Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Dee...Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 instances.Results indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s variance.When computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction times.The GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational speed.In contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery management.Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep discharging.These findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained resources.Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.展开更多
Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) ...Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.展开更多
A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].T...A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].The proposed equivalent circuit model from[1]is reproduced in Fig.1.展开更多
Driving range and battery aging, which are two important topics considered by consumers when purchasing an electric vehicle, are studied in this research. This research started with experiments on LiNiMnCoO<sub>...Driving range and battery aging, which are two important topics considered by consumers when purchasing an electric vehicle, are studied in this research. This research started with experiments on LiNiMnCoO<sub>2</sub> battery cells. Experimental results of discharging voltage, OCV, and internal resistance are obtained under different ambient temperatures. Cycle aging of battery cells is also investigated by experiments. The obtained experimental data is used to develop the battery pack model in the electric vehicle model as well as the battery aging model. The developed electric vehicle model is used to investigate electric vehicle’s driving range. Within the ambient temperature range between -30°C to 50°C, the driving range decreases with the ambient temperature. The driving range can also be heavily reduced by high-speed and aggressive driving. By using the developed battery aging model, cycle aging of the onboard battery of an electric vehicle after its 15,000-hour usage is investigated. Simulation results show that battery cell has a quicker cycle aging process under higher ambient temperatures. Large discharging and charging currents involved in aggressive driving can also accelerate battery aging. In addition, cycle aging of the onboard battery will be accelerated if the battery is almost used up before recharging every time. This research presents a novel approach to studying the driving range and battery aging of electric vehicles and includes valuable results for automotive engineers and consumers of electric vehicles.展开更多
Estimating battery states such as State of Charge(SOC)and State of Health(SOH)is an essential component in developing energy storage technologies,which require accurate estimation of complex and nonlinear systems.A si...Estimating battery states such as State of Charge(SOC)and State of Health(SOH)is an essential component in developing energy storage technologies,which require accurate estimation of complex and nonlinear systems.A significant challenge is extracting pertinent spatial and temporal features from original battery data,which is crucial for efficient battery management systems.The emergence of digital twin(DT)technology offers a novel opportunity for performance monitoring and management of lithium-ion batteries,enhancing collaborative capacity among different battery state estimation techniques and enabling optimal operation of battery storage units.In this study,we propose a DT-supported battery state estimation method,in collaboration with the temporal convolutional network(TCN)and the long short-term memory(LSTM),to address the challenge of feature extraction.Firstly,we introduce a 4-layer hierarchical DT to overcome computational and data storage limitations in conventional battery management systems.Secondly,we present an online algorithm,TCN-LSTM for battery state estimation.Compared to conventional methods,TCN-LSTM outperforms other cyclic networks in various sequence modelling tasks and exhibits reduced reliance on the initial state conditions of the battery.Our methodology employs transfer learning to dynamically adjust the neural network parameters based on fresh data,ensuring real-time updating and enhancing the DT's accuracy.Focusing on SOC,SOH and Remaining Useful Life(RUL)estimation,our model demonstrates exceptional results.When testing with 90 cycle data,the average root mean square error(RMSE)values for SOC,SOH,and RUL are 1.1%,0.8%,and 0.9%respectively,significantly outperforming traditional CNN's 2.2%,2.0%and 3.6%and others.These results un-equivocally demonstrate the contribution of the DT model to battery management,highlighting the outstanding robustness of our proposed method,showcasing consistent performance across various conditions and superior adaptability compared to other models.展开更多
The inconsistency of the cells in a battery pack can affect its lifespan,safety and reliability in the electric vehicles. The balanced system is an effective technique to reduce its inconsistency and improve the opera...The inconsistency of the cells in a battery pack can affect its lifespan,safety and reliability in the electric vehicles. The balanced system is an effective technique to reduce its inconsistency and improve the operating performance. A hybrid equilibrium strategy based on decision combing battery state-of-charge（ SOC） and voltage has been proposed. The battery SOC is estimated through an improved least squares method. An equalization hardware in loop（ HIL） platform has been constructed. Based on this HIL platform,equilibrium strategy has been verified under the constant-current-constant-voltage（ CCCV） and dynamicstresstest（ DST） conditions. Experimental results indicate that the proposed hybrid equalization strategy can achieve good balance effect and avoid the overcharge and over-discharge of the battery pack at the same time.展开更多
electrolyte. The properties of lithium-ion (Li-ion) battery, such as cycle life, irreversible capacity loss, self-discharge rate, electrode corrosion and safety are usually ascribed to the quality of the SEI, which ar...electrolyte. The properties of lithium-ion (Li-ion) battery, such as cycle life, irreversible capacity loss, self-discharge rate, electrode corrosion and safety are usually ascribed to the quality of the SEI, which are highly dependent on the thickness. Thus, understanding the formation mechanism and the SEI thickness is of prime interest. First, we apply dimensional analysis to obtain an explicit relation between the thickness and the number density in this study. Then the SEI thickness in the initial charge-discharge cycle is analyzed and estimated for the first time using the Cahn-Hilliard phase-field model. In addition, the SEI thickness by molecular dynamics simulation validates the theoretical results. It has been shown that the established model and the simulation in this paper estimate the SEI thickness concisely within order-of-magnitude of nanometers. Our results may help in evaluating the performance of SEI and assist the future design of Li-ion battery.展开更多
文摘Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 instances.Results indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s variance.When computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction times.The GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational speed.In contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery management.Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep discharging.These findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained resources.Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.
基金supported by the National Natural Science Foundation of China(62373224,62333013,U23A20327)the Natural Science Foundation of Shandong Province(ZR2024JQ021)
文摘Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.
文摘A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].The proposed equivalent circuit model from[1]is reproduced in Fig.1.
文摘Driving range and battery aging, which are two important topics considered by consumers when purchasing an electric vehicle, are studied in this research. This research started with experiments on LiNiMnCoO<sub>2</sub> battery cells. Experimental results of discharging voltage, OCV, and internal resistance are obtained under different ambient temperatures. Cycle aging of battery cells is also investigated by experiments. The obtained experimental data is used to develop the battery pack model in the electric vehicle model as well as the battery aging model. The developed electric vehicle model is used to investigate electric vehicle’s driving range. Within the ambient temperature range between -30°C to 50°C, the driving range decreases with the ambient temperature. The driving range can also be heavily reduced by high-speed and aggressive driving. By using the developed battery aging model, cycle aging of the onboard battery of an electric vehicle after its 15,000-hour usage is investigated. Simulation results show that battery cell has a quicker cycle aging process under higher ambient temperatures. Large discharging and charging currents involved in aggressive driving can also accelerate battery aging. In addition, cycle aging of the onboard battery will be accelerated if the battery is almost used up before recharging every time. This research presents a novel approach to studying the driving range and battery aging of electric vehicles and includes valuable results for automotive engineers and consumers of electric vehicles.
文摘Estimating battery states such as State of Charge(SOC)and State of Health(SOH)is an essential component in developing energy storage technologies,which require accurate estimation of complex and nonlinear systems.A significant challenge is extracting pertinent spatial and temporal features from original battery data,which is crucial for efficient battery management systems.The emergence of digital twin(DT)technology offers a novel opportunity for performance monitoring and management of lithium-ion batteries,enhancing collaborative capacity among different battery state estimation techniques and enabling optimal operation of battery storage units.In this study,we propose a DT-supported battery state estimation method,in collaboration with the temporal convolutional network(TCN)and the long short-term memory(LSTM),to address the challenge of feature extraction.Firstly,we introduce a 4-layer hierarchical DT to overcome computational and data storage limitations in conventional battery management systems.Secondly,we present an online algorithm,TCN-LSTM for battery state estimation.Compared to conventional methods,TCN-LSTM outperforms other cyclic networks in various sequence modelling tasks and exhibits reduced reliance on the initial state conditions of the battery.Our methodology employs transfer learning to dynamically adjust the neural network parameters based on fresh data,ensuring real-time updating and enhancing the DT's accuracy.Focusing on SOC,SOH and Remaining Useful Life(RUL)estimation,our model demonstrates exceptional results.When testing with 90 cycle data,the average root mean square error(RMSE)values for SOC,SOH,and RUL are 1.1%,0.8%,and 0.9%respectively,significantly outperforming traditional CNN's 2.2%,2.0%and 3.6%and others.These results un-equivocally demonstrate the contribution of the DT model to battery management,highlighting the outstanding robustness of our proposed method,showcasing consistent performance across various conditions and superior adaptability compared to other models.
基金Supported by the National Natural Science Foundation of China(51507012)Beijing Nova Program(Z171100001117063)
文摘The inconsistency of the cells in a battery pack can affect its lifespan,safety and reliability in the electric vehicles. The balanced system is an effective technique to reduce its inconsistency and improve the operating performance. A hybrid equilibrium strategy based on decision combing battery state-of-charge（ SOC） and voltage has been proposed. The battery SOC is estimated through an improved least squares method. An equalization hardware in loop（ HIL） platform has been constructed. Based on this HIL platform,equilibrium strategy has been verified under the constant-current-constant-voltage（ CCCV） and dynamicstresstest（ DST） conditions. Experimental results indicate that the proposed hybrid equalization strategy can achieve good balance effect and avoid the overcharge and over-discharge of the battery pack at the same time.
基金supported by the National Natural Science Foundation of China (Grant Nos. 11372313, U1562105, and 11611130019)the Chinese Academy of Sciences (CAS) through CAS Interdisciplinary Innovation Team Project, the CAS Key Research Program of Frontier Sciences (Grant No. QYZDJ-SSW-JSC019)the CAS Strategic Priority Research Program (Grant No. XDB22040401)
文摘electrolyte. The properties of lithium-ion (Li-ion) battery, such as cycle life, irreversible capacity loss, self-discharge rate, electrode corrosion and safety are usually ascribed to the quality of the SEI, which are highly dependent on the thickness. Thus, understanding the formation mechanism and the SEI thickness is of prime interest. First, we apply dimensional analysis to obtain an explicit relation between the thickness and the number density in this study. Then the SEI thickness in the initial charge-discharge cycle is analyzed and estimated for the first time using the Cahn-Hilliard phase-field model. In addition, the SEI thickness by molecular dynamics simulation validates the theoretical results. It has been shown that the established model and the simulation in this paper estimate the SEI thickness concisely within order-of-magnitude of nanometers. Our results may help in evaluating the performance of SEI and assist the future design of Li-ion battery.
