Accurate remaining useful life (RUL) prediction and state-of-health (SOH) diagnosis are of extreme importance for safety, durability, and cost of energy storage systems based on lithium-ion ...batteries. It is also a crucial challenge for energy storage systems to predict RUL and diagnose SOH of batteries due to the complicated aging mechanism. In this paper, a novel method for battery RUL prediction and SOH estimation is proposed. First, a novel support vector regression-based battery SOH state-space model is established to simulate the battery aging mechanism, which takes the capacity as the state variable and takes the representative features during a constant-current and constant-voltage protocol as the input variables. The estimated impedance variables are taken as the output due to the correlation between battery capacity and the sum of charge transfer resistance and electrolyte resistance. Second, in order to suppress the measurement noises of current and voltage, a particle filter is employed to estimate the impedance degradation parameters. Furthermore, experiments are conducted to validate the proposed method. The results show that the proposed SOH estimation method can provide an accurate and robustness result. The proposed RUL prediction framework can also ensure an accurate RUL prediction result.
•An online RUL estimation method for lithium-ion battery is proposed.•RUL is described by the difference among battery terminal voltage curves.•A feed forward neural network is employed for RUL ...estimation.•Importance sampling is utilized to select feed forward neural network inputs.
An accurate battery remaining useful life (RUL) estimation can facilitate the design of a reliable battery system as well as the safety and reliability of actual operation. A reasonable definition and an effective prediction algorithm are indispensable for the achievement of an accurate RUL estimation result. In this paper, the analysis of battery terminal voltage curves under different cycle numbers during charge process is utilized for RUL definition. Moreover, the relationship between RUL and charge curve is simulated by feed forward neural network (FFNN) for its simplicity and effectiveness. Considering the nonlinearity of lithium-ion charge curve, importance sampling (IS) is employed for FFNN input selection. Based on these results, an online approach using FFNN and IS is presented to estimate lithium-ion battery RUL in this paper. Experiments and numerical comparisons are conducted to validate the proposed method. The results show that the FFNN with IS is an accurate estimation method for actual operation.
An accurate battery pack state of health (SOH) estimation is important to characterize the dynamic responses of battery pack and ensure the battery work with safety and reliability. However, the ...different performances in battery discharge/charge characteristics and working conditions in battery pack make the battery pack SOH estimation difficult. In this paper, the battery pack SOH is defined as the change of battery pack maximum energy storage. It contains all the cells' information including battery capacity, the relationship between state of charge (SOC) and open circuit voltage (OCV), and battery inconsistency. To predict the battery pack SOH, the method of particle swarm optimization-genetic algorithm is applied in battery pack model parameters identification. Based on the results, a particle filter is employed in battery SOC and OCV estimation to avoid the noise influence occurring in battery terminal voltage measurement and current drift. Moreover, a recursive least square method is used to update cells' capacity. Finally, the proposed method is verified by the profiles of New European Driving Cycle and dynamic test profiles. The experimental results indicate that the proposed method can estimate the battery states with high accuracy for actual operation. In addition, the factors affecting the change of SOH is analyzed.
•A novel battery pack SOH definition is proposed.•A PSO-GA estimator is applied in parameters identification.•The accuracy and robustness of the method is verified by different profiles.•The influential battery pack SOH factors are performed.
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•Build a method for joint estimation of both state-of-charge and state-of-energy.•Data of an IFP1865140-type battery have been analyzed comprehensively in order to better understand ...the cell character.•The particle filter is used for simultaneous SOC and SOE estimation to improve the accuracy.•Dynamic temperature experiments are performed to verify the robustness of the new method.
The state-of-charge (SOC) is a critical index in battery management system (BMS) for electric vehicles (EVs). However in the energy storage systems, the available energy also acts as a significant role. Through the estimating result of state-of-energy (SOE), we can further estimate how long the battery is going to last if we apply a low power demand, a high power demand, or even a dynamic power demand. Unlike the SOC, the SOE is not only the integral of current but also the integral of voltage which include the nonlinearity of Li-ion batteries. Since there are accumulated errors caused by current or voltage measurement noise, a joint estimator based on particle filter is proposed for the estimation of both SOC and SOE. Validation experiments are carried out based on IFP1865140-type batteries under both constant and dynamic current conditions. To further verify the robustness of the proposed method, experiments are performed under dynamic temperatures. The experiment results have verified that accurate and robust SOC and SOE estimation results can be obtained by the proposed method.
•The equivalent circuit model is estimated for battery states estimation.•Battery peak current is analyzed by multi-constrained conditions.•A novel multi-time-scale observer is used to estimate SOE ...and SOP concurrently.•The accuracy of the proposed method is verified under different conditions.
The battery state of energy and state of power are two important parameters in battery usage. The state of energy represents the residual energy storage in battery and the state of power represents the ability of battery discharge/charge. To estimate the two states with high accuracy, the characteristics of battery maximum available capacity and open-circuit voltage are analyzed under different working temperatures. Meanwhile, the equivalent circuit model of the battery is employed to embody the battery dynamic performance. To improve the accuracy of the battery states estimation, the multi-time-scale filter is applied in battery model parameters identification and battery states prediction. Besides, the state of power is analyzed by multi-constrained conditions to ensure battery work with safety. The proposed approach is verified by experiments operated on lithium-ion battery under new European driving cycle profiles and dynamic test profiles. The experimental results indicate the proposed method can estimate the battery states with high accuracy for actual application. In addition, the factors affecting the change of battery states are analyzed.
•The architecture of network collaboration for future battery management is presented.•The digital twin model is used for management of the batteries in their entire life cycle.•Online learning and ...model updating are used to fix model parameters.•The key technologies of state estimation and battery equalization are introduced.
Nowadays the wave of digital economy has swept the world, and the competition in the field of battery management has become increasingly vigorous. The application of digital twin technology gives a new concept of networked management and service of lithium-ion batteries. In this paper, the digital twin technology and cloud-side-end collaboration for the future battery management system is discussed. A four layer networked architecture of cloud-side-end collaboration for battery management system is presented which breaks through the computing capacity and storage space limitations of the conventional battery management and enables high performance algorithms. The digital twin model of the battery is established, which enables refined and safety management of the batteries in their entire life cycle. Furthermore, the digital twin model and key technologies such as state estimation and cloud assisted equalization of the batteries are introduced. The results indicate that digital twin models are helpful for battery management and the full life cycle data are useful to build the upgrade route of the battery.
Accurate estimation of battery pack state-of-charge plays a very important role for electric vehicles, which directly reflects the behavior of battery pack usage. However, the inconsistency of ...battery makes the estimation of battery pack state-of-charge different from single cell. In this paper, to estimate the battery pack state-of-charge on-line, the definition of battery pack is proposed, and the relationship between the total available capacity of battery pack and single cell is put forward to analyze the energy efficiency influenced by battery inconsistency, then a lumped parameter battery model is built up to describe the dynamic behavior of battery pack. Furthermore, the extend Kalman filter-unscented Kalman filter algorithm is developed to identify the parameters of battery pack and forecast state-of-charge concurrently. The extend Kalman filter is applied to update the battery pack parameters by real-time measured data, while the unscented Kalman filter is employed to estimate the battery pack state-of-charge. Finally, the proposed approach is verified by experiments operated on the lithium-ion battery under constant current condition and the dynamic stress test profiles. Experimental results indicate that the proposed method can estimate the battery pack state-of-charge with high accuracy.
•A novel space state equation is built to describe the pack dynamic behavior.•The dual filters method is used to estimate the pack state-of-charge.•Battery inconsistency is considered to analyze the pack usage efficiency.•The accuracy of the proposed method is verified under different conditions.
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•A temperature-compensated battery model is developed to determine the effect of temperature.•Temperature-based models of internal resistance and OCV are built to identify the model ...parameters at various temperatures.•A dual-particle-filter estimator is proposed for simultaneous SOC and drift current estimation to eliminate the drift noise.•More accurate SOC is obtained by the proposed approach than that without regard to the temperature and drift current.
The accurate state-of-charge (SOC) estimation of power Li-ion batteries is one of the most important issues for battery management system (BMS) in electric vehicles (EVs). Temperature has brought great impact to the accuracy of the SOC estimation, which greatly depends on appropriate battery models and estimation algorithms. The fact that the model parameters, such as the internal resistance and the open-circuit voltage, are dependent on battery temperature and current detection precision is greatly related to the drift noise in current measurements will lead to errors in SOC estimation. Aiming at this problem, we present a temperature-compensated model with a dual-particle-filter estimator for SOC estimation of power Li-ion batteries in EVs. To overcome the effect of model parameter perturbations caused by temperature, a practical temperature-compensated battery model, in which the temperature and current are taken as model inputs, is presented to study and describe the relationship between the internal resistance, voltage and the temperature comprehensively. Additionally, the drift current is considered as an undetermined static parameter in the battery model to eliminate the effect of the drift current. Then, we build a dual-particle-filter estimator to obtain simultaneous SOC and drift current estimation based on the temperature-compensated model. The experimental and simulation results indicate that the proposed method based on the temperature-compensated model and the dual-particle-filter estimator can realize an accurate and robust SOC estimation.
The state-of-energy of lithium-ion batteries is an important evaluation index for energy storage systems in electric vehicles and smart grids. To improve the battery state-of-energy estimation ...accuracy and reliability, an online model-based estimation approach is proposed against uncertain dynamic load currents and environment temperatures. Firstly, a three-dimensional response surface open-circuit-voltage model is built up to improve the battery state-of-energy estimation accuracy, taking various temperatures into account. Secondly, a total-available-energy-capacity model that involves temperatures and discharge rates is reconstructed to improve the accuracy of the battery model. An extended-Kalman-filter and particle-filter based dual filters algorithm is then developed to establish an online model-based estimator for the battery state-of-energy. The extended-Kalman-filter is employed to update parameters of the battery model using real-time battery current and voltage at each sampling interval, while the particle-filter is applied to estimate the battery state-of-energy. Finally, the proposed approach is verified by experiments conducted on a LiFePO4 lithium-ion battery under different operating currents and temperatures. Experimental results indicate that the battery model simulates battery dynamics robustly with high accuracy, and the estimates of the dual filters converge to the real state-of-energy within an error of ±4%.
•A novel open circuit voltage model is developed in considering temperature.•Temperature and current are considered to model total available energy capacity.•A novel model-based state of energy estimator is established using dual filters.•The robustness of new method is validated under dynamic experimental conditions.
•Build a method for battery pack SOC estimation.•Analyze the effect of the uneven cells problems to the pack SOC.•The SOC is estimated with consideration of different balance control strategies.•The ...UPF method is used to estimate the SOC to improve the accuracy.
The state-of-charge (SOC) is a critical parameter of a Li-ion battery pack. Differences among in-pack cells are inevitable and can change the total capacity of a pack and the remaining available capacity. Because the traditional methods for the estimation of the SOC of a pack did not consider the difference among the cells and the impact of balance control, we developed a new method that accounts for these problems. To accurately estimate the pack SOC, we establish the relationship between the parameters of the pack and those of in-pack cells under different balance control strategies. This paper also studies the two different types of connections of a battery pack: in series and in parallel. Based on the model of the first over-charged cell and that of the first over-discharged cell, the estimation of the SOC of a battery pack is realized by the Unscented Particle Filter (UPF) algorithm. A simulation experiment verified the method for the estimation of the SOC for a battery pack based on actual data and proved that an accurate estimation value can be obtained by the method.
