Lithium-ion Battery Pack Research Articles

Economic and Environmental Viability of Lithium-Ion Battery Recycling—Case Study in Two Canadian Regions with Different Energy Mixes

Lithium-ion battery (LIB) pack is the core component of electric vehicles (EVs). As the demand is continuously increasing, it puts a lot of strain on the battery raw material supply chains. Likewise, the large quantity of spent LIBs from different sources will add to the complexity of end-of-life (EoL) management. Battery recycling processing is a potential source of critical cathode precursor materials as an alternative to virgin raw material sourcing. Indeed, metal sulfates (nickel, cobalt, and manganese) and lithium carbonate could be recovered through EoL processing. This study aims to provide an economic and environmental life cycle sustainability assessment of recycled battery materials. This assessment is based on a bottom-up approach considering geographical boundaries and process data inputs. The two sources of critical cathode battery materials, virgin and recycled battery materials, are compared based on economic and environmental indicators. This study identified the province of Quebec in Canada as the geographical boundary where several battery processing plants have been recently announced. The best available recycling process (hydrometallurgy) was selected. For the virgin materials, this study considers the option of importing from other jurisdictions by using global average supply chain values. Furthermore, a comparison of alternative supply chain configurations was performed using a spatially differentiated approach. The main findings of this study are as follows: (i) the environmental credit of recycled cathode active materials (CAMs) is estimated as −6.46 kg CO2e/kg CAM, and (ii) the overall cost and environmental impacts of producing LIB cathode active material from recycled battery materials can be 48% and 54% lower than production from virgin materials, respectively, considering the upstream, midstream, and downstream stages of the CAM supply chain. The main drivers for the reduction in these financial costs and emissions are the local transportation and the hydrometallurgical process. The assessment results provide insights to support the development of appropriate policies and R&D solutions adapted to local considerations as well as offer additional possibilities to improve the design of sustainable supply chains for LIB recycling.

Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network

Lithium-ion batteries (LIBs) are widely used in electrical vehicles (EVs), but safety issues with LIBs still occur frequently. State of charge (SOC) and state of health (SOH) are two crucial parameters for describing the state of LIBs. However, due to inconsistencies that may occur among hundreds to thousands of battery cells connected in series and parallel in the battery pack, these parameters can be difficult to estimate accurately. To address this problem, this paper proposes a joint SOC and SOH estimation method based on the nonlinear state space reconstruction (NSSR) and long short-term memory (LSTM) neural network. An experiment testbed was set up to measure the SOC and SOH of battery packs under different criteria and configurations, and thousands of charging/discharging cycles were recorded. The joint estimation algorithms were validated using testbed data, and the errors for SOC and SOH estimation were found to be within 2.5% and 1.3%, respectively, which is smaller than the errors obtained using traditional Ah-Integral and LSTM-only algorithms.

Research on Thermal Management Coupling by CPCM and Liquid Cooling for Vehicle Lithium-Ion Batteries

This study addresses the issue of heat dissipation in 18,650 cylindrical lithium-ion battery packs and proposes a novel heat dissipation model that combines paraffin wax-expanded graphite composite phase change material (CPCM) with liquid cooling. Initially, a comparison is conducted between the heat dissipation effects of the battery pack under natural convection and the heat dissipation achieved through the utilization of CPCM. Subsequently, the CPCM model is employed to identify the optimal battery arrangement. Subsequently, a heat dissipation model is developed by coupling CPCM with liquid cooling. The simulation outcomes obtained using COMSOL software demonstrate that employing the paraffin-expanded graphite CPCM liquid cooling coupled heat dissipation model can achieve a reduction in battery spacing to 0 mm while maintaining the maximum surface temperature of the battery between 20–45 °C and improving the temperature uniformity of the battery during 1–3 C cyclic charging and discharging. This approach ensures the battery pack’s normal operation, enhances safety, and prolongs the battery pack’s service life.

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

Numerical investigation of suppressing thermal runaway propagation in a lithium-ion battery pack using thermal insulators

Thermal runaway propagation (TRP) in a lithium-ion battery pack is crucial to its safety concerning the potential hazards of fire or explosion. In current study, a TRP suppression method for a 4 × 4 battery pack using three insulation materials, silicate, ceramic and glass fiber boards, is numerically investigated. Reliability of the model is first verified by experimental temperature of a single battery during TR. Then, three sets of TRP scenarios initiated by external heating are studied to reveal the effects of insulation type, thickness (0.5–4 mm) and layout. The results show that thermal conductivity of insulation impacts its performance more greatly than thickness, and glass fiber outperforms silicate and ceramic fibers in preventing TRP. Bidirectional layout (BL) of insulation boards performs better than unidirectional layout (UL). For UL, row-to-row TRP exists and the TRP process is accelerated compared with non-insulation case if insulation boards fail, implying UL can prevent TRP only if the row-to-row TRP is inhibited. While for BL, TRP is only observed for 0.5 mm silicate fiber boards, and the critical heating power (6.5 kWm−2) triggering TRP in battery pack is much lower. The outcomes may provide useful theoretical bases and suggestions for safety design and risk assessment of battery pack.

Modeling and Validation of a Lithium-Ion Battery Pack

Abstract Batteries are part of an extremely nonlinear electrochemical system, and their performance is determined by various key factors such as law of thermodynamics, electrode kinetics, ion transport, and diffusion. To develop an accurate model of a battery, it is important to take into consideration both the internal and external conditions that would determine both the battery dynamics and its performance. Electrochemical, mathematical, and electrical equivalent circuit models are three common ways to model the electrochemical battery system. However, using these models involves certain limitations such as increased computation, empirical equations, and components (both active and passive). A simple electrical battery model with a voltage source and a resistor is insufficient to attain the desired level of accuracy in the model. To overcome these limitations, this study presents an electrothermal model that has been designed by adding “n” number of resistance capacitance (RC) pairs connected parallelly to ensure both dynamics and accuracy. An electrothermal model of a lithium-ion cell with a one-RC model and two-RC model has been developed, and a performance comparison has also been made to check the voltage accuracy of all the models. The electrical and thermal parameters (Em (state of charge [SOC], T), RO (SOC, T), R1 (SOC, T), R2 (SOC, T), C1 (SOC, T), C2 (SOC, T)) required for an RC model have been extracted through the pulse charge-discharge test. A battery pack of 55 V, 50 Ah using the RC model based on the Indian driving cycle condition has also been developed. Pulse charge-discharge and constant current-constant voltage profiles have been applied to the developed battery pack model to simulate and validate the accuracy of voltage in a real-time scenario.

A Novel Set-Valued Sensor Fault Diagnosis Method for Lithium-Ion Battery Packs in Electric Vehicles

Sensor fault diagnosis is of great significance to ensure safe battery operation. This paper proposes a novel sensor fault diagnosis method that achieves the simultaneous fault detection, fault source and type identification, and fault estimation in a comprehensive way. Specifically, a set-valued observer, featuring a state predictor and a state estimator, is first constructed and designed to guarantee the inclusion of the unavailable actual battery state due to unknown modeling errors and noises at every instant of time. Compared with the traditional observers, a distinct feature of the proposed one lies in that the calculated state predictions and estimations of the battery system at each time step are ellipsoidal sets in state space rather than single vectors. The boundedness of state prediction and estimation errors is formally proved, and the tractable design criteria for determining the real-time optimal prediction and estimation ellipsoids are also derived. As for diagnosis algorithm, fault detection is implemented based on the intersection between the prediction and estimation ellipsoids. Then, a two-layer Pearson correlation coefficient analysis mechanism is developed to identify the source and type of sensor faults. Another set-valued observer based on an augmented battery model is further designed to estimate the fault level. Finally, experimental studies of a battery cell under different sensor fault sources, types and values are elaborated to verify the effectiveness of the proposed method.

Electric Aircraft Battery Performance: Examining Full Discharge Under Two Conditions

The understanding of battery performance, particularly over the full course of discharge and over battery life is critical to allow pilots to maintain safety in electric aviation. In this study, an electric aircraft powered by two Lithium-Ion battery packs is used as a test article. The objectives of the current study are to build on previous work by conducting two full tests discharging both battery packs from a 100% charge. This allows examination of the battery performance a) under a constant power output and b) with periodic tests of the maximum power available. In addition to state of charge (SOC), remaining flight time, battery temperature, and motor power, this study presents data on motor RPM, torque, voltage, current, battery internal resistance, and available energy. The data on motor power indicates that the available maximum power decreases with lower SOCs when the throttle settings are varied; however, at lower power settings, such as an optimum cruise at 27 kW, the motor power remains constant and as expected during discharge. Also, the constant power setting illustrates that there are inflection points in physical battery characteristics. These results confirm that electric aircraft performance changes during a flight are different than what a pilot expects from a gasoline-powered aircraft. The longer the aircraft flies at different throttle settings, the faster the batteries discharge; the discharge curve is nonlinear. Thus, a piston-trained pilot’s expectation for an aircraft’s performance later in a flight will not match an electric aircraft.

Correction to: Comparison of Fire Suppression Techniques on Lithium-Ion Battery Pack Fires

Correction to: Comparison of Fire Suppression Techniques on Lithium-Ion Battery Pack Fires

Обзор алгоритма программного обеспечения для диагностики остаточного ресурса литий-ионных аккумуляторных батарей тягового подвижного состава

Purpose: Review of the algorithm for assessing the residual life of the lithium iron phosphate battery in the process of functional diagnostics of the electric energy storage device, based on the measurement of disturbing influences of current pulsations. Methods: Using the result of previously conducted experimental studies on the measured parameters of the lithium-ion battery pack when working with the electric energy converter, a set of statistic data is assumed, which by means of the described algorithm will produce an estimate of the remaining life of the battery cell. Results: The software algorithm for functional diagnostics of the lithiumion battery pack when working in the direct current link with the frequency converter based on the previously conducted physical experiment that collected statistical parameters of the lithium-ion battery pack in operation is considered. Practical significance: The conducted review of the statistical data of the investigated storage device gives the possibility to carry out functional diagnostics of the current resource state of the lithium-ion battery pack.

LIFE CYCLE ASSESSMENT AND CIRCULAR ECONOMY INDICATORS TO DESIGN SUSTAINABLE ELECTRIC OUTBOARDS: RESULTS FROM WORKSHOPS WITH INDUSTRIAL EXPERTS

AbstractTo help industries in their sustainable and circular transition from internal combustion engine production to electric motor production, the deployment of (i) a sound environmental impact assessment methodology, such as life cycle analysis, coupled with (ii) Design for Re-X tools, such as circularity indicators, is instrumental. To demonstrate the industrial relevance and complementary of both approaches, two consecutive workshops are conducted with a major original equipment manufacturer of recreational boats and their associated engines. On this basis, two circularity indicator-based tools were used to quantify and enhance (i) the circularity potential of the electric outboard as a whole, and (ii) the circularity performance of the two most impactful components, based on the LCA results: the electric motor unit and the lithium-ion battery pack. In all, the practice sessions supported the generation of strategic and operational ideas to improve the circularity of the electric outboard. As the industrial participants found both frameworks easy to use and efficient, all the details and resources used to conduct, replicate, or adapt such workshops in other industrial contexts are shared.

Field programmable gate arrays implementation of a Kalman filter based state of charge observer of a lithium ion battery pack

This paper presents a study on the state of charge observation of lithium-Ion batteries for energy management in embedded applications. The knowledge of the state of charge is fundamental for the safety and optimal usage of these batteries. The study focuses on the development and implementation of a Kalman filter-based observer algorithm on a Spartan 6 FPGA that can accurately estimate the state of charge of a battery, even if it is initialized differently from its actual state. In this paper we have focused on the opportunities of FPGA for fast calculation which allow to use the FPGA as a slave component in a BMS and allow to observe the SOC a great deal of cells with a low cost. The implementation of this observer on a low-cost FPGA can lead to cost reduction for battery management systems in various applications, such as electric cars and any other systems requiring the observation of the state of charge of a battery pack. The observer model was validated through simulations and real-time testing. This study presents a promising approach to accurately estimate the state of charge of Lithium-Ion batteries for efficient energy management in various applications.

Learning-based sparse spatiotemporal modeling for distributed thermal processes of Lithium-ion batteries

Distributed thermal modeling of Lithium-ion batteries (LIBs) is critical for the safety of electric vehicles. Due to the installation and cost constraints, only limited sensors are allowed for practical applications. In this paper, a learning-based framework is proposed for online spatiotemporal modeling of distributed thermal processes in pouch-type LIBs under sparse sensing. It consists of two stages. In the offline learning stage under full sensing, the Karhunen–Loève (KL) decomposition is used to extract the full spatial basis functions (BFs). In the subsequent online modeling stage under sparse sensing, a spatial mapping filter is first designed to recover the missing spatial information using the initial full BFs, which are then dynamically updated by the incremental KL technique as the thermal process evolves. By iteratively repeating these two steps, the streaming sparse spatiotemporal output can be accurately completed. Finally, the typical KL-based time-space separation method can be used for online temperature prediction. The simulation results of the distributed thermal processes on a pouch-type cell and a LIB pack demonstrate the effectiveness of the proposed method.

Design and Performance Evaluation of Liquid-Cooled Heat Dissipation Structure for Lithium Battery Module

The current global resource shortage and environmental pollution are becoming increasingly serious, and the development of the new energy vehicle industry has become one of the important issues of the times. In this paper, a nickel–cobalt lithium manganate (NCM) battery for a pure electric vehicle is taken as the research object, a heat dissipation design simulation is carried out using COMSOL software, and a charging heat generation model of the battery pack is established. Combined with the related research on the thermal management technology of the lithium-ion battery, five liquid-cooled temperature control models are designed for thermal management, and their temperature control simulation and effect analysis are carried out. Finally, the performance evaluation system of the thermal management scheme of the lithium-ion battery pack is established based on the analytic network process (ANP) and system dynamics (SD), and the performance of the above five thermal management design models is comprehensively scored and analyzed. The results show that liquid-cooled Models 1 (86.7075) and 5 (89.1055) have the highest overall scores, meeting both the temperature control requirements and the overall thermal management performance, and it is recommended to apply the working condition settings for which they are evaluated as Level I.

A novel battery thermal management system utilizing ultrathin thermal ground planes for prismatic Lithium-ion batteries

Temperature significantly affects the energy efficiency, safety, life and performance of a lithium-ion battery pack in electric vehicles (EVs). High overall temperature and large temperature difference of the battery in fast charging (FC) can cause degradation in performance and even catastrophic failure like a thermal runaway. Besides, the increasing energy density of battery packs limits the space for the thermal management system. Therefore, we developed an ultrathin thermal ground plane-based battery thermal management system (UTTGP-BTMS), which utilizes 0.4 mm thick novel UTTGP with air cooling to dissipate the heat from the gap between batteries. Double-layer high pores per inch (PPI) mesh and wettability modification was adopted to enhance the thermal performance of the UTTGP. Before the evaluation test of the BTMS, the heat generation rates of the batteries in 2.2C to 4C fasting charging conditions are estimated by Bernardi’s model. Then, the thermal performance of the novel battery thermal management system (BTMS) is experimentally investigated under 2.2C to 4C FC regimes at ambient temperatures from 10 °C to 50 °C. The BTMS is able to maintain a battery surface temperature of 55Ah Lithium iron phosphate (LiFeO4, LFP) batteries below 42.7 °C under a 4C charge rate, 57.3 °C at 50°C ambient temperature, respectively, and still achieve good surface temperature uniformity in all cases studied. Compared to a BTMS with copper heat spreaders, the temperature rise temperature non-uniformity, and thermal resistance are reduced by up to 23.3%, 28.4%, and 62.6%, respectively. Besides this, the effects of the pores density of the mesh in the UTTGP-BTMS are also studied by changing the sieve number of mesh, showing that a higher pore density gives better performance under large C-rates. The proposed UTTGP-BTMS showed significant performance with only submillimeter thickness in the thermoregulation of a battery pack, with the potential of being a viable solution for high-power battery thermal management in EVs.

EV Battery Cooling using PCM and Force Convention Method

The thermal battery management system (BTMS)'s goal is to preserve the health and effectiveness of the battery. as well as ensure that the temperature of the battery inside is in safe operating range. a conventional air cooling system not only does it need extra power, but it also can't meet. The demand for new lithium-ion battery (LIB) packs is high energy density, while liquid cooling requires BTMS complex devices to ensure the effect. Therefore, Phase change materials (PCMs) based on BTMS technology are now becoming commonplace. Using PCM s to Absorb heat, the temperature of the battery pack can be maintained Within the normal operating range for a long time without using any external force. It was an experimental platform developed to study thermal phenomena in a Li-ion battery With PCM material. CFD analysis will be performed to find out Battery EV temperature and PCM while operating state.

A fast active balancing strategy based on model predictive control for lithium-ion battery packs

The consistency of lithium-ion battery packs is extremely important to prolong battery life, maximize battery capacity and ensure safety operation in electric vehicles. In this paper, a model predictive control (MPC) method with a fast-balancing strategy is proposed to address the inconsistency issue of individual cell in lithium-ion battery packs. Firstly, an optimal energy transfer direction is investigated to improve equalization efficiency and reduce energy loss. Then, a MPC-based equalization algorithm is developed to obtain the optimal constant equalization current by directly minimizing equalization time of battery's SOC. Moreover, a fast-solving strategy for MPC is designed to reduce the computational burden of cells' equalization. Finally, the performance of proposed MPC algorithm has been compared with other MPC-based equalization methods in three different equalization topologies (cell-to-cell, cell-to-pack and module-based equalization topology), the results indicate that the proposed algorithm achieves faster equalization speed and less energy loss in three equalization topologies. Importantly, the proposed algorithm avoids the repeated charging and discharging of intermediate batteries effectively, and ensures the single-point convergence of cells' SOC. Furthermore, the effectiveness and accuracy of proposed fast-solving strategy for MPC algorithm is verified by comparison with common solving strategies, the results show the proposed method takes less computational time to obtain the accurate optimal balancing current, indicating that the proposed fast-solving strategy can improve computation speed and reduce computational burden.

OCV-SOC-Temperature Relationship Construction and State of Charge Estimation for a Series– Parallel Lithium-Ion Battery Pack

Accurate state of charge (SOC) estimation of a battery pack is more meaningful than that of a cell in practical applications. The existing methods are difficult to provide an accurate SOC of a battery pack under a wide range of temperature due to cell inconsistency. In this paper, a SOC estimation method for a series-parallel lithium-ion battery pack based on the newly constructed OCV-SOC-temperature relationship was proposed. In the proposed method, firstly the cosine similarity is used to quantify cell inconsistency and the representative cells in the battery pack are determined by the largest cosine similarity value. Secondly, the OCV-SOC curves of the representative cells at different temperatures are used to construct the OCV-SOC-temperature relationship of the battery pack. Finally, a multi-branch fusion method is developed to estimate the SOC of the battery pack through the SOC of each branch by means of a Bayesian probability formula. The effectiveness of the newly constructed OCV-SOC-temperature relationship and the proposed SOC estimation method of the battery pack is verified under various dynamic conditions. The estimated SOC error of the series–parallel battery pack is less than 1.5%, which is approximately 60% lower than the traditional methods.

Effects of pulsating flow on the performance of multi-channel cold plate for thermal management of lithium-ion battery pack

Effects of pulsating flow on the performance of multi-channel cold plate for thermal management of lithium-ion battery pack

A manifold channel liquid cooling system with low-cost and high temperature uniformity for lithium-ion battery pack thermal management

A manifold channel liquid cooling system with low-cost and high temperature uniformity for lithium-ion battery pack thermal management