Electric Vehicle Battery Pack Research Articles

Voltage-fault diagnosis for battery pack in electric vehicles using mutual information

The safety of battery system is compromised by the abusive operation and aging, potentially resulting in the abnormal voltage levels. Rapid detection and accurate diagnosis of voltage fault are crucial for ensuring the safety of battery packs. A battery voltage fault diagnosis method is proposed by using the mutual information in this work, which can identify faulty cells timely. Specifically, the voltage of battery pack in an electric vehicle is collected, and the mutual information of voltages between each paired-cells is calculated. The presence of faulty cells disturbs the original distribution of mutual information. Therefore, the Ryan Joiner (RJ) test is used to judge whether the normal distribution of calculated mutual information or not. The interquartile test is used to detect the outliers. The faulty cell is diagnosed by matching the cell corresponding to the outliers. The defaults resulting from an internal short circuit and mechanical vibration are analyzed and diagnosed. The method can avoid misdiagnosis caused by the mutual influence between different faults. Furthermore, the fault diagnosis capability of presented method is discussed under various states of aged cells. The experimental results verify the effectiveness, robustness, model-free, and easiest-to-implement of the presented method.

Theoretical and experimental investigations on liquid immersion cooling battery packs for electric vehicles based on analysis of battery heat generation characteristics

With the increasingly severe challenges of the thermal management of battery packs for electric vehicles, the liquid immersion cooling technology has gradually attracted more attention due to its superior characteristics such as high heat dissipation efficiency, well temperature uniformity and low risk of thermal runaway. To investigate the heat transfer characteristics of the liquid immersion cooling BTMSs, the 3D model of the 60-cell immersion cooling battery pack was established, and a well-established heat generation model that leveraged parameters derived from theoretical analysis and experiments was incorporated into the 3D simulation to analyze the thermal characteristics of battery pack. The simulation results showed that under the discharging rate of 2C, even with the lowest coolant flow rate in this work (0.2 L/min), no localized abnormal overheating within the battery pack was observed, and the highest temperature was 34.22 °C. The temperature uniformity within the battery pack improved with the increase of the coolant flow rate. It was recommended to maintain a flow rate above 0.5 L/min to ensure a temperature difference below 5 °C. The experimental apparatus of the immersion cooling battery pack was also developed to explore the heat dissipation and temperature uniformity at 2C discharge rate. The simulation results were in well agreement with the experimental results, with the deviation less than 0.43 °C when the flow rate exceeded 0.6 L/min. The effect of the localized high-rate discharge events (4.5C and 6.5C) at different battery pack locations on temperature distribution and uniformity was further discussed based on the verified 3D model. The results suggested that liquid immersion cooling systems offered promising potential for achieving both efficient heat dissipation and preventing thermal runaway in battery packs.

A strategy to assess the use-phase carbon footprint from energy losses in electric vehicle battery

The electric vehicles are not completely emission-free. The vehicles consume power and the power generation processes involve carbon emissions, resulting in indirect vehicle usage carbon emissions. A use-phase carbon emissions assessment can help electric vehicle designers and manufacturers analyze the onboard carbon footprint and mitigate vehicle-related carbon emissions through energy consumption optimization. In this article, we focus on the use-phase carbon footprint based on the energy losses in electric vehicle battery packs. A battery pack energy loss model is established to examine the carbon footprint of four main subsystems of the battery pack, including the energy storage system, thermal management system, and battery junction box. We use the proposed method to evaluate the battery pack use-phase carbon emissions in four different cities from four main continents. The results show that the highest emissions appear in Tokyo (436.2 kgCO2) due to the relatively high greenhouse gas factor on the grid, the London has the lowest 207.5 kgCO2 emissions due to the massive adoption of sustainable energy during power generation. With a similar high greenhouse gas factor, but Sydney shows lower emissions (347.5 kgCO2) compared to Tokyo, which illustrates the closer the annual average temperature to the battery suitable operation range, the lower energy loss happens inside the battery pack due to the low thermal management load and resistance energy loss in the cells. This electric vehicle has the highest energy loss rate (20.8%) in New York, 343.6 kgCO2 emissions, and the highest thermal management system energy losses show the high seasonal temperature difference causing the high thermal management load to maintain the battery temperature. The proposed method can be used for different battery pack configurations and vehicle uses, providing researchers with a robust and versatile use-phase carbon emissions assessment strategy.

Full-Scale Fire Extinguishing Experiments to Analyze the Adaptability of Firefighting Facilities for Electric Vehicle Fires in Underground Parking Lots

Recently, the number of electric vehicles (EVs) has rapidly increased owing to an increase in international environmental problems and domestic eco-friendly policies. With the increase in the number of EVs, the number of EV fires is also increasing. However, full-scale experiments for fire risk analysis or verification studies on the adaptability of present firefighting facilities are lacking , except for EV fire suppression experiments. In this study, to analyze the adaptability of firefighting facilities to EV fires in underground parking lots, fire extinguishing experiments on EV fires were conducted by considering the conditions of firefighting facilities applied to existing buildings. The first experiment simulated a current underground parking sprinkler system (pre-action system). In the second experiment, a lower sprinkler system was added to the upper sprinkler system. Experiment #1 confirmed that preventing the spread of thermal runaway in EV battery packs was difficult but blocking fire spread to adjacent vehicles was possible. Experiment #2 confirmed that thermal runaway occurred in approximately 50% of EV battery packs. These experiments concluded that fire spread to adjacent vehicles could be blocked by the spray from the upper sprinkler system.

Welding Challenges and Quality Assurance in Electric Vehicle Battery Pack Manufacturing

Electric vehicles’ batteries, referred to as Battery Packs (BPs), are composed of interconnected battery cells and modules. The utilisation of different materials, configurations, and welding processes forms a plethora of different applications. This level of diversity along with the low maturity of welding designs and the lack of standardisation result in great variations in the mechanical and electrical quality of the joints. Moreover, the high-volume production requirements, meaning the high number of joints per module/BP, increase the absolute number of defects. The first part of this study focuses on associating the challenges of welding application in battery assembly with the key performance indicators of the joints. The second part reviews the existing methods for quality assurance which concerns the joining of battery cells and busbars. Additionally, the second part of this paper identifies the general trends and the research gaps for the most widely adopted welding methods in this domain, while it renders the future directions.

Transformer-based lithium battery fault diagnosis research for electric vehicles

With the fast advances of new energy vehicles, the EV battery technology needs to be further improved to follow the step. How to effectively diagnose the electric vehicle’s lithium battery fault becomes a hotspot in the academic circle. This study has proposed new method that uses the state of charge of the battery and self-coder depth to detect faults in the lithium battery group of electric vehicles. First, the study investigates the single lithium battery faults. Then, it builds a lithium battery group fault diagnosis model by integrating the battery charge state and denoising converter network. Finally, it uses a dataset and retired battery group to validate the model’s performance. The results show that the proposed model achieves an accuracy of 93.18% and a recall rate of 93.73% in identifying the faults in the lithium batteries of the electric vehicles and its F1 value is as high as 0.95. Moreover, the modeling method has the lowest prediction error, indicating its high accuracy and robustness in diagnosing the faults of battery packs. This study aims to provide an effective solution for the fault diagnosis of lithium battery packs in electric vehicles.

Employing extended surfaces for a hybrid battery thermal management system comprising PCM and refrigerated cooling air: An experimental investigation

If electric vehicles (EVs) are to supersede a larger share of the automobile market, developing safer battery packs with longer life and larger energy and power capacities is crucial. An effective battery thermal management system (BTMS) can promise many critical attributes of an EV battery pack, such as safety, life, and reliability. This article presents an effective BTMS with a novel hybrid design in which phase change material (PCM) is hybridized with a refrigerated air stream equipped with fins. The presented hybrid design is investigated experimentally and the role of fin structure and environmental conditions are examined. In addition, active and passive cooling strategies are also investigated to provide a more comprehensive analysis. The results show that the pure passive BTMS cannot control the module temperature in hot environments. On the other hand, although an active BTMS incorporating the refrigerated air provides a fast cooling process, the maximum temperature difference exceeds the improper value of 15 °C. The results demonstrate that the hybrid BTMSs can effectively control the temperature as well as minimize the maximum temperature difference. Furthermore, employing fins in the hybrid BTMSs leads to faster cooling. Among the three investigated fin structures, the spiral structure shows the best performance in controlling the average temperature of the module with a 4.2 °C temperature reduction relative to a similar case with no fin. The results of this study show that in long-term operation, a hybrid cooling strategy requires about one-third less energy in comparison to an active cooling strategy.

An integrated cell-to-pack design based on an origami sandwich structure to enhance cooling and mechanical performances of battery packs in electric vehicles

The rapid growth in the energy density of batteries in electric vehicles has led to the need for complex structural components and cooling systems to ensure the safety of the battery pack. However, these inactive materials occupy a significant portion, accounting for at least 35% of the pack's volume. To enhance the volume utilization rate of the battery pack, this paper proposes a novel origami structure with its optimized geometry that enhances thermal and mechanical performances as well as crashworthiness performance. The simulation results of temperature evaluation show that the proposed origami channels for a battery pack can reduce the maximum temperature by more than 2.6 °C at a 5C discharging rate and by over 20 °C under internal short-circuit conditions compared with a flat cooling channel. The experimental results of a quasi-static punch compression test demonstrate the enhanced mechanical properties of the origami channels with a significant increase in peak crushing force and impact energy absorption compared with flat cooling channels.

Service life estimation of electric vehicle lithium-ion battery pack using arrhenius mathematical model

Durability is a desired characteristic for all battery packs in Electric Vehicles. In this study, the service life of the EV battery pack under real-world operating conditions is projected using an Arrhenius mathematical simulation model. The model comprises a 39.2 kWh EV Lithium-Ion battery pack integrated with a three-phase inverter to convert the battery pack’s Direct Current output to Alternating Current. In addition, the Alternating Current output is coupled to a 100 kW permanent magnet synchronous motor, which is regarded as the load. A field-oriented controller provides pulse width-modulated output signals that are supplied back to the inverter to generate the correct driving current. Variable conditions of charge rate (C-rate: 1.25C − 4C), discharge rate (C-rate: 0.5C − 4C), temperature (25°C–60°C), and depth of discharge (30%–90%) are evaluated to determine the battery pack’s service life. Under a 4C charge rate/0.5C discharge rate and 50% depth of discharge, the modeling results indicate the battery pack has a service life of approximately 6,000 h at low temperatures (25°C) and roughly 3,000 h at high temperatures (60°C). The model has been validated by comparing the results with experimental data from the literature.

Robotic Disassembly Platform for Disassembly of a Plug-In Hybrid Electric Vehicle Battery: A Case Study

Efficient processing of end-of-life lithium-ion batteries in electric vehicles is an important and pressing challenge in a circular economy. Regardless of whether the processing strategy is recycling, repurposing, or remanufacturing, the first processing step will usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. This work was conducted as a case study to demonstrate the concept of the autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported, and future research directions are suggested.

A Multimode Power Processor With Wired and Wireless Battery Charging for Electric Vehicle

The propulsion unit is the prime power module in an Electric Vehicle (EV) that keeps it moving. This module includes a propulsion motor and a power electronic interface that converts DC to three phase AC for driving the motor. In addition to this, separate power electronic modules are used for on-board wired charging and wireless charging. This paper proposes a multimode power processor for EV capable of serving the purpose of three different power modules required for propulsion, wired charging and wireless charging. This optimizes the weight, volume, and cost of the EV as separate power modules are avoided for three different modes. This paper also proposes a new scheme for the transmitting side power electronic module that taps power from the single-phase utility grid and generates high frequency AC using a resonant inverter for wireless power transfer. In both the wired and wireless charging method, the proposed technique draws power from the single-phase wall outlet, charges the EV battery pack with constant current-constant voltage (CC-CV) charging logic and performs power factor correction operation at the input AC grid side. A laboratory scale prototype is developed for experimentally validating the proposed concept with a 24 V, 30 Ah battery set and a 24 V, 400 W BLDC motor.

Employment of Artificial Intelligence (AI) Techniques in Battery Management System (BMS) for Electric Vehicles (EV): Issues and Challenges

Rechargeable Lithium-ion batteries have been widely utilized in diverse mobility applications, including electric vehicles (EVs), due to their high energy density and prolonged lifespan. However, the performance characteristics of those batteries, in terms of stability, efficiency, and life cycle, greatly affect the overall performance of the EV. Therefore, a battery management system (BMS) is required to manage, monitor and enhance the performance of the EV battery pack. For that purpose, a variety of Artificial Intelligence (AI) techniques have been proposed in the literature to enhance BMS capabilities, such as monitoring, battery state estimation, fault detection and cell balancing. This paper explores the state-of-the-art research in AI techniques applied to EV BMS. Despite the growing interest in AI-driven BMS, there are notable gaps in the existing literature. Our primary output is a comprehensive classification and analysis of these AI techniques based on their objectives, applications, and performance metrics. This analysis addresses these gaps and provides valuable insights for selecting the most suitable AI technique to develop a reliable BMS for EVs with efficient energy management.

Enhancement in air-cooling of lithium-ion battery packs using tapered airflow duct

Temperature uniformity and peak-temperature reduction of lithium-ion battery packs are critical for adequate battery performance, cycle life, and safety. In air-cooled battery packs that use conventional rectangular ducts for airflow, the insufficient cooling of cells near the duct outlet leads to temperature nonuniformity and a rise in peak temperature. This study proposes a simple method of using a converging, tapered airflow duct to attain temperature uniformity and reduce peak temperature in air-cooled lithium-ion battery packs. The conjugate forced convection heat transfer from the battery pack was investigated using computational fluid dynamics, and the computational model was validated using experimental results for a limiting case. The proposed converging taper provided to the airflow duct reduced the peak temperature rise and improved the temperature uniformity of the batteries. For the conventional duct, the boundary layer development and the increase in air temperature downstream resulted in hotspots on cells near the outlet. In contrast, for the proposed tapered duct, the flow velocity increased downstream, resulting in improved heat dissipation from the cells near the outlet. Furthermore, the study investigated the effects of taper angle, inlet velocity, and heat generation rate on the flow and thermal fields. Notably, with the increase in taper angle, owing to the increase in turbulent heat transfer near the exit, the location of peak temperature shifted from the exit region to the central region of the battery pack. The taper-induced improvement in cooling was evident over the entire range of inlet velocities and heat generation rates investigated in the study. The peak temperature rise and maximum temperature difference of the battery pack were reduced by up to 20% and 19%, respectively. The proposed method, being effective and simple, could find its application in the cooling arrangements for battery packs in electric vehicles.

Discharge and Thermal Distribution Characteristics of Electric Vehicle Battery Pack in Closed Circuit System

This paper presents an experimental study of the depth of discharge (DOD) and temperature distribution characteristics at different locations of the lithium-ion battery (LIB) pack in the closed circuit system. Three different discharge power setups i.e., 600 W, 800 W, and 1000 W are prepared for investigating the depth of discharge and temperature characteristics of commercial LIB. Voltage measurement was implemented to achieve the DOD curve, while thermocouple measurement was used to identify real-time temperature at four different locations of the LIB. As a result, internal resistance and discharging time tend to be increased, while the voltage and current decline linearly from 0% to 80% of LIB capacity. Discharge power greatly affected the four variables when the process continued to the 10% cut-off voltage. Furthermore, the heat generation of the LIB caused a rise in temperature on its surface. The highest temperature was identified on the LIB cell surface followed by an air gap, internal surface casing, and external surface casing temperature. Among all locations, the real-time temperature on the LIB surface operated close to the upper limit of optimum temperature. Due to that reason, increasing of discharge power should be maintained to extend battery cycle life as well as to prevent battery failure. The high-temperature difference between the LIB surface and air gap during the discharging process indicated that there is required heat transfer enhancement.

Effects of air inlet and outlet on thermal management of electric vehicle battery pack equipped with fins

AbstractTo solve the high heat dissipation problem of lithium ion battery, the air‐cooled heat dissipation with fins is adopted for the thermal management. Heat dissipation characteristics of two types of air inlets and outlets (single air inlet and outlet, and double air inlet and outlet) and two types of surfaces (smooth and fin structure) were simulated and compared. When the air inlet and outlet are single inlet and single outlet, compared with smooth battery packs, finned construction packs can increase the Nusselt number by 24.9%. When the air inlet and outlet are double inlet and double outlet, compared with smooth battery packs, finned construction packs can increase the Nusselt number by 13.3%. The double‐air inlet and outlet battery pack can significantly reduce the average temperature of the battery pack compared with the single‐air inlet and outlet battery pack. Compared with the single‐inlet and single‐outlet solar smooth battery pack, it can increase the Nu by 131.6%, and the comprehensive performance index can reach 1.66.

Optimization design of a hybrid thermal runaway propagation mitigation system for power battery module using high-dimensional surrogate models

The demand for high energy density in lithium-ion battery packs for electric vehicles poses a challenge to maintaining its optimum operating temperature while reducing the risk of thermal runaway (TR) propagation. This study proposes a novel hybrid TR propagation mitigation system that balances heat transfer and thermal insulation requirements using low and high thermal conductivity phase change materials (PCM), heat pipes (HP), and air-cooling. The design and optimization of such a mitigation system are complex due to the many design parameters involved. The Adaptive-Kriging-High dimensional model representation (Adaptive-Kriging-HDMR) is used to establish a surrogate model of the system, and the sensitivity of the system's design parameters is evaluated with the maximum battery temperature and the system weight as the targets, thereby improving the efficiency of model calculation and reducing the dimension of optimization parameters. Then, the design of the sensitive parameters is optimized using an extended elitist non‐dominated sorting genetic algorithm (E-NSGA-II) multi-objective optimization algorithm. The results show that the modeling difficulty and optimization calculation time are significantly reduced by using a surrogate model. The calculation time for a single surrogate model only takes a few seconds instead of several hours for the original three-dimensional heat transfer and flow calculation. The thermal conductivity of high thermal conductivity-PCM, the distance between battery and low thermal conductivity-PCM, the battery spacing, and the HP length significantly affect the system. The optimized system substantially reduces the overall weight of the battery system while ensuring its good heat dissipation capability. In the case of TR in a single battery, the system succeeds in limiting the TR propagation to the same row, with the maximum battery temperature in the second row being only 64.3 °C, well below the battery TR trigger point. Under more severe conditions, such as TR occurring in two batteries simultaneously, the maximum battery temperature in the second row is 155.5 °C, and no TR spreads to the adjacent row. This study provides a rapid and effective method for designing a TR propagation mitigation system. It can serve as a reference for the engineering design and optimization of battery thermal management systems.

Electro-thermal performance evaluation of a prismatic battery pack for an electric vehicle

Abstract In recent years, electric vehicles (EVs) have grown in popularity as a viable way to reduce greenhouse gas emissions by replacing conventional vehicles. The need for EV batteries is steadily increasing. An essential and expensive part of electric transportation is the battery. The operating temperature of the lithium-ion (Li-ion) battery significantly impacts the performance of the EV battery pack. Battery packs undergo temperature fluctuations during the charging and discharging procedures due to internal heat generation, necessitating an examination of the temperature distribution of the battery pack. The geometrical spacing between cells is considered larger and identical and is kept open on two sides for free air circulation. A novel battery thermal management system (BTMS) design is required to effectively dissipate heat from the prismatic battery pack module. The electro-thermal behaviour of the prismatic Li-ion battery pack module was investigated based on the high charge/discharge rate. This study presents the development of a three-dimensional free open-source OpenFOAM computational fluid dynamics model for prismatic cell battery packs that simulates heat generation, air flow field, and temperature distribution across the width and depth of the battery pack module. The prismatic battery pack simulation results are compared with the experimental and simulation results of the cylindrical battery pack. It was also revealed that prismatic cells generate more heat on the backside, requiring battery packs to have increased cooling and space between individual cells to ensure sufficient air circulation for cooling and gas removal. The BTMS is improved by designing with increased space among the prismatic battery cells as compared with the conventional prismatic cell battery pack design.

Aging behavior of an electric vehicle battery system considering real drive conditions

In this article, a comprehensive study of the aging behavior of an electric vehicle battery pack considering the vehicle's operation under real driving conditions, such as ambient temperature, humidity, and solar irradiation conditions, in all different months of the year for a 10-year time horizon with two different working scenarios is investigated for the first time. The vehicle duty cycle represents a personal vehicle traveling once at 7:00 AM from home to work and returning at 5:00 PM from work to home for scenario 1. For the second scenario, 8:30 AM is set for outbound trips, and 6:30 PM is set for return trips via the WLTP driving cycle. The target temperature for the cabin and battery pack are set to 22 °C and 28 °C, respectively. The results indicate the highest energy consumption is for the cold season. The EV's energy consumptions in scenario 2 for outbound and return trips were 20 % less and 0.6 % more than the EV's energy consumptions in scenario 1 for outbound and return trips, respectively. Next, the battery aging was evaluated for ten years, resulting in 7.16 % and 6.99 % total capacity loss for scenario 1 and scenario 2, respectively.

Soft-switching dual active bridge converter-based bidirectional on-board charger for electric vehicles under vehicle-to-grid and grid-to-vehicle control optimization

Electric vehicles (EVs) are rapidly replacing conventional fuel vehicles, offering powerful, emission-free performance. This paper introduces an innovative three-phase bidirectional charger for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) applications, strengthening the connection between EVs and the power grid. The charger employs a two-stage power conversion approach with advanced converters and a simplified dq-based charging control strategy. An efficient AC-DC converter facilitates smooth transitions between modes, responding to grid directives for active and reactive power. A soft-switching dual active bridge (SS-DAB) DC-DC converter optimally interfaces with the EV battery pack, while dual active LCL filters suppress harmonics, enhancing system performance. Simulated results confirm the charger’s effectiveness in a 3.5-kW prototype using MATLAB/Simulink. The proposed SS-DAB converter-based bidirectional on-board charger introduces a groundbreaking unified Voltage Source Converter (VSC) control approach, enabling efficient power transfer in both vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes. This innovation ensures rapid dynamic response, exceptional steady-state performance, and robustness against grid demand changes, optimizing EV integration.

A novel time series hybrid model for online prediction of electric vehicles battery pack capacity with real charging data

Accurate on-road vehicle capacity degradation path prediction model is of great significance to guide vehicle maintenance and performance optimization and ensure safety performance. This paper proposes a novel hybrid model, which mainly focus on the characteristics of the data set and combines the random forest (RF), particle swarm optimization (PSO), variational mode decomposition (VMD), and convolutional long short-term memory (ConvLSTM) algorithm to online forecast the capacity degradation path of electric vehicles (EVs) battery pack. First, battery capacity mean values during a week are regarded as the real capacity. Then, the PSO method is mainly used to obtain the parameters of the VMD offline and online to reduce the noise of the real capacity trajectory. In addition, the importance of the extracted battery pack features is evaluated through the RF method, and features with high importance are screened out. Finally, ConvLSTM is designed to extract the spatial relationship between feature sets to obtain better prediction performance. The charging data of 20 EVs are used to verify the performance of the proposed hybrid model. The experiment results show that the performance of proposed model outperforms other baseline models and can be used to accuracy predict the battery pack capacity degradation path.