Selective recovery of lithium from spent lithium iron phosphate batteries using oxidation pressure sulfuric acid leaching system

Oxidation pressure leaching was proposed to selectively dissolve Li from spent LiFePO_(4) batteries in a stoichiometric sulfuric acid solution.Using O_(2) as an oxidant and stoichiometric sulfuric acid as leaching agent,above 97% of Li was leached into the solution,whereas more than 99% of Fe remained in the leaching residue,enabling a relatively low cost for one-step separation of Li and Fe.And then,by adjusting the pH of leachate,above 95% of Li was recovered in the form of the Li_(3)PO_(4) product through iron removal and chemical precipitation of phosphate.

Synthesis of Low-cost LiFePO_4 from Li_2CO_3 by a Novel Hydrothermal Method and Investigation on the Reaction Mechanism

Phspho-olivine Li Fe PO4 was synthesized from the relatively insoluble lithium source Li2CO3, proper iron and phosphorus sources（n（Li）：n（Fe）：n（P）=1：1：1） by a novel hydrothermal method. Afterwards, the optimal sample was mixed with glucose and two-step calcinated（500 ℃ and 750 ℃） under high-purity N2 to obtain the Li Fe PO4/C composite. The resultant samples were characterized by X-ray diffraction（XRD）, atomic absorption spectrometry（AAS）, scanning electron microscops（SEM）, transmission electron microscopy（TEM）, energy dispersive spectrometry（EDS）, elementary analysis（EA） and electrochemical tests. The results show that the optimal reaction condition is to set the reactant concentration at 0.5 mol·L^-1, the reaction temperature at 180 ℃ for 16 h duration. During the reaction course, an intermediate product NH4 Fe PO4·H2O was first synthesized, and then it reacted with Li＋ to form Li Fe PO4. The optimized Li Fe PO4 sample with an average particle size（300 to 500 nm） and regular morphology exhibits a relatively high discharge capacity of 84.95 m Ah· g^-1 at the first charge-discharge cycle（0.1C, 1C=170 m A·g^-1）. Moreover, the prepared Li Fe PO4/C composite shows a high discharge capacity of 154.3 m Ah·g^-1 at 0.1C and 128.2 m Ah·g^-1 even at 5C. Besides it has good reversibility and stability in CV test.

Estimation of Battery State of Health Using Back Propagation Neural Network

100 pieces of 26650-type Lithium iron phosphate（LiFePO4） batteries cycled with a fixed charge and discharge rate are tested, and the influence of the battery internal resistance and the instantaneous voltage drop at the start of discharge on the state of health（SOH） is discussed. A back propagation（BP） neural network model using additional momentum is built up to estimate the state of health of Li-ion batteries. The additional 10 pieces are used to verify the feasibility of the proposed method. The results show that the neural network prediction model have a higher accuracy and can be embedded into battery management system（BMS） to estimate SOH of LiFePO4 Li-ion batteries.

A Novel Real-Time State-of-Health and State-of-Charge Co-Estimation Method for LiFePO_4 Battery

The state of charge （SOC） and state of health （SOH） are two of the most important parameters of Li-ion batteries in industrial production and in practical applications. The real-time estimation for these two parameters is crucial to realize a safe and reliable battery application. However, this is a great problem for LiFePO4 batteries due to the large constant potential plateau in the charge/discharge process. Here we propose a combined SOC and SOH co-estimation method based on the experimental test under the simulating electric vehicle working condition. A first-order resistance-capacitance equivalent circuit is used to model the battery cell, and three parameter values, ohmic resistance （Rs）, parallel resistance （Rp） and parallel capacity （Cp）, are identified from a real-time experimental test. Finally we find that Rp and Cp could be utilized to make a judgement on the SOIl. More importantly, the linear relationship between Cp and the SOC is established to make the estimation of the SOC for the first time.

Modeling and Simulating a Battery for an Electric Vehicle Based on Modelica

Battery is the key technology to the development of electric vehicles,and most battery models are based on the electric vehicle simulation.In order to accurately study the performance of LiFePO4 batteries,an improved equivalent circuit model was established by analyzing the dynamic characteristics and contrasting different-order models of the battery.Compared to the traditional model,the impact of hysteresis voltage was considered,and the third-order resistance-capacitance(RC)network was introduced to better simulate internal battery polarization.The electromotive force,resistance,capacitance and other parameters were calibrated through battery charge and discharge experiments.This model was built by using Modelica,a modeling language for object-oriented multi-domain physical systems.MWorks was used to implement the cycle conditions and vehicle simulation.The results show that the third-order RC battery model with hysteretic voltage well reflects the dynamics of a LiFePO4 battery.The difference between the simulated and measured voltages is small,with a maximum error of 1.78%,average error of 0.23%.The validity and feasibility of the model are verified.It can be used in unified modeling and simulation of subsequent multi-domain systems of electric vehicles.