Vehicle Battery Pack Research Articles

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

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

Simulation on the thermal management of electrical vehicle battery pack with different cooling methods

Electrical vehicles (EVs) are becoming more popular every day. Lithium ion (Li-ion) batteries in the cylindrical form are utilized as the power source of both electrical vehicles and hybrid electric vehicles due to their small size and high-power density. There is typically very little space between the batteries. Heat dissipation must be properly evaluated to ensure that the battery operates as intended. The study goal is examine how well a battery pack performs overall while using alternating cooling strategies. Three models were created in solidworks 2016 for the purpose of determine the best cooling approach. Three battery thermal management (BTMs) options were also chosen, analyzed, and simulated in Ansys Fluent 19.2 to ensure accurate and thermal modelling. According to the findings, geometry 2 ethynyl glycol exhibits a more efficient temperature distribution and maximum temperature than the other cooling methods. Furthermore, channel cooling based on BTMs, the consistent temperature distribution is carried out, and the maximum temperature is regulated to 306.66 K, with a minimum temperature of 293. 20 K being attained.

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

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

Monitoring Single-Phase LV Charging of Electric Vehicles

The paper presents the results of the monitoring process of the charging of an electric vehicle battery pack. Battery pack charging with a capacity of 58kWh was monitored in a single-phase 230V/50Hz circuit. The slow charging system used was configured to obtain a current of 10A. During monitoring, the focus was on the recognition of the charging, considering the impact of this process on power quality and, consequently, on the reliability of electrical machines. Research results show that the monitored charges are one-, two-, or three-stage processes. The variations in the currents, power, and higher harmonic contents were observed. The effects of such variations depend on the properties of the power grid at the point of connection of the charging system. Knowledge of the variation of the voltages, currents, and active and reactive power allows for the determination of the requirements of the measuring equipment used for charging the monitoring, including the selection of discrimination/averaging time of monitored quantities. The research results also indicate the need for continuous monitoring of the power quality in the power supply circuit of electrical loads, e.g., electrical machines. Continuous monitoring supports the diagnostics of electrical machines and allows the appropriate measures to increase their reliability.

Improving the air-cooling performance for lithium-ion battery packs by changing the air flow pattern

Air-cooling battery thermal management system (BTMS) is commonly used to maintain the performance and safety of lithium-ion battery packs in electric vehicles. In this paper, based on the conventional Z-type and U-type flow BTMSs, several novel BTMSs with different flow patterns are designed on the 4 × 9 21700 battery module by changing the outlet positions, and the performance of these BTMSs are evaluated with numerical simulations. The results show that the flow pattern of the air-cooling BTMSs has a significant influence on the BTMS cooling performance. When the outlet is located at the top of the battery module and the center plane of the outlet is closer to the inlet, the BTMS reveals a better cooling performance. In this system, when the inlet wind speed reaches 5 m/s, the average temperature and maximum temperature of the battery packs both reduce about 3 °C, with the Z-type BTMS flow pattern. Meanwhile, the temperature uniformity of the battery packs is greatly improved. Comparing with the U-type flow pattern, the average and the maximum temperatures reduce about 0.8 °C and 2.4 °C, respectively. The numerical simulations also demonstrate that with the increase of the inlet wind speed, the flow pattern of the BTMS has a significant effect on the cooling performance.

Parallel optimization of design and manufacturing—Carbon fiber battery pack for electric vehicles

According to the requirement of “structural design and manufacturing feasibility” of the electric vehicle battery pack, the design of carbon fiber composite material instead of metal material is studied in this paper. Firstly, the cross-section performance of the composite battery pack was determined by the principle of equal stiffness. Moreover, the different layups were verified from a manufacturing point of view and the advantages and disadvantages of the three improvements, linear shear, V-shear, and V-shear + linear shear, were compared. Secondly, the three steps of free size optimization, size optimization, and layup sequence optimization were carried out for the lower box to find the lightest structure; the upper box was directly reinforced with a “King” type to alleviate the excessive amplitude. The total mass of the final pack is 14.09 kg, with a weight reduction rate of 56%. Finally, the volume-of-fluid (VOF) method in ANSYS Fluent is chosen to simulate the curing process. The molding results of four injection methods are discussed, while resin viscosity and pressure are considered to find the optimal process parameters. The work in this paper provides a reference for the lightweight application of composite materials in new energy vehicles.

Quadratic Programming-Based Simultaneous Charging Strategy for Battery Packs of Electric Vehicles

An optimal strategy for simultaneous charging of electric vehicle battery packs is proposed as a multiobjective optimization problem in this article. The charger consists of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{n}$</tex-math></inline-formula> constant voltage current sources for charging <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{n}$</tex-math></inline-formula> batteries, respectively. Considering the energy loss and the charging criteria, a battery charging issue for the charging mode is formulated as a multiobjective optimization problem and the optimal solution is obtained with the application of quadratic programming. In addition, an adaptive momentum-based steepest descent algorithm is propounded to minimize the simultaneous charging time in the charging mode. Simulation and experimental results show that the actual state of charge converges to the same value within the theoretical shortest time for under the charging mode.

A Critical Analysis of Helical and Linear Channel Liquid Cooling Designs for Lithium-Ion Battery Packs

Thermal management systems are integral to electric and hybrid vehicle battery packs for maximising safety and performance since high and irregular battery temperatures can be detrimental to these criteria. Lithium-ion batteries are the most commonly used in the electric vehicle (EV) industry because of their high energy and power density and long life cycle. Liquid cooling provides superior performance with low power draw and high heat transfer coefficient. Two liquid cooling designs-the Linear Channel Design (LCD) and Helical Channel Design (HCD)-underwent multiple numerical and geometrical optimisations, where inlet mass flow rate, channel diameter, and inlet and outlet locations were analysed using CFD (computational fluid dynamics). The primary objectives were to maintain maximum temperatures and thermal uniformity within the operational limits derived from the literature. These were both achieved with the LCD using a mass flow rate of 7.50E-05 kgs−1. The Tmax goal was met for the HCD but not the thermal uniformity goal. The LCD achieved 1.796 K lower in maximum temperature and 8.740 K lower in temperature difference compared to the HCD, proving itself superior in both metrics. The HCD required a higher mass flow rate than the LCD to regulate temperatures, resulting in an undesirably high power consumption.

SOC prediction of Volterra adaptive filter based on chaotic time series

This paper presents an SOC (State of Charge) prediction method based on a chaotic time series Volterra adaptive filter. This method first verifies the chaotic characteristics of the SOC time series of lithium-ion batteries and then implements the phase space reconstruction method to reorganize the voltage and current data into multi-dimensional data. Furthermore, we use the Volterra adaptive filter to predict the SOC of the lithium-ion battery pack on the reconstructed multi-dimensional data. This method can effectively solve the problem that the SOC estimation error increases due to the poor real-time performance of battery voltage, current, and other parameters of vehicular lithium-ion battery packs in electric vehicles obtained by the battery management system. According to the simulation and test results, the error of the SOC estimation algorithm studied in this paper is less than 0.6%, which indicates that the Volterra adaptive filter based on chaotic time series comprehensively uses the linear and non-linear parameter characteristics of lithium-ion batteries and recombines the voltage and current data into multi-dimensional data through phase space reconstruction technology. It can reveal the relationship between the battery SOC and other variables better, achieve higher precision lithium-ion battery SOC prediction, and have better realizability.

Cellular Structure Design and Manufacturability for Electric Vehicle: A Review

Cellular structures can be classified into foams, honeycombs, and lattice structures. Each type of structure has its characteristics. Various applications of cellular structures can be found in aviation, bioengineering, automotive, and other fields. In the automotive sector, cellular structures have been used for structural applications and impact- absorbing modules, for example, for protecting the electric vehicle battery pack against impact loading. The challenges that limit the application of cellular structures today include systematically designing pseudo-random cellular structures, assessing which cellular patterns are most suitable for a particular application, and mastery of manufacturing technology for efficient mass production of cellular structures. In this paper, the authors examine the state-of-the-art technology in geometry, applications, and manufacturing of various cellular structures carried out by researchers to obtain an overview of the current conditions for further development of these cellular structures. Limited manufacturing capabilities encourage researchers to design an optimal cellular structure to be applied to a particular function but have high manufacturability. The development of additive manufacturing technology has provided opportunities for researchers to produce an optimal cellular structure commercially soon.

Fault Diagnosis Method for Lithium-Ion Battery Packs in Real-World Electric Vehicles Based on K-Means and the Fréchet Algorithm.

Battery failure has traditionally been a major concern for electric vehicle (EV) safety, and early fault diagnosis will reduce many EV safety accidents. However, the short-circuit signal is generally very weak, so it is still a challenge to achieve a timely warning of battery failure. In this paper, an initial microfault diagnosis method is proposed for the data of electric vehicles in actual operation. First, a robust locally weighted regression data smoothing method is proposed that can effectively remove noisy data and retain fault characteristics. Second, an ordinary-least-squares-based voltage potential feature extraction method is proposed, which can effectively capture the small fault features of battery cells and achieve early warning. Third, a reference cell selection method based on K-means clustering is proposed, which can effectively reduce the false alarms caused by the inconsistency of each cell. Fourth, the Fréchet algorithm is introduced into the field of battery pack fault diagnosis and combined with thresholds for battery pack fault diagnosis and localization to accomplish the diagnosis and early warning of minor faults. Finally, the fault diagnosis method is validated by three actual running electric vehicles to verify the effectiveness, reliability, and robustness of the method.

Multi-physical contact simulation in Vehicle applications

Abstract Multi-physical contact behaviour is important in multiple disciplines related to the automotive industry. Nowadays battery-electric vehicles' (BEV) thermal management systems deal with contact between bodies where mechanical, electric, and thermal interaction occurs. The battery thermal management itself is crucial for cell life, safety, and everyday vehicle performance. Thus, comprehensive and accurate simulation of the multi-physical contact is a vital part of vehicle development. The multi-physical contact is represented by two or more bodies under applied mechanical load and a current or heat conducted throughout the realized contact area. The amount of conducted current/heat or generated Joule heat is the function of the contact area as well as contact pressure, thus the structural simulation should be essential for such thermal management system simulations Most of the current full vehicle battery pack CFD cooling simulations simplified the multi-physical contact as ideal. Detailed contact modelling is time-consuming, hence not applicable for the full vehicle modelling. In this work, a feasible approach based on contact resistance curves was implemented. Furthermore, the work demonstrates the necessity of correct structural contact prediction for a joule heating and thermal solution.

Detecting mechanical indentation from the time constants of Li-ion batteries

Lithium-ion batteries pose severe hazards if their safety is compromised. Previous work has shown that mechanical damage to the battery may not affect its voltage, capacity, or other primary specifications. Therefore, currently, there is no method to check the integrity of battery cells inside an electric vehicle battery pack once it has been subjected to a shock or impact. Here, we report a method to detect mechanical damage to Li-ion cells from their electrical response. We formulate the distribution function of relaxation times (DRT) by a series of passive electrical elements consisting of inductors, resistors, and capacitors. Using our DRT formulation and criteria, we show that the indented cells have substantially different high-frequency time constant characteristics than the control group. This non-invasive method has the potential for detecting hazardous mechanical damage to the batteries of electric vehicles after a road crash or impact landings of drones.

Safety warning analysis for power battery packs in electric vehicles with running data

The Safety warning of battery packs can effectively prevent thermal runaway accidents in electric vehicles. The inconsistency evaluating of the battery pack accurately is a prerequisite for safety warning. In this work, the safety warning model for electric vehicles (EVs) power battery packs based on operational data is proposed, where the voltage, temperature, internal resistance, and electric quantity are extracted from accident vehicles over two years as the four factors for consistency evaluation of battery packs, and their changes during vehicle operation and before thermal runaway are analyzed. The Fuzzy analytic hierarchy process (FAHP) and the entropy weighting method are used to assign weights to the four factors, and the weighting coefficients of the subjective and objective assignment methods are determined to ensure accurate assignment of weights through the game theory approach. Each factor is scored comprehensively using the weighted scoring criteria, and the consistency status of the battery pack is determined based on the scoring results. The lowest consistency score of the single battery can be discerned by analyzing the real accident vehicle data through the distribution cloud map. The results reveal that the evaluation system can accurately quantify the degree of inconsistency of battery packs and identify problematic single cells timely, which is able to provide a reference for safety warning in the electric vehicles field.

A novel thermal management system for battery packs in hybrid electrical vehicles utilising waste heat recovery

• Waste heat from HEVs is applied to the ejector battery thermal management system. • Accurate prediction of complex flow behaviors in ejector by the condensation model. • Ejector's COP and ER shows inverse trend with increasing inlet superheating. • Ejector's performance reaches maximum with an increase of 11 K inlet superheating. Thermal management system generally ensures the safe operating conditions and heat resilience of battery packs in hybrid electric vehicles (HEVs). The current study raised a novel approach to reduce fire risks related to HEVs through a novel battery thermal management system powered by low-grade combustion waste heat running on steam ejectors for the first time. In this paper, an ejector operating at a low temperature under 100 °C for HEV's battery thermal management system is proposed and investigated. An in-house wet-steam model considering the condensation effect has been developed to characterise the ejector's internal flow structure and further analyse its feasibility as a thermal management system. The results show that the model considering the condensation process is more feasible in evaluating the performance of the steam ejector than the dry gas assumption. To improve the performance of the proposed ejector battery thermal management system, the effect of superheating of primary steam has been investigated. The results showed that an optimum point exists with 11 K superheating between improvement of entrainment ratio, the system's coefficient of performance and the power efficiency for the current case. The entrainment ratio at that point reaches around 0.45, while the coefficient of performance reaches 0.225.

Optimal sizing of hybrid high-energy/high-power battery energy storage systems to improve battery cycle life and charging power in electric vehicle applications

Design of the Electric Vehicle (EV) battery pack involves different requirements related to the driving range, acceleration, fast-charging, lifetime, weight, volume, etc. Therefore, sizing of the EV battery pack necessitates a multi-objective optimization study to achieve the right trade-off considering the aforementioned factors. This “trade-off” can vary depending on the type and size of the EV, as well as use cases. In this regard, a nice solution is to use a hybridized battery pack consisting of both High-Energy (HE) and High-Power (HP) battery cells, which will help to meet a wider range of customer requirements. Hybridization decouples energy and power and thus increases design flexibility to achieve a better trade-off for a wider range of EV applications. This paper proposes an effective framework for optimal sizing of such hybridized battery packs for a typical EV, namely the Mitsubishi MiEV. Lithium-ion cells with Nickel Manganese Cobalt Oxide (NMC) and Lithium Titanate Oxide (LTO) chemistry are chosen as HE and HP cells, respectively. A detailed analysis is fulfilled to determine the best hybridization topology, e.g. voltage profile, interface with DC-link, etc. The genetic algorithm is used to solve the multi-objective optimization problem considering two different driving cycles, namely the New European Driving Cycle (NEDC) and Worldwide Harmonised Light Vehicle Test Procedure (WLTP). The results favor the usefulness of the hybrid battery pack to simultaneously achieve lifetime and charge power requirements compared to mono battery systems. The hybrid pack offers >+40,000 km improvement in the achievable driving when an end-of-life criterion of 70 % for the cell capacity is considered. Remarkably, the optimized hybrid pack can also be fast charged up to 70 % within 6 min.

A methodology to model and validate electro-thermal-aging dynamics of electric vehicle battery packs

It is becoming increasingly important for power system engineers to have accurate yet simple-to-realize models that simulate electric vehicle (EV) battery pack behaviour. This paper proposes a methodology to model and validate the main dynamics – electrical, thermal and aging – that characterize Li-ion batteries without disassembling them from the vehicle. The methodology consists of three steps. The first is the model implementation of the battery pack, considering the mentioned dynamics and their interdependencies. Next, the electrical and thermal parameters are experimentally derived from a Nissan LEAF 40 kWh battery pack. Third, the overall model is validated through real tests. Only electrical power and ambient temperature are provided as model inputs, and the output is compared with the collected measurements from the battery pack. Results show that the model predicts the electro-thermal battery pack behaviour with errors below 5%. The estimated capacity decrease deviates from the measurements and battery management system (BMS) readings up to one percentage point. We conclude our paper by discussing the uncertainty regarding this estimation and the limitations introduced by working with EV battery packs, both on model implementation and field validation.

Edge computing for vehicle battery management: Cloud-based online state estimation

The adoption of electric vehicles (EVs), including battery EVs and hybrid EVs, makes it possible to reduce fossil fuel consumption and greenhouse gas emission. However, an accurate battery model and an effective battery management system should be established to enable this benefit. This paper proposes a novel cloud-assisted online battery management method based on artificial intelligence and edge computing technologies. Integration of cloud computation and big data resources into real-time vehicle battery management is realized by establishing a novel cloud-edge battery management system (CEBMS). A deep learning algorithm-based cloud data mining and battery modeling method is developed to estimate the voltage and energy state of the battery. The accuracy of the established cloud battery model outperforms the onboard battery management system by utilizing multi-sources information from different EVs. Meanwhile, a cloud-assisted battery management method is established at edge nodes in the onboard battery management unit to realize real-time state estimation locally. By using precise battery state estimation provided by the cloud platform, vehicle battery model accuracy can be significantly improved. The performance of the proposed battery management method is verified by a vehicle big data platform and battery pack experimental test bench. Experimental results justify the effectiveness of the proposed method in battery state estimation, which can help the EVs use and manage the battery more effectively.

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

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

Dynamic Bayesian network-based disassembly sequencing optimization for electric vehicle battery

The sharply increasing end-of-life (EOF) battery volume in the global complex energy market has created significant challenges for its recycling and reuse, to reduce environmental pollution and resource waste, and efforts have been focused on the disassembly process considering the uncertainty of electric vehicle (EV) battery pack categories and quality. Compared with traditional disassembly, the EV battery disassembly process needs to consider more uncertainty factors for each EOF battery pack to represent its disassembly structure, which significantly reduces disassembly production efficiency. Even though sequence optimization methods for the disassembly process have been developed to solve these problems, there are still two important challenges that remain: uncertain disassembly structure representation and optimal disassembly sequence selection. To address these challenges, this paper proposes a dynamic disassembly Bayesian network approach based on an EV battery disassembly graph model. This method offers dynamic process optimization to manufacturers to deduce the optimal disassembly sequences using the forward–backward algorithm and the Viterbi decoding algorithm. To validate the proposed method, an EOF battery is used to demonstrate the disassembly sequence selection, which indicates the possibility of massive EV battery disassembly prediction.