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Key Technologies for Electric Vehicles
... L ithium-ion batteries (LiBs) are currently preferred as key energy storage devices for consumer electronics, electric vehicles, and energy storage systems [1,2] . Compared with traditional lead-acid or nickel-metal hydride batteries, LiBs have the advantages of low self-discharge rate, long cycle life, high power, high energy density, and so on [3] . To ensure the safe and efficient application of LiBs, an advanced battery management system (BMS) is essential to realize real-time, efficient, and accurate monitoring and management of batteries [4,5] . ...
... Other aging characteristic parameters have obvious monotonic decay paths and have no obvious sensitivity to temperatures. Eventually, C e0 and c total,p are applied to calculate SOH 1 , R p and R n are applied to calculate SOH 2 , k p and k n are applied to calculate SOH 3 , and R f is applied to calculate SOH 4 . Due to differences in capacity estimation accuracy of different aging characteristic parameters, the calculation weights of SOH 1 to SOH 4 are designed using the verification results in Table 2. Further, the final SOH is calculated by fusing the selected 7 aging characteristic parameters. ...
Electric vehicles and battery energy storage are effective technical paths to achieve carbon neutrality, and lithium-ion batteries (LiBs) are very critical energy storage devices, which is of great significance to the goal. However, the battery's characteristics of instant degradation seriously affect its long life and high safety applications. The aging mechanisms of LiBs are complex and multi-faceted, strongly influenced by numerous interacting factors. Currently, the degree of capacity fading is commonly used to describe the aging of the battery, and the ratio of the maximum available capacity to the rated capacity of the battery is defined as the state of health (SOH). However, the aging or health of the battery should be multifaceted. to realize the multi-dimensional comprehensive evaluation of battery health status, a novel SOH estimation method driven by multidimensional aging characteristics is proposed through the improved single-particle model. The parameter identification and sensitivity analysis of the model were carried out during the whole cycle of life in a wide temperature environment. Nine aging characteristic parameters were obtained to describe the SOH. Combined with aging mechanisms, the current health status was evaluated from four aspects: capacity level, lithium-ion diffusion, electrochemical reaction, and power capacity. The proposed method can more comprehensively evaluate the aging characteristics of batteries, and the SOH estimation error is within 2%.
... In recent years, with the continuous development of lithium-ion battery technology, lithium-ion battery energy storage is currently one of the most feasible ways. To ensure the safe use of lithium-ion batteries, extend the service life of the battery, optimize the power management strategy, and reduce the cost of the battery, it is very necessary to carry out effective management of the battery [4][5][6]. BMS often uses the state of charge (SOC) parameter to characterize the lithium-ion lithium battery's residual electric capacity [7,8]. However, the SOC parameter does not consider the voltage change and cannot fully respond to the energy state of a lithium-ion battery [9,10]. ...
... The velocity and position of each particle are updated by Eqs. (6) and (7) during the iteration of the algorithm: ...
State of energy (SOE) estimation of lithium-ion batteries is the basis of electric vehicle driving range prediction. To improve the estimation accuracy of SOE under complex dynamic working conditions, this paper takes the ternary lithium-ion battery as the research object, chooses the second-order RC-PNGV model to model the polarization reaction inside the battery, and adopts the improved adaptive forgetting factor recursive least squares (MAFFRLS) method to identify the model parameters. For battery SOE estimation, an improved unscented particle filtering algorithm for particle swarm optimization is proposed, which introduces quantum theory into particle swarm optimization to solve the sub-depletion problem of unscented particle filtering and improves the accuracy and adaptability of real-time estimation of SOE in complex environments. Experimental validation is carried out by constructing different working conditions at multiple temperatures, and the results show that the maximum error of parameter identification using recursive least squares based on improved adaptive-forgotten factor is stabilized within 2%. Under the HPPC, BBDST, and DST working conditions, the MAE and RMSE are limited to within 1% when the quantum particle swarm optimized-unscented particle filtering (QPSO-UPF) algorithm is applied to estimate the SOE estimation, which indicates that the proposed algorithm has strong tracking ability and robustness to the SOE of lithium batteries.
... In BMS, the reliability analysis of battery SOC [4] is the basis of BMS and the key to estimating the remaining capacity of batteries [5][6][7]. Accurate and effective SOC estimation is the key to electric vehicle battery research [8], and it guarantees the prevention of safety accidents caused Ionics by vehicle battery overcharging and over-discharging. However, how to obtain high-precision SOC values is a challenge for researchers. ...
... Among them, the resistance and capacitance of the secondorder Thevenin model can be represented by i , and the five unknown parameters can be obtained by five equations, as shown in Eq. (8). ...
State of charge (SOC) is a crucial parameter in evaluating the remaining power of commonly used lithium-ion battery energy storage systems, and the study of high-precision SOC is widely used in assessing electric vehicle power. This paper proposes a time-varying discount factor recursive least square (TDFRLS) method and multi-scale optimized time-varying bounded layer smoothing variable structure filtering (TSVSF) to obtain a more accurate SOC. Firstly, the TDFRLS algorithm is formed by introducing a time-varying discount factor, which effectively solves the problem of data saturation and simultaneous optimization of speed and accuracy in other improved RLS algorithms and is conducive to realizing high-precision identification of battery model parameters. Then, based on TSVSF, extended Kalman filter (EKF) gain is combined to ensure the stability and accuracy of the predicted system. In addition, the square root algorithm ensures the non-negative quality of error covariance matrix and effectively solves the non-convergence problem in the prediction process. Finally, the professional lithium-ion battery test platform is used to obtain the real-time parameters of the battery under different temperatures and working conditions, and comparative experiments of various SOC estimation algorithms are carried out. Among the four working conditions, the SOC estimation accuracy is the highest at HPPC at 35 °C, and the error is kept within 0136. The proposed algorithm has the highest accuracy and stability compared with four new algorithms. The experimental results show that this method has high accuracy and is significant for accurately estimating electric vehicle electricity.
... To ensure that electric vehicles are utilized safely and reliably, a battery management system (BMS) is required for real-time battery-state monitoring. In the BMS, state of charge (SOC) is one of the core parameters to monitor [9]. Thus, obtaining an accurate real-time estimation value of the SOC is a significant goal for the future development of electric vehicles. ...
... After the parameters were determined, the variational EKF algorithm was utilized, which optimizes the model accuracy and the estimation performance with simultaneous iterations. The main contribution Batteries 2023, 9,583 3 of 15 of this study is the variational EKF algorithm in the SOC estimation for batteries. With the determination of the unique characteristics of the RC model in SOC estimation, the feasibility of the combination for the variation idea and the EKF algorithm is proposed. ...
State of charge (SOC) is a very important variable for using batteries safely and reliably. To improve the accuracy of SOC estimation, a novel variational extended Kalman filter (EKF) technique based on least square error method is herein provided by establishing a second-order equivalent circuit model for the battery. It was found that when SOC decreased, resistance polarization occurred in the electrochemical model, and the parameters in the equivalent RC model varied. To decrease the modeling error in the equivalent circuit model, the system parameters were identified online depending on the SOC’s estimated result. Through the SOC-estimation process, the variation theorem was introduced, which enabled the system parameters to track the real situations based on the output measured. The experiment results reveal the comparison of the SOC-estimation results of the variational EKF algorithm, the traditional EKF algorithm, the recursive least square (RLS) EKF algorithm, and the forgotten factor recursive least square (FFRLS) EKF algorithm based on different indices, including the mean square error (MSE) and the mean absolute error (MAE). The variational EKF algorithm provided in this paper has higher estimation accuracy and robustness than the traditional EKF, which verifies the superiority and effectiveness of the proposed method.
... Lithium-ion batteries (LIBs) have become an indispensable energy medium in the felds of electric vehicles, mobile facilities, and new energy storage due to their high density of energy, long service life, and decreasing manufacturing costs [1][2][3]. However, under actual complex and variable working conditions, LIBs are accompanied by performance degradation phenomena such as capacity decay and output power reduction during use [4][5][6]. ...
Lithium-ion batteries (LIBs) have wide-ranging applications in areas such as electric vehicles and mobile devices. Accurate estimation of the state of health (SOH) of batteries is an important aspect of battery state estimation. Battery capacity cannot be precisely measured due to negative factors such as aging effects. To address this issue, this paper proposes a LIB’s SOH estimation method based on incremental energy analysis (IEA) and transformer. First, data collected during the constant-current (CC) charging phase of the battery are used to create and analyze the IEA curve. Then, the peaks and areas of the curve are proposed as health characteristics of the LIB. Cosine similarity analysis (CSA) is employed to determine the correlation between each health characteristic and SOH, as well as the correlations between different health characteristics. Finally, an accurate estimation model of battery health was developed using the Bayesian-transformer model by plotting the relationship between health characteristics and battery health. To validate the reliability of the model, comparisons with regression evaluation metrics of other models such as support vector regression (SVR) and recurrent neural network (RNN) are conducted under different charging rates. The conclusion is drawn that this model exhibits an R2 greater than 98%, MAE less than 0.22%, RMSE less than 0.26%, and MAPE less than 0.0026%. Its accuracy is significantly improved compared to the same type of methods, and it can be used for high accuracy estimation of SOH in LIBs. The multistep prediction of a single step adopted by the model can effectively overcome the capacity regeneration problem in the field of SOH estimation, which will inspire future experts and scholars to improve the accuracy of SOH estimation.
... [1][2][3] Much attention has been devoted to both the energy storage characteristics and energy loss of capacitors used in these and other microsystems. [4][5][6][7][8] Especially in projectiles and missiles, tantalum electrolytic capacitors provide a very important secondary power supply for sensors, signal processing circuits, and detonation actuators of fuzes. In recent years, there has been grow-Nanotechnology and Precision Engineering ARTICLE pubs.aip.org/aip/npe ...
Tantalum electrolytic capacitors have performance advantages of long life, high temperature stability, and high energy storage capacity and are essential micro-energy storage devices in many pieces of military mechatronic equipment, including penetration weapons. The latter are high-value ammunition used to strike strategic targets, and precision in their blast point is ensured through the use of penetration fuzes as control systems. However, the extreme dynamic impact that occurs during penetration causes a surge in the leakage current of tantalum capacitors, resulting in a loss of ignition energy, which can lead to ammunition half-burst or even sometimes misfire. To address the urgent need for a reliable design of tantalum capacitor for penetration fuzes, in this study, the maximum acceptable leakage current of a tantalum capacitor during impact is calculated, and two different types of tantalum capacitors are tested using a machete hammer. It is found that the leakage current of tantalum capacitors increases sharply under extreme impact, causing functional failure. Considering the piezoresistive effect of the tantalum capacitor dielectric and the changes in the contact area between the dielectric and the negative electrode under pressure, a force–electric simulation model at the microscale is established in COMSOL software. The simulation results align favorably with the experimental results, and it is anticipated that the leakage current of a tantalum capacitor will experience exponential growth with increasing pressure, ultimately culminating in complete failure according to this model. Finally, the morphological changes in tantalum capacitor sintered cells both without pressure and under pressure are characterized by electron microscopy. Broken particles of Ta–Ta2O5 sintered molecular clusters are observed under pressure, together with cracks in the MnO2 negative base, proving that large stresses and strains are generated at the micrometer scale.
... In the rapidly evolving landscape of intelligent transport systems, where the main objective is to upgrade existing traffic systems, autonomous driving vehicles and the implementation of proper control strategies are increasingly gaining significance (e.g., [1][2][3]). As we witness the advent of autonomous agents and multi-agent systems reshaping the future of transport, ensuring the stability of vehicles, particularly those with only two wheels, emerges as a critical enabling technology [4]. ...
In the dynamic landscape of autonomous transport, the integration of intelligent transport systems and embedded control technology is pivotal. While strides have been made in the development of autonomous agents and multi-agent systems, the unique challenges posed by two-wheeled vehicles remain largely unaddressed. Dedicated control strategies for these vehicles have yet to be developed. The vertical balance of an autonomous two-wheeled single-track vehicle is a challenge for engineering. This type of vehicle is unstable and its dynamic behaviour changes with the forward velocity. We designed a scheduled-gain proportional–integral controller that adapts its gains to the forward velocity, maintaining the vertical balance of the vehicle by means of the steering front-wheel angle. The control law was tested with a prototype designed by the authors under different scenarios, smooth and uneven floors, maintaining the vertical balance in all cases.
... Recently, A.I. has rapidly developed and provided numerous viable tools for battery SOH assessment [21]. A.I.-based battery SOH estimation methods can learn intricate knowledge from massive data without understanding the underlying mechanisms, and are promising to analyze the influencing factors of battery ageing. ...
The Incremental Capacity Analysis (ICA) method is a typical data-driven method with great potential in battery ageing assessment for electric vehicles (EVs). However, the battery health features generated through the ICA method are subject to battery State-of-Health (SOH) and environmental factors, which compromises the accuracy of battery ageing assessment in real-world situations. This paper proposes a novel model structure that combines the Fuzzy Logic and the Radial Basis Function Neural Network (RBFNN) to decouple the influencing factors of battery ageing using operating data collected from real-world EVs. First, the distortion phenomenon of the battery ageing trajectory is discussed, and the relationships between influencing factors and battery health features are carefully analyzed. Secondly, a Fuzzy-RBFNN model for battery ageing assessment is constructed considering two influencing factors as inputs. Finally, employing an artificially adjusted method, the influence of temperature on battery ageing assessment is decoupled using the trained Fuzzy-RBFNN model. The comparison results with the sole RBFNN model demonstrate the effectiveness and necessity of combining with fuzzy logic for battery ageing assessment.
... These batteries are increasingly used in electric vehicles (EVs), providing essential attributes like high energy density, low self-discharge rates, and extensive recharge cycles, positioning them as a practical and environmentally friendly alternative to conventional energy sources. The development and widespread adoption of lithium-ion batteries in EVs are closely linked to advancements in battery technology, marked by a balance of cost-efficiency and safety [1,2]. This progress necessitates a sophisticated BMS to ensure optimal performance, longevity, and safety. ...
The improvement of battery management systems (BMSs) requires the incorporation of advanced battery status detection technologies to facilitate early warnings of abnormal conditions. In this study, acoustic data from batteries under two discharge rates, 0.5 C and 3 C, were collected using a specially designed battery acoustic test system. By analyzing selected acoustic parameters in the time domain, the acoustic signals exhibited noticeable differences with the change in discharge current, highlighting the potential of acoustic signals for current anomaly detection. In the frequency domain analysis, distinct variations in the frequency domain parameters of the acoustic response signal were observed at different discharge currents. The identification of acoustic characteristic parameters demonstrates a robust capability to detect short-term high-current discharges, which reflects the sensitivity of the battery’s internal structure to varying operational stresses. Acoustic emission (AE) technology, coupled with electrode measurements, effectively tracks unusually high discharge currents. The acoustic signals show a clear correlation with discharge currents, indicating that selecting key acoustic parameters can reveal the battery structure’s response to high currents. This approach could serve as a crucial diagnostic tool for identifying battery abnormalities.
... The transportation sector is a significant contributor to global greenhouse gas emissions, and the widespread adoption of EVs represents a tangible action towards reducing these emissions. As countries worldwide commit to reducing their carbon footprints and adhering to international agreements like the Paris Accord, EVs' promotion and adoption become integral to these efforts [9,10]. The importance of EVs is also reflected in the policy measures being adopted by governments worldwide. ...
This study explores the synergies between marketing strategies, analytical insights, and consumer education in propelling electric vehicle (EV) adoption. We uncover intricate sales patterns in Turkey’s EV sales data using advanced statistical models such as ARIMA, SARIMA, and ETS. Our findings underscore the efficacy of aligning marketing strategies with analytical insights, demonstrating the significance of education in shaping positive consumer attitudes. Education-driven marketing emphasizing economic benefits, reduced emissions, and technological advancements is a potent catalyst in overcoming adoption barriers. Digital campaigns, experiential marketing, and sustainability messaging validated by our analysis, play pivotal roles in influencing consumer behavior. Strategic partnerships with energy companies address infrastructure challenges, while incentive-based marketing, personalized strategies, and after-sales support foster a sense of community and loyalty. This research contributes a holistic framework for marketers, policymakers, and stakeholders to navigate the evolving landscape of EV adoption successfully, providing actionable insights and paving the way for future research directions in sustainable transportation.
... The electric vehicle industry has entered a rapid growth stage driven by the technological revolution and industrial transformation in recent years [1]. Its capacity decreases as a power battery is charged and discharged over time. ...
Monitoring battery health is critical for electric vehicle maintenance and safety. However, existing research has limited focus on predicting capacity degradation paths for entire battery packs, representing a gap between literature and application. This paper proposes a multi-horizon time series forecasting model (MMRNet, which consists of MOSUM, flash-MUSE attention, and RNN core modules) to predict the capacity degradation paths of battery packs. First, domain knowledge (DK) extracts the features from extensive battery aging datasets. The moving sum (MOSUM) and improved flash multi-scale attention (MUSE) methods are proposed to capture capacity curve mutations and multi-scale trends. Dynamic dropout training, transposition linear architecture, residual connections, and module stacking improve model generalization and accuracy. Experiments on battery pack and cell datasets demonstrate the superior performance of MMRNet over six baseline time series models. The proposed data-driven approach effectively predicts battery degradation trajectories, with implications for condition monitoring and the safety of electric vehicles.
... With the maturity of vehicle electrification, electrified powertrains have allowed for high-power electrical transmission, making it possible for electric motors to do work instead of engines. While the power performance is improved, the development of battery technology provided more adequate energy reserves with far less pollution than fossil fuels [26]. Relevant technologies have helped to develop a series of multi-power source electric vehicle configurations. ...
The escalating environmental concerns and energy crisis caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) integrate various clean energy systems to enhance the powertrain efficiency. The energy management strategy (EMS) is plays a pivotal role for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement Learning (RL) has emerged as an effective methodology for EMS development, attracting continuous attention and research. However, a systematic analysis of the design elements of RL-based EMS is currently lacking. This paper addresses this gap by presenting a comprehensive analysis of current research on RL-based EMS (RL-EMS) and summarizing its design elements. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. It highlights the contributions of advanced algorithms to training effectiveness, provides a detailed analysis of perception and control schemes, classifies different reward function settings, and elucidates the roles of innovative training methods. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Potential development directions are suggested for implementing advanced artificial intelligence (AI) solutions in EMS.
... They constitute a promising solution to a set of global challenges such as climate change and air pollution. 2 Developing new energy vehicles has been a global consensus, and developing new energy vehicles with pure electric characteristics has become China's national strategy. 3 Due to its advantages of high energy density, low self-discharge rate, high cycle life, and no memory effect, 4−6 the lithium-ion battery (LIB) has gradually replaced the nickel−cadmium battery, nickel−metal hydride battery, and lead acid battery as a mainstream choice for an electric vehicle power battery. ...
Abnormalities in individual lithium-ion batteries can cause the entire battery pack to fail, thereby the operation of electric vehicles is affected and safety accidents even occur in severe cases. Therefore, timely and accurate detection of abnormal monomers can prevent safety accidents and reduce property losses. In this paper, a battery cell anomaly detection method is proposed based on time series decomposition and an improved Manhattan distance algorithm for actual operating data of electric vehicles. First, time series decomposition is performed on the voltage data of all battery cells in the battery pack to obtain the voltage trend component of each cell. Then, the improved Manhattan distance algorithm is utilized to calculate and compare the Manhattan distance values between adjacent cell trend components, to determine the abnormal cells inside the battery pack. Furthermore, the Manhattan distance values at the same sampling moment are calculated within the data sequence to detect the specific time when the abnormal cells malfunction. The data analysis and experimental verification results based on actual vehicle operating conditions indicate that this method can accurately identify an abnormal cell within the battery pack and diagnose the specific moment of abnormality in the battery cell at an early stage of failure, with good robustness.
... In response to the issues of energy consumption and decarbonization, automotive companies have developed zero-emission vehicles, such as battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs) [1,2]. Among these, power consumption has become a research focus. ...
The control strategy of thermal management systems is crucial for electric vehicles to ensure thermal comfort of the cabin and thermal safety of the battery and motor. However, this will also impact the energy consumption of the vehicle. In this study, a three-heat source segmented heating control strategy was proposed to reduce the heating energy consumption of electric vehicles, with the three sources denoted motor waste heat, air, and positive temperature coefficient (PTC) heaters. Specifically, a simulation model of an integrated thermal management system for heat pump air conditioning, motor thermal management, and battery thermal management systems was first established and validated using published experimental results. Subsequently, the performance of three cabin heating modes was investigated based on the standard driving cycle. The optimal opening and closing strategies for each mode were discussed to ensure the temperature requirements of the cabin, and the optimal method for using motor waste heat and the PTC heater to heat the battery at low temperatures was explored. The segmented heating control strategy was developed by determining the heating priority for the battery and cabin, as well as the heating capacity of heating modes using air source. Compared to the traditional heating strategy of the cabin and battery, the proposed control strategy could reduce energy consumption by 18.49 % while running at an ambient temperature of − 10 • C for 2.5 h.
... The TR mechanism of thermal runaway still has unresolved issues that require further research by the scientific community [54] . Several methods for detecting thermal runaway in LIBs have been proposed: ① finding suitable detection methods to prewarn regarding the diffusion and overflow of electrolytes during thermal runaway, ② using impedance detection to prevent internal short circuits that cause thermal runaway through lithium dendrite growth, and ③ applying safety detection methods for lithium batteries to emerging solid-state battery-safety detection. ...
Energy-storage technologies based on lithium-ion batteries are advancing rapidly. However, the occurrence of thermal runaway in batteries under extreme operating conditions poses serious safety concerns and potentially leads to severe accidents. To address the detection and early warning of battery thermal runaway faults, this study conducted a comprehensive review of recent advances in lithium battery fault monitoring and early warning in energy-storage systems from various physical perspectives. The focus was electrical, thermal, acoustic, and mechanical aspects, which provide effective insights for energy-storage system safety enhancement.
Accurately predict the remaining useful life (RUL) of lithium‐ion batteries for energy storage is of critical significance to ensure the safety and reliability of electric vehicles, which can offer efficient early warning signals in a timely manner. Considering nonlinear changes in the aging trajectory of lithium‐ion batteries, a method for predicting the RUL of lithium‐ion batteries was proposed in this study based on a complementary ensemble empirical mode decomposition (CEEMD) as well as transformer and long short‐term memory (LSTM) neural network dual‐drive machine learning model. First, the CEEMD algorithm was adopted to decompose the raw aging data of lithium‐ion batteries into intrinsic mode function (IMF) sequences and residual sequence, where the number of modal layers was produced by the proposed posterior feedback entropy and relevance (PFER) method. Second, prediction models of LSTM and transformer neural networks were established to predict IMF and residual sequences. Simultaneously, the sparrow search algorithm (SSA) was used to obtain the optimal value of the hyperparameter learning rate for the RUL prediction model. Finally, the predicted IMF and residual sequences were combined to comprehensively calculate the future lifespan aging trajectory of lithium‐ion batteries. The aging data of two groups of lithium‐ion batteries were obtained from the CALCE at the University of Maryland as well as the laboratory at AQNU University to verify the proposed method. Experimental results demonstrated that the proposed method can effectively predict the RUL of lithium‐ion batteries; moreover, it exhibited better robustness and generalization ability.
Electric vehicles (EVs) can reduce reliance on finite resources such as oil, aiding in the reduction of carbon emissions, while an accurate battery state of health (SOH) helps ensure the safety of EVs. Because EVs generally have a nonzero battery state of charge (SOC) at the start of charging, obtaining complete charging data for the power battery is challenging. Therefore, a method for estimating the SOH of lithium-ion batteries based on multiphysics features and Convolutional Neural Network-Enhanced Feature Combination-Bidirectional Long Short-Term Memory (CNN-EFC-BiLSTM) is proposed in this paper. First, various data of the battery during the constant-voltage charging phase are measured by the sensors of the battery testing system, and the analysis of battery temperature, current, time, and energy data during the phase is conducted. Multiphysics features, including the average charging temperature, length of the current trajectory, and incremental energy, which are highly correlated with the battery SOH, are extracted from the measured data. A CNN-EFC-BiLSTM model is proposed to map features to battery SOH and establish a SOH estimation model through training. Experiments were conducted with batteries at different charging currents, and the results indicated that even with significant nonlinearity during battery SOH degradation, the method can achieve a rapid and accurate estimation of SOH. The maximum MAE is only 0.3219%, the maximum RMSE is only 0.4723%, and the average training time is reduced by approximately 32%.
Mechanical safety of lithium-ion batteries (LIBs) is the key factor restricting the development of electric vehicles. A critical assessment of their mechanical safety involves the evaluation of mechanical-electrical-thermal characteristics of lithium-ion batteries during internal short circuits (ISCs) induced by mechanical abuse. This study comprehensively analyzes these characteristics under the coupling influence of state of charge (SOC) and loading rate. The findings reveal the “densification→fracture→secondary densification→secondary fracture” process of the battery at 1 mm/min loading rate. The separator assumes a pivotal role in shaping the fracture failure characteristics of the battery components. Besides, within conventional SOC, the ISC duration displacement increases with SOC increasing at 1 mm/min loading rate, while the slightly overcharged LIBs trigger thermal runaway rapidly. The results also present that coupled SOC and loading rate effects are present in the mechanical and electrical characteristics, while absence in the thermal characteristics. The battery performs the SOC dependence at 500 mm/min loading rate, but not at 1 mm/min and 60 mm/min loading rate. The variation of ISC duration time with SOC also differs at 500 mm/min loading rate compared to others. While the association between maximum temperature and SOC at different loading rates perform less variability. The insights derived from this study could contribute valuable theoretical guidance for the mechanical simulation of the battery and the design of the mechanically safe battery pack.
To meet stringent performance of electric vehicle controllers, it is highly needed to optimize the electrical and thermal performance for power semiconductor modules. As stacked DBC (Direct Bonded Copper) power modules have higher power density and enable the parallel of more chips, the demand for optimization is increasingly imperative. In this paper, an automated optimization method based on GA (genetic algorithm) and LBM (lattice Boltzmann method) was proposed for stacked DBC power modules with water-cooled PinFin heatsink. With proposed automatic generation method for electrical layout and heatsink structures, GA is used to select candidates. Because of the expanded optimization space, there is a growing requirement for faster and more precise evaluation methods. The proposed evaluation method based on LBM can assess layout parasitic inductance and the flow velocity and temperature distribution of power module with PinFin heatsink, which verifies an advantage in balancing speed and accuracy compared to traditional evaluation methods. According to optimization results, a 1200 V/ 1400 A, 16 chips in parallel, SiC MOSFET power module was fabricated in a standard EconoDUAL packaging size. Dynamic and static parameter testing as well as thermal testing were conducted, and experiment results demonstrate the effectiveness of proposed automatic optimization method.
Predicting the capacity and temperature of lithium-ion batteries is of critical significance to ensure their safety and stability, and consequently, extend the service life of battery systems. However, the degradation of capacity and thermal performance is typically regarded as independent processes, disregarding their coupling relationship. In response, this study constructs a combined model based on convolutional long short-term memory for the joint prediction of the reversible capacity and peak discharge temperature of batteries. The model’s feature extraction and pattern reconstruction capabilities are well-acknowledged. A variety of charging and discharging features (e.g., current, voltage, temperature, and incremental capacity) are analyzed and correlated with the evolution trends of battery capacity and temperature during long-term operation. Moreover, the evident phenomenon of capacity regeneration caused by intermittent rest is considered. Finally, the prediction results for different cells from public datasets show that the root-mean-square errors of capacity prediction vary from 0.01179 to 0.03304, and the mean absolute percentage error of peak discharge temperature prediction can be basically kept lower than 0.6%.
Lithium plating during the fast charging process significantly decreases the health and safety of lithium-ion batteries (LIBs). Hence, detecting the onset of lithium plating is essential to realize the highly effective and low-damage fast charging. However, an online lithium plating onset detection method that can be used in constant current (CC) and multiage constant current (MCC) fast charging under different working conditions without interrupting the charging process of LIBs has not been reported yet. To fill this gap, a novel method based on dynamic impedance measurement has been proposed. The abnormal drop of dynamic impedance at 1 Hz during the charging processes was treated as a universal feature of lithium plating onset. A method based on real-time impedance feedback and a long short-term memory (LSTM) was first proposed to identify this feature. It was validated in various CC and MCC charging rates at different temperatures and battery types. With the cross-validation of the voltage relaxation method, an error of <2.5% SOC in detecting the lithium plating onset was achieved. Besides, the proposed method effectively optimizes the misidentification and the time delay in detection. For practical applications, the method's accuracy and robustness were additionally validated in a rapid prototyping system. The method proposed in this paper solves the complex problem of lithium plating detection during the CC and MCC charging procedures, which is essential for improving the health and safety of LIBs under fast charging conditions.
Accurate early prediction of the degradation trajectory of lithium-ion batteries (LIBs) can accelerate battery development, production, and design optimization. However, existing early-stage prediction methods for degradation trajectory prediction face challenges in dealing with insufficient data and the generalization under different operational conditions. To address this issue, a novel transfer learning based data-driven method is proposed, which integrates convolutional neural network (CNN) and long short-term memory (LSTM) neural networks. Additionally, we incorporate a temporal attention (TA) mechanism to selectively focus on informative capacity fade patterns and leverage Bayesian Optimization (BO) for hyper-parameters optimization. The proposed method is validated using experimental degradation data from six 5Ah low-temperature batteries (−20 °C, −10 °C) and public datasets. Results demonstrate high accuracy leveraging merely 10% of initial battery data. For the low-temperature aging experimental data, the best prediction result proposed is 0.025 Ah root mean square error (RMSE) and 0.019 Ah mean absolute error (MAE). Compared to baseline CNN-LSTM models, our framework achieved an average reduction of 62.6% in RMSE for early battery capacity trajectory prediction, while maintaining high accuracy across diverse operational conditions. The proposed framework puts forward a promising solution toward enabling adaptive data-driven capacity monitoring under different practical application conditions.
Electric vehicles (EVs) are gaining worldwide popularity as a means to reduce greenhouse gas emissions and decrease dependence on fossil fuels. This comparative study examines the factors influencing the EV market share in the European Union (EU) and the United States (US), aiming to identify regional differences and similarities using statistical and spatial models. The study findings indicate that in the EU, ownership of charging outlets, personal income, education levels, and ages over 55 are positively correlated with the EV market share. However, population density does not support the adoption of EVs. In contrast, in the US, ownership of charging outlets is positively associated with the EV market share in the Central US, while higher personal income is strongly negatively correlated. These findings emphasize the need for tailored location-specific policies that target specific sociodemographic groups and prioritize the development of robust charging infrastructure.
Lithium-ion batteries (LIBs) are widely used in transportation, energy storage, and other fields. The prediction of the remaining useful life (RUL) of lithium batteries not only provides a reference for health management but also serves as a basis for assessing the residual value of the battery. In order to improve the prediction accuracy of the RUL of LIBs, a two-phase RUL early prediction method combining neural network and Gaussian process regression (GPR) is proposed. In the initial phase, the features related to the capacity degradation of LIBs are utilized to train the neural network model, which is used to predict the initial cycle lifetime of 124 LIBs. The Pearson coefficient’s two most significant characteristic factors and the predicted normalized lifetime form a 3D space. The Euclidean distance between the test dataset and each cell in the training dataset and validation dataset is calculated, and the shortest distance is considered to have a similar degradation pattern, which is used to determine the initial Dual Exponential Model (DEM). In the second phase, GPR uses the DEM as the initial parameter to predict each test set’s early RUL (ERUL). By testing four batteries under different working conditions, the RMSE of all capacity estimation is less than 1.2%, and the accuracy percentage (AP) of remaining life prediction is more than 98%. Experiments show that the method does not need human intervention and has high prediction accuracy.
The main difficulty in designing predictive controllers for permanent magnet motors with different topologies is the control of the additional current subspaces. The frequency of current components in these additional subspaces (harmonic subspaces and zero-sequence loop/subspaces) is several times the fundamental current frequency. Moreover, the inner voltage source would vary with operation conditions, and there are also different views about the causes of inner voltage sources. This paper analyses the inner voltage sources in these additional current subspaces for permanent magnet motors with different drive topologies using simulation and experiment. Based on the results of the simulations and experiments, we analyze the sources and characteristics of the inner voltage sources and provide a predictive model calibration method for the components in these additional subspaces. Finally, the deadbeat current control scheme based on calibrated look-up table is designed and verified.
The successful market uptake of all-electric propulsion systems is closely related to the performance metrics of the electrical motor used within. In light of this, various road-maps have been set for the next two decades by aerospace and automotive bodies targeting ambitious future targets of the motor’s power densities and efficiencies. In achieving motors with such step-improvement performance metrics, often the thermal management is a key challenge. In this paper, a cooling structure for a propulsion motor of solar unmanned aircraft is proposed which combines the stator windings with heat pipes, and which is shown to simultaneously improve the heat dissipation as well as the efficiency. This paper firstly determines the heat transfer characteristic of the heat pipe experimentally which is then used in the development of a bespoke thermal network model of the motor. The effects of the cooling structure on the motor’s temperature rise, copper losses, torque, and efficiency are studied in detail. Finally, a prototype is developed and a test platform is built. The experimental results are consistent with the analytical result, verifying the correctness of the thermal network model and the benefits of the proposed mechanism. Compared to the motor without heat pipes, the temperature rise of the motor is reduced by 35%, while its efficiency is improved by a significant 1.5%.
Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles (EVs). The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies, which are difficult to distinguish from faults. A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this paper. Symplectic geometry mode decomposition (SGMD) is introduced to obtain the components characterizing battery states, and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of batteries. The 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of values. The proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal runaway. And the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness, high reliability, and long time scale warning, and the method is easy to implement online.
New energy vehicles play a positive role in reducing carbon emissions. To improve the dynamic performance and durability of vehicle powertrain, the hybrid energy storage system of “fuel cell/power battery plus super capacitor” is more used in new energy vehicles. Bidirectional DC–DC converters with wide voltage conversion range are essential for voltage matching and power decoupling between super capacitor and vehicle bus, helping to improve the low input voltage characteristics of super capacitors and realize the recovery of feedback energy. In recent years, the topologies of bidirectional converters have been widely investigated and optimized. Aiming to obtain bidirectional DC–DC converters with wide voltage conversion range suitable for hybrid energy storage system, a review of the research status of non-isolated converters based on impedance networks and isolated converters based on transformer are presented. Additionally, an evaluation system for bidirectional DC–DC topologies for hybrid energy storage system is constructed, providing a reference for designing bidirectional DC–DC converters. The performance of eight typical non-isolated converters and seven typical isolated converters are comprehensively evaluated by using this evaluation system. On this basis, issues about DC–DC converters for hybrid energy storage system are discussed, and some suggestions for the future research directions of DC–DC converters are proposed. The optimization of bidirectional DC–DC converters for hybrid energy storage system from the perspectives of wide bandgap device application, electromagnetic compatibility technology and converter fault diagnosis strategies is the main research direction.
In order to suppress the battery aging of electric vehicles (EVs), a multi-objective optimization function is established to describe the battery aging behavior based on a high-precision battery aging model, and the state-space equation is then constructed to reveal the intrinsic relationship between vehicle speed, acceleration, and battery state-of-charge (SOC). The constructed optimization model is solved by using a sequential quadratic programming (SQP) algorithm, and based on the model predictive control (MPC) theory, the efficient real-time control of vehicle speed is achieved. Simulation results show that the developed strategy extends the battery life by 10.33% compared to the baseline strategy when the traffic flow is not involved. In the case of involving the traffic flow, the optimization results of battery aging improves as the look-ahead time period increases, while the computational burden increases. The results show that the developed strategy reduces the battery aging of the target vehicle by 33.02% compared to the preceding vehicle while meeting the real-time requirement.
In the actual use of a parallel battery pack in electric vehicles (EVs), current distribution in each branch will be different due to inconsistence characteristics of each battery cell. If the branch current is approximately calculated by the total current of the battery pack divided by the number of the parallel branches, there will be a large error between the calculated branch current and the real branch current. Adding current sensors to measure each branch current is not practical because of the high cost. Accurate estimation of branch currents can give a safety warning in time when the parallel batteries of EVs are seriously inconsistent. This paper puts forward a method to estimate and correct branch currents based on dual back propagation (BP) neural networks. In the proposed method, one BP neural network is used to estimate branch currents, the other BP neural network is used to reduce the estimation error cause by current pulse excitations. Furthermore, this paper makes discussions on the selection of the best inputs for the dual BP neural networks and the adaptability of the method for different battery capacity and resistence differences. The effectiveness of the proposed method is verified by multiple dynamic conditions of two cells connected in parallel.
Vehicles using a single fuel cell as a power source often have problems such as slow response and inability to recover braking energy. Therefore, the current automobile market is mainly dominated by fuel cell hybrid vehicles. In this study, the fuel cell hybrid commercial vehicle is taken as the research object, and a fuel cell/battery/supercapacitor energy topology is proposed, and an energy management strategy based on a double-delay deep deterministic policy gradient is designed for this topological structure. This strategy takes fuel cell hydrogen consumption, fuel cell life loss, and battery life loss as the optimization goals, in which supercapacitors play the role of coordinating the power output of the fuel cell and the battery, providing more optimization ranges for the optimization of fuel cells and batteries. Compared with the deep deterministic policy gradient strategy (DDPG) and the nonlinear programming algorithm strategy, this strategy has reduced hydrogen consumption level, fuel cell loss level, and battery loss level, which greatly improves the economy and service life of the power system. The proposed EMS is based on the TD3 algorithm in deep reinforcement learning, and simultaneously optimizes a number of indicators, which is beneficial to prolong the service life of the power system.
To promote the intelligent vehicle safety and reduce the driver steering workload, stackelberg game theory is adopted to design the shared steering control strategy that takes the driver neuromuscular delay characteristics into account. First, a shared steering control framework with adjustable driving weight is proposed, and a coupling interaction model considering the driver neuromuscular delay characteristics is constructed by using the stackelberg game theory. Moreover, the driver-automation optimal control strategy is deduced theoretically when the game equilibrium is reached. Finally, simulation and virtual driving tests are carried out to verify the superiority of the proposed method. The results illustrate that the raised method can enhance the vehicle safety with low driving weight intervention, and it can achieve better auxiliary effect with less control cost. In addition, the driver-in-the-loop test results show that the proposed strategy can achieve better performance in assisting drivers with low driving skills.
Nowadays, with increasing concerns about environmental pollution and energy crisis electric vehicles are rapidly developing owing to their significant environmental-friendly benefits. To meet the diverse driving requirements of electric vehicles, the PM motors with high-performance rare-earth PMs have been widely employed in electric vehicle powertrains, which shows the performance merits of high power density and high efficiency. Yet, rare-earth PMs, as non-renewable strategic resources, usually suffer from unstable supply and fluctuation prices, which increases the potential risks of further large-scale application of rare-earth PM motors. And this also poses a negative factor for the long-term sustainable development of electric vehicles or other applications that rely heavily on rare-earth PM materials. Under this background, a type of less-rare-earth PM motors, which aims to effectively alleviate the dependence of high-performance PM motors on rare-earth PMs, has recently drawn increasing attention from experts and scholars. It implies that the investigation and development of less-rare-earth PM motors without compromising performances is becoming a new and hot research direction in the motor field. This paper reviews the existing main alternatives for less-rare-earth PM motors. Based on the dominated torque component, the less-rare-earth PM motors are divided into two types, which are the less-rare-earth PM-dominated motor and less-rare-earth PM-assisted motor. The operation principle, design considerations and restrictions of each type of less-rare-earth PM motor is sequentially discussed. Finally, combined with the potential electric vehicle application, the key problems of less-rare-earth PM motor are summarized and the corresponding technological means are prospected.
Developing new energy vehicles has been a worldwide consensus, and developing new energy vehicles characterized by pure electric drive has been China’s national strategy. After more than 20 years of high-quality development of China’s electric vehicles (EVs), a technological R & D layout of “Three Verticals and Three Horizontals” has been created, and technological advantages have been accumulated. As a result, China’s new energy vehicle market has ranked first in the world since 2015. To systematically solve the key problems of battery electric vehicles (BEVs) such as “driving range anxiety, long battery charging time, and driving safety hazards”, China took the lead in putting forward a “system engineering-based technology system architecture for BEVs” and clarifying its connotation. This paper analyzes the research status and progress of the three core components of this architecture, namely, “BEV platform, charging/swapping station, and real-time operation monitoring platform”, and their key technological points. The three major demonstration projects of the 2008 Beijing Olympic Games, the 2022 Beijing Winter Olympics, and the intelligent and connected autonomous battery electric bus project are discussed to specify the applications of BEVs in China. The key research directions for upgrading BEV technologies remain to be further improving the vehicle-level all-climate environmental adaptability and all-day safety of BEVs, systematically solving the charging problem of BEVs and improving their application convenience, and safeguarding safety with early warning and implementing active/passive safety protection for the whole life cycle of power batteries on the basis of BEVs’ operation big data. BEVs have acquired new technological features such as intelligent and networked technology empowerment, extensive integration of control-by-wire systems, a platform of chassis hardware, and modularization of functional software.
Due to the high mileage and heavy load capabilities of hybrid electric vehicles (HEVs), energy management becomes crucial in improving energy efficiency. To avoid the over-dependence on the hard-crafted models, deep reinforcement learning (DRL) is utilized to learn more precise energy management strategies (EMSs), but cannot generalize well to different driving situations in most cases. When driving cycles are changed, the neural network needs to be retrained, which is a time-consuming and laborious task. A more efficient transferable way is to combine DRL algorithms with transfer learning, which can utilize the knowledge of the driving cycles in other new driving situations, leading to better initial performance and a faster training process to convergence. In this paper, we propose a novel transferable EMS by incorporating the DRL method and dueling network architecture for HEVs. Simulation results indicate that the proposed method can generalize well to new driving cycles, with comparably initial performance and faster convergence in the training process.
With the continuous advancement and exploration of science and technology, the future trend of energy technology will be the deep integration of digitization, networking, intelligence with energy applications. The increasing maturity of digital technologies, such as the Internet of Things, Big Data, and cloud computing, has given rise to the creation and use of a potential technology – Digital Twin. Currently, research on Digital Twin has produced many concepts and outcomes that have been applied in many fields. In the energy sector, while some relevant ideas and case studies of Digital Twin have been generated, there are still many gaps to be explored. As a potential technology with advantages in many aspects, Digital Twin is bound to generate more promotion and applications in the energy fields. This paper systematically reviews the existing Digital Twin approaches and their possible applications in the energy fields. In addition, this paper attempts to analyze Digital Twin from different perspectives, such as definitions, classifications, main features, case studies and key technologies. Finally, the directions and challenges of possible future applications of Digital Twin in the energy fields have been presented.
This paper proposes a novel power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning algorithm. Three parallel soft actor-critic (SAC) networks are trained for high speed, medium speed, and low-speed conditions respectively; the reward function is designed as minimizing the cost of energy cost and battery aging. During operation, the driving condition is recognized at each moment for the algorithm invoking based on the learning vector quantization (LVQ) neural network. On top of that, a driving cycle reconstruction algorithm is proposed. The historical speed segments that were recorded during the operation are reconstructed into the three categories of high speed, medium speed, and low speed, based on which the algorithms are online updated. The SAC-based control strategy is evaluated based on the standard driving cycles and Shenyang practical data. The results indicate the presented method can obtain the effect close to dynamic programming and can be further improved by up to 6.38% after the online update for uncertain driving conditions.
In order to improve the driving dynamics and riding comfort of pure electric vehicles, taking a two-speed I-AMT (Inverse-Automatic Mechanical Transmission) with rear friction clutch as the research object, a gear shift strategy, which consists of the open-loop control of the clutch position control and the closed-loop control of the drive motor speed control, is proposed. Considering the inherent time-delay and external disturbances within the motor speed adjustment system, a two DOF (degree-of-freedom) Smith predictor with feedforward input is designed to track the target speed of the drive motor. The feedforward input is used to eliminate the influence of clutch sliding friction on the motor speed control, while the feedback speed tracking controller is applied to realize the speed tracking performance with the existence of time-delay and the external disturbance. In order to verify the effectiveness of the gear shift control strategy and the accuracy of the two DOF Smith controller with feedforward control, simulation results comparison is firstly carried out to illustrate the effectiveness of the control scheme. Then, a light pure electric vehicle equipped with I-AMT was used for downshift experiments under large throttle, which is the most difficult working scenario to control the transmission. The experimental results show that the two DOF Smith controller can eliminate the influence of time-delay on the closed-loop control, and the proposed whole gear shift control strategy can limit the clutch slippage time within 1.5 , resulting in a smaller shift jerk, thus guarantee the driving dynamics and riding comfort simultaneously.
This paper presents a segmented trajectory planning strategy for active collision avoidance system. Considering the longitudinal and lateral movement of the obstacle vehicle, as well as the ego vehicle and obstacle outer contour limitations, the collision avoidance trajectory is divided into three segments: lane changing, overtaking and back to original lane. The starting point and end point of lane-change are decided based on longitudinal and lateral safety distance model according to the relative speed and distance as well as the outer contour of the two vehicles. Based on system objective function and lane-change trajectory cluster, vehicle states, dynamic constraints and vehicle body kinematics constraints, the optimal trajectory can be selected, which can monitor the relative location of the obstacle vehicle constantly and then ensure the vehicle can accomplish the collision avoidance safely and smoothly. Simulation and experiment results demonstrate the effectiveness and feasibility of proposed trajectory planning strategy for the active collision avoidance.
Batteries often show the coupling change of multiple physical field characteristic parameters in the charging or discharging processes. Conventional battery modeling and characterization methods mainly focus on electrical or thermal parameters. Considering that battery state change can cause a change in the mechanical structure, research on the vibration characteristic modeling and characterization of a plastic-cased lithium-ion battery is carried out. By analyzing the mechanical structure of the battery, the first principles modeling method is selected to model the battery’s mechanical performance. The vibration data in the process of battery charging and discharging are measured by a laser Doppler vibrometer (LDV). The obtained experimental data are used to identify the mechanical parameters of the battery. Based on the experimental results, the root mean square error (RMSE) of the frequency domain amplitude fitting result of the model is less than 11.36%. The effectiveness of the battery mechanical vibration model is verified. The model and the characterization method provide tools for nondestructive battery state evaluation.
To address the driving conflicts of connected automated vehicles (CAVs) at unsignalized roundabouts, a cooperative decision-making framework is proposed. The personalized driving preferences of CAVs are considered in the decision-making algorithm, which are reflected by different driving styles. A motion prediction algorithm is used to improve the decision-making performance. The effect of the motion prediction algorithm on the decision-making performance is evaluated, including the advancement of driving safety and the computational load for the hardware. The cooperative game theoretic approach is applied to the interaction modelling and collaborative decision making of CAVs. Finally, hardware-in-the-loop (HIL) tests are carried out to evaluate the feasibility and real-time performance of the proposed algorithm.
To improve the efficiency of safety tests of driver-automation cooperation, a method for generating a scenario library is proposed that considers the probability of scenario occurrence and driver-handling challenges in real driving situations. First, the original scenario data under cut-in conditions stored in a time series are extracted from the scenario data set. Then, a mathematical performance index is used to model the scenario and a significance function in terms of the occurrence frequency of the scenario, and the performance challenge between the driver and the vehicle is established. Next, the important scenario set is extracted from the original scenario set by constructing and optimizing a significance auxiliary function. Finally, the extracted important scenario sets are filtered by using the significance function values of the scenarios to generate a scenario library. Simulation results show that the proposed method for scenario-library generation can effectively identify scenarios with potential adventure during driver-automation cooperation and thus accelerate safety tests compared with traditional methods.
With the rapid development and widespread application of electric vehicles (EV) around the world, the wireless power transfer (WPT) technology is also accelerating for commercial applications in EV wireless charging (EV-WPT) because of its high reliability, safety, and convenience, especially high suitability for the future self-driving scenario. Foreign object detection (FOD), mainly including metal object detection and living object detection, is required urgently and timely for the practical application of EV-WPT technology to ensure electromagnetic safety. In the last decade, especially in the past three years, many pieces of research on FOD have been reported. This article reviews FOD state-of-the-art technology for EV-WPT and compares the pros and cons of different approaches in terms of sensitivity, reliability, adaptability, complexity, and cost. Future challenges for research and development are also discussed to encourage commercialization of EV-WPT technique.
With the wide application of the LFP lithium-ion batteries, more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging monitoring. In recent years, long-term aging trajectory prediction of the lithium-ion battery is always a challenge due to its complex nonlinear aging behaviors especially the aging behaviors in the two aging stages are quite different when the battery experiences the two-stage aging process under fast-charging conditions. Thus, it is harder to achieve accurate long-term aging trajectory prediction of the LFP lithium-ion batteries on the condition of the two-stage aging process. To address it, a novel transfer learning strategy combined with the cycle life prediction technology is presented in this paper. Specifically, a new cycle life prediction method is proposed based on feature extraction and deep learning technology and achieves accurate cycle life prediction. The transfer learning is started by developing a base aging model offline to learn the information of the two-stage aging process. Then, taking the predicted cycle life as its prior information, the Bayesian model migration technology is employed to predict the aging trajectory accurately, and the uncertainty of the aging trajectory is quantified. Two batches of the battery datasets are used for performance evaluation and comparison with two benchmarks. It is novel to combine the cycle life prediction and transfer learning technique to achieve accurate two-stage aging trajectory prediction with only a few data available (first 30%).
Accurate battery state estimation is the premise of battery management for electric vehicles and energy storage, and the relationship between battery Open-Circuit-Voltage (OCV) and State-of-Charge (SOC) is the basis for accurate state estimation. To solve the long time-consuming test period for Li-ion battery state estimation algorithm development, this study attempts to make three efforts: (1) An operating data-driven universal method for estimating the OCV-SOC relationship is proposed. The OCV vs. Ampere-hour (Ah) segments are extracted from operating data segments based on a battery model, and then a complete OCV-SOC curve with the full SOC is formed via connecting these OCV-Ah segments. (2) A series of limitations have been further set in the estimated process to guarantee the reliability of the estimated OCV-SOC relationship, including the minimum total electric quantity of a data segment, the minimum OCV overlapping range between different data segments, and the maximum model voltage error. (3) Validated using different data segments show high accuracy in SOC and SOH estimation with a maximum error of less than 3.0% and 2.9% respectively. Compared with the test data-based OCV-SOC relationship, the estimated OCV-SOC relationship can realize higher accuracy in SOC estimation, especially for "flat voltage characteristic" LiFePO4 battery, the maximum error is less than 3.5% and the accuracy is higher than the complete test data method. This method realizes the leap from relying on time-consuming accurate measurements and complete test periods to directly using operational data for battery state estimation.