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The state estimation technology of lithium-ion batteries is one of the core functions elements of the battery management system (BMS), and it is an academic hotspot related to the functionality and safety of the battery for electric vehicles. This paper comprehensively reviews the research status, technical challenges, and development trends of state estimation of lithium-ion batteries. First, the key issues and technical challenges of battery state estimation are summarized from three aspects of characteristics, models, and algorithms, and the technical challenges in state estimation are deeply analyzed. Second, four typical battery states (state of health, state of charge, state of energy, and state of power) and their joint estimation methods are reviewed, and feasible estimation frameworks are proposed, respectively. Finally, the development trends of state estimation are prospected. Advanced technologies such as artificial intelligence and cloud networking have further reshaped battery state estimation, bringing new methods to estimate the state of the battery under complex and extreme operating conditions. The research results provide a valuable reference for battery state estimation in the next-generation battery management system. [ABSTRACT FROM AUTHOR]

Summary: Calendar life accounts for most of the lifetime of the power lithium‐ion battery applied in electric vehicles, and thus should be investigated in detail. In this paper, a mechanistic calendar aging model of lithium‐ion battery is developed by adding the solid electrolyte interface (SEI) growth side reaction to the pseudo‐two‐dimensional electrochemical model. The model can accurately simulate the capacity degradation and evolution of the charging voltage profiles of a Lix(NiCoMn)1/3O2 ‐ graphite battery during high‐temperature (55°C) storage under various states of charge. The model is further validated by comparing the electrode degradations inside the batteries after calendar aging. The model predicted electrode degradations are consistent with the results identified using a dual‐tank model and post‐mortem analysis, confirming that SEI growth is the dominant degradation mechanism. Based on the validated model, internal changes in electrodes' structure induced by SEI growth during calendaring aging are discussed. The anodes of the calendar‐aged batteries suffer from severe pore clogging and film resistance increase, resulting in apparent power performance degradation of lithium‐ion battery. Finally, modeling analysis is conducted to find possible solutions to mitigate the battery calendar aging process. Rational designs of SEI by reducing the porosity and solvent diffusivity inside the SEI and the kinetic rate constant turn out to be effective in improving the calendar lifetime of lithium‐ion batteries. Novelty Statement: Calendar life accounts for most of the lifetime of the lithium‐ion battery in electric vehicles. This study establishes a mechanistic calendar aging model of lithium‐ion battery considering solid electrolyte interface growth. The model can not only simulate the battery capacity degradation and the evolution of battery charging voltage profiles but also capture the internal degradation of the electrodes during the aging process. Modeling analysis is further conducted to find possible solutions to mitigate the calendar aging process of lithium‐ion battery. [ABSTRACT FROM AUTHOR]

Summary: Cell‐to‐pack (CTP) structure has been proposed for electric vehicles (EVs). However, massive heat will be generated under fast charging. To address the temperature control and thermal uniformity issues of CTP module under fast charging, experiments and computational fluid dynamics (CFD) analysis are carried out for a bottom liquid cooling plate based–CTP battery module. The impact of the channel height, channel width, coolant flow rate, and coolant temperature on the temperature and temperature difference are analyzed. A liquid cooling control method of exchanging the coolant inlet and outlet is proposed to optimize the temperature uniformity. Besides, the temperature uniformity enhancement by this control method at different intervals is compared. Results indicate that the flow rate and temperature positively affect the battery temperature; the maximum temperature can be reduced by 10.93% and 15.12%, respectively, under the same operations. However, the coolant temperature increment increases the maximum temperature difference by about 41.58%. Reversing flow enhances the cooling effect of conventional unidirectional flow of the CTP battery module under fast charging, especially for the thermal uniformity, which provides guidance for the battery thermal management system (BTMS) control under fast charging. [ABSTRACT FROM AUTHOR]

Accurate and reliable capacity estimation for lithium-ion batteries adapting to uncertain environment plays a significant role in the fields of electric vehicles and renewable energy systems. This paper proposes a novel capacity fusion estimation approach based on three-parameter capacity fade model and constant current charging curves using dual unscented Kalman filters. In view of the battery cycle aging problem under variable temperature situation, a three-parameter capacity fade model is introduced to continuously estimate the battery cell capacity for unknown changes of ambient temperature. Nevertheless, the model parameters may mismatch in practical applications, so the first unscented Kalman filter views the discrete capacity based on constant current charging curves as observations to periodically update the parameters. Thereafter, the second one performs a fusion operation depending on the capacity estimated by constant current charging curves and the three-parameter capacity fade model with the corrected parameters, so as to improve the capacity estimation accuracy online. The results show that the fusion estimation errors of capacity are controlled within 2% by updating the model parameters, and the robustness of the proposed method is also validated. [ABSTRACT FROM AUTHOR]

Summary: The timely and accurate detection of an internal short circuit (ISC) is critical for improving electric vehicle (EV) safety. Typical ISC detection approaches are based on consistency differences between each cell in a module. However, the indispensable equalizer in a battery management system reduces inconsistency, affecting the accuracy and timeliness of ISC detection. Hence, an online ISC detection method considering equalization electric quantity (EEQ) is proposed. First, the remaining charge capacity and state‐of‐charge difference approaches are investigated. Next, the negative impact of EEQ on ISC detection is analyzed; thereafter, improved approaches based on EEQ compensation are proposed. Moreover, an ISC routing inspection approach for dormant EVs is proposed. Lastly, the proposed approaches are verified. The main results are as follow: (a) The ISC detection method without EEQ compensation has issues, such as significant errors, undetected ISC, and longer detection periods; our approaches help effectively overcome these issues, while the detection error is maintained below 5%; (b) Supplementing the above approaches, the proposed ISC routing inspection approach can help detect a moderate ISC even for a dormant EV, while further improving timeliness in ISC detection. [ABSTRACT FROM AUTHOR]

Micro-short-circuit (MSC) is a latent risk in power batteries, which may give rise to thermal runaway and even catastrophic safety hazards. The motivation of this paper is to quantitatively analyze MSC in an initial stage, particularly for lithium-ion batteries. To verify the feasibility of the proposed method, an equivalent MSC experiment is carried out. Based on a cell difference model, the cell state of charge (SOC) differences with the mean SOC for a battery pack are estimated by extended Kalman filter. The evaluated SOC difference can track the actual value well. Furthermore, an MSC diagnostic method is developed by employing recursive least squares filter. The method is demonstrated to examine the short-circuit resistance accurately. The results also show that the proposed method requires low computational load for the SOC difference and short-circuit resistance diagnosis. [ABSTRACT FROM AUTHOR]

One of the key battery performances which can be described as the resistance characteristic is the cell voltage response with dynamic currents, and it requires synchronous cell voltages and current. But due to inevitable network latency in modular battery management system (BMS) with controller area network (CAN), cell voltages and current are usually asynchronous. We firstly analyze the sampling and the storage process of battery signals to study the asynchronous mechanism in BMS. We develop an on-line synchronization method using a “global clock” from the master controller to decrease the time delay as much as possible. And we further propose a model based sync method based on the frequency division equivalent circuit model (FDECM) for the battery pack. The low frequency cell difference model is used to identify cell “resistances difference”, and then the optimal time compensation for cell voltages is obtained when the minimum mean absolute derivative (MAD) value of identified resistance differences is reached according to the low frequency characteristic of cell “resistances difference”. The proposed methods are verified by simulation and experiment. The current and cell voltages in the data logger of BMS can be synchronized when the optimal compensation time is applied respectively for each cell. The data after synchronization can meet the requirements of further data analysis and processing, which is of great significance to enhance and improve the control strategy of BMS. [ABSTRACT FROM AUTHOR]

Massive data storage is an advanced function in a fully functional battery management system (BMS). Reducing the recording signal length undoubtedly saves the precious memory space for BMS. And it also reduces the network and computation loads. However, it leads to a side effect that the trend of signal distortion is enhanced. The optimal recording frequency in practice should be as low as possible on the condition that little signal distortion happens. This paper presents a novel method which uses a multi-frequency recording technology that cooperates two approaches according to the signal dynamics. A flexible recording frequency method is applied for stationary signals which only records signals when their values are changed. While for dynamic signals, the most dynamic period is found using discrete wavelet transformation (DWT) and further analyzed by fast Fourier transformation (FFT). By comparing two recording signal indicators for four different recording frequencies, we conclude that recording at 1 Hz is not qualified for the cell voltage and current during the dynamic period in our system due to the high dynamic performance of the vehicle. In the demonstrated vehicle, only by increasing the recording frequency to at least 2 Hz, can the accuracy of the recorded cell voltage achieve the level the same as the measurement accuracy in engineering. And we also verify that when the recording frequency is reduced to the optimal frequency compared to the high frequency recorded original signals, the accuracy of the SOC estimation is not influenced. [ABSTRACT FROM AUTHOR]

Power capability of lithium-ion batteries is strongly correlated with electric vehicle's accelerating and braking performance. However, the estimate of state-of-power relies highly on battery models, whose accuracy usually increases with the complexity. We here propose a simple and accurate frequency-dependent integer-order model for battery state-of-power estimation. First, a random search-pseudo gradient descent algorithm is proposed to identify the parameters of our model from electrochemical impedance spectroscopy in the frequency domain. Then, the proposed model is mathematically derived in the time domain. Next, two strategies are developed to estimate battery state-of-power under different constraints — using particle swarm optimization and direct inversion algorithms. Finally, our method is experimentally verified: the proposed frequency-dependent model shares similar complexity compared with the conventional integer-order model, while its accuracy is competitive to that of the fractional-order model. With such a simple and accurate model, our state-of-power estimation error is 90% smaller than that based on the conventional integer order model, and the computational time is 99.8% lower than that corresponds to the fractional-order model. Since the proposed method is developed upon the conventional integer-order model, it has a strong potential for real-life application and can be easily integrated into the existing battery management systems. • Replace the fractional-order model with the Frequency-dependent integer- order model. • Models are derived and analyzed in both time and frequency domains. • The frequency-dependent model supports direct state-of-power calculations. • High accuracy and low computational burden. • Algorithms are verified with experimental-based state-of-power ground truth. [ABSTRACT FROM AUTHOR]

Due to the incompleteness of charging data, the voltage step caused by fast charging conditions and sampling accuracy of the battery management system, the conventional mechanism model is not applicable to the aging mechanism analysis and capacity estimation of electric vehicle batteries. Therefore, this study applies support vector regression to achieve the actual charging condition equivalence based on the variable operating conditions charging data of electric vehicles. The aging parameters and open circuit voltage reconstruction based on the dual-tank model are applied to obtaining the aging state and the capacity of cells. The capacity of the battery pack is calculated by the pack formation theory. The maximum error of the aging parameters obtained by the multiple stage constant current is 5.572% compared with the 1/20 C (C is the charge/discharge current rate unit) constant current charging of the experimental battery. As to the maximum relative error of cell capacity estimation based on vehicle data is 0.99%, and battery pack capacity estimation is 0.86%. The method proposed in this paper is not only able to quantitatively analyze the dominant factors of battery capacity decay, but also achieves high accuracy capacity estimation of the vehicle battery pack and its individual cells. • Support vector regression model is used to process operating voltage data of vehicle battery. • The effect of current switching on voltage is eliminated in the multi-stage constant current charging process of electric vehicles. • The dominant decay mode of the vehicle battery is analyzed based on the mechanism model. • The dual-tank model is successfully applied to the aging analysis of vehicle batteries. • The capacity of the vehicle battery pack and its cells are accurately estimated. [ABSTRACT FROM AUTHOR]

Accurate estimation of the capacity of a battery pack is essential for the battery management system (BMS) in electric vehicles. The SOCs and capacities of individual cells are the prerequisites for accurately estimating the capacity of a battery pack. This paper proposes quantitative analysis on how the estimation errors of individual cells’ SOCs and capacities influence the estimation error of the battery pack capacity using an approach named Capacity–Quantity diagram (C–Q diagram). The analysis concludes that the estimation error of cell SOC has more influence on the estimation error of pack capacity than the estimation error of cell capacity does. The theoretical analysis is further validated by an experiment using six NCM batteries connected in series with different initial SOC variations. The results help to guide the determination of specifications, e.g., the estimation error of the SOC and that of the capacity, during the design process of a BMS. [ABSTRACT FROM AUTHOR]

• A novel internal short circuit (ISC) diagnosis method based on cloud data is proposed. • The proposed diagnosis method is based on mean-normalization. • Experimental data and cloud data of real vehicle are used for validation. • DBSCAN algorithm realizes the automatic identification of ISC cells. To promote electrified transportation and achieve carbon neutrality, Li-ion batteries with excellent energy storage performance are widely adopted in electric vehicles (EVs). However, internal short circuit (ISC) of batteries is a serious safety hazard of EVs. ISC diagnosis using the powerful storage and computing capabilities of the cloud platform is a promising method to enhance the safety performance of battery packs in EVs, but the recording period of cloud data is larger than that of battery management systems in EVs, which brings great challenges to the existing ISC diagnosis methods. In this paper, the new mean-normalization is employed to amplify the voltage characteristics of the ISC cell in the battery pack. The adaptive Kalman filter algorithm is deployed to filter the mean-normalization values to obtain the vivid ISC features. Furthermore, the density-based spatial clustering of applications with noise algorithm is exploited to automatically detect and locate the ISC cell. Even with the 40 s sparse data in the real vehicle, the ISC cell also can be timely diagnosed within 2.51 h. [ABSTRACT FROM AUTHOR]

Early internal short circuit (ISC) diagnosis is critical for a battery management system (BMS) to prevent the thermal runaway of lithium-ion batteries. However, it is difficult to be diagnosed owing to few obvious electric and thermoelectric characteristics in the early stage of ISC. In this study, a novel ISC diagnosis method based on the incremental capacity (IC) curves is proposed. Different charging rates on four ISC situations are carried out on a cell to verify the proposed method. The leakage current of the ISC battery can be obtained by the area difference between the normal cell and the ISC cell, and it can be converted into the ISC resistance. The experiments of different initial charging states of charge (SOC) in a series-connected battery pack are conducted to verify the method in the real EVs working environment. The diagnosis results of the battery cell and battery pack indicate the proposed method is feasible and effective to quantitatively diagnose the ISC. • A novel internal short circuit diagnosis method based on the peak area of the IC curve is proposed. • The battery cell and series-connected battery pack charging experiments are designed and conducted. • The influence of internal short circuit on IC curves is revealed. • The experimental results show that the proposed approach is feasible and effective. [ABSTRACT FROM AUTHOR]

To avert the degradation information island brought by a single model for state estimation of lithium-ion batteries (LIBs), multi-model integration is well worth being extended for health evaluation. A cloud-edge collaboration strategy that integrates multi-model adaptation and machine learning is proposed for battery capacity prediction in lifespan. In this respect, multiple individual algorithms are promoted relying on online data from battery management system (BMS) and induced ordered weighted average (IOWA) operator is introduced for joint capacity estimation. Taking the massive data storage and computing power into account, the operation of the neural network optimized by genetic algorithm (GA) is elevated to the big data platform for strong robustness and accurate prediction, accompanied by the historical state message from BMS. With the validation of NASA battery aging data, the error of the collaborative prediction strategy is kept within 5%, and the feasibility of cloud-edge collaboration is confirmed for future battery management. • A novel "Induced ordered weighted average operator" is applied to capacity estimation. • Sequential cloud-edge collaboration strategy that integrates multi-model adaptation and machine learning is proposed. • Simulations relying on experiments confirm ideal accuracy and convergence rate. • Excellent anti-interference ability against system uncertainties is achieved. [ABSTRACT FROM AUTHOR]