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This article utilizes the research method of the Life Cycle Assessment (LCA) to scrutinize Lithium Iron Phosphate (LFP) batteries and Ternary Lithium (NCM) batteries. It develops life cycle models representing the material, energy, and emission flows for power batteries, exploring the environmental impact and energy efficiency throughout the life cycles of these batteries. The life cycle assessment results of different power battery recycling process scenarios are compared and analyzed. This study focuses on retired LFP batteries to assess the environmental and energy efficiency during the cascade utilization stage, based on a 50% Single-Cell Conversion Rate (CCR). The findings of the research reveal that, in terms of resource depletion and environmental emission potential, LFP batteries exhibit lower impacts compared to NCM batteries. The use of hydrometallurgy in recovering LFP power batteries leads to minimal life cycle resource consumption and environmental emission potential. During the cascade utilization stage of LFP batteries, significant benefits are noted, including a 76% reduction in mineral resource depletion (ADP e) and an 83% reduction in fossil energy depletion (ADP f), alongside notable reductions in various environmental impact factors. Simultaneously, considering the sensitivity of life cycle assessment indicators and their benefit percentages to different CCRs, it is observed that ODP exhibits the highest sensitivity to CCR changes, while evaluation indicators such as HTP, AP, and GWP show relatively lower sensitivity. This study can provide an effective reference for the establishment of an energy saving and emission reduction evaluation system of power batteries. [ABSTRACT FROM AUTHOR]

In order to improve the screening accuracy of decommissioned power batteries and reduce the regulation errors of batteries in the process of echelon utilization, a cascade batteries selection method based on confidence interval probability prediction is proposed. Firstly, according to the internal and external characteristic parameters, residual capacity, state of health (SOH) and other historical parameters, K-means clustering is used to divide static clusters of retired power batteries with similar parameters. Secondly, the bootstrap probability method is used to analyze the deviation subset of SOH estimation in echelon utilization process, and the probability interval density function of SOH subset is established. Finally, further optimization grouping is carried out according to the predicted SOH characteristics. By comparative analysis, the influence of confidence interval estimation on the screening accuracy is determined, and the effectiveness of the proposed screening method is determined. The consistency of dynamic characteristics is improved while the static parameters are similar in the screening process, which lays a foundation for accurate regulation of retired power battery echelon utilization. [ABSTRACT FROM AUTHOR]

Electricity powered vehicles/Electric vehicles using renewable energy are becoming more and more popular, since they have become an effective way to solve energy shortage, and environmental pollution. Battery electric vehicles with zero emission characteristics are being developed on a large scale. With the scale of electric vehicles, electric vehicles with controllable load and vehicle-to-grid functions can optimize the use of renewable energy in the grid. This puts forward the higher request to the battery performance. The energy density of the batteries and renewable energy conversion efficiency have greatly also affected the application of electric vehicles. This paper presents an overview of the research for improving lithium-ion battery energy storage density, safety, and renewable energy conversion efficiency. It is discussed that is the application of the integration technology, new power semiconductors and multi-speed transmissions in improving the electromechanical energy conversion efficiency, and the issues and challenges of increasing battery energy density. In order to improve the safety, it is analyzed how to improve the estimation accuracy and temperature control performance of the battery management system. As the demand for fast charging and renewable energy of electric vehicles increases, the latest developments and technical challenges of on-board rapid charging technology are introduced. • We present an overview on energy storage density and energy conversion efficiency of electricity powered vehicles. • Methods to increase the energy storage density of electricity powered vehicles are proposed. • Efficient inverter and multi-speed transmission improving renewable energy conversion efficiency are discussed. • The integration improves the energy efficiency of electricity powered vehicles. • Integrated fast charging and coordinated charging for renewable energy are discussed. [ABSTRACT FROM AUTHOR]

The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. Here, we used a unique hybrid approach to enable complex SOH estimations. The approach hybridizes the Delphi method known for its simplicity and effectiveness in applying weighting factors for complicated decision-making and the grey relational grade analysis (GRGA) for multi-factor optimization. Six critical factors were used in the consideration for SOH estimation: peak power at 30% state-of-charge (SOC), capacity, the voltage drop at 30% SOC with a C/3 pulse, the temperature rises at the end of discharge and charge at 1C; respectively, and the open circuit voltage at the end of charge after 1-h rest. The weighting of these factors for SOH estimation was scored by the ‘experts’ in the Delphi method, indicating the influencing power of each factor on SOH. The parameters for these factors expressing the battery state variations are optimized by GRGA. Eight battery cells were used to illustrate the principle and methodology to estimate the SOH by this hybrid approach, and the results were compared with those based on capacity and power capability. The contrast among different SOH estimations is discussed. [ABSTRACT FROM AUTHOR]

With the popularity of electric vehicles, the ever-increasing demand for high-capacity batteries highlights the need for monitoring the health status of batteries. In this article, we proposed a magnetic imaging technique (MIT) to investigate the health status of power batteries nondestructively. This technique is based on a magnetic sensor array, which consists of a 16-channel high-performance magnetoelectric sensor, and the noise equivalent magnetic induction (NEB) of each channel reaches 3–5 pT/Hz1/2@10 Hz. The distribution of the magnetic field is imaged by scanning the magnetic field variation of different positions on the surface. Therefore, the areas of magnetic anomalies are identified by distinguishing different magnetic field abnormal results. and it may be possible to classify the battery failure, so as to put forward suggestions on the use of the battery. This magnetic imaging method expands the application field of this high-performance magnetoelectric sensor and contributes to the battery's safety monitoring. Meanwhile, it may also act as an important role in other nondestructive testing fields. [ABSTRACT FROM AUTHOR]

Abstract: LiMn1.5Ni0.5O4 has been synthesized by an ultrasonic-assisted sol–gel method. The precursor is heat treated at a series of temperatures from 650°C to 1000°C. The structure and physical–chemical properties of the as-prepared powder are investigated by powder X-ray diffraction (XRD), scanning electron microscopy (SEM), cyclic voltammetry (CV) thermal gravimetric and galvanostatic charge–discharge tests in detail. As temperature goes up, the particle size increases, the reactivity of the material in 4V region becomes more obvious, the structure of the samples become more stable and it behaves optimal electrochemical properties as the material is heat treated at 850°C. When it is used as cathode active material in a lithium battery, it delivers high initial capacity of 134.5mAhg−1 (corresponding to 91.7% of the theoretical capacity), and high rate discharge capability, e.g., 133.4, 120.6, 111.4, 103.2 and 99.3mAhg−1 as discharged at 0.5, 1, 5, 10 and 15C (1C=148mAg−1)-rates, respectively. It also shows satisfactory capacity retention even at high rate of 5C, which is about 99.83% of the capacity retention per cycle. [Copyright &y& Elsevier]

In order to improve the convergence time and stabilization accuracy of the real-time state estimation of the power batteries for electric vehicles, a fuzzy unscented Kalman filtering algorithm (F-UKF) of a new type is proposed in this paper, with an improved second-order resistor-capacitor (RC) equivalent circuit model established and an online parameter identification used by Bayes. Ohmic resistance is treated as a battery state of health (SOH) characteristic parameter, F-UKF algorithms are used for the joint estimation of battery state of charge (SOC) and SOH. The experimental data obtained from the ITS5300-based battery test platform are adopted for the simulation verification under discharge conditions with constant-current pulses and urban dynamometer driving schedule (UDDS) conditions in the MATLAB environment. The experimental results show that the F-UKF algorithm is insensitive to the initial value of the SOC under discharge conditions with constant-current pulses, and the SOC and SOH estimation accuracy under UDDS conditions reaches 1.76% and 1.61%, respectively, with the corresponding convergence time of 120 and 140 s, which proves the superiority of the joint estimation algorithm. [ABSTRACT FROM AUTHOR]