Safety Of Electric Vehicles Research Articles

Preparation, Characterization and Conductivity of SiO2 doped rGO-WO3 Composite for Battery Application

High-performance solid polymer electrolytes are regarded as one of the most promising materials to be used in the development of lithium ion batteries with improved extended life, increased recyclability and increased safety for electric vehicles and energy storage. In this work, a microwave-assisted hydrothermal technique was used to produce SiO2-doped rGO-WO3 composite, which possesses outstanding supercapacitor capabilities. This method is simple, low-cost method without using any other capping and reducing agents. The obtained SiO2 doped rGO-WO3 composite structural, morphology and components have been examined by XRD, FT-IR, FE-SEM, TEM, EDX, UV-visible techniques. It was found that the SiO2 doped rGO-WO3 composite well crystalline and the size range is about 20-30 nm. The SEM morphology results showed that the nanoparticles were stabilized by rGO, having cavity like structure and randomly distributed on the rGO-WO3 matrix. The solid state electrolyte revealed superior lithium ion transference number and cyclic stability. The SiO2 doped rGO-WO3 composite shows improved ionic conductivity of 2.74 × 10-5 S cm-1 at room temperature.

Multi-fault diagnosis of lithium battery packs based on comprehensive analysis of locally weighted Manhattan distance and voltage ratio

The diagnosis of faults in lithium-ion battery packs is pivotal to ensuring the operational safety of electric vehicles. A fault diagnosis method is introduced to address the lack of a discharge phase and the existence of a single fault type in traditional diagnostic methods. This method relies on a comprehensive assessment involving locally weighted Manhattan distance and voltage ratio anomalies. The KD-Tree-MLLE dimensionality reduction technique is utilized for fast data processing, which improves the computational efficiency by 1.3 % compared to the original MLLE algorithm. The fault detection method utilizing locally weighted Manhattan distance can meet the same threshold evaluation criteria as the charging phase. This approach resolves the constraint of a single phase and accomplishes comprehensive fault detection. The precision and efficacy of the methodology are confirmed through F1-score evaluation. Accuracy and recall rates are compared under varying thresholds during the charging and discharging phases to determine the optimal threshold value. The ratio between the voltage data of the defective battery and the average voltage data of the normal batteries is calculated, and the comprehensive anomaly analysis method based on the voltage ratio and temperature is proposed. The use of comprehensive data such as voltage ratio and temperature to judge the type of fault can simultaneously achieve the judgment of a single fault and mixed fault types. The applicability of locally weighted Manhattan distance under dynamic circumstances and the efficacy of the voltage ratio analysis method across charging and discharging phases are confirmed through the establishment of an experimental and simulation platform dedicated to lithium-ion batteries. Findings indicate that all faults in both phases can be accurately located when the threshold is set to 0.17. This affirms the superior accuracy of the proposed method in detecting and localizing individual faults and combinations of faults quickly and reliably.

A comprehensive review of BMS fault analysis in pure electric vehicles

Pure electric vehicle technology faces numerous technical challenges during the process of independent research and development, with the safety and endurance of power batteries being particularly critical. The Battery Management System (BMS) is a core factor affecting the performance and safety of electric vehicles. The normal operation of the BMS is essential for ensuring the reliability of electric vehicles. However, in practical applications, the BMS may encounter faults that can lead to a decline in battery performance, a reduction in lifespan, and even trigger safety incidents. Therefore, the development of an efficient and accurate fault analysis and diagnostic system is particularly important. This paper systematically explores the fault issues of electric vehicle BMS, including the composition, working principles, and main functions of the BMS, as well as the classification, level definition, case analysis, and resolution measures of faults. The article also provides a detailed introduction to fault analysis methods, including qualitative and quantitative analyses. Finally, based on recent hot topics and the latest research progress, it is concluded that model-based fault diagnosis will become increasingly important. This paper aims to provide a reference for the development of the field of electric vehicle battery management fault diagnosis technology.

A label-free battery state of health estimation method based on adversarial multi-domain adaptation network and relaxation voltage

The state of health (SOH) estimation of lithium-ion batteries is crucial for the operational reliability and safety of electric vehicles. However, traditional data-driven methods face problems of label shortage and domain discrepancy caused by diverse battery types and operating conditions. This paper proposes a label-free SOH estimation method based on adversarial multi-domain adaptation technique and relaxation voltage (RV). Firstly, the raw RV and integral voltage are proposed to construct the two-dimensional input sequence to ensure high adaptability across various target domains. Secondly, a two-dimensional convolutional neural network integrated with fully connected layers is developed to establish the feature extractor and SOH estimator, which paves the way for effective knowledge transfer. Furthermore, an adversarial multi-domain adaptation method with a domain discriminator is developed to optimize the extraction of domain-invariant features and enable accurate SOH estimation without SOH labels. Datasets of 50 batteries with different temperatures and materials are used for the validation. The proposed method shows outstanding performance, achieving the average RMSE and MAE of 0.0274 and 0.0240 across various working temperatures, and 0.0177 and 0.0148 across different battery types. It suggests that the proposed method can achieve robust adaptive battery SOH estimation across multiple target domains without using SOH labels.

Thermal Runaway in Battery Cells: Guiding Principles for Continuum Scale Modeling

Lithium-ion batteries are sensitive to temperature. Low-temperature or high-temperature operation may lead to lithium plating and dendrites, SEI decomposition, and separator collapse. Such phenomena may cause internal short circuits, rapid heat generation and, subsequently, may trigger thermal runaway, resulting in fire or explosion [1][2]. Hence, accurate modeling and prediction of the thermal response of battery cells inside the pack is critical to preventing safety hazards as well as optimizing battery performance. Battery thermal management system (BTMS) plays a significant role in maintaining an optimal operation temperature and ensuring thermal safety for electric vehicles [3]. However, the large number of battery cells inside one module makes it challenging for BTMS algorithms to realize real-time simulations based on fully resolved physics-based models. The conventional lumped capacitance (LC) thermal model achieves reduced computation costs under the hypothesis that temperature is spatially uniform. Nevertheless, such a model works better for small Biot number conditions, usually under low C-rate operation and moderate cooling scenarios, and it lacks justification under a broad range of battery operating conditions and materials [4]. To overcome such limitations, rigorous multiscale models, based on mathematical coarse-graining theories, have been developed to predict the thermal response of batteries at the module scale [5] while not compromising on computational cost. In this work, we investigate the accuracy of the classical LC thermal model for twenty potential battery modules constructed with commercial batteries and packing materials. We show that the classical LC thermal model may be inaccurate under a broad range of design conditions. In the context of multiscale modeling, we identify which effective models should be instead used, and analyze the impact of SOC and the relative strength of heat insulation and heat dissipation on model selection and accuracy. This work provides guidance, based on first principles and rigorous multiscale theory, on best practices for model selection in practical applications involving thermal behavior and thermal runaway in battery cells at the module scale.

Improvement of electric vehicle safety using a new hybrid fuzzy Q-learning algorithm for lithium-ion battery state-of-charge estimation

Improvement of electric vehicle safety using a new hybrid fuzzy Q-learning algorithm for lithium-ion battery state-of-charge estimation

A novel battery SOC estimation method based on random search optimized LSTM neural network

Battery state of charge (SOC) estimation is crucial for assessing electric vehicle safety and evaluating the remaining driving range. Owing to the complexity, variability of operating conditions, and the highly nonlinear internal mechanisms of batteries, accurate SOC estimation remains a focal point of current research. Therefore, this paper proposes a random search optimization-based Long Short-Term Memory (RS-LSTM) neural network for precise SOC estimation. The paper firstly uses the CALCE dataset, extracting discharge capacity and discharge energy as critical features from six battery parameters by employing the random forest algorithm. The Look-back, Epoch, Batch size, and Learning rate parameters in the LSTM neural network optimized by random search algorithm. The study result reveals optimal settings (Look back: 45, Epoch: 177, Batch size: 64, Learning rate: 0.0026) achieving superior estimation accuracy, evidenced by mean average error（MAE） and root mean square error（RMSE）of 0.221 % and 0.262 %, respectively. Furthermore, the method's superiority, effectiveness, robustness, and applicability were verified by conducting tests across various estimation methods, various SOC estimation intervals, various temperature conditions, the addition of Gaussian noise, and tests on experimental and real-world vehicle data. The research process demonstrates that the proposed method has superior precision and indicates promising potential for future applications.

Intelligent recognition of structural health state of EV lithium-ion Battery using transfer learning based on X-ray computed tomography

The lithium-ion battery (LIB) achieves wide applications on electric vehicles (EVs). The morphology of LIB electrode is highly correlated to the performance and safety of EV. Therefore, it is significant to monitor the structural health state of LIB to guarantee a reliable operation of EV. Currently, few research focused on the intelligent recognition of LIB structural health state. To bridge the gap, this paper proposes an image-based method to conduct structural health state recognition of LIB. The geometric deformation of LIB electrode is recorded by X-ray computed tomography (CT) images under different structure health state. Then a novel transfer learning network is incorporated to enhance the generalization ability of model. In the network, prior knowledge is first acquired from source domain data to initialize a convolutional autoencoder (CAE). Then the CAE is fine-tuned on CT images (target domain data), and a structural deviation index is established to reflect the structure health condition. Once the index exceeds the pre-set threshold, the alarm is triggered. The performance of the proposed method was evaluated on experimental data, and the results proved that the proposed method can perform structural health state monitoring effectively, and transfer learning can improve the generalization ability of detection model significantly.

Charging control of lithium‐ion battery and energy management system in electric vehicles

AbstractIn terms of electric vehicle architectures, the drivetrain offers unprecedented freedom, but it also creates new obstacles in terms of achieving all needs. The architecture of electric vehicles is simplified and adjustable at the component level because they don't have a combustion engine or fuel tank, only an electric motor and a battery. Implementing safe zones within electric vehicles (EVs) to accommodate battery packs necessitates significant adjustments to ensure the secure integration of the battery. A Battery EV, also known as a pure EV, solely relies on rechargeable battery packs as its source of energy, without any additional propulsion system. The Battery Management System (BMS) plays a significant role in maintaining the safety of electric vehicles by controlling the electronics of rechargeable batteries, whether they are individual cells or battery packs. The BMS plays crucial role in protecting both the user and the battery by monitoring and maintaining the cell's operation within safe limits. This research paper focuses on the control of solar‐powered charging for lithium‐ion batteries. An optimized FOPID controller is utilized to maximize power extraction from PV array and efficiently charge the battery. A hybrid optimization model is employed to optimize the gain parameters of the FOPID controller.

A transfer learning-based ensemble learning model for electric vehicles lithium-ion battery capacity estimation using electrochemical impedance spectroscopy

Lithium-ion battery capacity estimation is crucial to ensure the operational reliability and safety of electric vehicles. Electrochemical Impedance Spectroscopy (EIS) can provide rich physical degradation information of the battery, which makes the EIS-based data-driven method a promising solution for accurate battery capacity estimation. However, batteries tend to present diverse degradation patterns due to the diverse operating conditions and manufacturing, resulting in large capacity estimation errors in practical applications. Therefore, this paper proposes a transfer learning (TL) based ensemble learning (EL) method based on multi-layer perception for the battery capacity estimation. First, the influential impedance features are identified from the EIS using Spearman rank coefficient analysis. Second, three TL models are developed based on distinct TL strategies to enable sufficient knowledge transfer considering the different degradation pattern. Further, an EL method with Gaussian kernel-based weighted assignment technique is proposed to integrate the developed TL models and improve the estimation robustness. The performance of the developed model has been successfully validated only using the first 1/3 cycles of the historical data from target battery obtained under different operating conditions and batteries. The results reveal that the proposed method can largely suppress estimation errors, presenting more accurate capacity estimation compared to other methods with an RMSE lower than 1%.

An early-fault diagnostic method based on phase plane for lithium-ion batteries under complex operation conditions

Timely and accurate diagnosis of lithium-ion battery faults is crucial for ensuring the safety of electric vehicles. While incremental capacity (IC)-based diagnostic methods are effective, they can only be employed under charging conditions. To address this issue, a fault diagnostic method based on phase plane analysis is proposed to extend the application to various operating conditions. In a phase plane, the battery voltage is plotted on the x-axis, while the differential in voltage is plotted on the y-axis. During charging, diagnostic vectors indicating the occurrence and type of faults are extracted from the trajectories in the phase plane. Under complex operating conditions, early faults can be detected based on the morphological and scale deviation of the trajectories. Experimental results have shown that the proposed method can effectively detect minor faults. The fault detection rate and detection accuracy rate under charging conditions are 96.25 % and 97.73 %, respectively, which are 17.5 % and 8.54 % higher than those of the IC-based diagnostic method. Furthermore, the fault detection rate and accuracy rate under UDDS (Urban Dynamometer Driving Schedule) are 91.76 % and 97.23 %, respectively. Additionally, the diagnostic speed of the proposed method is twice as fast as that of the IC-based method.

Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA

Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%.

Experiment study of pseudo-passive heat removal system for power battery thermal runaway propagation inhibition

Approximately 41% of power battery thermal runaway propagation accidents happen at rest conditions; hence, developing passive technologies to stop such accidents is highly necessary. Existing passive power battery thermal runaway propagation inhibition approaches involve thermal insulation. However, the relatively large thermal resistance is incompatible with thermal management, which is a bottleneck that needs to be addressed properly. In this paper, a pseudo-passive heat removal system is proposed, to be combined with less thermal resistance to cut off thermal runaway propagation. The pseudo-passive heat removal system is based on a pump-driven cooling and thermoelectric conversion process, that is, the latter converts battery heat generation into electric energy to power the former. A prototype was developed, and its operational characteristics were studied. First, steady performance of pump-driven cooling processes with large temperature differences were analyzed. Second, the transient processes of the prototype for different internal electric-energy allocations were compared. It was found that the system heat removal rate in the working mode in which more energy was assigned to the pump-driven cooling process was 35% better than the rate when more energy was assigned to the thermoelectric conversion process. The novelty of this study lies in proposing a pseudo-passive power battery heat removal system and highlighting the effects of key parameters. This study provides new insights into heat removal from power batteries and will be helpful in enhancing electric vehicle safety.

Revealing and Overcoming Unfavorable Electrochemical Behaviors of Thick LiNbO3-Coated NCM523 for All-Solid-State Lithium Batteries

All-solid-state batteries (ASSBs) are very promising for next-generation energy storage technologies owing to several key advantages including higher power density, better thermal and electrochemical stability, and improved safety for electric vehicles. In this work, bulk-type ASSB cells were prepared with 0–25 nm thick LiNbO3 coatings, and their electrochemical behaviors at different upper cutoff voltages (upper cutoff potential ≥ 4.40 V) were systematically compared. A thicker coating caused three unfavorable electrochemical behaviors in the first three cycles: (1) a higher overpotential, (2) sluggish discharge kinetics, and (3) capacity fading. The measured electronic conductivity decreased drastically with increasing coating thickness, suggesting that this may have caused behaviors (1–3). To overcome this, a carbon additive was used to improve electronic transport in the composite cathode and successfully suppressed the aforementioned behaviors. Our findings indicate that the combination of a thick LiNbO3 coating on NCM523 and carbon additive can achieve synergistic effects to improve both the electrochemical properties and durability of ASSB cells.

Efficient Design of Battery Thermal Management Systems for Improving Cooling Performance and Reducing Pressure Drop

The air-cooled system is one of the most widely used battery thermal management systems (BTMSs) for the safety of electric vehicles. In this study, an efficient design of air-cooled BTMSs is proposed for improving cooling performance and reducing pressure drop. Combining with a numerical calculation method, a strategy with a varied step length of adjustments (∆d) is developed to optimize the spacing distribution among battery cells for temperature uniformity improvement. The optimization results indicate that the developed strategy reduces the optimization time by about 50% compared with a strategy using identical ∆d values while maintaining good performance of the optimized system. Furthermore, the system’s pressure drop does not increase after the spacing optimization. Based on this characteristic, a structural design strategy is proposed to improve the cooling performance and reduce the pressure drop simultaneously. First, the appropriate flow pattern is arranged and the secondary outlet is added to reduce the pressure drop of the system. The results show that the BTMS with U-type flow combined with a secondary outlet against the original outlet can effectively reduce the pressure drop of the system. Subsequently, this BTMS is further improved using the developed cell spacing optimization strategy with varied ∆d values while the pressure drop is fixed. It is found that the final optimized BTMS achieves a battery temperature difference below 1 K for different inlet airflow rates, with the pressure drop being reduced by at least 45% compared with the BTMS before the optimization.

Early warning for thermal runaway in lithium-ion batteries during various charging rates: Insights from expansion force analysis

Thermal runaway introduces a significant challenge in the widespread application of lithium-ion batteries, necessitating advanced early-warning technologies to ensure safety, particularly during charging. Only monitoring the temperature and voltage limit the performance of diagnostic algorithms. The expansion behavior of batteries, which is linked to their operating status, offers a vital indicator of safety. In this study, the evolution of multidimensional signals during overcharging experiments at different current rates is comprehensively investigated. The result shows that the abnormal expansion force can be detected at temperatures as low as 35.4 °C, which achieves an early warning signal 11 min earlier than the onset of battery thermal runaway. The effect of charging rate on battery safety is comprehensively analyzed, showing that the time interval between the warning signal of the expansion force and temperature increases steadily from 151 s to 682 s as the charging rate decreases. However, the charging rate hardly affects the stage of charge boundary of venting, which is around ±118 %. These insights are crucial for understanding early warning mechanisms in overcharged batteries, offering valuable guidance for enhancing the safety of electric vehicles and energy storage systems.

Case study on thermal management of planar elements with various polymeric heat exchangers: experiment and simulation

A reliable battery thermal management system (BTMS) is essential to ensure proper performance, a long life span and high electric vehicle safety. The primary objective of BTMS is to maintain the cells’ temperature in the range of 15–35 °C while limiting the temperature spread between cells to below 5 °C. Active thermal management with polymeric hollow fibers (PHFs) has been reported in a few articles, but its tremendous flexibility is mainly advantageous for cylindrical cells. Extruded polymeric cold plate heat exchangers with rounded rectangle channels (RRCs) are proposed as a more elegant solution for planar batteries. Heat exchangers using PHFs and RRCs were experimentally compared, with a strong focus on minimizing the maximum temperature and temperature spread of the experimental setup while simultaneously achieving minimal pressure drops. The system behavior with different parameters, including materials, geometry and thermophysical properties, was further studied using properly validated CFD models.

A Study on Securing the Safety of Electric Vehicle Charging Areas

This study analyzes the risk and risk factors of fire based on the location of a specific firefighting object where an electric vehicle charging area is installed, and proposes a way to ensure safety through fire risk assessment. First, as an institutional measure, individuals should be prohibited from installing charging areas in high-risk underground parking lots. Second, as a managerial measure, fire agencies should develop and disseminate standardized charging-area checklists and manuals. Third, as a structural measure, the charging area should be fire-protected or bulkheads should be installed. Fourth, as a facility measure, a dedicated firefighting facility and smoke control and ventilation systems should be installed in the charging areas. Fifth, firefighting measures should be equipped with dedicated firefighting equipment in the case of new constructions.

A Novel Feature Engineering-Based SOH Estimation Method for Lithium-Ion Battery with Downgraded Laboratory Data

Accurate estimation of lithium-ion battery state of health (SOH) can effectively improve the operational safety of electric vehicles and optimize the battery operation strategy. However, previous SOH estimation algorithms developed based on high-precision laboratory data have ignored the discrepancies between field and laboratory data, leading to difficulties in field application. Therefore, aiming to bridge the gap between the lab-developed models and the field operational data, this paper presents a feature engineering-based SOH estimation method with downgraded laboratory battery data, applicable to real vehicles under different operating conditions. Firstly, a data processing pipeline is proposed to downgrade laboratory data to operational fleet-level data. The six key features are extracted on the partial ranges to capture the battery’s aging state. Finally, three machine learning (ML) algorithms for easy online deployment are employed for SOH assessment. The results show that the hybrid feature set performs well and has high accuracy in SOH estimation for downgraded data, with a minimum root mean square error (RMSE) of 0.36%. Only three mechanism features derived from the incremental capacity curve can still provide a proper assessment, with a minimum RMSE of 0.44%. Voltage-based features can assist in evaluating battery state, improving accuracy by up to 20%.

Study on the electrical-thermal properties of lithium-ion battery materials in the NCM622/graphite system.

The phenomenon of fire or even explosion caused by thermal runaway of lithium-ion power batteries poses a serious threat to the safety of electric vehicles. An in-depth study of the core-material thermal runaway reaction mechanism and reaction chain is a prerequisite for proposing a mechanism to prevent battery thermal runaway and enhance battery safety. In this study, based on a 24 Ah commercial Li(Ni0.6Co0.2Mn0.2)O2/graphite soft pack battery, the heat production characteristics of different state of charge (SOC) cathode and anode materials, the separator, the electrolyte, and their combinations of the battery were investigated using differential scanning calorimetry. The results show that the reaction between the negative electrode and the electrolyte is the main mode of heat accumulation in the early stage of thermal runaway, and when the heat accumulation causes the temperature to reach a certain critical value, the violent reaction between the positive electrode and the electrolyte is triggered. The extent and timing of the heat production behaviour of the battery host material is closely related to the SOC, and with limited electrolyte content, there is a competitive relationship between the positive and negative electrodes and the electrolyte reaction, leading to different SOC batteries exhibiting different heat production characteristics. In addition, the above findings are correlated with the battery failure mechanisms through heating experiments of the battery monomer. The study of the electro-thermal properties of the main materials in this paper provides a strategy for achieving early warning and suppression of thermal runaway in batteries.