Vehicle Battery Research Articles

Research on power battery anomaly detection method based on improved TimesNet

Health monitoring and abnormality detection of power batteries for new energy vehicles has been one of the hot topics in recent years. Accurate and efficient power battery anomaly detection is crucial to ensure stable operation of the battery system and energy saving. However, power battery data are often non-linear and unstable due to external factors, such as temperature conditions, which pose challenges for anomaly detection. Existing methods for collecting battery data’s temporal and spatial features are available, but many of them yield suboptimal detection accuracy and feature extraction efficiency due to their disregard for the multi-periodicity of the data. This paper proposes a novel network structure for power battery anomaly detection based on an improved TimesNet. Firstly, the original battery data undergo preprocessing, and the feature correlation coefficient matrix is established using the MIC algorithm. Secondly, the improved TimesNet network is employed to convert the one-dimensional time feature sequence into a two-dimensional tensor, enabling the extraction of temporal features and dependencies at various cycle scales. Finally, the two-dimensional tensor is transformed into a one-dimensional time series, which undergoes information-weighted aggregation to obtain the final anomaly detection results. To assess the effectiveness and generalization of the proposed model, experiments are conducted using Battery and four public datasets for anomaly detection. The experimental results demonstrate that the proposed model outperforms the transformer, autoformer, TimesNet, and Dlinear models, achieving an improvement of 1%–19% in the F1 value and 1%–3% in the ACC compared to the other models.

Predicting the most economical option of managing electric vehicle battery at the end of its serviceable life

It is predicted that millions of electric vehicle batteries (EVBs) will retire in the next decade. With the global Electric Vehicles (EVs) market share increasing to over 40 million by 2030, the replacement of unwanted EVBs will become more abundant and may end up in landfills. These discarded EVBs can become a threat to the environment and to human health if they are not treated, disposed of, and recycled appropriately. Numerous end-of-life (EOL) cost estimation methods that have been developed are mainly for evaluating fossil fuel vehicle components. Research within the scope of EOL of EVBs however, is slow in progress, therefore, this work focuses on developing a cost estimation method for predicting EOL costs of EVBs by adopting a circular economy approach. This proposed method is validated through a case study using two Nissan's EV battery packs. The significance of this study may provide valuable information which could, indirectly lead to the redesign of EVBs for better business circular economy and less waste pollution for future EVs. The main results show that the potential economic benefit that generated by reusing/repurposing EOL EVBs is 92% more profitable than recycling and 52% more profitable than remanufacturing. This study concludes that reuse/repurpose becomes the most profitable process when EVBs reach the end of their serviceable life. Remanufacturing method can also be economically viable if it considers multi-cell replacement of an EVB.

Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation

The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. In this work, a novel Mamba network architecture called BMPTtery (Bidirectional Mamba Predictive Battery Temperature Representation) is proposed to overcome these challenges. First, a two-step hybrid model of trajectory piecewise–polynomial regression and exponentially weighted moving average is created and used to an operational dataset of EVs in order to handle the problem of noisy and incomplete time-series data. Each time series is then individually labeled to learn the representation and adaptive correlation of the multivariate series to capture battery performance variations in complex dynamic operating environments. Next, a prediction method with multiple steps based on the bidirectional Mamba is suggested. When combined with a failure diagnosis approach, this scheme can accurately detect heat failures due to excessive temperature, rapid temperature rise, and significant temperature differences. The experimental results demonstrate that the technique can accurately detect battery failures on a dataset of real operational EVs and predict the battery temperature one minute ahead of time with an MRE of 0.273%.

Intelligent Robust Controllers Applied to an Auxiliary Energy System for Electric Vehicles

This paper presents two intelligent robust control strategies applied to manage the dynamics of a DC-DC bidirectional buck–boost converter, which is used in conjunction with a supercapacitor as an auxiliary energy system (AES) for regenerative braking in electric vehicles. The Neural Inverse Optimal Controller (NIOC) and the Neural Sliding Mode Controller (NSMC) utilize identifiers based on Recurrent High-Order Neural Networks (RHONNs) trained with the Extended Kalman Filter (EKF) to track voltage and current references from the converter circuit. Additionally, a driving cycle test tailored specifically for typical urban driving in electric vehicles (EVs) is implemented to validate the efficacy of the proposed controller and energy improvement strategy. The proposed NSMC and NIOC are compared with a PI controller; furthermore, an induction motor and its corresponding three-phase inverter are incorporated into the EV control scheme which is implemented in Matlab/Simulink using the “Simscape Electrical” toolbox. The Mean Squared Error (MSE) is computed to validate the performance of the neural controllers. Additionally, the improvement in the State of Charge (SOC) for an electric vehicle battery through the control of buck–boost converter dynamics is addressed. Finally, several robustness tests against parameter changes in the converter are conducted, along with their corresponding performance indices.

Collaborative Optimization Framework for Coupled Power and Transportation Energy Systems Incorporating Integrated Demand Responses and Electric Vehicle Battery State-of-Charge

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

The Complex and Spatially Heterogeneous Nature of Degradation in Heavily Cycled Li-ion Cells

Abstract As service lifetimes of electric vehicle (EV) and grid storage batteries continually improve, it has become increasingly important to understand how cells perform after extensive cycling. The multifaceted nature of degradation in Li-ion cells can lead to complex behavior that may be difficult for battery management systems or operators to model. Accurate characterization of heavily cycled cells is critical for developing accurate models, especially for cycle-intensive applications like second-life grid storage or vehicle-to-grid charging. In this study, we use operando synchrotron X-ray diffraction (SR-XRD) to characterize a commercially manufactured polycrystalline NMC622 pouch cell that was cycled for more than 2.5 years. Using spatially resolved synchrotron XRD, the complex kinetics and spatially heterogeneous behavior of such cells can be mapped and characterized under both near-equilibrium and non-equilibrium conditions. The resulting data is complex and multifaceted, requiring a different approach to analysis and modelling than what has been used in the literature. To show how material selection can impact the extent of degradation, we compare the results from polycrystalline NMC622 cells to an extensively cycled single-crystal NMC532 cell with over 20,000 cycles – equivalent to a total EV traveled distance of approximately 8 million km (5.5 million miles) over six years.

A fusion algorithm of multidimensional element space mapping architecture for SOC estimation of lithium-ion batteries under dynamic operating conditions

Accurate estimation of the state of charge (SOC) is pivotal for ensuring the reliable operation of lithium-ion batteries in electric vehicles, particularly under complex real-world conditions. This paper introduces a novel SOC estimation method named Multidimensional Elemental Space Mapping Architecture (MESMA), which integrates the battery equivalent circuit model with a data-driven approach to tackle adaptability under intricate conditions and high-dimensional input data correlation. Initially, a least squares method incorporating a forgetting factor is employed to identify the battery circuit model parameters and analyze their correlation with SOC. Subsequently, a feature fusion algorithm is introduced to discern spatial variables highly correlated with SOC by leveraging both original voltage and current data and circuit model parameters as inputs to a Convolutional Neural Network (CNN) model, and an eight-input and one-output spatially mapped feature CNN model is established. Finally, the XGBoost model is harnessed for battery SOC estimation. The effectiveness and adaptability of MESMA are validated using experimental data from both public single condition and mixed condition datasets, including FUDS, Mixed-1, etc. The results unequivocally demonstrate that MESMA accurately estimates the SOC of lithium-ion batteries under varying operating conditions. Furthermore, it exhibits remarkable adaptability and stability across different temperatures, effectively capturing SOC trends.

Conceptual model for extending electric vehicle battery lifetime

To maximise the resource efficiency of electric vehicle lithium-ion batteries (LIBs), their lifetimes can be extended through cascading second- and third-life applications. Using expert input, this study establishes a conceptual model for understanding these applications’ state of health (SOH) thresholds and user requirements. Using a qualitative methodology, including focus group discussions with multistakeholder experts and policy analysis of the European battery regulatory landscape, we propose extending LIB use. Our model outlines potential second- and third-life applications aiming to maximise battery value retention. The findings highlight gaps in current European Union regulations that inadequately support battery-repurposing strategies. The conceptual model with an SOH threshold and key performance indicators serves as a foundation for researchers and industries to explore cascading battery applications, foster long-term resource efficiency and contribute to the circular economy by extending LIB lifespans through repurposing initiatives.

Sharing channel choices for electric vehicle battery manufacturers when considering range anxiety among heterogeneous consumers

PurposeThis study aims to investigate the optimal pricing decisions and shared channel strategy selection of battery manufacturers considering heterogeneous consumers' range anxiety.Design/methodology/approachAmidst the rapid growth of the electric vehicle sector, countries are promoting upgrades in the automotive industry. However, insufficient driving range causes consumer range anxiety. The study utilizes the Stackelberg game model to assess how range anxiety influences battery manufacturers' pricing and channel strategy decisions across three strategies.FindingsWe find that electric vehicle battery manufacturers' decisions to cooperate with third-party sharing platforms (TPSPs) are primarily influenced by fixed costs and consumer range anxiety levels. As range anxiety increases, the cost threshold for joining shared channels rises, reducing cooperation likelihood. However, considering diverse consumer needs, especially a higher proportion of leisure-oriented consumers, increases the likelihood of cooperation. Furthermore, higher battery quality makes direct participation in shared channels more probable.Originality/valueIn the electric vehicle industry, range anxiety is a significant concern. While existing literature focuses on its impact on consumer behavior and charging infrastructure, this study delves into battery manufacturers' strategic responses, offering insights into channel options and pricing strategies amidst diverse consumer segments.

Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks

Accurate estimation of the state of charge (SoC) in electric vehicle (EV) batteries is essential for effective battery management and optimal performance. This study investigates the application of Kolmogorov-Arnold Networks (KAN) for SoC estimation, comparing its performance against Artificial Neural Networks (ANN) and a hybrid Barnacles Mating Optimizer-deep learning model (BMO-DL). The dataset, derived from simulations involving a lithium polymer cell model (ePLB C020) in an electric car similar to Nissan Leaf EV, encompasses 68,741 instances, divided into training and testing sets. Three KAN models were developed and evaluated based on root mean square error (RMSE), mean absolute error (MAE), maximum error (MAX), and coefficient of determination (R2). Residual analysis indicates that KAN-Model 1 performs the best, with residuals closely clustered around zero and no significant patterns, suggesting reliable and unbiased predictions. KAN-Model 2 also performs well but exhibits some nonlinear trends in the residuals. ANN and BMO-DL models show larger deviations and less consistent performance. These findings highlight the potential of KAN for enhancing SoC estimation accuracy in EV applications.

A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier

The escalating applications of Lithium-ion (Li-ion) batteries in renewable energy and electric vehicles underscore the need for enhanced prognostics and health management systems to reduce the risk of sudden failures. Remaining useful life (RUL) determination is one of the most critical tasks in the field of battery prognostics nowadays. Even though statistical and machine learning (ML) methods have proven effective in research setups, many challenges prevent applying these prediction methods to real-life scenarios. These challenges include (1) scarcity of run-to-failure datasets with similar experimental conditions, (2) low data granularity when presented in capacity vs. discharge cycle pairs, and (3) lack of “temporal identifiers” in real-life scenarios. A temporal identifier is any label that provides knowledge about the current degradation state of a working battery. The research question developed for this study was, ‘Can the remaining useful life of a Li-ion battery having limited data without a temporal identifier be predicted?’ The specific aims were to estimate the temporal identifier of limited data and to predict the remaining useful life (RUL). An innovative framework incorporating reliability analysis and deep learning addresses these specific aims. Experimental data is used to test the framework's capabilities, limiting the training dataset to only three batteries and the testing dataset to a small sample (< 10 data points) of another battery. This new approach enabled the RUL prediction to achieve errors as low as ∼5 cycles and root mean square error of 6.24 cycles, outperforming other benchmark studies on Li-ion battery RUL prediction that use more battery degradation data without temporal identifier.

A Study on the Green Laser Weldability of Copper-aluminum Battery Materials

Copper is widely utilized in electric vehicle batteries due to its excellent electrical conductivity, lightweight, and excellent corrosion resistance. However, copper has a high reflectivity of about 97% for infrared lasers, which makes it difficult to achieve stable welding quality. This study investigated using a green laser to weld nickel-coated copper and aluminum materials. After performing welding by changing the laser power and scan speed, the weld cross-section was observed with an optical microscope, and it was confirmed that the welding penetration depth and bead width increased as the heat input increased. The spatter on the bead surface and defects such as pores and cracks in the weld were caused by excessive heat input. Analysis of the weld cross-section using SEM-EDS showed that high heat input increased the formation of intermetallic compounds including CuAl2 and Cu9Al4. The mechanical properties of the weld were examined using a shear tensile test. The analysis results showed that intermetallic compounds caused brittleness in the weld joint, which lowered mechanical properties and caused defects such as pores and cracks. P60 (1.2 kW, scan speed: 250 mm/s), P80 (1.6 kW, scan speed: 375 mm/s), and P100 (1.2 kW, scan speed: 375 mm/s) were examples of excellent mechanical quality. The fastest welding condition, P100 (power: 2 kW, scan speed: 450 mm/s), was suggested as the most efficient welding condition.

Towards a circular business ecosystem of used electric vehicle batteries – A modelling approach

The increase in electric mobility will result in a growing number of used batteries that require an efficient system for handling and recycling valuable materials. The purpose of the research is to create a model of the business ecosystem for post-consumer electric vehicle batteries to identify circular economy business opportunities and limitations. Applying the ecosystem pie modelling methodology, the critical actors involved in electric vehicle battery repurpose and recycling are mapped, including the interdependencies and risks. The modelling is based on interviews with several industrial ecosystem actors and existing research literature. Opportunities and challenges in the circular business ecosystem are identified, relevant to industry strategists and decision-makers. The research findings reveal that downstream circular business ecosystems are more unpredictable and unplanned than upstream linear business ecosystems. The study also highlights the trend towards vertical integration among actors in the battery industry to secure the supply of materials.

Vibration reduction of an electric vehicle heat exchanger by controlling the polarization of its ferroelectric ceramics

The growing requirements in terms of CO2 emissions reduction in transports renders necessary the development of new electric traction technologies. This results in new technical constraints for equipment manufacturers, forcing them to reduce the acoustic emissivity of their new products inside and outside vehicles. Heat exchangers for electric cars are part of this new product category. High voltage supplied by the battery of electrical vehicles requires introduction of a new energy conversion process and technology. To this end, components based on ceramics containing barium titanate (BaTiO3) with high electrocaloric coupling properties are selected to provide the heating function. Baryum titanate is ferroelectric. This category of materials is subjected to piezoelectric and electrostrictive effects. The vibrations induced in the components (rods) containing the BaTiO3 ceramics are transmitted through the system, and cause undesired acoustic radiation. This coupling is a suffered effect and is not taken into account in the integration process during the rods manufacturing. In this study, an experimental characterization is proposed to identify the involved electromechanical coupling coefficients. Numerical simulations of the vibrations induced by BaTiO3 are carried out and used to study the possibility of attenuating them by controlling the polarisation of the ceramics or by geometric modifications.

Optimizing the influence of refrigerant superheat on the cooperative thermal management system performance for the vehicle cabin and battery

The refrigerant superheat significantly impacts on the energy consumption of the vehicle cabin and battery collaborative thermal management system. The study investigates the problem using a genetic algorithm. Firstly, the model of collaborative thermal system is established and verified. Then, the coefficient of performance (COP) maximization is taken as the optimization target during steady-state conditions. The results show that the optimal refrigerant superheat at the evaporator outlet (SEva) is very low for different heat dissipation of the battery (HBat) as well as ambient temperature, while the optimal refrigerant superheat at the chiller outlet (SChi) is affected by the HBat as well as ambient temperature. Finally, according to conclusions under the steady state, the SEva is set to 1 K and taking the energy consumption (EC) as the response under transient condition, the SChi is determined. The results indicate that when the SChi is decided by single-point optimization, the optimal SChi is 18 K, and the EC is 1490 kJ, which is 2.3 % energy saving compared to the basic condition. When the SChi is decided by two-point optimization, the optimal two superheats are 12 K and 37 K and the EC is 1460 kJ, which is 4.3 % energy saving compared to the basic condition.

Mechanism and Data-Driven Fusion SOC Estimation

An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order Thevenin battery model is developed to obtain the physical parameters of the battery. Second, data from the Thevenin battery model and data from four standard cycling conditions in the electric vehicle industry are added to the dataset of the feed-forward neural network data-driven model to construct the test and training sets of the data-driven model. Finally, the error of the mechanism and data-driven fusion modeling method is quantitatively analyzed by comparing the estimation error of the method for the battery SOC at different temperatures with the accuracy of the data-driven SOC estimation method. The simulation results show that the root mean square error, the mean age absolute error, and the maximum error of mechanism and data-driven method for the estimation error of battery SOC are lower than those of the data-driven method by 0.9%, 0.65%, and 1.3%, respectively. The results show that the mechanism and data-driven fusion SOC estimation method has better generalization performance and higher SOC estimation accuracy.

Wireless power transfer for deep cycle lithium-ion batteries in electric vehicles using inductive coupling

Wireless power transfer for deep cycle lithium-ion batteries in electric vehicles using inductive coupling

Optimization of thermal management performance of direct-cooled power battery based on backpropagation neural network and deep reinforcement learning

The batteries of new energy vehicles generate a large amount of heat when discharged at large multiplication rates, which can cause safety hazards if the heat is not eliminated in time. In this paper, a Swiss roll-type cooling belt is used to improve the thermal performance of lithium-ion battery packs. This paper selects six key parameters as optimization parameters, including the structure of Swiss roll-type cooling belt, coolant type, inlet coolant mass flow rate, inlet coolant gas volume fraction, battery discharge rate, and ambient temperature. Use neural network fitting and deep reinforcement learning to optimize these six parameters in order to achieve optimized cooling performance. The results show that after the cold plate has been structurally optimised and the inlet parameters have been optimised by deep reinforcement learning, the thermal performance has been significantly improved. Not only does the cold plate inlet require less mass flow, but the battery pack also performs better in terms of temperature difference.

Batteries for electric vehicles: Technical advancements, environmental challenges, and market perspectives

Batteries for electric vehicles: Technical advancements, environmental challenges, and market perspectives

Analysis of cooperative cooling performance between the battery and cabin of pure electric vehicles

Abstract The cooperative cooling of the cabin and battery in pure electric vehicles (PEVs) is crucial for the comfort, thermal safety of the occupants, and the lifespan of the batteries. Therefore, a well-designed thermal management system (TMS) is particularly important. Thus, this paper constructs an cooperative cooling for the battery pack circuit and the cabin circuit in parallel, aiming to enhance the vehicle system’s integration and reduce energy consumption. Firstly, a one-dimensional simulation model of the cooperative TMS is established, and the model is calibrated through experiments. Subsequently, the system’s performance is analyzed under different ambient temperatures. The study finds that as the ambient temperature increases, the system’s energy consumption rises, and the Coefficient of Performance (COP) decreases. Moreover, when the system switches from the simultaneous cooling mode of the cabin and battery to the sole cooling mode of the cabin, the temperature of the cabin will fluctuate to a certain extent, within a range of 2°C. At same time, the COP value also increases with the switch of the mode. Finally, the analysis of the simultaneous cooling mode of the cabin and battery side under different ambient temperatures reveals that as the ambient temperature increases from 34°C to 42°C, the exhaust temperature, pressure ratio, and compressor speed increase accordingly, while the compressor efficiency decreases. The condenser and chiller experience a reduction in their heat transfer efficiency by 59.4 watts per degree Celsius and 218.5 watts per degree Celsius, respectively. Conversely, the evaporator’s heat transfer efficiency undergoes an enhancement of 6.77 watts per degree Celsius.