Electric Vehicle Power Battery Research Articles

Preparation of LiFePO4/Li3V2(PO4)3/C composite cathode materials and their electrochemical performance analysis

In this study, 9LiFePO4·Li3V2(PO4)3/C (LFVP-91/C) composite cathode materials (molar ratio of LiFePO4:Li3V2(PO4)3 = 9:1) were prepared using a sol–gel (SG) technique and spray drying (SP) technique, followed by a calcination process at 650–750 °C for 10 h. Oxalic acid and citric acid were used as chelating agents, l-ascorbic acid (AA) was used as a reducing agent, and sucrose (SUC) was used as a carbon source. For improving the electrochemical properties of LFVP-91/C, different conductive carbon materials were used as carbon additives, including graphene oxide (GO), graphene nanosheet (GNS), and carbon nanotube (CNT). The experimental results show that the best discharge capacities of as-prepared SG-LFVP-91/C are approximately 151.1 mAh/g at 0.1C/0.1C, 115.2 mAh/g at 0.2C/5C, and 98.2 mAh/g at 0.2C/10C. The capacity retention after 100 cycles at 1C/1C is 84.0%. After addition of GO, the best discharge capacity retention increases to 91.66% after 100 cycles at 1C/1C. Moreover, after addition of GNS + CNT, the discharge capacity of SP-LFVP-91/C/GNS + CNT composite material increases to 160.1 mAh/g at 0.1C/0.1C, 129.3 mAh/g at 0.2C/5C, and 117.8 mAh/g at 0.2C/10C. No capacity fading is observed after 30 cycles at 0.1C/0.1C. The capacity retentions after 100 cycles and 500 cycles at 1C/1C increase to 97.58% and 85.26%, respectively. This study demonstrates that the prepared SP-LFVP-91/C/GNS + CNT composite cathode materials are excellent candidates for use in lithium-ion power batteries for electric vehicles.

Life cycle assessment of lithium nickel cobalt manganese oxide (NCM) batteries for electric passenger vehicles

This study evaluated and quantified the life cycle environmental impacts of lithium-ion power batteries (LIBs) for passenger electric vehicles to identify key stages that contribute to the overall environmental burden and to find ways to reduce this burden effectively. Primary data for the assessment were collected onsite from the two Chinese leading LIB suppliers, two leading cathode material producers and two battery recycling corporations from 2017 to 2019. Six environmental impact categories, including primary energy demand (PED), global warming potential (GWP), acidification potential (AP), photochemical oxidant creation potential (POCP), eutrophication potential (EP) and human toxicity potential (HTP), were considered in accordance with the ISO 14040/14044 standards.The results indicate that material preparation stage is the largest contributor to the LIB’s life cycle PED, GWP, AP, POCP, EP and HTP, with the cathode active material, wrought aluminum and electrolytes as the predominant contributors. In the production stage, vacuum drying and coating and drying are the two main processes for all the six impact categories. In the end-of-life stage, waste LIBs recycling could largely reduce the life cycle POCP and HTP.Sensitivity analysis results depict that replacing NCM 622 by NCM 811 as the cathode active material could increase all the six environmental impacts. We hope this study is helpful to reduce the uncertainties associated with the life cycle assessment of LIBs in existing literatures and to identify opportunities to improve the environmental performance of LIBs within the whole life cycle.

Highly concentrated dual-anion electrolyte for non-flammable high-voltage Li-metal batteries

With a higher demand towards the energy density of power batteries for electric vehicles, the Li-metal batteries (LMBs) have staged a comeback in recent years. However, the low cycle efficiency and safety issues associated with Li dendrite growth and electrolyte combustion severely limit their practical applications. Here we prepare a highly concentrated TFSI-FSI dual-anion electrolyte (HCDE) based on lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), N-ethyl-N-methyl-pyrrolidinium bis(fluorosulfonyl)imide ([C2mpyr][FSI]), and dimethyl carbonate (DMC). The co-solvent of [C2mpyr][FSI] and DMC exhibits promoted ionic conductivity, low viscosity and non-flammability. In HCDE, the TFSI-FSI dual-anion is preferentially decomposed to obtain a robust inorganic solid electrolyte interphase (SEI), which induces a dense and uniform Li deposition on the Li-metal surface and effectively suppresses Li dendrite growth. Meanwhile, it also enables good suppression of Al current collector corrosion and stable cycling performance at high-voltage owing to the high salt-to-solvent ratio. The LFP (LiFePO4)/Li cells steadily deliver a long-term cycle life and obtain ~80% capacity retention with an extremely high CE (~99.9%) after 500 cycles, and the NCM523 (LiNi0.5Co0.2Mn0.3O2)/Li cells retain ~95% of their original capacity after 100 cycles at a high cut-off voltage of 4.5 ​V. According to X-ray photoelectron spectroscopy (XPS) and molecular dynamic (MD)-density functional theory (DFT) simulations, the forming mechanism of SEI layer is suggested.

A Normal Wiggly Pythagorean Hesitant Fuzzy Bidirectional Projection Method and Its Application in EV Power Battery Recycling Mode Selection

As a useful mathematical tool to solve the multi-attribute decision-making (MADM) problems, Pythagorean hesitant fuzzy set (PHFS) permits decision makers (DMs) to give several evaluation values in membership and non-membership degrees. Given that much cognitive preferences of DMs may be hidden in the original evaluation information and cannot be fully expressed, we propose the normal wiggly Pythagorean hesitant fuzzy set (NWPHFS) to comprehensively mine the uncertain preferences from original Pythagorean hesitant fuzzy information. NWPHFS can help DMs more accurately express their potential valuable preferences for objects while giving the original evaluation information. We put forward some operational laws and comparison rules of NWPHFS, then present the formulas of projection and bidirectional projection measures between normal wiggly Pythagorean hesitant fuzzy elements (NWPHFEs). Furthermore, we propose the normal wiggly Pythagorean hesitant fuzzy bidirectional projection (NWPHFBP) method and its specific application steps to solve MADM problems. The proposed approach can accurately reflect the projection relationship between the alternatives evaluated and ideal solutions by the NWPHF information. Through constructing a reasonable criteria system, we apply the NWPHFBP method to the issue of electric vehicle (EV) power battery recycling mode selection. Finally, we conduct the sensitivity analysis and verify the effectiveness of the proposed method by comparisons with other approaches.

Fractional calculus based modeling of open circuit voltage of lithium-ion batteries for electric vehicles

Abstract Open circuit voltage (OCV) and state of charge (SOC) are key characteristic parameters of power batteries for electric vehicles. The OCV-SOC model is an integral of the design of the effective model and SOC estimation of the battery. However, there is a strong nonlinear relationship between OCV and SOC of lithium-ion batteries (LIBs). A novel OCV-SOC model of LIBs based on fractional calculus was proposed for the first time in this paper. The root mean squared error and the coefficient of determination were compared with the benchmarking models under a relatively fair condition. The sensitivity of the accuracy of the OCV-SOC models to temperature, numbers of data points, and SOC regions, were verified by the experimental results respectively, which showed that the proposed OCV-SOC model has good performance under different conditions, satisfying the accuracy requirements for modeling and state estimation of the battery in battery management systems.

(Invited) Considerations of Ionic Charge Carriers for Aqueous Rechargeable Batteries

There is a pressing need for new battery technologies in two key areas: high-energy power batteries for electric vehicles, and large-scale storage batteries to buffer the output from the renewable-but-intermittent solar and wind power generation. For storage batteries, the goal is to minimize the levelized energy cost over the devices’ lifetime. This means that electrode materials containing rare and expensive elements should be avoided. Secondly, the cycle life of storage batteries must be excellent — ideally over 10,000 cycles. Thirdly, these batteries should be quickly rechargeable to store, for example, energy from wind farms. Most importantly, storage batteries should be intrinsically safe, i.e., nonflammable. To meet this list of requirements, batteries with aqueous electrolytes relish several distinct advantages. Aqueous electrolytes are cheaper and safer. The simplicity of an aqueous chemistry environment may facilitate long battery longevity, and its high conductivity brings an innate power advantage. In battery chemistry design, one of the most important considerations is the choice of working charge carriers. To date, a majority of battery technologies rely on metal-ion charge carriers. Surprisingly, non-metal cations, particularly proton-containing cations, i.e., H+, H3O+,1 and NH4 +,1,2, have received exceedingly little attention. The simplest form of hydrogen cation, a single proton, is nearly “invisible” with a measured radius of ~0.89 fm or ~2.1 fm, using muon or e- spectroscopy, respectively. Due to the negligible strain of hosting protons, the rate capability and cycle life of proton batteries have the potential to be far superior to those of existing batteries. In this talk, I will introduce some of our new results, experimental and computational, on the storage of new charge carriers in battery chemistry for grid-storage purposes, particularly related to the Grotthuss mechanism3 and the ion/electrode interactions. I will compare different charge carriers in terms of their correlations to electrochemical properties, such as capacity fading, polarization, and operation potentials.

SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

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.

Electric Vehicle Battery Simulation System for Mobile Field Test of Off-Board Charger

An electric vehicle power battery simulation system simulating different power battery packs for the field test of the off-board charger is designed, which can be used to test the performance of an off-board charger. Specifically, the improved power battery model is combined with the improved lightweight charging load and the online estimation of the state of charge as well as the electromotive force of the battery model are used to adjust charging load parameters in real time to simulate the charging response. An acceleration coefficient is introduced into the traditional battery model to improve test efficiency, and the type, specification, temperature and voltage parameters of the battery can be set online according to the test requirements. An improved charging load scheme is proposed, in which a DC converter cascaded power battery pack of the mobile test vehicle is used to form a lightweight charging load with the mode of constant voltage, constant current, constant power and constant resistance and the ability to be adjusted continuously within the rated range. As a result, the size and weight of the charging load are reduced and the autonomous test of the off-board charger is realized. The performances of the proposed battery simulation system are validated through the various experimental results.

Research on adaptability of charging strategy for electric vehicle power battery

Charging is an important part in daily use and maintenance of power battery, while charging strategy will have different effects on performance and charging efficiency of power battery. In this paper, it use 18650 cell of Lithium nickel cobalt manganese oxide as the research object to explore effects of key parameters on battery charging and discharging capacity, charging time, charging temperature rise and energy efficiency in constant current charge, Constant current constant voltage charge, multi-stage constant current charge, intermittent charge Strategy with variable current, pulse current charge. At the same time, it compares differences of battery performance under different charging strategies. Based on data analysis, it selects three optimal charging strategies with significant advantages over conventional charging strategies. The results of this paper have a guiding significance for formulation of vehicle charging strategy.

State of Charge Estimation of a Lithium Ion Battery Based on Adaptive Kalman Filter Method for an Equivalent Circuit Model

Due to its accuracy, simplicity, and other advantages, the Kalman filter method is one of the common algorithms to estimate the state-of-charge (SOC) of batteries. However, this method still has its shortcomings. The Kalman filter method is an algorithm designed for linear systems and requires precise mathematical models. Lithium-ion batteries are not linear systems, so the establishment of the battery equivalent circuit model (ECM) is necessary for SOC estimation. In this paper, an adaptive Kalman filter method and the battery Thevenin equivalent circuit are combined to estimate the SOC of an electric vehicle power battery dynamically. Firstly, the equivalent circuit model is studied, and the battery model suitable for SOC estimation is established. Then, the parameters of the corresponding battery charge and the discharge experimental detection model are designed. Finally, the adaptive Kalman filter method is applied to the model in the unknown interference noise environment and is also adopted to estimate the SOC of the battery online. The simulation results show that the proposed method can correct the SOC estimation error caused by the model error in real time. The estimation accuracy of the proposed method is higher than that of the Kalman filter method. The adaptive Kalman filter method also has a correction effect on the initial value error, which is suitable for online SOC estimation of power batteries. The experiment under the BBDST (Beijing Bus Dynamic Stress Test) working condition fully proves that the proposed SOC estimation algorithm can hold the satisfactory accuracy even in complex situations.

Experimental investigation on electro-thermal characteristics of the commercial Li-ion battery

As the most commonly used power battery in electric vehicles, lithium-ion battery is sensitive to the operation temperature. The performance and lifespan of lithium-ion battery are strongly dependent on its working temperature. Various thermal management systems have been developed to maintain batteries’ operating temperature within an appropriate range. A properly designed thermal management system relies upon the understanding of battery’s heat-generation characteristics and the temperature effect on its discharging performance. In this research, an experimental investigation is carried out to study the electro-thermal characteristics of the commercial prismatic ternary lithium-ion battery under three thermal conditions. Constant temperature condition is built to explore the effect of the operating temperature, while near-adiabatic condition and natural-convection condition are employed to study the heat-generation characteristics of lithium-ion battery. The results show that the effect of operating temperature on discharge performance becomes more pronounced as the discharge rate increases. It is found that the surface temperature decreases when the battery discharges at small rates within the capacity ranged from 17Ah to 27Ah, demonstrating that the reversible heat makes up a large proportion of battery heat generation.

A Novel Method for Estimating State-of-Charge in Power Batteries for Electric Vehicles

Estimation of the state-of- charge (SOC) of power batteries has always been the focus of electric vehicle users’ criticism. Accurate SOC is beneficial for extending the mileage of electric vehicles and the life of the battery pack. The key to improving SOC accuracy is to establish its accurate model and combine it with an appropriate estimation algorithm. Based on characterization experiments related to SOC, this paper describes a second-order charge–discharge resistor–capacitor model that can accurately simulate external characteristics of the battery and identify them online. An improved adaptive unscented Kalman filter algorithm based on Sage–Husa is introduced to estimate SOC. The reliability of the algorithm is verified by building a MATLAB/Simulink simulation model. The results show that the improved algorithm displays increased robustness and can quickly converge to the true value; the steady-state error is also within a small range.

A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter

Accurate state of charge (SOC) estimation is a fundamental guarantee for effective development of lithium-ion power battery in electric vehicles. To improve the SOC estimation precision and robustness, a novel model-based estimation approach has been proposed. Fully giving consideration to the effect of measurement errors, the dynamic external electrical property of lithium-ion battery is approximated by a controlled auto-regressive and moving average (controlled ARMA)-based equivalent circuit model. An improved adaptive extended Kalman filter approach is developed for SOC estimation based on the multi-innovation principle. Meanwhile, the different weighting factor is added into each innovation to reduce cumulative influence of historical interference. Since the flat characteristic in OCV-SOC fitting curve enlarges the OCV-based SOC estimation error, a feedforward compensation method is introduced to reduce OCV identification error to improve OCV-based SOC estimation. The simulation and experimental results verify the validity of the proposed methodology over other estimation methods. Besides, simulated current noise is added to the condition data to prove the high precision and strong robustness of the proposed algorithm.

Optimization of the Heat Dissipation Structure and Temperature Distribution in an Electric Vehicle Power Battery

Optimization of the Heat Dissipation Structure and Temperature Distribution in an Electric Vehicle Power Battery

State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network

Accurately estimating the state of charge (SOC) of power batteries in electric vehicles is of great significance to the measurement of the endurance mileage of electric vehicles, as well as the safety protection of the power battery. In view of lithium ion batteries’ nonlinear relation between SOC estimation and current, voltage, and temperature, the improved Back Propagation (BP) neural network method is proposed to accurately estimate the SOC of power batteries. To address the inherent limitations of BP neural network, particle swarm algorithm is adopted to modify the relevant weighting coefficients. In this paper, the lithium iron phosphate battery (3.2 V/20 Amper-Hour) was studied. Charge and discharge experiments were conducted under a constant temperature. The training data were used to construct the surrogate model using the improved BP neural network. It is noted that the accuracy of the developed algorithm is increased by 2% as compared to that of conventional BP. Finally, an actual vehicle condition experiment was designed to further verify the accuracy of these two algorithms. The experimental results show that the improved algorithm is more suitable for real vehicle operating conditions than the traditional algorithm, and the estimation accuracy can meet the industry standards to a greater extent.

Estimation algorithm research for lithium battery SOC in electric vehicles based on adaptive unscented Kalman filter

The state of charge (SOC) is a significant part of energy management for electric vehicle power battery, which has important influence on the safe operation of power battery and the judgment of driver’s operation. Because the battery SOC cannot be measured directly, many researchers use various estimation methods to obtain accurate SOC values. But the SOC is affected by the temperature, current, cycle life and other time-varying nonlinear factors, which make difficult to construct prediction model. The key problem of battery SOC estimation is the change rule of battery capacity. The Peukert equation is a good method for calculating the battery capacity. The traditional Peukert equation without considering the influence of temperature, but the differences of temperature lead to changes in the constants n and K of the Peukert equations. In this paper, the Peukert equation based on temperature, current change and cycle life is established to estimate the battery capacity. And the battery model state equation is established for estimation and measurement equations of charge and discharge parameters $$ \left\{ {C_{\text{e}} ,R_{\text{e}} ,C_{\text{d}} ,R_{\text{d}} ,R_{0} } \right\} $$ and $$ V_{\text{OC}} $$ by using the ampere-hour method and the second-order RC model. And the dynamic estimation of charge state of battery is realized by AUKF. The results show that the accuracy of the lithium battery SOC estimation algorithm based on the temperature, current and cycle life of the modified Peukert equation is about 8% higher than that of the traditional KF ampere-hour method.

A New Method for State of Charge and Capacity Estimation of Lithium-Ion Battery Based on Dual Strong Tracking Adaptive H Infinity Filter

As one of the most important features representing the operating state of power battery in electric vehicles (EVs), state of charge (SOC) and capacity estimation is a crucial assessment index in battery management system (BMS). This paper presents a fusion method of SOC and capacity estimation with identified model parameters. The equivalent circuit model (ECM) parameters are obtained online by variable forgetting factor recursive least squares (VFFRLS), which is based on incremental ECM analysis to respond to the inconsistent rates of parameters variation. The independent open-circuit voltage (OCV) estimation way is designed to reduce the effect of mutual coupling between OCV and ECM parameters. Based on the identified ECM parameters and OCV, a dual adaptive H infinity filter (AHIF) combined with strong tracking filter (STF) is proposed to estimate battery SOC and capacity. A new quadratic function as capacity error compensation is introduced to represent the relationship between capacity and OCV. The adaptive strategy of the AHIF can adjust noise covariance and restricted factor, while the STF can regulate prior state covariance by adding suboptimum fading factor. The results of experiment and simulation show the merits of proposed approach in SOC and capacity estimation.

State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles

State of charge estimation is one of the most critical factors to solve the key issues of monitoring and safety concerns of an electric vehicle power battery. In this paper, a state of charge estimation approach using subtractive clustering based neuro-fuzzy system is presented and evaluated by the simulation experiments using advanced vehicle simulator in comparison with back propagation neural network and Elman neural networks. Input parameters to model the state of charge estimation approach using subtractive clustering based neuro-fuzzy system are current, temperature, actual power loss, available and requested power, cooling air temperature and battery thermal factor. Data collected from 10 different drive cycles are utilized for the training and testing stages of the state of charge estimation model. Experimental results illustrated that the proposed model exhibits sufficient accuracy and outperforms both neural network and Elman neural network based models. Thus, the proposed model under different drive cycles show remarkable advancement in state of charge estimation with high potential to overcome the drawbacks in traditional methods and therefore provides an alternative approach in state of charge estimation. In addition, a sensitivity analysis is also performed to determine the importance of each input parameter on output i.e. state of charge.

Experimental investigation on thermal management performance of electric vehicle power battery using composite phase change material

The development of electric vehicles (EVs) is beneficial to a cleaner environment. As the powertrain of EVs, power battery is limited obviously by its fast operating temperature rise and large temperature variation among different parts, so an efficient battery thermal management (BTM) system is desirable. In this paper, a battery thermal management system using kaolin/expanded graphite (EG)/paraffin composite was designed. By contrasting different thermal properties of ternary composites with various proportions, the most suitable composite with 10 wt% EG and 10 wt% kaolin was selected for the thermal management testing, which has a conductivity of larger than 6 W/(m·K) and did not shows any visible leakage after 30 min at 60 °C. In the thermal management test, the temperature of a single battery was effectively controlled under 45 °C even at the discharge rate of 4C, and the temperature difference of the single battery was restrained within 5 °C. Besides, the composite had reduced the temperature of the battery pack by 25.77% at 4C rate, and the temperature difference of the battery pack or each battery in the pack could be greatly controlled once the phase change occurred. In conclusion, the excellent temperature controlling performance of the new phase change material (PCM) was verified in this paper, and the importance of reasonable dosage distribution of PCM in battery pack was put forward.

Research on Multi-Parameter Evaluation of Electric Vehicle Power Battery Consistency Based on Principal Component Analysis

Electric vehicle power battery consistency is the key factor affecting the performance of power batteries. it is not scientific to evaluate the consistency of the battery depending on voltage or capacity. In this paper, multi-parameter evaluation method for battery consistency based on principal component analysis is proposed. Firstly, the characteristic parameters of battery consistency are analyzed, the principal component score can be used as the basis for evaluating the consistency of the battery. Then, the function that multi-parameter evaluation of battery consistency is established. Finally, battery balancing strategy based on fuzzy control is developed. The basic principle of fuzzy control is to fuzzy the input quantity based on expert knowledge, and the fuzzy control quantity is obtained by fuzzy control rule. The results are verified by test.