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
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
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
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.
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.
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.
