Battery Pack Research Articles

PPG opens $30-million battery pack application center in China

PPG opens $30-million battery pack application center in China

A Wireless Wearable Ecosystem for Social Network Analysis in Free-living Animals.

Studying animal social systems requires understanding variations in contact and interaction, influenced by factors like environmental conditions, resource availability, and predation risk. Traditional observational methods have limitations, but advancements in sensor technologies and data analytics provide new opportunities. We developed a wireless wearable sensor system, "Juxta," with features such as modular battery packs and a smartphone app for data collection. A pilot study on free-living prairie voles (Microtus ochrogaster), a species with complex social behavior, demonstrated Juxta's potential for studying social networks and behavior. We propose a framework for merging temporal, spatial, and event-driven data, which can help explore complex social dynamics across species and environments.

Two-Phase Cooling System for Electric Vehicle Battery Based on a 3D Pulsating Heat Pipe

The primary objective pursued in this research is the creation and thorough evaluation of an inventive cooling system designed to uphold optimal temperatures within the batteries employed in electric vehicles. Nowadays, the prevailing equipment underpinning electrical motion hinges on Lithium-Ion cells. These cells frequently necessitate the expeditious delivery of substantial power, thereby giving rise to a consequential generation of heat. Consequently, there is an emergence of elevated operational temperatures, potentially causing noteworthy declines in battery performance, or, in extreme cases, operational failures. Furthermore, deviating from the recommended temperature range (20–40 °C) significantly expedites the aging process of the battery and elevates the probability of premature malfunction. In response to these crucial challenges, the implementation of a battery thermal management system assumes a pivotal role in maximizing battery efficiency. Within the framework of this investigation, we propose the adoption of a cooling system founded on a three-dimensional pulsating heat pipe as the designated thermal management technology for a battery pack. The preliminary phase of our study involved the assessment of the suggested pulsating heat pipe’s performance. Following this, the efficacy of the pulsating heat pipe is subjected to rigorous scrutiny through practical experimentation on an authentic electric battery. The empirical findings conclusively highlight the substantial cooling capacity of the proposed system, thereby presenting a robust and efficacious solution for the thermal management challenges faced by electric vehicle batteries.

A Smart and Efficient Cooling System Design for High-Performance Electric Vehicle Battery Packs

A Smart and Efficient Cooling System Design for High-Performance Electric Vehicle Battery Packs

Distributed fiber optic strain sensing for structural health monitoring of 70 MPa hydrogen vessels

We report on the development and testing of 70 MPa hydrogen pressure vessels with integrated fiber optic sensing fibers for the automotive use. The paper deals with the condition monitoring of such composite pressure vessels using the optical backscatter reflectometry (OBR) which was applied for a distributed fiber optic strain sensing along fully integrated polyimide-coated single-mode glass optical fiber (SM-GOF). The sensing fibers were embedded into the vessel structure by wrapping them over the inside polymer liner during the manufacturing process of the outside carbon fiber reinforced polymer (CFRP). Detecting local strain events by the integrated fiber optic sensors might be an opportunity for monitoring the material degradation. Regarding the investigation of the ageing behavior, both ambient hydraulic cycling tests and slow burst tests were conducted on 70 MPa pressure vessels with embedded fiber optic sensors. The achieved results contribute to the knowledge about material fatigue to guarantee the safe usage during the entire lifetime of the composite hydrogen storage vessels. The presented work stands in context of the European Horizon 2020 research project SH2APED focused on the development of an innovative hydrogen storage system composed of an assembly of nine 70 MPa tubular pressure vessels used as an automotive battery pack.

A Precise Temperature Control Method for Lithium-ion Battery Pack based on the Nonlinear Model Predictive Control Algorithm

Abstract To utilize the maximum performance of the battery while ensuring its thermal safety, a battery thermal management system is used to control the battery maximum temperature within a safe range. This paper centres on the establishment of a temperature prediction model and the development of the nonlinear-based model predictive control (MPC) strategy. First, to address the need of predicting battery temperature, this paper develops a distributed parameter thermal resistance model to predict battery temperature quickly and accurately. Secondly, the open-loop formulation of the nonlinear-based MPC is derived based on the established state space equations. Then the soft and hard constraints of the model are established based on the actual current conditions, pump conditions, temperature difference and temperature rise indexes, so as to establish the objective function of the MPC algorithm. Finally, the established temperature nonlinear MPC algorithm is embedded on the board and the hardware platform of battery liquid cooling system is established. The experiment test result shows that the maximum error of temperature control is less than 0.1°C, and the effectiveness of the temperature control strategy of lithium-ion battery is verified through experiments.

Thermal management of lithium-ion batteries with direct and counter flow channels: A comparative study of different cooling fluids

The study addresses the crucial issue of thermal management in lithium-ion batteries (LIBs) and the maintenance of safe operational temperatures. It emphasizes the importance of efficient cooling mechanisms in preventing damage. A comparative analysis was conducted to assess the impact of various cooling fluids on the thermal performance of LIBs using direct and counter fluid flow channels. The study involved the use of different cooling fluids including water, water-glycol, Novec7000, mineral oil, and silicon oil. The researchers examined temperature variation, surface Stanton number (SN), surface Nusselt number (Nu), surface heat transfer coefficient (HTC), battery surface temperature, and pressure drop to evaluate the battery cooling process with different cooling configurations. The battery cooling part proved highly effective in managing heat, achieving a significant maximum temperature reduction of 30.70 % and 37.43 % for direct and counter flow channels, respectively. The Nu values of various fluids showed notable differences, with water-glycol showing a 3.76 % increase, Novec 7000 with an 11.39 % increase, mineral oil with a 15.46 % improvement, and silicon oil with a 20.09 % increase. The HTC values in the direct flow channel proved a consistent decrease from 16.09 to 57.73 to 11.07–25.69 W/m2K, respectively. Similarly, the HTC values for the counter flow channel ranged from 15.88 to 55.38 to 11.07–25.69 W/m2K. Additionally, it was seen that silicon oil outperformed other cooling fluids with a 6.0 % improvement in HTC in both direct flow and counter flow channels. The SN showed increases of 17.34 %, 20.11 %, 26.76 %, 29.57 %, and 29.60 %, respectively, at all five liquid flow velocities through the direct channel, and 19.39 %, 22.18 %, 28.34 %, 36.58 %, and 37.64 %, respectively, through the counter flow channel. These findings clearly show the superior heat transfer capabilities of silicon oil compared to other coolants. However, it is important to note that the use of silicon oil also resulted in a lower pressure drop, a critical consideration for ensuring efficient cooling. These findings have significant implications for the advancement of battery thermal management systems for electric vehicles, as designers can optimize the cooling process by considering different cooling configurations to enhance the thermal performance of battery packs while ensuring safe and stable operation.

A Deep Learning Model Based on the Introduction of Attention Mechanism is Used to Predict Lithium-Ion Battery SOC

In order to enhance the accuracy of state of charge (SOC) prediction for lithium-ion batteries, this paper developed a deep learning model optimized by slime mold algorithm (SMA). This model combines convolutional neural networks, bidirectional gated recurrent units, and attention mechanisms. Through SMA optimization of critical parameters in the model, the predictive performance has been significantly improved. In the experimental phase, the paper collected discharge data from an 18650 battery pack under 8 different operating conditions, totaling 10596 sets of data. These data were used to fully train and validate the model. The test results show that the new model demonstrates exceptional accuracy in SOC prediction, with average absolute error, root mean square error, and mean absolute percentage error reaching 0.46462%, 0.56406%, and 6.8028%, respectively. Moreover, the decision coefficient R reaches 0.962. This result not only surpasses single models and unoptimized models but also provides important technical support for improving the battery life and driving range of electric vehicles.

An intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making

Multiple lithium-ion battery cells and multi-contact connection methods increase the chances of connection failures in power battery packs, posing a significant threat to the operational safety of electric vehicles. To this end, the study proposes an intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making. The method uses Pearson correlation coefficients (PCC), Spearman correlation coefficients (SCC), and Kendall correlation coefficients (KCC) to simultaneously quantify the voltage synchronization between different cells in the battery pack, which is used to shield against the voltage measurement noise. The images of the correlation series transformed by the Gramian angular field are used as the input values of the convolutional neural network to classify the fault states by combining the adaptive weights of the three correlation series images on the fault levels. The experimental data shows that the accuracy of the method is 97.75 %, which is an improvement of 4.56 %, 5.98 %, and 2.39 % over the fault diagnosis accuracy using only PCC, SCC, and KCC, respectively, and effectively avoids misdiagnosis and omission due to the limitation of a single measurement. The proposed battery pack connection fault diagnosis method is robust and reliable, and has great practical application value.

Cost/Performance Analysis of Battery Pack placed in Spare Tire Area for Extending the Range of Hybrid, and Electric Vehicles

Recently, electric vehicles have been an alternative form of transportation promising a bright future as sales figures are increasing rapidly beyond expectations. In the near fu-ture, electric vehicles are expected to dominate the market with numerous advantages over vehicles with internal combustion engines. The hybrid electric vehicles are often con-sidered a transition between the two due to added advantages of both models. Out of hy-brid models, plugin hybrid vehicles have more resemblance to the fully electric vehicles because of longer electric range and lower cost per mile. This paper investigates the use of the spare tire location to convert an existing battery pack or to install a new battery pack for a hybrid vehicle. Cost and range calculations are performed based on two most com-monly sold vehicles that use 16”, 17” and 18” size tires. Resulting battery packs are demonstrated through various battery dimensions and chemistries. Results are provided with cost and environmental benefits of utilizing an electric power for driving.

Modeling and validation of hydrogen porosity formation in aluminum laser welding

Laser welding is used to weld aluminum alloy 1100 for battery cell manufacturing in the automotive industry. However, these welds can have porosity that can decrease their performance in the battery packs. One type of porosity is gas porosity, commonly understood to be due to hydrogen entrapment in the weld during solidification. Hydrogen is dissolved in the molten aluminum at elevated temperatures during laser heating and can be entrapped in the weld pool upon solidification. When welding anodized aluminum, the amount of hydrogen porosity is increased because the anodized oxide layer is broken up and the broken-up oxide can act as heterogeneous nucleation sites for hydrogen pores. A cellular automaton model has been developed to predict the nucleation and growth of hydrogen porosity during laser welding. This model has been validated with microstructure analysis, LECO® hydrogen analysis, and X-ray micro-computed tomography. This model can be used by welding engineers to reduce porosity and control weld quality during laser welding of aluminum alloys.

Optimization and working performance analysis of liquid cooling plates in refrigerant direct cooling power battery systems

Refrigerant direct cooling is currently being considered as an efficient thermal management technology in power battery systems. In this paper, four types of liquid cooling plates for power battery modules are designed and the computational model is constructed. With the model being validated, it is applied to analyze the effects of the cooling plate structure and cooling channel on the cooling and heat dissipation performances. The results reveal that for the conventional cooling method, i.e., when the cooling plate is placed on the bottom of the battery pack, a significant temperature gradient in the vertical direction is observed. On the contrary, adopting a serpentine cooling plate structure in the main wall or parallel cooling channels in the narrow side wall could significantly reduce the temperature difference of the battery pack, which can keep the temperature difference within 5 °C even under extreme conditions. Further, a detailed analysis of the heat dissipation performance based on the optimal cooling plate structure is performed to determine the safe operating range of the battery pack. It is shown that as the humidity is less than 0.73 and the evaporation temperature is below 15.43 °C, the battery pack can operate safely. However, beyond this condition range the heat dissipation characteristics of the battery pack cannot satisfy the operating requirements. This work sheds light upon the potential of refrigerant direct cooling strategy in power battery thermal management systems by properly arranging the cooling plate.

Experimental study on liquid immersion preheating of lithium-ion batteries under low temperature environment

An experimental platform to examine the effects of single-phase immersion preheating on lithium-ion battery performance at low temperatures was set up in this study. The performance of lithium-ion batteries at low temperatures can be improved through immersion preheating. After preheating from −15 °C to 15 °C, the battery capacity can recover to over 80 % of its rated capacity. The three influencing factors are inlet flow rate, fluid temperature, and cell spacings. The inlet temperature exerts the most influence on the preheating performance. The warming rate can reach 1.63 °C/min at a temperature of 50 °C. While the increase in flow rate has less influence on the heating rate, it has a greater impact on the temperature difference between cells. The maximum temperature difference between cells can still be kept below 4 °C. The impact of spacing on the heating rate and temperature difference is minimal. The temperature difference can be controlled within 6 °C. Finally, it's important to note that immersion preheating consumes significant amount of energy. The energy consumption of preheating at an ambient temperature of −25 °C exceeds 80 % of the rated capacity of the experimental battery pack.

Battery pack passive insulation strategies of electric vehicles under a frigid environment

Further application of electric vehicles (EVs) in frigid regions is hindered by the diminished performance of lithium-ion batteries (LIBs) at sub-zero temperatures. Developing efficient heat preservation strategies has significant implications for the broad application of EVs and LIBs. This study focuses on passive heat preservation strategies (PHPS) for battery packs in frigid environments (−30 °C). A validated 3D battery pack heat preservation model integrated with airflow is constructed in this paper, and the model analysis results indicate that arranging aerogels at the pack level provides superior heat preservation compared to cell and module levels. Additionally, the influence of flow field distribution (FFD) schemes on heat preservation performance is discovered. Both experimental and simulation results confirm that the proposed scheme can optimize the cooling rate to about 2.3 °C/h. Commencing from an initial temperature of 30 °C, extending the battery pack retention time above 0 °C to over 12 h under −30 °C can significantly expand the application of electric vehicles in frigid regions. This paper explores the primary factors affecting the heat dissipation of battery packs in the frigid temperature environment, which have great significance in guiding the heat preservation design of battery systems.

Estimation Procedure for the Degradation of a Lithium-Ion Battery Pack

This paper proposes a test procedure for evaluating the degradation of cells in a battery pack. The test can be performed using only the charger’s converters and the battery management system (BMS) without requiring sophisticated instrumentation. The method circumvents the difficulties related to the evaluation of derivative quantities for estimating the state of health (SOH) using integral quantities in the evaluation. The method introduces a 'degradation function' that is calculated with respect to the reference performance of pristine cells. The procedure was applied to the JuiceRoll Race Edition system, an innovative electric vehicle (EV) DC charger with internal storage, made in ENEL X and used during the MotoE championship races. Using this procedure, the degradation of performance in individual groups of cells composing the battery pack was quantified in comparison to the reference group. The procedure helps identify modules that have aged too early or show reliability issues. The method is mature for field operational applications.

Expression of Concern for article entitled “Numerical investigation of the effect of inlet dimensions air duct and distance of battery packs for thermal management of three lithium-ion battery packs”

Expression of Concern for article entitled “Numerical investigation of the effect of inlet dimensions air duct and distance of battery packs for thermal management of three lithium-ion battery packs”

Expression of Concern for article entitled “Effect of inlet and outlet size, battery distance, and air inlet and outlet position on the cooling of a lithium-ion battery pack and utilizing outlet air of cooling system to heat an air handling unit”

Expression of Concern for article entitled “Effect of inlet and outlet size, battery distance, and air inlet and outlet position on the cooling of a lithium-ion battery pack and utilizing outlet air of cooling system to heat an air handling unit”

Expression of Concern for article entitled “Assessment of the effect of distance between lithium-ion batteries with a number of triangular blades, on the thermal management of the battery pack in a chamber full of phase change material”

Expression of Concern for article entitled “Assessment of the effect of distance between lithium-ion batteries with a number of triangular blades, on the thermal management of the battery pack in a chamber full of phase change material”

An efficient buck-boost converter for fast active balancing of lithium-ion battery packs in electric vehicle applications

This article proposes a fast active cell balancing circuit for lithium-ion battery packs. The proposed architecture incorporates a modified non-inverting buck-boost converter to improve balancing efficiency, an equivalent circuit model technique for battery designing, and an extended Kalman Bucy filter for accurate SOC estimation. The proposed balancing technique analyses a six-series and one parallel (6S1P) battery pack combination in static, charging, and discharging modes. With fewer components, the proposed architecture reduces the losses and improves the balancing performance. The design limitations, balancing principle, loss analysis, and control strategies are thoroughly investigated. The proposed topology is modelled in the MATLAB/Simulink platform to perform energy transformation analysis between stronger and weaker cells. The FPGA-based real-time simulator OPAL-RT (OP5700) validates the balancing performance of the proposed topology. For the 22V, 2200mAh battery pack the proposed topology had an overall balancing efficiency of 97.05% and a balancing speed of 1328s. The proposed topology has a 1.3% deviation in balancing efficiency and a 57s deviation in balancing speed compared to MATLAB and real-time simulation. The proposed active cell balancing architecture considerably increases balancing speed and efficiency.

Design and research of heat dissipation system of electric vehicle lithium-ion battery pack based on artificial intelligence optimization algorithm

This research focuses on the design of heat dissipation system for lithium-ion battery packs of electric vehicles, and adopts artificial intelligence optimization algorithm to improve the heat dissipation efficiency of the system. By integrating genetic algorithms and particle swarm optimization, the research goal is to optimize key design parameters of the cooling system to improve temperature control and extend battery life. In the process of algorithm implementation, genetic algorithm improves the diversity of population through crossover and mutation operations, thus enhancing the global search ability. Particle swarm optimization (PSO) improves local search accuracy and convergence speed by dynamically adjusting inertia weight and learning factor. The effects of different design schemes on heat dissipation performance were systematically evaluated by using computational fluid dynamics (CFD) software. The experimental results show that the efficiency of the cooling system is significantly improved after the application of the optimization algorithm, especially in the aspects of temperature distribution uniformity and maximum temperature reduction. The optimization algorithm also successfully shortens the thermal response time of the system and improves the adaptability and stability of the system under different working conditions. The computational complexity and execution time of these algorithms are also analyzed, which proves the efficiency and feasibility of these algorithms in practical applications. This study demonstrates the practicability and effectiveness of artificial intelligence optimization algorithm in the design of heat dissipation system of lithium-ion battery pack for electric vehicles, and provides valuable reference and practical guidance for the progress of heat dissipation technology of electric vehicles in the future.