battery packs in electric vehicles based on mechanical vibration signals and broad belief network

The connection faults between the cells of a battery pack can increase contact resistance and thus result in abnormal heating at the connections, which can seriously damage or even fail the battery pack. This work therefore proposes a novel connection fault diagnosis method based on mechanical vibration signals rather than voltage and current measurements. Firstly, this work simulates the vibration environment, which resembles that of the actual operation of a lithium-ion battery pack in electric vehicles. The optimal sensor placement is achieved via a sparse-learning algorithm, and the vibration signals are collected on this basis. Following that, this work proposes a broad belief network (BBN) for detecting and locating connection faults in lithium-ion battery packs based on the vibration signals. Since fault diagnosis needs to adapt to new data as they become progressively available in real-time, two incremental-learning algorithms are paired with the BBN, such that the network can achieve fast reconstruction and expansion without re-training from scratch. Empirical evidence suggests that the diagnostic accuracy of the proposed method is 93.25%, which demonstrates the effectiveness and feasibility of the proposed method.

Economic Payback Time of Battery Pack Replacement for Hybrid and Plug-In Hybrid Electric Vehicles

This article investigates the economic viability of replacing the high-voltage battery pack of a power-split hybrid electric vehicle (HEV) and a plug-in hybrid electric vehicle (PHEV) by estimating the impact of battery aging on the fuel economy, drivability capability, and electric range. The HEV is modeled first, an optimal energy management strategy based on dynamic programming is then implemented, and experimental characterization data for the battery cell is presented. The batteries are tested to a heavily aged state, with up to an 84% loss of capacity. The battery pack payback period is estimated by assessing the vehicle operative costs in terms of fuel and electricity as obtained through numerical simulations as a function of battery aging. Replacing the battery pack at the conventional end-of-life limit of 80% residual capacity is suggested not to be convenient from an economic standpoint for both the HEV and the PHEV. On the other hand, acceptable payback periods (i.e., 2–5 years) can be achieved for the battery pack when being replaced at 20% to 40% residual capacity. The proposed methodology can be implemented to advise an HEV or PHEV user regarding the benefit of replacing the battery pack due to excessive aging.

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries.

Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxide, Lithium Iron Phosphate, Lithium Manganese Oxide, Lithium Titanium Oxide, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. Results: Prediction results with overall Mean Absolute Percentage Error of 0.0126 have been obtained for XGBoost algorithm. Among these results, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide type cell chemistries stand out with their mean absolute percentage errors of 0.0035 and 0.0057 respectively. Also, algorithm fitting performance is relatively better for these chemistries at 100% state of charge and 60°C temperature compared to ANN results. ANN algorithm predicts with mean absolute error of approximately 0.0472 overall and 0.0238 and 0.03825 for Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. The fitting performance of ANN for Nickle Manganese Cobalt Oxide at 100% state of charge and 60°C temperature is especially poor compared to XGBoost. Conclusions: For an electric vehicle battery calendar ageing prediction application, XGBoost can establish itself as the primary choice more easily compared to ANN. The reason is XGBoost's error rates and fitting performance are more usable for such application especially for Nickel Cobalt Aluminum Oxide and Nickel Manganese Cobalt Oxide chemistries, which are amongst the most demanded cell chemistries for electric vehicle battery packs.

The Use of a Vortex Generator for the Efficient Cooling of Lithium-Ion Batteries in Hybrid Electric Vehicles

High heat flux dissipation from the Lithium-ion battery pack of hybrid electric vehicles is one of the major concerns in the automotive sector, since it directly affects the performance and it may also lead to permanent failure. Among various thermal management systems, forced air cooling is most favorable due to its light weight, compactness, lower cost, and design flexibility. In the present work, a battery thermal management system with the two types of vortex generator in the coolant passage has been used to enhance the performance. A numerical model has been developed in commercial code to investigate the performance of the delta winglet and circular protrusion type vortex generator. Apart from that, both types of vortex generator have been compared in terms of various parameters such as pressure drop, weight, and maximum temperature. From the results of the simulation, it has been observed that both vortex generators performed better at the attack angle of 30° in comparison to the attack angles of 45° and 60°. Furthermore, it has also been found that the cooling system with protrusion as the vortex generator has performed better in terms of thermos-hydraulic performance compared to the cooling system with the delta winglet vortex generator. In addition to that, the protruded system is lighter in weight compared to both the plain and delta winglet systems, and is recommended.

General Decoupling and Sampling Technique for Reduced-Sensor Battery Management Systems in Modular Reconfigurable Batteries

The capacity and voltage rating of battery packs for electric vehicles or stationary energy storages are increasing, which challenge battery management and monitoring. Breaking the larger pack into smaller modules and using power electronics to achieve dynamic reconfiguration can be a solution. Reconfigurable batteries come with their own set of problems, including many sensors and complex monitoring systems, high-bandwidth communication interfaces, and additional costs. Online parameter estimation methods can simplify or omit many of these problems and reduce the cost and footprint of the system. However, most methods require many sensors or can only estimate a subset of the elements in the module’s equivalent circuit model (ECM). This paper proposes a simple decoupling technique to derive individual modules’ voltage and current profiles from the output measurements without direct measurement at the modules. The determined profiles can achieve a high sampling rate with minimum communication between the battery management system (BMS) and the modules. With accurate profiles, an estimation technique can easily determine the parameters of the modules. Provided simulations and experiments confirm this claim by estimating the parameters of a first-order ECM with a parallel capacitor. The proposed technique reduces the number of sensors from 2N + 2 to only two at the pack’s output terminals.

Analysis and design of battery thermal management under extreme fast charging and discharging

Thermal management is critical for the safety of electric vehicle (EV) battery packs, especially under ultra-fast and extreme fast charging and discharging use conditions. Liquid cooling is particularly attractive over other battery thermal management technologies due to its high heat transfer coefficient and low power usage. This paper systematically studies a liquid-cooled battery thermal management system for limiting the maximum temperature and voltage excursions of a standard 18650 lithium-ion battery pack. Considering two different coolant flow configurations, the influences of coolant flow rate, inlet temperature, battery capacity, charge rate, and discharge rate are investigated through a detailed computational study. Based on the simulations, design windows are developed for keeping the maximum temperature during charging below 45 °C and during discharging within 60 °C, as well as maintaining the cell voltage < 4.2 V. The design windows provide the maximum charge and discharge rates that can be achieved with a given coolant flow rate and inlet temperature for a battery capacity. Equivalently, the design windows may be utilized for a desired charge and discharge rate to obtain the required coolant flow design. The study provides valuable insight into advancing the ultra and extreme fast charge and discharge capabilities of battery packs for EV applications.

Analysing unbalanced ageing in EV battery Packs using the Low-Cost Lumped Single Particle Model (LSPM): the impact of temperature gradients among parallel-connected cells

Lithium-ion battery cells are widely used in electric vehicles (EVs) due to their high energy density and long lifespan. However, these cells are sensitive to temperature and can age at different rates depending on the temperature they are subjected to. While EV battery packs are equipped with a battery thermal management system (BTMS) to regulate the temperature of the pack, it cannot completely eliminate temperature gradient within the pack due to the limited cooling capabilities. As a result, a maximum temperature gradient of 5°C is allowed in BTMS designs for EVs, which leads to uneven ageing speeds within the pack. Due to practical limitations of battery management system (BMS), it is difficult to directly measure the currents flowing through each parallel-connected cell, making it challenging to actively balance the cells. To investigate the pack-level current distribution and ageing trends caused by temperature gradient, this paper adopts the low computational cost Lumped Single Particle Model (LSPM) coupled with Arrhenius dependency to investigate the influence of temperature on current distribution as well as the resulting unbalanced ageing rates among parallel-connected cells in a 4s6p Panasonic NCR18650PF battery pack.

Review of on-board conductive charger topologies for electric transportation

This paper presents an overview of on-board conductive charger topologies for electric vehicles (EVs). Battery packs in electric and plug-in hybrid electric vehicles (EVs/PHEVs) need frequent energy refills to fulfil their duty cycles and overcome the range anxiety of vehicle owners. Due to the limited availability of fast charging infrastructures, all automotive manufacturers prefer an on-board battery charger in their vehicles. However, there are power level limitations for these chargers because of the weight, space and cost constraints. On-board charging systems can be either conductive or inductive. This paper reviews the topologies that are involved in conductive charging only. Globally, all automotive companies provide levels 1 and 2 for residential charging of vehicles. Single- and two-stage power converter topologies are summarised with their limitations. Furthermore, the current trends in the DC-DC converters for the two-stage conversion battery charger topologies are also outlined. This paper provides an overview of various topologies and configurations involved in EV charging with their limitations.

Joint Estimation of State-of-Health and State-of-Charge for Lithium-Ion Battery Based on Electrochemical Model Optimized by Neural Network

Accurate estimation of battery state including state-of-charge (SOC) and state-of-health (SOH) plays an important role in improving the performance and lifespan of battery packs in electric vehicles. From the perspective of electrochemical mechanisms, the battery SOC and SOH strongly depends on the status of battery electrochemical reaction processes and associated variables, especially the number of lithium-ion in solid phases. In this article, a method based on a simplified battery electrochemical model, which is optimized by back propagation neural network, was proposed to estimate the battery SOC and SOH. The simplified battery electrochemical model called single particle model is employed to obtain the number of recyclable lithium-ions at different aging degrees and the lithium-ion changes of solid particles with different loaded currents. The parameters of the electrochemical model are identified by an algorithm hybridizing Coyote Optimization Algorithm and Grey Wolf Optimizer, abbreviated to Hybrid Coyote Optimization Algorithm and Grey Wolf Optimizer for its great balance in operational speed and optimization accuracy. To improving the speed and accuracy of model parameter identification, the rang of the number of recyclable lithium-ions is narrowed by battery capacity, which is estimated by the back propagation neural network. The accuracy and robustness of the proposed method are systematically evaluated, and with the mean absolute percentage errors of promising battery capacity and SOC prediction results are less than 2% and 1.8%, respectively. The operation time of the battery SOC prediction during federal urban driving schedule cycles are less than 2 s.

Використання часткового зарядно-розрядного циклу акумуляторної батареї для збільшення її ресурсу

The work investigates a way to extend the lifespan of electric vehicle battery packs. Currently, there is a notable absence of solutions for recycling and disposal of lithium-based battery packs. Therefore, investigating possible ways to prolong the lifespan of existing electric vehicle battery packs becomes crucial to delay their disposal and allow more time to find new effective recycling solutions. This study analyzes the average daily mileage of vehicles in Ukraine and other countries, revealing that it does not exceed 50 km per day. This mileage is significantly lower than the full charge-discharge cycle range of any electric vehicle traction battery pack. The authors investigated the impact of limiting the depth of discharge of the battery pack on its lifespan and found that by restricting the depth of discharge to 50%, the battery lifespan could increase by 20%. Statistical data from other scientists were gathered and organized to analyze the influence of the depth of discharge on the battery lifespan across different charge levels during the charge-discharge cycle. It was shown that when using battery packs with the same depth of discharge but in different charge-discharge cycle ranges, the batteries degrade differently. In conclusion, it was noted that to increase the battery lifespan, it is essential to keep it in the optimal charge level range for as long as possible. The paper provides recommendations for identifying this optimal charge level range. It proposes ways for upgrading electric vehicle control systems that can increase the lifespan of their traction battery pack without setting substantial restrictions for users.

Optimal battery state of charge parameter estimation and forecasting using non-linear autoregressive exogenous

The lithium-ion battery (LiB) has become the most widely used energy storage system for electric vehicles (EVs) due to its many advantages. The EV battery pack needs a battery management system (BMS) to estimate the state of charge (SOC) and balance the energy capacity through the cells. Apart from the fact that it is still challenging to accurately solve, the SOC forecasting represents an important concern in the study sector. This research proposes an effective battery SOC forecasting approach utilizing the non-linear autoregressive exogenous model (NARX) time’s series optimized Levenberg-Marquardt training algorithm, and Bayesian-Regularization (BR). The suggested technique is well-known for its resilience and high performance in nonlinear and complex system prediction, and it is extensively used in a wide range of disciplines. Also, the precision of the NARX technique has been investigated as a function of training data sets, error classifications based on experimental data of LiB. Both algorithms were evaluated with experimental data. Discharging followed by resting process was conducted on a 2.6 Ah LiB. They demonstrate good convergence in the low error and regression. In an effort to address a gap in the field, this paper offers a comparison between NARX-LM and NARX-BR algorithms for the LiB SOC prediction. Both algorithms are optimized the ANN using times series analysis based in the same training data. The results show that NARX-BR is more rapid and accurate with a low mean square error (MSE) of 2.39 10-5 than NARX-LM, which achieved an MSE of 1.11. Thus, it shows NARX-BR as an effective technique for LiB SOC prediction.

Consistency Evaluation of Electric Vehicle Battery Pack: Multi-feature Information Fusion Approach

Consistency Evaluation of Electric Vehicle Battery Pack: Multi-feature Information Fusion Approach

전기자동차 배터리팩 내부 자연대류 열전달 특성에 관한 수치해석적 연구

This study primarily aimed to explore the natural convection heat transfer characteristics inside the battery pack of electric vehicle equipped with effusion holes and rectangular fins. Three-dimensional numerical simulations were performed to examine the effects of effusion hole diameter, rectangular fin thickness and height, and effusion hole and rectangular fin arrangements. The local and average heat transfer coefficients on the inner surface of the battery pack upper cover were obtained from numerical simulations. The results showed that the flow velocities between the battery modules and under the battery pack upper cover increased with the aid of the effusion holes and rectangular fins. This increased flow velocity has a significant influence on the improvement of the natural convection heat transfer of the battery pack upper cover.