Search

Accurate state-of-charge (SoC) estimation is remarkably difficult due to nonlinear characteristics of batteries and complex application environment in electric vehicles (EVs), particularly low temperature and low SoC. In this paper, an improved battery model is first built using a feedforward neural network (FFNN) by introducing newly defined inputs. Based on the FFNN model and the extended Kalman filter algorithm, a FFNN-based SoC estimation method is designed, and its robustness is verified and discussed using the experimental data obtained at different temperatures. Finally, a hardware-in-loop test bench is built to further evaluate the real-time and generalization of the designed FFNN model. The results show that the SoC estimation can converge to the reference value at erroneous settings of an initial SoC error and an initial capacity error, and the SoC estimation errors can be stabilized within 2% after convergence, which applies to all the cases discussed in this paper, including low temperature and low SoC. This indicates that the FFNN-based method is an effective method to estimate SoC accurately in complex EV application environment. • Battery model is built using a feedforward neural network with newly defined inputs. • The SoC estimation method performs well even at low SoC and low temperature. • The proposed method can result in a good accuracy even using an inaccurate capacity. • The effectiveness of the method is verified by hardware-in-loop test. [ABSTRACT FROM AUTHOR]

Accurate estimation of the state-of-charge (SOC) is crucial for efficient and safe battery applications. However, existing SOC estimation methods fail to provide accurate SOC estimation for LiFePO 4 batteries that have a flat voltage-SOC relationship. The analysis of the voltage-SOC characteristics shows that the failure of the present model-based methods can be ascribed to their inability to simultaneously accommodate the differences in voltage characteristics between different open-circuit-voltage (OCV) ranges. To overcome this limitation, an adaptive recursive square root algorithm is used to online identify OCV and other battery model parameters. Then, the parameters of the extended Kalman filter are adaptively updated in different OCV ranges, which are distinguished based on the identified OCV. Additional filtering methods are employed to enhance the stability of the estimation. Large-scale experiments are conducted at different temperatures with various driving profiles for method validation. While conventional methods fail to converge, the proposed method ensures both high accuracy and stability, with a maximum absolute error of <2%. The viability of the proposed method is further verified using data collected from real battery systems. Our work lays a foundation for the reliable management of LiFePO 4 batteries in electric vehicles. • A novel state-of-charge estimation method for LiFePO 4 batteries is proposed. • State updating strategies are different in various open-circuit voltage ranges. • A series of methods are proposed to improve the robustness of the SOC estimation. [ABSTRACT FROM AUTHOR]

This paper developed an effective health indicator to indicate lithium-ion battery state of health and moving-window-based method to predict battery remaining useful life. The health indicator was extracted based on the partial charge voltage curve of cells. Battery remaining useful life was predicted using a linear aging model constructed based on the capacity data within a moving window, combined with Monte Carlo simulation to generate prediction uncertainties. Both the developed capacity estimation and remaining useful life prediction methods were implemented based on a real battery management system used in electric vehicles. Experimental data for cells tested at different current rates, including 1 and 2 C, and different temperatures, including 25 and 40 °C, were collected and used. The implementation results show that the capacity estimation errors were within 1.5%. During the last 20% of battery lifetime, the root-mean-square errors of remaining useful life predictions were within 20 cycles, and the 95% confidence intervals mainly cover about 20 cycles. [ABSTRACT FROM AUTHOR]

Battery models are the cornerstone to battery state of charge (SOC) estimation and battery management systems in electric vehicles. This paper proposes a novel fractional-order model for a battery, which considers both Butler–Volmer equation and fractional calculus of constant phase element. The structure characteristics of the proposed model are then analyzed, and a novel identification method, which combines least squares and nonlinear optimization algorithm, is proposed. The method is proven to be efficient and accurate. Based on the proposed model, a fractional-order unscented Kalman filter is developed to estimate SOC, while singular value decomposition is applied to tackle the nonlinearity of Butler–Volmer equation and fractional calculus of constant phase element. The systematic comparison between the proposed model and traditional fractional order model is carried out on two LiNiMnCo lithium-ion batteries at different temperatures, ageing levels, and electric vehicle current profiles. The comparison results show that the proposed model has higher estimation accuracy in battery terminal voltage and SOC than the traditional model over wide range of temperature and ageing level under electric vehicle operation conditions. Furthermore, the hardware-in-the-loop test validates that the proposed SOC estimation method is suitable for SOC estimation in electric vehicles. [ABSTRACT FROM AUTHOR]

State of health (SOH) estimation of lithium-ion batteries is a key but challengeable technique for the application of electric vehicles. Due to the ambiguous aging mechanisms and sensitivity to the applied conditions of lithium-ion batteries, the recognition of aging mechanisms and SOH monitoring of the battery might be difficult. A novel SOH estimation and aging mechanism identification method is presented in this paper. First, considering the dispersion effect, a fractional-order model is constructed, and the parameter identification approach is proposed, and a comparison between integer-order model and fractional-order model has been done from the prospect of predicting accuracy. Then, based on the identified open-circuit voltage, the battery aging mechanism can be analyzed by the means of an incremental capacity analysis method. Moreover, the normalized incremental capacity peak is used to estimate the remaining capacity. Finally, the robustness of the SOH estimation method is validated by batteries aged at different conditions based on the idea of cross validation, and the estimation error of the remaining capacity can be reduced within 3.1%. [ABSTRACT FROM AUTHOR]

The current lithium-ion battery remaining useful life (RUL) prediction techniques are mainly developed dependent on offline training data. The loaded current, temperature, and state of charge of lithium-ion batteries used for electric vehicles (EVs) change dramatically under the working conditions. Therefore, it is difficult to design acceleration aging tests of lithium-ion batteries under similar working conditions as those for EVs and to collect effective offline training data. To address this problem, this paper developed an RUL prediction method based on the Box–Cox transformation (BCT) and Monte Carlo (MC) simulation. This method can be implemented independent of offline training data. In the method, the BCT was used to transform the available capacity data and to construct a linear model between the transformed capacities and cycles. The constructed linear model using the BCT was extrapolated to predict the battery RUL, and the RUL prediction uncertainties were generated using the MC simulation. Experimental results showed that accurate and precise RULs were predicted with errors and standard deviations within, respectively, [-20, 10] cycles and [1.8, 7] cycles. If some offline training data are available, the method can reduce the required online training data and, thus, the acceleration aging test time of lithium-ion batteries. Experimental results showed that the acceleration time of the tested cells can be reduced by 70%–85% based on the developed method, which saved one to three months’ acceleration test time compared to the particle filter method. [ABSTRACT FROM AUTHOR]

Abstract To ensure the driving safety and avoid potential failures for electric vehicles, evaluating the health state of the battery properly is of significant importance. This study aims to serve as a useful support for researchers and practitioners by systematically reviewing the available literature on state of health estimation methods. These methods can be divided into two types: experimental and model-based estimation methods. Experimental methods are conducted in a laboratory environment to analyze battery aging process and provide theoretical support for model-based methods. Based on a battery model, model-based estimation methods identify the parameters, which have certain relationships with battery aging level, to realize state of health estimation. On the basis of reading extensive literature, methods for determining the health state of the battery are explained in a deeper way, while their corresponding strengths and weaknesses of these methods are analyzed in this paper. At the end of the paper, conclusions for these methods and prospects for the development trend of health state estimation are made. Highlights • A detailed classification of battery SOH estimation methods was presented. • The strengths and weaknesses of SOH methods were compared and analyzed. • Deficiencies of the existing research and the improving directions were pointed out. • SOH estimation with ultrasonic is expected to add one-dimensional data to batteries. • A prospect of future SOH management for battery application has been presented. [ABSTRACT FROM AUTHOR]

Highlights • A dual-estimators-based joint estimation framework is set up to estimate SOC. • The influence of temperature deviation on the SOC accuracy is discussed. • Four filter-based algorithms have been systematically compared. • The proposed algorithm is validated by a hardware testing platform. Abstract The state of charge (SOC) estimation is extremely important for the wide commercialization and safe operation of electric vehicle (EV), especially under cold conditions, which is also a critical technology for battery system in EVs used in the 2022 Beijing winter Olympics. Three efforts have been made in this paper: (1) A general joint estimation framework with dual estimators is set up. Based on this frame, a joint algorithm using the recursive least square (RLS) and the adaptive H infinity filter (AHIF) is realized. (2) Four filter-based algorithms have been systematically compared and analyzed at the wide temperature range. The results show that RLS-AHIF algorithm has better performance for SOC estimation even at low temperatures, such as −10 °C, and the SOC error is within 3.5%. (3) A hardware-in-loop validation platform including the battery management system (BMS) and battery test instruments has been built to verify the proposed method. The results from the platform show that the maximum error of SOC is less than 2% at 0 °C and 25 °C. Consequently, the proposed algorithm can achieve the application over a wide temperature range in an actual BMS. [ABSTRACT FROM AUTHOR]

Abstract: The impact of SOC and temperature on external short circuit (ESC) faults characteristics of lithium-ion batteries, including the current and voltage variation and temperature increase, are analyzed. A fractional-order model (FOM) and a first-order RC model are both employed to describe the electrical behavior of the battery cells with the ESC fault. While the model parameters are identified by the genetic algorithm (GA). A comparison study is made on the prediction accuracy for the two models. An effective classification method based on a random forests (RF) model is proposed to recognize the electrolyte leakage behavior that occurs during the ESC fault experiments. Based on the above efforts, the three steps model-based diagnosis algorithm for identifying the ESC fault and even electrolyte leakage of the battery in real-time is proposed. Two indicators of the root mean square error (RMSE) of battery predicting voltage are applied to diagnose for the ESC fault only and ESC-leakage merged fault. The result of the leakage condition is obtained by a pre-trained RF classifier to confirm the leakage detection result based on the RMSE indicator. Several cases are verified that all the ESC cells can be diagnosed efficiently. [ABSTRACT FROM AUTHOR]

In order to meet the requirements of high specific energy and high specific power together and extend the service life of the energy storage system in temperature abusive conditions, a multi-power configuration with high specific energy lithium-ion battery and high specific power ultracapacitor is the best choice for the all-climate electric vehicle (ACEV). Aiming at real-time power management of a hybrid energy storage system (HESS), three power management strategies, which are respectively based on rules, dynamic programming algorithm, and real-time reinforcement learning algorithm, have been systematically compared in this study. To verify the performance of the control strategies, the hardware-in-loop (HIL) simulation test platform based on xPC Target has been built. The results show that the real-time power management strategy based on reinforcement learning algorithm is superior to the others. This strategy can reduce the charge and discharge ratio of the battery pack, which extends the life of battery pack and improves the efficiency of the system. [ABSTRACT FROM AUTHOR]

This paper investigates the inverter nonlinearities in a drive system based on silicon carbide metal-oxide-semiconductor field-effect transistor (SiC-mosfets) and compares its performance with that of an equivalent silicon insulated-gate bipolar transistor (Si-IGBT) system. Initially, a novel comprehensive analytical model of the inverter voltage distortion is developed. Not only voltage drops, dead time, and output capacitance, but also switching delay times and voltage overshoot of the power devices are taken into account in the model. Such a model yields a more accurate prediction of the inverter's output voltage distortion, and is validated by experimentation. Due to inherent shortcomings of the commonly used double pulse test, the switching characteristics of both SiC-mosfets and Si-IGBTs in the pulse width modulation inverter are tested instead, such that the actual performances of the SiC and Si devices in the motor drive system are examined. Then, the switching performance is incorporated into the physical model to quantify the distorted voltages of both the SiC-based and Si-based systems. The results show that, despite its existing nonlinearities, the SiC-based drive has lower voltage distortion compared to the conventional Si-based drive as a result of its shorter switching times and smaller voltage drop, as well as a higher efficiency. Finally, the overriding operational advantages of the SiC-based drive over its Si-based counterpart is fully demonstrated by comprehensive performance comparisons. [ABSTRACT FROM PUBLISHER]

This study investigates the external short circuit (ESC) fault characteristics of lithium-ion battery experimentally. An experiment platform is established and the ESC tests are implemented on ten 18650-type lithium cells considering different state-of-charges (SOCs). Based on the experiment results, several efforts have been made. (1) The ESC process can be divided into two periods and the electrical and thermal behaviors within these two periods are analyzed. (2) A modified first-order RC model is employed to simulate the electrical behavior of the lithium cell in the ESC fault process. The model parameters are re-identified by a dynamic-neighborhood particle swarm optimization algorithm. (3) A two-layer model-based ESC fault diagnosis algorithm is proposed. The first layer conducts preliminary fault detection and the second layer gives a precise model-based diagnosis. Four new cells are short-circuited to evaluate the proposed algorithm. It shows that the ESC fault can be diagnosed within 5 s, the error between the model and measured data is less than 0.36 V. The effectiveness of the fault diagnosis algorithm is not sensitive to the precision of battery SOC. The proposed algorithm can still make the correct diagnosis even if there is 10% error in SOC estimation. [ABSTRACT FROM AUTHOR]

A bidirectional (Bi) DC/DC converter is one of the key components in a hybrid energy storage system for electric vehicles and plug-in electric vehicles. Based on the detailed analysis of the losses in the converter, this paper firstly develops a model to theoretically calculate the efficiency of the converter. Then, the influences of temperature, switching frequency, duty cycle and material of switching device on the converter’s efficiency are experimentally investigated. The analysis of the experimental results has shown that (1) The efficiency at the switching frequency of 15 kHz is about 2% higher than that of 25 kHz. (2) The efficiency at 25 °C is similar to that at 85 °C for the MOSFET SiC while the efficiency at 25 °C is 2% higher than that at 85 °C for the IGBT Si for both buck and boost modes. (3) In buck mode, when the duty cycles are decreasing from 66.7%, 50% to 33.33%, the peak efficiencies are also decreasing from 97.6%, 94.5% to 90.3%, respectively. In boost mode, when the duty cycle is increasing from 33.33%, 50% to 75%, the peak efficiency is decreasing from 96.9%, 96.5% to 92.4%, respectively. (4) The developed model can calculate the converter’s efficiency accurately [ABSTRACT FROM AUTHOR]

Due to the inconsistent and varied characteristics of lithium-ion battery (LiB) cells, battery pack modeling remains a challenging problem. To model the operation of each cell in the battery pack, considerable work effort and computation time are needed. This paper proposes a data-driven bias-correction-based LiB modeling method, which can significantly reduce the computation work and remain good model accuracy. [ABSTRACT FROM AUTHOR]

In order to maximize the capacity/energy utilization and guarantee safe and reliable operation of battery packs used in electric vehicles, an accurate cell state-of-charge (SoC) estimator is an essential part. This paper tries to add three contributions to the existing literature. (1) An integrated battery system identification method for model order determination and parameter identification is proposed. In addition to being able to identify the model parameters, it can also locate an optimal balance between model complexity and prediction precision. (2) A radial basis function (RBF) neural network based uncertainty quantification algorithm has been proposed for constructing response surface approximate model (RSAM) of model bias function. Based on the RSAM, the average pack model can be applied to every single cell in battery pack and realize accurate terminal voltage prediction. (3) A systematic SoC estimation framework for multi-cell series-connected battery pack of electric vehicles using bias correction technique has been proposed. Finally, three cases with twelve lithium-ion polymer battery (LiPB) cells series-connected battery pack are used to verify and evaluate the proposed framework. The result indicates that with the proposed systematic estimation framework the maximum absolute SoC estimation error of all cells in the battery pack are less than 2%. [ABSTRACT FROM AUTHOR]

To develop an advanced battery estimation unit for electric vehicles application, the state-of-charge (SoC) estimation is proposed with an unscented Kalman filter (UKF) and realized with the RTOS μCOS-II platform. Kalman filters are broadly used to deploy various battery SoC estimators recently. Herein, an UKF algorithm has been employed to develop a systematic adaptive SoC estimation framework. Compared with traditional used extended Kalman filter, it uses an unscented transform to deal with the state estimation problem, thus it has the potential to achieve third order accuracy of the Taylor expansion for tracking posterior estimate of the inner inhabited state. Beneficial from it, the SoC estimation accuracy has been improved with higher tracking accuracy and faster convergence ability. To further evaluate and verify the performance of the proposed online SoC estimation approach, a battery-in-loop platform is built and the SoC estimation is calculated with a RTOS μCOS-II platform. The analog acquisition, communication system and SoC estimation algorithms were programmed, the performance of the proposed SoC estimation with UKF algorithm was finally investigated. The battery management system with UKF algorithm and RTOS μCOS-II platform has good performance and it can apply for electric vehicles. [ABSTRACT FROM AUTHOR]

To achieve the optimal energy allocation for the engine-generator, battery and ultracapacitor of a plug-in hybrid electric vehicle, a novel adaptive energy management strategy has been proposed. Three efforts have been made. First, the hierarchical control strategy has been proposed for multiple energy sources from a multi-scale view. The upper level is for regulating the energy between the engine-generator and hybrid energy-storage system, while the lower level is for the battery and ultracapacitor. Second, a driving pattern recognition based adaptive energy management approach has been proposed. This approach uses a fuzzy logic controller to classify typical driving cycles into different driving patterns and to identify the real-time driving pattern. Dynamic programming has been employed to develop optimal control strategies for different driving blocks, and it is helpful for realizing the adaptive energy management for real-time driving cycles. Third, to improve the real-time and robust performance of the energy management, the previous 100 s duration of historical information has been determined to identify a real-time driving pattern. Finally, an adaptive energy management strategy has been proposed. The simulation results indicate that the proposed energy management strategy has better fuel efficiency than the original and conventional dynamic programming-based control strategies. [ABSTRACT FROM AUTHOR]

The state of energy (SoE) of Li-ion batteries is a critical index for the remainder range forecasting, energy optimization and management. The paper attempts to make three contributions. (1) The definition of SoE is proposed and elaborated, which includes the output energy of battery, the internal resistance heating and the energy consumed on the electrochemical reactions. Based on this definition, the new mathematical model of estimating SoE is built, which can realize the real-time estimation of SoE. (2) Based on the combined general battery model, the recursive least square (RLS) method with an optimal forgetting factor is used to identify the model parameters. The parameter identification results are obtained at relative SoE points, and the verification results indicate that the proposed battery model is accurate enough to simulate the battery characteristics. (3) Based on the SoE mathematical model and the combined general battery model, the extended Kalman filter (EKF) is built to estimate the SoE online. The simulation results show that the EKF-based SoE estimator performs well even under different incorrect initial SoE. [ABSTRACT FROM AUTHOR]

This paper studies the characteristics of battery packs with parallel-connected lithium-ion battery (LiB) cells. To investigate the influence of the cell inconsistency problem in parallel-connected cells, a group of different degraded LiB cells were selected to build various battery packs and test them using a battery test bench. The physical model was developed to simulate the operation of the parallel-connected packs. The experimental results and simulation indicate that, with different degraded cells in parallel, there could be capacity loss and large difference in discharge current, which may cause further accelerated degradation and a more serious inconsistency problem. [ABSTRACT FROM PUBLISHER]

To improve the estimation accuracy of battery’s inner state for battery management system, an online parameters identification algorithm for Thevenin battery model is researched. The Thevenin model and parameters identification algorithm based on recursive least square adaptive filter algorithm was built with the Simulink/xPC Target. The results of hardware-in-loop experiment, which uses Federal Urban Driving Schedule test to verify the parameters identification approach, show the proposed approach can accurately identify the model parameters within 1% maximum terminal voltage estimation error, and the State of Charge error which calculated by the open circuit voltage estimates can be efficiently reduced to 4%. [ABSTRACT FROM AUTHOR]

Accurate estimations of cell state-of-charge for series-connected battery pack are remaining challenge due to the inhabited inconsistency characteristic. This paper tries to make three contributions. (1) A parametric modeling method is proposed for developing model-based SoC estimation approach. Based on the analysis for the mapping relationship between battery parameters and its SoC, a three-dimensional response surface open circuit voltage model is proposed for correcting erroneous SoC estimation. (2) An improved battery model considering model and parameter uncertainties is developed for modeling multiple cells in battery pack. A filtering process for selecting cell having “average capacity” and “average resistance” of battery pack has been developed to build the nominal battery model. Then a bias correction for single cells based on an average cell model is proposed for improving the expansibility of the nominal battery model. (3) A novel model-based dual-scale cell SoC estimator has been proposed. It uses micro and macro time scale to estimate the SoC of the selected cell and unselected cells respectively. Lastly, the proposed approach has been verified by two lithium-ion battery packs. The results show that the maximum estimation errors for cell voltage and SoC are less than 30 mV and 1% respectively against uncertain diving cycles and battery packs. [ABSTRACT FROM AUTHOR]

Abstract: Battery state-of-charge (SoC) and state-of-power capability (SoP) are two of the most significant decision factors for energy management system in electrified vehicles. This paper tries to make two contributions to the existing literature. (1) Based on the adaptive extended Kalman filter algorithm, a data-driven joint estimator for battery SoC and SoP against varying degradations has been developed. (2) To achieve accurate estimations of SoC and SoP in the whole calendar-life of battery, the need for model parameter updates with lowest computation burden has been discussed and studied. The robustness of the joint estimator against dynamic loading profiles and varying health conditions is evaluated. We subsequently used data from cells that have different aging levels to assess the robustness of the SoC and SoP estimation algorithm. The results show that battery SoP has close relationship with its aging levels. And the prediction precision would be significantly improved if recalibrating the parameter of battery capacity and resistance timely. What's more, the method reaches accuracies for new and aged battery cells in electrified vehicle applications of better than 97.5%. [Copyright &y& Elsevier]

This paper is concerned with model-based dynamic peak-power evaluation for LiNMC and \LiFePO4 batteries under different operating conditions. The battery test and our prior study on linear-parameter-varying (LPV) battery modeling are briefly introduced. The peak-power estimation method that incorporates an explicit prediction horizon and design constraints on the battery current, voltage, and SOC are elaborated, and its computational load is analyzed. The discharge and charge peak powers are quantitatively assessed under different dynamic characterization tests, in which a comparison with the conventional PNGV-HPPC method and approaches using the less accurate models is conducted. The robustness of the peak-power estimation approach against varying battery temperatures and aging levels is investigated. The methods to improve the credibility of the peak-power assessment in the context of battery degradation are explored. [ABSTRACT FROM PUBLISHER]

Highlights: [•] A lumped parameter battery model against different battery aging levels is proposed. [•] The RLS based method is used to identify the parameter of battery model in real-time. [•] A data-driven based adaptive SoC estimator is developed by RLS and AEKF algorithm. [•] The robustness of the SoC estimator against varying loading profiles is evaluated. [•] The robustness of the SoC estimator against different aging levels is evaluated. [ABSTRACT FROM AUTHOR]

Highlights: [•] A data-driven multi-scale extended Kalman filtering is developed for battery system. [•] A lumped parameter battery model against different aging levels has been proposed. [•] The proposed approach has less computation efficiency but higher estimation accuracy. [•] The proposed approach can estimate battery parameter, capacity and SoC concurrently. [•] The robustness of the proposed approach against different aging levels is evaluated. [ABSTRACT FROM AUTHOR]

Abstract: An accurate SoC (state of charge) and SoP (state of power capability) joint estimator is the most significant techniques for electric vehicles. This paper makes two contributions to the existing literature. (1) A data-driven parameter identification method has been proposed for accurately capturing the real-time characteristic of the battery through the recursive least square algorithm, where the parameter of the battery model is updated with the real-time measurements of battery current and voltage at each sampling interval. (2) An adaptive extended Kalman filter algorithm based multi-state joint estimator has been developed in accordance with the relationship of the battery SoC and its power capability. Note that the SoC and SoP can be predicted accurately against the degradation and various operating environments of the battery through the data-driven parameter identification method. The robustness of the proposed data-driven joint estimator has been verified by different degradation states of lithium-ion polymer battery cells. The result indicates that the estimation errors of voltage and SoC are less than 1% even if given a large erroneous initial state of joint estimator, which makes the SoP estimate more accurate and reliable for the electric vehicles application. [Copyright &y& Elsevier]

Abstract: A fuzzy logic-based energy management strategy for a hybrid power system used in electric vehicles was developed and verified in this paper. First, the topology structure of a hybrid power system was put forward that the ultracapacitors connected with the battery pack in parallel after a bidirectional DC/DC converter. To improve the systematic efficiency, a fuzzy logic-based energy management strategy was designed and the control model was built. We proposed an active electricity management module for the ultracapacitors on the basis of the real-time vehicle velocity. Then, the vehicle model, the interface model of the electrical load and the xPC Target were built with the Simulink/State flow soft. Finally, the hybrid power/energy system-in-loop simulation experiment was carried out to verify the energy management strategy under the Urban Dynamometer Driving Schedule (UDDS) dynamic driving cycle. The results show the proposed fuzzy logic-based energy management strategy can ensure the battery pack working in high efficiency range and show better performance than the traditional logic threshold-based control strategy. The hybrid power system’s electricity economy was improved by 4.1% and the bad influences of the high-current discharging and charging on battery pack were avoided successfully. [Copyright &y& Elsevier]

Abstract: Due to cell-to-cell variations in battery pack, it is hard to model the behavior of the battery pack accurately; as a result, accurate State of Charge (SoC) estimation of battery pack remains very challenging and problematic. This paper tries to put effort on estimating the SoC of cells series lithium-ion battery pack for electric vehicles with adaptive data-driven based SoC estimator. First, a lumped parameter equivalent circuit model is developed. Second, to avoid the drawbacks of cell-to-cell variations in battery pack, a filtering approach for ensuring the performance of capacity/resistance conformity in battery pack has been proposed. The multi-cells “pack model” can be simplified by the unit model. Third, the adaptive extended Kalman filter algorithm has been used to achieve accurate SoC estimates for battery packs. Last, to analyze the robustness and the reliability of the proposed approach for cells and battery pack, the federal urban driving schedule and dynamic stress test have been conducted respectively. The results indicate that the proposed approach not only ensures higher voltage and SoC estimation accuracy for cells, but also achieves desirable prediction precision for battery pack, both the pack's voltage and SoC estimation error are less than 2%. [Copyright &y& Elsevier]

Abstract: Battery model plays an important role in the simulation of electric vehicles (EVs) and states estimation of the batteries in the development of the model-based battery management system. To build a battery model with enough precision and suitable complexity, firstly this paper summarizes the seven representative battery models, which belong to the simplified electrochemical models or the equivalent circuit models. Then the model equations are built and the model parameters are identified with an online parameter identification method. The battery test bench is built and the experiment schedule is designed. Finally an evaluation is performed on the seven battery models by an experiment approach from the aspects of the estimation accuracy of the terminal voltages. To evaluate the effect of the number of RC networks on the model’s precision, the battery general equivalent circuit models (GECMs) with different RC networks are also discussed further. The results indicate the equivalent circuit model with two RC networks, the DP model, has an optimal performance. [Copyright &y& Elsevier]

Abstract: The accurate estimation of internal parameters and state-of-charge (SoC) of battery, which greatly depends on proper models and corresponding high-efficiency, high-accuracy algorithms, is one of the critical issues for the battery management system. A model-based online estimation method of a LiFePO4 battery is presented for application in electric vehicles (EVs) by using an adaptive extended Kalman filter (AEKF) algorithm. The Thevenin equivalent circuit model is selected to model the LiFePO4 battery and its mathematics equations are deduced to some extent. Additionally, an implementation of the AEKF algorithm is elaborated and employed for the online parameters’ estimation of the LiFePO4 battery model. To illustrate advantages of the online parameters’ estimation, a comparison analysis is performed on the terminal voltages between the online estimation and the offline calculation under the Hybrid pulse power characteristic (HPPC) test and the Urban Dynamometer Driving Schedule (UDDS) test. Furthermore, an efficient online SoC estimation approach based on the online estimation result of open-circuit voltage (OCV) is proposed. The experimental results show that the online SoC estimation based on OCV–SoC can efficiently limit the error below 0.041. [Copyright &y& Elsevier]

To improve the use of lithium-ion batteries in electric vehicle (EV) applications, evaluations and comparisons of different equivalent circuit models are presented in this paper. Based on an analysis of the traditional lithium-ion battery equivalent circuit models such as the Rint, RC, Thevenin and PNGV models, an improved Thevenin model, named dual polarization (DP) model, is put forward by adding an extra RC to simulate the electrochemical polarization and concentration polarization separately. The model parameters are identified with a genetic algorithm, which is used to find the optimal time constant of the model, and the experimental data from a Hybrid Pulse Power Characterization (HPPC) test on a LiMn2O4 battery module. Evaluations on the five models are carried out from the point of view of the dynamic performance and the state of charge (SoC) estimation. The dynamic performances of the five models are obtained by conducting the Dynamic Stress Test (DST) and the accuracy of SoC estimation with the Robust Extended Kalman Filter (REKF) approach is determined by performing a Federal Urban Driving Schedules (FUDS) experiment. By comparison, the DP model has the best dynamic performance and provides the most accurate SoC estimation. Finally, sensitivity of the different SoC initial values is investigated based on the accuracy of SoC estimation with the REKF approach based on the DP model. It is clear that the errors resulting from the SoC initial value are significantly reduced and the true SoC is convergent within an acceptable error. [ABSTRACT FROM AUTHOR]

Actions to reduce greenhouse gas emissions are required from all actors. Adopting plug-in electric vehicles (EV) would reduce light motor vehicle travel emissions, a significant and rising emissions source. To encourage EV uptake, many governments have implemented policies which may be less effective than desired. Using New Zealand as a case study, we surveyed private motorists. The results show that consumers are heterogeneous, with varying car-buying motivations, perceptions, attitudes to EVs and awareness of policies. Uniquely, we segmented motorists into four attitudinal groups to ascertain characteristics potentially affecting EV readiness to provide evidence to improve policies and aid social marketing. Our results show the next-most-ready to buy EVs are early mainstream consumers—designated the EV Positives—who were most concerned about vehicle range, perceptions of EV expense, charging-related inconvenience and the unknown value proposition of batteries, and were relatively unaware of incentives compared to EV Owners. The EV Positives favored incentives designed to effect purchase price reductions and increase nation-wide fast-charger deployment. To increase awareness of EVs and shift perceptions of EV expense and inconvenience, we suggest policies that potentially increase EV adoption rates and suggest reframing the language to appeal to EV Positives through information programs. Increasing EV procurement by organizations could increase opportunities for positive information dissemination via employees. [ABSTRACT FROM AUTHOR]

This editorial summarizes the content of the Special Issue entitled “The International Symposium on Electric Vehicles (ISEV2017)”, which was published in MDPI’s Energies journal. The Special Issue was compiled in 2017 and accepted a total of 26 papers. Lithium-ion battery, energy management of electric vehicles, and motor control in electric vehicles were the most discussed topics, introducing brand new methods with very sound results. [ABSTRACT FROM AUTHOR]

Obtaining an estimation of the parameters and state of charge (SoC) of a lithium-ion battery is crucial for an electric vehicle. The parameters of a battery model are usually different throughout the battery lifetime. To obtain an accurate SoC and parameters and reduce the computational cost, a double-scale dual adaptive particle filter for online parameters and SoC estimation of lithium-ion batteries is proposed. First, the lithium-ion battery is modeled using the Thevenin model. Second, a double-scale dual particle filter is proposed and applied to the battery parameter and SoC estimation. To improve the accuracy and convergence ability to the initial environmental offset, a double-scale dual adaptive particle filter is proposed. Finally, the effectiveness and applicability of the two algorithms are verified by Lithium Nickel Manganese Cobalt Oxide (NMC) batteries of different ages. [ABSTRACT FROM AUTHOR]

In order to ensure the safety and reliability of electric vehicles (EVs), the accurate center of gravity (CG) position estimation is of great significance. In this study, a novel approach based on combined H ∞ –extended Kalman filter (H ∞ –EKF) is proposed. Utilizing the characteristics of the wheel torque controlled independently, the estimation method only requires the longitudinal stimulus of vehicles and avoids other possible disadvantageous stimulus, such as the vehicle yaw or roll motion. Furthermore, additional parameters (suspension parameters, tire parameters, etc.) are unessential. To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation considering of the front wheel speed, the rear wheel speed and the longitudinal velocity of the vehicle. The designed estimator consists of two layers: the H ∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown. Simultaneously, the other EKF estimator uses the states derived by the former filter to identify the CG position of the vehicle. Results indicate that the performance of the H ∞ filter is superior to the standard KF and the proposed synthetic estimation algorithm is able to estimate the longitudinal location and the height of CG with acceptable accuracy. [ABSTRACT FROM AUTHOR]

State-of-energy (SoE) is an important index for batteries in electric vehicles and it provides the essential basis of energy application, load equilibrium and security of electricity. To improve the estimation accuracy and reliability of SoE, a novel multi-model fusion estimation approach is proposed against uncertain dynamic load and different temperatures. The main contributions of this work can be summarized as follows: (1) Through analyzing the impact on the estimation accuracy of SoE due to the complexity of models, the necessity of redundant modeling is elaborated. (2) Three equivalent circuit models are selected and their parameters are identified by genetic algorithm offline. Linear matrix inequality (LMI) based H -infinity state observer technique is applied to estimate SoEs on aforementioned models. (3) The concept of fusion estimation is introduced. The estimation results derived by different models are merged under certain weights which are determined by Bayes theorem. (4) Batteries are tested with dynamic load cycles under different temperatures to validate the effectiveness of this method. The results indicate the estimation accuracy and reliability on SoE are elevated after fusion. [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity and complex time-variant property of batteries, the existing state of charge (SOC) estimation approaches based on a single equivalent circuit model (ECM) cannot provide the accurate SOC for the entire discharging period. This paper aims to present a novel SOC estimation approach based on a multiple ECMs fusion method for improving the practical application performance. In the proposed approach, three battery ECMs, namely the Thevenin model, the double polarization model and the 3rd order RC model, are selected to describe the dynamic voltage of lithium-ion batteries and the genetic algorithm is then used to determine the model parameters. The linear matrix inequality-based H-infinity technique is employed to estimate the SOC from the three models and the Bayes theorem-based probability method is employed to determine the optimal weights for synthesizing the SOCs estimated from the three models. Two types of lithium-ion batteries are used to verify the feasibility and robustness of the proposed approach. The results indicate that the proposed approach can improve the accuracy and reliability of the SOC estimation against uncertain battery materials and inaccurate initial states. [ABSTRACT FROM AUTHOR]

Lithium plating in lithium-ion batteries for electric vehicles, occurring due to low-temperature or high-rate charging, is a significant factor impacting safety and service life. To address this issue, a novel adaptive charging approach is proposed, combining ultrasound-assisted diagnosis and model predictive control (MPC). In the method, a discrete state-space electrochemical model is used to describe the dynamic characteristics of the battery, and a model predictive controller (MPC) is utilized to optimize the charging current to avoid lithium plating. Considering that factors such as battery performance degradation and variable working temperature affect the battery model's judgment of lithium plating, an ultrasound-assisted diagnosis method is used to determine the critical point of lithium plating. The effectiveness of the method is validated through low-temperature charging and cycle aging experiments. The results indicate that without complex model parameter calibration in different temperatures, the new charging method not only has a higher charging speed than constant current charging, but also can effectively suppress the occurrence of lithium plating on the negative electrode of the battery. The method is expected to be applied in electrochemical energy storage systems to enhance safety and service life. • Simplified battery electrochemical model is designed for charging control. • Model predictive controller is utilized to optimize the charging current. • Ultrasound-assisted diagnosis method is used to diagnose lithium plating. • Model parameters do not need to be recalibrated at different temperatures. [ABSTRACT FROM AUTHOR]

State-of-energy (SoE) is a very important index for battery management system (BMS) used in electric vehicles (EVs), it is indispensable for ensuring safety and reliable operation of batteries. For achieving battery SoE accurately, the main work can be summarized in three aspects. (1) In considering that different kinds of batteries show different open circuit voltage behaviors, the Gaussian model is employed to construct the battery model. What is more, the genetic algorithm is employed to locate the optimal parameter for the selecting battery model. (2) To determine an optimal tradeoff between battery model complexity and prediction precision, the Akaike information criterion (AIC) is used to determine the best hysteresis order of the combined battery model. Results from a comparative analysis show that the first-order hysteresis battery model is thought of being the best based on the AIC values. (3) The central difference Kalman filter (CDKF) is used to estimate the real-time SoE and an erroneous initial SoE is considered to evaluate the robustness of the SoE estimator. Lastly, two kinds of lithium-ion batteries are used to verify the proposed SoE estimation approach. The results show that the maximum SoE estimation error is within 1% for both LiFePO 4 and LiMn 2 O 4 battery datasets. [ABSTRACT FROM AUTHOR]

• An improved model with the voltage as input and current as output (VICO) is proposed. • The established VICO model is extended to an n -order VICO model. • The fault diagnosis method of current sensor is realized with the first-order VICO model. • The adaptability under different operating conditions and merit in detecting time are verified. Battery management systems (BMSs) are very important to ensure the safety of electric vehicles. The normal operation of BMSs is highly dependent on the accuracy of battery sensors. The present fault diagnosis efficiency of current sensors is much lower than that of voltage sensors due to model limitations in conventional methods. In this paper, a fault diagnosis method based on an improved model with voltage as input and current as output (VICO) is proposed to detect current sensor faults, where the least squares method combined with the unscented Kalman filter is used to estimate the fault current of current sensor. By comparing the estimated fault current with the diagnosis threshold, the fast fault diagnosis of current sensor is realized. The proposed method is verified under different operating conditions and compared with the methods based on state of charge and open-circuit voltage residuals. To highlight the importance of the proposed method, the influence and possible causes of minor faults and temperature on diagnosis are analyzed. The experimental results show that the method can detect the fault of the current sensor more accurately and quickly compared with the conventional methods, and has the ability to detect minor faults and adaptability under different operating conditions and temperatures. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a method to estimate the state-of-charge (SOC) of a lithium-ion battery, based on an online identification of its open-circuit voltage (OCV), according to the battery’s intrinsic relationship between the SOC and the OCV for application in electric vehicles. Firstly an equivalent circuit model with n RC networks is employed modeling the polarization characteristic and the dynamic behavior of the lithium-ion battery, the corresponding equations are built to describe its electric behavior and a recursive function is deduced for the online identification of the OCV, which is implemented by a recursive least squares (RLS) algorithm with an optimal forgetting factor. The models with different RC networks are evaluated based on the terminal voltage comparisons between the model-based simulation and the experiment. Then the OCV-SOC lookup table is built based on the experimental data performed by a linear interpolation of the battery voltages at the same SOC during two consecutive discharge and charge cycles. Finally a verifying experiment is carried out based on nine Urban Dynamometer Driving Schedules. It indicates that the proposed method can ensure an acceptable accuracy of SOC estimation for online application with a maximum error being less than 5.0%. [Copyright &y& Elsevier]

• ESC behaviors at various temperatures are investigated experimentally. • ESC-induced heat generation and its impacts on electrical behaviors is modeled. • Distribution and anisotropy diffusion of ESC-caused heat generation is delineated. • An electro-thermal coupling model is proposed for batteries under ESCs. • Effectiveness of the proposed model is verified by experimental data. External short circuit (ESC) fault, which can cause large current and high temperature, is one of the main reasons for battery failure. Its analysis and diagnosis remains a challenging task due to complex electro-thermal characteristics of batteries under ESCs. In this paper, ESC experiments at various temperatures are conducted to investigate the impact of temperature on battery electro-thermal behaviors. Based on the analysis of the experimental data, heat generation inside a battery caused by ESC-induced high current and side reactions is modeled. The heat distribution and diffusion are also modeled by considering battery's internal jellyroll structure. The combination of the heat generation, distribution and diffusion models forms a novel electro-thermal coupling model, which is used to predict the complex thermal and electrical properties of a battery under ESCs. The presented model is simulated and verified by the test data. The maximum root mean square error of ESC current prediction is less than 1.73A and the maximum errors of the internal temperatures and the surface temperatures are only 1.771% and 3.915%, respectively. These results verify the effectivceness of the presented model. It is expected that the presented model is useful for safety analysis, temperature prediction and fault diagnosis applications of the lithium-ion batteries under ESC. [ABSTRACT FROM AUTHOR]

The safety and availability of lithium-ion batteries are greatly affected by environmental temperature. Fast preheating of batteries is considered an effective technology for promoting the globalization of electric vehicles. This study establishes a coupled model of electro-thermal-aging to explore the advantages of pulse preheating methods in improving environmental adaptability and extending the service life of batteries. Firstly, the response of battery heating rate and capacity loss to the state of charge, positive pulse rate, pulse period, and the ratio of positive to negative pulse amplitude was analyzed. Additionally, the effects of various factors on the preheating effect of pulse current were explored. Secondly, appropriate factors and their levels were selected to construct an orthogonal experimental table. Then, the time and capacity loss rate were adopted as output response, multivariate analysis of variance and main effect analysis were performed on the factors and their levels in the orthogonal table. Moreover, the strategy of minimizing capacity loss and the shortest charging time for the pulse preheating method can be quickly determined based on the analysis results. Finally, the experimental results show that the developed preheating strategy achieves good results in terms of the heating effect and capacity retention rate. • Coupled model of electro-thermal-aging has been established. • An orthogonal design table is constructed to reduce experimental workload. • Univariate and multivariate analysis are combined to accurately and quickly identify target strategies. [ABSTRACT FROM AUTHOR]

Highlights • A multi-agent system is developed to simulate the operation of an energy hub with electric vehicles. • Different penetration rates and various charging patterns of electric vehicle are modelled. • A full random dispatch algorithm is integrated in smart charging strategy. • The maximum capacity and potential of vehicle to grid is calculated. Abstract In this paper, a multi-agent system (MAS) was developed to simulate the operation of an energy hub (EH) with different penetration rates (PRs) and various charging patterns of electric vehicle (EV). Three charging patterns, namely uncontrolled charging pattern (UCP), rapid charging pattern (RCP) and smart charging pattern (SCP), together with vehicle to grid (V2G), were simulated in the MAS. The EV penetration rates (EV-PRs), from 10% to 90% with a step of 20%, are considered in this study. Under the UCP, the peak load increases by 3.4–17.1% compared to the case without EVs, which is the reference case in this study. A main part of the increased electricity demand can be supplied by the gas turbine (GT) when the PR is lower, i.e. 71.7% under 10% PR and 37.4% under 50% PR. Under the SCP, the charging load of EVs is shifted to the valley period and thus the energy dispatch of the EH at 07:00–23:00 remain the same as that in the reference case. When V2G is considered, the electricity demand from the grid becomes the largest in all of the cases, e.g. the demand with 50% PR doubles the electricity demand in the reference case. However, the GT output decreases by 2.9–15.7% at 07:00–23:00 due to the effect of V2G. The variations in the EH's operation further raise the changes in energy cost, i.e. the electricity and cooling prices are lowered by 18.3% and 33.8% due to the availability of V2G and the heating and cooling prices increase by 3.5% and 4.3% under the UCP with the PR of 50%. Regarding the V2G capacity, near 39% of the EVs' battery capacity can be discharged via V2G. In addition, the paper also produced a V2G potential line, which is an effective tool to provide the maximum potential of the EVs for peak shaving at any specific time. [ABSTRACT FROM AUTHOR]

• A model for SOH estimation and degradation diagnosis is present. • Proposing a method to select appropriate segments of charging data as model input. • This model development process does not rely on aging data. • The method is validated on real aging data. Lithium-ion batteries (LiBs) have been widely used in electric vehicles and portable electronics. However, the performance and safety of these applications are highly dependent on degradation of LiBs. In this paper, three contributions have been made to achieve reliable degradation diagnosis and State-of-Health (SOH) estimation: (1) Open-circuit voltage is reconstructed to diagnose degradation modes of LiBs by performing scaling and translation transformations on open-circuit potential curves. (2) A degradation diagnosis model is developed to quantify aging characteristics of LiBs. In this model, a segment of charging data is taken to estimate SOH and the degradation modes in a degradation path. (3) An appropriate voltage range of the charging data is selected to improve model estimation accuracy. Experimental results show that the proposed method can achieve reliable degradation diagnosis and accurate SOH estimation with the maximum error of 1.44%. [Display omitted] [ABSTRACT FROM AUTHOR]