Edge computing for vehicle battery management:Cloud-based online state estimation

The adoption of electric vehicles (EVs), including battery EVs and hybrid EVs, makes it possible to reduce fossil fuel consumption and greenhouse gas emission. However, an accurate battery model and an effective battery management system should be established to enable this benefit. This paper proposes a novel cloud-assisted online battery management method based on artificial intelligence and edge computing technologies. Integration of cloud computation and big data resources into real-time vehicle battery management is realized by establishing a novel cloud-edge battery management system (CEBMS). A deep learning algorithm-based cloud data mining and battery modeling method is developed to estimate the voltage and energy state of the battery. The accuracy of the established cloud battery model outperforms the onboard battery management system by utilizing multi-sources information from different EVs. Meanwhile, a cloud-assisted battery management method is established at edge nodes in the onboard battery management unit to realize real-time state estimation locally. By using precise battery state estimation provided by the cloud platform, vehicle battery model accuracy can be significantly improved. The performance of the proposed battery management method is verified by a vehicle big data platform and battery pack experimental test bench. Experimental results justify the effectiveness of the proposed method in battery state estimation, which can help the EVs use and manage the battery more effectively.</p

Physics-based model predictive control for power capability estimation of lithium-ion batteries

The power capability of a lithium-ion battery signifies its capacity to continuously supply or absorb energy within a given time period. For an electrified vehicle, knowing this information is critical to determining control strategies such as acceleration, power split, and regenerative braking. Unfortunately, such an indicator cannot be directly measured and is usually challenging to be inferred for today\u27s high-energy type of batteries with thicker electrodes. In this work, we propose a novel physics-based battery power capability estimation method to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power, but also the internal constraints on lithium plating and thermal runaway, can be readily taken into account. The online estimation of maximum power is accomplished by formulating and solving a constrained nonlinear optimization problem. Due to the relatively high system order, high model nonlinearity, and long prediction horizon, a scheme based on multistep nonlinear model predictive control is found to be computationally affordable and accurate

Multi-Level Data-Driven Battery Management: From Internal Sensing to Big Data Utilization

Battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of data-driven battery management from a multi-level perspective. The widely-explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multi-dimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for enhanced battery management is further overviewed. This belongs to the upper and the macroscopic level of the data-driven BMS framework. With this endeavor, we aim to motivate new insights into the future development of next-generation data-driven battery management

Electrochemical Model-Based Fast Charging: Physical Constraint-Triggered PI Control

This paper proposes a new fast charging strategy for lithium-ion (Li-ion) batteries. The approach relies on an experimentally validated high-fidelity model describing battery electrochemical and thermal dynamics that determine the fast charging capability. Such a high-dimensional nonlinear dynamic model can be intractable to compute in real-time if it is fused with the extended Kalman filter or the unscented Kalman filter that is commonly used in the community of battery management. To significantly save computational efforts and achieve rapid convergence, the ensemble transform Kalman filter (ETKF) is selected and tailored to estimate the nonuniform Li-ion battery states. Then, a health- and safety-aware charging protocol is proposed based on successively applied proportional-integral (PI) control actions. The controller regulates charging rates using online battery state information and the imposed constraints, in which each PI control action automatically comes into play when its corresponding constraint is triggered. The proposed physical constraint-triggered PI charging control strategy with the ETKF is evaluated and compared with several prevalent alternatives. It shows that the derived controller can achieve close to the optimal solution in terms of charging time and trajectory, as determined by a nonlinear model predictive controller, but at a drastically reduced computational cost

Embedded distributed temperature sensing enabled multi-state joint observation of smart lithium-ion battery

Accurate monitoring of the internal statuses are highly valuable for the management of lithium-ion battery (LIB). This paper proposes a thermal model-based method for multi-state joint observation, enabled by a novel smart battery design with embedded and distributed temperature sensor. In particular, a novel smart battery is designed by implanting the distributed fiber optical sensor (DFOS) internally and externally. This promises a real-time distributed measurement of LIB internal and surface temperature with a high space resolution. Following this endeavor, a low-order joint observer is proposed to co-estimate the thermal parameters, heat generation rate, state of charge, and maximum capacity. Experimental results disclose that the smart battery has space-resolved self-monitoring capability with high reproducibility. With the new sensing data, the heat generation rate, state of charge, and maximum capacity of LIB can be observed precisely in real time. The proposed method validates to outperform the commonly-used electrical model-based method regarding the accuracy and the robustness to battery aging

Up-regulated serum lactate dehydrogenase could become a poor prognostic marker in patients with bladder cancer by an evidence-based analysis of 2,182 patients

BackgroundA growing number of studies have considered serum lactate dehydrogenase (LDH) as an indicator of bladder cancer (BC) prognosis. However, a meta-analysis of the serum LDH’s influence on BC prognosis is still missing.MethodsPubMed, EMBASE, Web of Science and Cochrane Library were exhaustively searched for studies comparing oncological outcomes between high-LDH and low-LDH patients. Standard cumulative analyses using hazard ratios (HR) with 95% confidence intervals (CI) were performed using Review Manager (version 5.3) for overall survival (OS) in patients with BC.ResultsSix studies involving 2,182 patients were selected according to predefined eligibility criteria. The results showed that serum LDH level was significantly associated with OS (HR = 1.86, 95%CI = 1.54-2.25, p&lt;0.0001) in BC. Sensitivity analysis showed the stability of the results. Subgroup analysis revealed that the levels of serum LDH had a significant impact on the OS of BC patients among different groups including publication time, research country, sample size, tumor stage, LDH cut-off value, therapy and follow-up time (all HR&gt;1 and p&lt;0.05), revealing that the ability of serum LDH is not affected by other factors.ConclusionOur findings indicated that a high level of serum LDH was associated with inferior OS in patients with BC. However, caution must be taken before recommendations are given because this interpretation is based upon very few clinical studies and a small sample

Dynamic evolution and numerical analysis of rock deformation under impact failure based on corner correlation method

To fulfill the requirement for deformation measurement within the failure area during rock failure, a newly developed digital image algorithm, known as the corner correlation method, was implemented. A corner correlation measurement system was established by utilizing a Split Hopkinson Pressure Bar (SHPB) and a high-speed camera. The study focused on monitoring the deformation and failure characteristics of sandstone samples under dynamic loading. The results show that the corner correlation method has its unique advantages in rock dynamic mechanics experiments, and can obtain the deformation of the failure region during the rock failure process. Specifically, the initiation, extension, and contraction processes of surface cracks on sandstone were examined. Parameters such as crack width, width propagation rate, and extension shrinkage rate were measured. The entire crack development process was analyzed, including crack width, crack initiation point, crack extension and contraction trajectory, elongation and contraction velocity, width expansion rate, and longitudinal crack penetration, which were obtained at any given time on the surface of the rock sample. Additionally, the attenuation law of stress, characterized by an exponential decay of the stress peak value, was obtained through numerical simulation using a similar model