Vehicle Battery Pack Research Articles

Deep reinforcement learning based fast charging and thermal management optimization of an electric vehicle battery pack

Existing optimal strategies for fast-charging electric vehicle batteries are predominantly at the cell level. The proposed high-fidelity methods for extending available cell models to pack level are associated with a large computational burden, making real-world implementation impossible. Further, fast charging optimization and thermal management problems are dependent. That is, the cooling system reduces battery temperature, allowing for higher charging current, while optimal current minimizes the need for cooling and, in turn, reduces thermal system power consumption. There is a lack of studies where fast charging optimization and battery thermal management problems are jointly solved. Therefore, this paper proposes a simulation study using a deep reinforcement learning (RL) approach that concurrently solves fast charging and thermal management problems for a battery pack with low computational complexity. In this regard, we formulate each cell using an electro-thermal-aging model, which accounts for the heat exchange between adjacent cells. The electro-thermal-aging model plays the role of the environment for RL and is not the focus of novelty in this work. The RL agent is then trained to output the optimal charging current and coolant mass flow rate. Moreover, the proposed methodology is examined through a numerical study where the outperformance of our model is showcased by comparing it with baseline algorithms of model predictive control (MPC) and CC–CV. Consequently, three battery packs comprising 20, 444, and 7104 cells are used, respectively. We demonstrate that RL requires less than a second to finish the simulation for 20 cells, while MPC requires more than 80 min. In addition, RL keeps the cells’ core temperature below 33 °C, but MPC results reach 40 °C. The RL performance in charging packs with 444 and 7104 cells is then compared with that of CC–CV. In terms of computation time, RL and CC–CV are nearly the same, while regarding the average core and surface temperature of the cells as well as the cell aging, RL attains better outcomes, extending the battery pack’s life by up to two years after 1000 fast charging cycles.

Impact load between spherical obstacles and electric vehicle battery packs

Impact load between spherical obstacles and electric vehicle battery packs

Electric Vehicle Battery Pack Charging Time Prediction

The electric vehicle's most crucial component is its battery, which provides the vehicle with power. A key element of electric vehicles (EVs) is the battery management system (BMS), which ensures the safe and efficient functioning of the battery pack. Previous research on electric vehicles has identified some drawbacks, such as lengthy charging times and the need for different charging methods depending on battery capacity and temperature. In the proposed work, the battery's state of charge and remaining capacity will be estimated by measuring the voltage and current with the use of a current sensor and temperature monitor. The novelty of the work lies in its ability to increase the range of electric vehicles. This is achieved through higher energy densities in high-voltage batteries, which allow for longer driving distances between charges. Additionally, faster charging systems can handle higher charging power levels, resulting in quicker charging times. These improvements in performance are made possible by the use of high-voltage batteries, which provide the necessary power for greater peak speeds and improved acceleration. As electric mobility becomes more widespread, the ability to accurately predict charging times based on layout becomes crucial in building user confidence, optimizing energy grid management, and promoting the widespread adoption of electric vehicles.

Experimental and Numerical Investigation of the Use of Ultrasonic Waves to Assist Laser Welding.

This study evaluates the enhancement of laser welding using ultrasonic waves aimed at reorganising the intermetallic position in such a fashion that leads to increased mechanical properties of welds in battery pack assemblies for electric vehicles. The experiment employed 20 kHz and 40 kHz High-Power Ultrasound Transducers (HPUTs) in both contact and contactless modes. A simplified experimental configuration is suggested to represent conditions similar to those found in electric vehicle battery pack assemblies. Measurements of vibration transmission to aluminium alloy 1050 plates revealed more than a 1000-fold increase in acceleration amplitude in contact mode compared to contactless mode. The 20 kHz transducer in contactless mode demonstrated superior performance, showing a 10% increase in load and 27% increase in extension compared to welding without ultrasonic assistance. On the other hand, the 40 kHz transducer, while still improved over non-ultrasonic methods, showed less pronounced benefits. This suggests that lower-frequency ultrasonic assistance (20 kHz) is more effective in this specific context. The study explores ultrasonic assistance in laser welding copper (Cu101) to aluminium alloy 1050 using 20 kHz and 40 kHz HPUTs, showing that both transducers enhance microstructural integrity by reducing copper homogenisation into aluminium, with the 20 kHz frequency proving more effective in this context. A numerical simulation was conducted to evaluate the transmission of pressure into the molten pool of the weld, correlated with the vibration results obtained from the 20 kHz transducer. The numerical simulation confirms that no cavitation is initiated in the molten pool area, and all improvements are solely due to the ultrasonic waves.

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.

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.

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.

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.

Online Adaptive Model Identification and State of Charge Estimation for Vehicle-Level Battery Packs

Accurate state of charge (SOC) estimation of traction batteries plays a crucial role in energy and safety management for electric vehicles. Existing studies focus primarily on cell battery SOC estimation. However, numerical instability and divergence problems might occur for a large-size lithium-ion battery pack consisting of many cells. This paper proposes a high-performance online model identification and SOC estimation method based on an adaptive square root unscented Kalman filter (ASRUKF) and an improved forgetting factor recursive least squares (IFFRLS) for vehicle-level traction battery packs. The model parameters are identified online through the IFFRLS, where the conventional method might encounter numerical stability problems. By updating the square root of the covariance matrix, the divergence problem in the traditional unscented Kalman filter is solved in the ASRUKF algorithm, where the positive semi-definiteness of the covariance matrix is guaranteed. Combined with the adaptive noise covariance matched filtering algorithm and real-time compensation of system error, the proposed method solves the problem of ever-degrading estimation accuracy in the presence of time-varying noise with unknown statistical characteristics. Using a 66.2-kWh vehicle battery pack, we experimentally verified that the proposed algorithm could achieve high estimation accuracy with guaranteed numerical stability. The maximum error of SOC estimation can be bounded by 1%, and the root-mean-square error is as low as 0.47% under real-world vehicle operating conditions.

Preparation of environmental-benign castor oil-derived polyurethane thermal conductive structural adhesives with superior strength

Thermal conductive structural adhesives offer a robust solution for injection into the battery core of power battery packs in new energy vehicles, effectively providing filling, protecting, and facilitating timely heat dissipation. In this work, a series of castor oil (CO)-based polyurethane prepolymers with different isocyanate (NCO) contents were synthesized by a solvent-free method, which designated as Component B (curing agent) for the CO-based polyurethane (CIPU) adhesives. Polyaspartic acid ester (PAE) resin compounded with poly (tetramethylene ether glycol) (PTMG) of different molecular weights, constituted Component A of the CIPU adhesives. The effect of mass ratio of PAE to PTMG (M value) on mechanical performance of the CIPU adhesives under different curing agent conditions was investigated. The results indicated optimal overall performance for the CIPU adhesive with an M value of 3, exhibiting a tensile strength of 10.25 MPa, an elongation at break of 107.14%, and a steel-steel tensile shear strength of 4.7 MPa. Subsequently, a series of CIPU thermal conductive structural adhesives were prepared by introducing thermal conductive fillers into the optimized CIPU adhesive formula. The introduction of a 35% Boron nitride content yielded a CIPU thermal conductive structural adhesive with a thermal conductivity of 0.64 W/m·K, and a steel-steel tensile shear strength of 6.97 MPa. These innovative CIPU thermal conductive structural adhesives prepared herein are expected to be applied in the new energy automotive and electronics industries.

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.

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.

Experimental investigation on efficient thermal management of autonomous underwater vehicle battery packs using anisotropic expanded graphite/paraffin composite materials

The thermal safety issue of battery packs is very prominent in the field of electrically powered AUVs. However, there has been few previous studies for AUV battery thermal management. In this study, anisotropic high-performance CENG/PCM composite materials were prepared and their internal microstructure was characterized by SEM, XRD, Raman spectroscopy, and TEM. A modified correlation was proposed to predict the latent heat and thermal conductivity of CPCM. In addition, the systematic experimental study was conducted on the thermal management of AUV battery modules using CENG/PCM. A new criterion called available mass energy density considering the weight and temperature control performance of CENG/PCM has been proposed to evaluate the comprehensive thermal management performance of battery packs. The results show that CENG can significantly improve the thermal conductivity of PCM. When the CENG density is 0.3 g/cm3, the thermal conductivity of CENG/PCM in the perpendicular and parallel compression directions is 118.7 and 31 times higher than that of pure PCM, respectively. CENG/PCM has demonstrated excellent thermal management performance. Compared to the battery module without CENG/PCM, the maximum temperature of the battery module with CENG/PCM decreased by 29.9 % and the temperature difference decreased by 55.3 % at 2C discharge rate. The optimal thickness of CENG/PCM is recommended to be 30 mm, and when the mass of the battery module increases by 16.84 %, the available mass energy density increases by 63.55 %. The findings of this study contribute to promoting the commercial application of CENG/PCM composite materials in thermal management of autonomous underwater vehicle battery packs.

In-depth analysis of electric vehicles battery pack structure and disassembly procedure for the application of circular economy strategies

The battery pack is the most valuable component of the electric vehicle and its disassembly is the key process to recover the inner value of the product and apply circular economy strategies, from repair to recycling and remanufacturing. Different models of EV battery packs have been analyzed to assess criticalities in the product structure and disassembly procedure. Regardless the absence of a standardized design, some similarities can be identified and considered for the implementation of disassembly procedures. From the comparison of the disassembly procedures of four in-depth analyzed battery pack models emerged that it is possible to identify six disassembly blocks, grouped in two main disassembly stages. The first stage includes the disassembly of the battery covers, the coolant removal (in case of liquid cooling) and the service plug removal, while the second stage involves the removal of the junction block, the battery management system and the modules. The precedencies and criticalities in the execution of the six disassembly blocks were deeply analyzed and the results reported in the paper.

Crashworthiness Evaluation of Electric Vehicle Battery Packs Using Honeycomb Structures and Explicit Dynamic Analysis

The battery pack system is crucial to safeguard battery units during any collision. The crashworthiness of battery packs could be improved with the incorporation of honeycomb structures. The objective of the current study is to evaluate the structural characteristics of battery encasing using ANSYS explicit dynamic analysis. The modal analysis is conducted to determine the natural frequency, mode shape, and peak displacement values. The CAD model of the battery pack is developed in Creo parametric design software. The use of a honeycomb structure enabled the reduction of the effect of impact on battery units. At the time of the collision, the honeycomb structure would absorb maximum crash impact and would save the battery unit cells from major damage. The natural frequency of a battery pack with a honeycomb structure has a higher first, 2nd, and 3rdnatural frequency. At the time of impact and without any honeycomb structure, the internal energy of the battery unit is 1021.8mJ while with the honeycomb lattice structure, it is 0.80376mJ. The results have shown that with the incorporation of a honeycomb structure, there is a substantial reduction in the internal energy of the cell with the incorporation of the lattice structure.