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In this paper, a model fusion method (MFM) is proposed for online state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs). Firstly, a particle swarm optimization-genetic algorithm (PSO-GA) method is cooperated with a 2-RCCPE fractional-order model (FOM) to construct battery open-circuit voltage (OCV)-SOC curve, which only relies on a part of dynamic load profile without the prior knowledge of an initial SOC. Secondly, a dual extended Kalman filter (DEKF) algorithm based on a 1-RC model is employed to identify the model parameters and estimate battery SOC with the extracted OCV-SOC curve. Furthermore, battery polarization dynamics in a SOP prediction window is analyzed from two aspects: (1) self-recovery; and (2) current excitation. They are separately simulated using 2-RCCPE FOM and 1-RC model, and then integrated through a model fusion for online SOP estimation, which enables an analytical expression of battery peak charge/discharge current in a prediction window without weakening the nonlinear characteristic of FOM. Experimental results demonstrate the improved performance of the proposed MFM for online discharge SOP estimation, where the mean absolute error and root mean square error are only 0.288 W and 0.35 W, respectively, under the urban dynamometer driving schedule profile. [ABSTRACT FROM AUTHOR]

With the widespread application of lithium-ion battery packs, it calls for efficient balancing methods to improve the performance of these battery systems. The relevant research has focused on the design of equalization circuits and control strategies while neglecting quantitative analysis and comparison of battery active equalization structures from the systematic level. In this paper, we propose an improved system-theoretic modeling approach for active equalization structures that takes into account the battery's constraints, including equalization current rate and SOC operation range. Accordingly, the mathematical models of various active equalization structures are derived that can describe their equalization dynamic behavior. Then, the analytical algorithms are developed to estimate the balancing time of active equalization structures online. Finally, extensive numerical simulations are conducted to evaluate and compare the average performance of these equalization systems in term of equalization time. It then comes up with a comprehensive comparison among them. Consequently, the Multicell-to-Multicell (MC2MC) balancing structure benefits the fast balancing speed and the lowest sensitive to initial batteries’ SOCs. The global structure has the lowest implementation cost and the series structure benefits the simplest control. Moreover, the module-based structures have faster balancing speed when each module contains about 4–6 batteries. [ABSTRACT FROM AUTHOR]

The article focuses on a technology developed by Stanford University associate professor Yi Cui and his team which turns a standard office paper into a battery that creates energy source for portable electronics such as laptops and hand held devices. Yi Cui and his team discovered that carbon-nanotube ink infused paper, used as electrodes, offered performance similar to the much-heavier metal electrodes found on lithium-ion batteries. The team is trying to make their technology commercially relevant, like improving their battery's performance.

Lithium-ion batteries have been widely applied in energy conversion sectors, where effective future ageing prediction is crucial to guarantee their safety and performance. Due to the highly nonlinear ageing behaviours, developing a reliable method that could not only consider the knee point effect but also predict the future ageing trajectory with uncertainty quantification poses a formidable task. This paper derives a machine learning solution, based on the migrated Gaussian process regression (GPR), for predicting future battery two-stage ageing trajectory. Specifically, a base model is first offline identified from the easier collected accelerated-speed ageing data, through which the long life ageing information can be effectively learned. With this base model, a migrated mean function is then designed and coupled within the GPR framework for battery ageing predictions. Experimental data from three different batteries are applied for model validation and performance evaluation. Results indicate that the proposed solution leads to effective improvements in prediction accuracy and uncertainty quantification for both cases of training before and after the knee point. This is the first time to couple migration concept within GPR, paving the way to reduce experimental cost and predict battery future two-stage ageing trajectory with only a few (first 30%) data available. [ABSTRACT FROM AUTHOR]

Long horizon state of health (SOH) monitoring and remaining useful life (RUL) prediction are of industrial value in prognostic and health management (PHM) of lithium-ion batteries (LIBs) to ensure their reliable functionality by early detection. Machine Learning, as a data-driven health diagnostic technique, has been widely utilized in solitary and hybrid structures. However, an accurate SOH estimation and RUL prediction method with less computational burden are highly desirable for the online state prediction in an electric vehicle application. This paper evaluates nonlinear autoregressive with external input (NARX) recurrent neural network (RNN) and time delay neural network (TDNN) in their prediction precision using the NASA dataset. The superior method, NARXRNN, is employed for two different datasets to estimate the battery's SOH and predict its RUL on a broad horizon. The results reveal the outstanding performance by presenting the root mean square error within 3% and mean absolute error within 2% for unseen data. Therefore, this method is capable to accurately predict the SOH of LIBS from historical data at low computational complexity. It is a promising model for long horizon SOH and RUL prediction and practical for online applications. [ABSTRACT FROM AUTHOR]

This paper proposes a cost-effective way to design and operate fuel cell hybrid electric trucks (FCHETs) where a chance-constrained optimization is formulated. The aim of the introduced problem is to minimize a summation of component cost and operational cost with consideration of fuel cell (FC) degradation and cycle life of energy buffer. We propose to decompose the problem into two sub-problems that are solved by sequential convex programming. The delivered power satisfies a cumulative distribution function of the wheel power demand, while the truck can still traverse driving cycles with a similar speed and travel time without delivering unnecessarily high power. This allows to downsize powertrain components, including electric machine, FC and energy buffer. A case study considering different energy buffer technologies, including supercapacitor (SC), lithium-ion battery (LiB), and lithium-ion capacitor (LiC) is investigated in a set of trucking applications, i.e. urban delivery, regional delivery, construction, and long-haul. Results show that the power rating of the electric machine is drastically reduced when the delivered power is satisfied in a probabilistic sense. Moreover, the configuration with LiB as the energy buffer has the lowest expense but the truck with LiC can carry more payload. [ABSTRACT FROM AUTHOR]

State of charge (SOC) estimation of lithium-ion batteries has been extensively studied and the estimation accuracy was mainly investigated through the development of various battery models and dynamic estimation algorithms. All battery models, however, contain inherent model bias due to the simplifications and assumptions, which cannot be effectively addressed through the development of various algorithms such as Kalman filtering (KF) or particle filtering (PF). Consequently, as observed in some study, battery SOC estimation using a typical extended KF in fact is not very accurate where the error could range from 5% to 10% or even more depending on the battery characteristics. This paper proposes bias characterization of the battery model, so that accuracy of the baseline model could be significantly improved and eventually SOC estimation could be much more accurate than the one only using the baseline model. This paper reports great potential for improving battery SOC estimation with the bias characterization and proposes two methods for actual bias modeling. In particular, the polynomial regression model and the Gaussian process regression model are proposed to examine the effects of the two methods on bias modeling and SOC estimation using a typical battery circuit model. Results are demonstrated in both simulation and lab testing using three battery charging/discharging profiles with the cross-validation technique. [ABSTRACT FROM AUTHOR]

Accurate state of energy (SOE) estimation of the battery pack is very important for the electric vehicle's driving range estimation, which is still a very challenging problem under actual vehicle application conditions at present. This paper proposes a method for estimating the SOE of the battery pack based on prediction, taking into account the future voltage and temperature changes of the battery pack. Firstly, a novel definition of the battery pack SOE is proposed, which considers the inconsistency of the battery pack. Secondly, the thermo-electric lumped parameter equivalent circuit model of a single cell in the battery pack is applied. The electrical parameters are identified using the forgetting factor recursive least-squares algorithm, and the extended Kalman filter is used to estimate the SOC of each cell. Finally, the future current, voltage and temperature response of the cell in the battery pack are accurately predicted and iterative calculations are performed to get SOE under the condition that the future output power of the battery pack is predicted by the Markov chain model method. The effectiveness of the proposed method is verified under dynamic conditions at different temperatures. The results show that the above method has high precision and good robustness, and can meet the needs of actual vehicle applications. [ABSTRACT FROM AUTHOR]

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

Lithium-ion batteries (LIBs) charging at low temperatures will easily accelerate the aging of LIBs and reduce the useful life. This paper applies advanced multi-factors coupling aging model and bi-objective particle swarm optimization (PSO) algorithm to derive suitable charging patterns for LIBs at low temperatures. Based on the results of orthogonal experiments, which consider the effects of charging rate, charging temperature and charging cut-off voltage on battery aging, the low-temperature capacity degradation model was established. Furthermore, two important yet competing charging objectives, including battery health and charging time, are taken into account simultaneously. These two conflicting charging objectives are traded off by solving a bi-objective optimization problem based on battery electrothermal and aging behavior. Combined with PSO algorithm, the optimal low-temperature charging strategy is obtained. As a result, the three-stage constant current and constant voltage (CC-CV) charging strategy is optimized to balance various combinations of charging objectives. Different tradeoffs are compared and analyzed based on the Pareto frontiers. The verification results show that the optimized three-stage CC-CV charging strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and battery health under low-temperature charging conditions. [ABSTRACT FROM AUTHOR]

Energy storage devices such as batteries and supercapacitors are arousing a growing interest in many areas of industrial applications since they allow improving the dynamic response and efficiency. In this sense, the availability of electric models of batteries and supercapacitors is necessary for the correct sizing, control, and energy management of power systems. In order to identify the parameters of the equivalent electric model, the identification current profile is often a pulse of charge or discharge. Subsequently, these models are validated with a current verification profile. However, the verification profile compared to the identification profile is more dynamic in amplitude and frequency, producing validation errors due to the identification signal's limited bandwidth. This paper proposes a novel Pulse – Pseudo Random Binary Sequence (Pulse – PRBS) identification current profile, with similar spectral characteristics to the verification profile, in order to improve the modeling accuracy. The parameters of a Lithium-ion battery and a supercapacitor are obtained with this new identification current profile and the Optimization Toolbox of Matlab, using the electrical models in Simulink or Simscape. Finally, the models are validated with the Urban Driving Cycle ECE-15 and the Hybrid Pulse Power Characterization (HPPC) for electric vehicle applications. [ABSTRACT FROM AUTHOR]

In order to effectively improve the role of battery energy storage system in power system, this paper studies the optimal scheduling strategy of BESS in power grid. Firstly, taking the minimum power exchange cost between power grid and the penalty cost of node voltage deviation as the objective function, an optimal scheduling model of BESS in distribution grid based on mixed integer programming is proposed. Secondly, when BESS tracks the scheduling instruction, the life attenuation of each BESS is inconsistent due to the difference of operation conditions. A scheduling method considering the state of health (SOH) of BESS is proposed. Finally, the SOC and total charge and discharge capacity of each BESS are analyzed, and the change characteristics of SOC under different SOH conditions (0.8, 0.85, 0.9, 0.95, 1.0) are verified effectively, which indirectly verifies that this scheduling method can effectively reduce the scheduling loss. [ABSTRACT FROM AUTHOR]

Accurate and reliable capacity estimation for lithium-ion batteries adapting to uncertain environment plays a significant role in the fields of electric vehicles and renewable energy systems. This paper proposes a novel capacity fusion estimation approach based on three-parameter capacity fade model and constant current charging curves using dual unscented Kalman filters. In view of the battery cycle aging problem under variable temperature situation, a three-parameter capacity fade model is introduced to continuously estimate the battery cell capacity for unknown changes of ambient temperature. Nevertheless, the model parameters may mismatch in practical applications, so the first unscented Kalman filter views the discrete capacity based on constant current charging curves as observations to periodically update the parameters. Thereafter, the second one performs a fusion operation depending on the capacity estimated by constant current charging curves and the three-parameter capacity fade model with the corrected parameters, so as to improve the capacity estimation accuracy online. The results show that the fusion estimation errors of capacity are controlled within 2% by updating the model parameters, and the robustness of the proposed method is also validated. [ABSTRACT FROM AUTHOR]

For timely maintenance and replacement in lithium-ion battery system, it is crucial to achieve fast and accurate State of Health (SOH) prediction. SOH is a dynamic status parameter of a battery indicated by its available capacity compared to the initial condition. SOH tends to decrease in the long-term because a battery is aged by its charging and discharging cycles due to the degradation of chemical composition. To ensure a battery has sufficiently good conditions, it is significant to predict the battery SOH precisely so that the depleted or weak battery can be replaced in time. However, the prediction of battery SOH is a difficult and complex task due to the following reasons (1) SOH degradation is a dynamic process because of the time-varying nature of both battery electrochemistry and working condition; (2) SOH prediction model usually involves numerous input attributes that lead to sheer complexity and computational cost for real-time battery management; (3) the balance of overhead-accuracy tradeoff is challenging to adapt to various application scenarios. In this paper, we propose a theoretical framework for fast and accurate SOH prediction based on the non-additive measure and overhead-accuracy balancing. Specifically, we characterize the interactions among battery attributes and their aggregated impacts on SOH prediction. By choosing the most significant subset of battery attributes, the computational complexity of SOH prediction can be substantially reduced while maintaining high accuracy. The tradeoff between accuracy and computational overhead can be balanced by adjusting the number of attributes and volume of training data. The experimental results validate the effectiveness and efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]

The thermal characteristics of a battery are vital in order to understand its health and safety. The thermal characteristics are influenced by charging techniques. This paper presents theoretical explanation, experimental validation, and an analytical method to quantify the heat loss influenced by charging techniques for a Li-Ion battery. Constant current constant voltage (CC-CV), sinusoidal ripple current constant voltage, and pulse current constant voltage charging profiles are used as test cases for validation. The electrical and entropic heat generation for different charging profiles are modeled and estimated based on battery equivalent parameters. The estimated heat generation is compared with the heat flux measurement from a differential scanning calorimeter. In addition to the heat flux measurement, temperature, state of charge, terminal voltage, and current were recorded and analyzed. [ABSTRACT FROM AUTHOR]

Online remaining charging-discharging cycle (RCDC) prognosis is of great significance for lithium-ion batteries. The conventional method is usually based on whether the state-of-health (SOH) of capacity reaches the end-of-life (EoL) threshold. However, the most available prediction methods have two problems that need to be solved. First, the SOH degradation curve of the lithium-ion battery is nonlinear and non-Gaussian, and the battery capacity regeneration phenomena (CRP) has a direct impact on RCDC estimation efficiency. These factors challenge the precise forecast of RCDC and increase the risk of prediction failure. Second, existing methods have insufficient early-stage prediction ability for capacity degradation because too little data are available to facilitate establishing and optimizing the prediction models. To overcome the above-mentioned drawbacks, this study introduces the Mann-Kendall trend analysis to generate an equivalent degradation indicator (EDI), and to replace the capacity-based SOH. The proposed EDI has good linearity and monotonicity, and is conducive to adopt a simple structured prediction model to determine the RCDC. Besides, this study is based on the “SOH-EDI” synchronization mapping relationship and applies an one-degree polynomial regression model to estimate the EoL threshold on the EDI curve. From the perspective of computational complexity, the proposed framework uses two polynomial prediction models with simple structures, which realizes a low computational burden and online RCDC prediction. To verify the efficiency of the proposed method, this paper introduces three methods for comparison. Experimental results show that the proposed framework has satisfied early-stage prediction ability of RCDC and has a superior prognosis efficiency. [ABSTRACT FROM AUTHOR]

Dynamic battery modeling uncertainties, even if low, may lead to significant performance degradation or even divergence of the state-of-charge estimation algorithm. This paper investigates the integration of the extended-Kalman-filter with the smooth-variable-structure-filter algorithms for state-of-charge estimation of lithium-ion batteries. The robustness of the presented approach to modeling uncertainty is assessed for batteries operating in highly dynamic environments. The presented approach combines the benefit of the smooth- variable-structure-filter in its robustness to model uncertainty with the benefit of the extended-Kalman-filter in its near-optimality for a given dynamics and measurement noise sequences. The algorithm is rigorously tested using various datasets including standardrized and artificial drive cycles with added dynamics. The drive cycle power profile is calculated for an electric Ford F150 truck and scaled for the 18650PF cell used in the tests. Experimental validation is performed by investigating four different scenarios in which knowledge of the initial conditions as well as accuracy of the battery model were varied. The results demonstrate a substantially enhanced estimation accuracy achieved by the adopted approach through its optimality to measurement and model noise as well as its robustness to model uncertainty. The adopted approach results in a reduction in the complexity of the state-of-charge control algorithm and therefore enhances the battery management system. [ABSTRACT FROM AUTHOR]

This paper studies the driving of white light emitting diodes (LEDs) from very low voltages in the 1.2 to 2 V range. The proposed idea is based on replacing the standard Schottky diode used in conventional converter topologies (i.e., buck, buck–boost, and boost) with an LED while shortcutting the output of the converter. In this configuration, the LED works both as the load and the rectifier diode of the converter, hence switching the LED at high frequencies (>100 kHz). Moreover, a thorough analytical study is carried out for the two topologies proposed in this paper, emphasizing in particular their static analysis and the obtaining of the boundaries between the different conduction modes. Finally, the idea is experimentally validated by means of the boost converter variation (i.e., the DL//S ac-LED driver). The DL//S ac-LED driver was also compared with a dc–dc boost converter, showing a better luminous efficacy while disposing of the Schottky diode and the output capacitor. The analysis was carried out when connected to a Li-ion battery using a simple control and integrated circuit for its development. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to review the recent literature regarding the effects of low temperatures on Lithium ion (Li-ion) batteries for electric vehicle, plug-in hybrid electric vehicle, and hybrid electric vehicle applications. Special consideration is given to nine major effects that directly or indirectly impact the battery use at low temperatures and it is presented that they are correlated to each other. The main discussions in this paper are capacity loss, power loss, life degradation, safety hazard, unbalanced capacity, charging difficulty, thermal management system complexity, battery model and state estimation method complexity, and incremental cost. The effects correlations and possible solutions are explained to provide a detailed, yet broader understanding of the Li-ion batteries low-temperature operating scenarios. [ABSTRACT FROM AUTHOR]

Existing charging techniques for lithium-ion batteries use a largely open-loop approach, where the charge profile is predecided based on a priori knowledge of cell parameters. There is a need for closed-loop charging techniques that use instantaneous cell voltage and/or temperature to modulate the charging current magnitude. This paper addresses this gap by proposing a constant-temperature constant-voltage (CT-CV) charging technique, considering cell temperature as a key degradation metric. The proposed CT-CV charging scheme employs a simple and easy-to-implement proportional-integral-derivative (PID) controller aided by a feed-forward term. The charging current is dynamically adjusted in response to the battery temperature, which indirectly reflects its aging and thermal environment. As per experimental results, the proposed method achieves 20% faster charging with the same total temperature rise as constant-current constant-voltage (CC-CV) technique. Alternatively, it causes 20% lower cell temperature rise for given total charge time. It can easily accommodate applications that demand even faster charging by simply raising the set temperature. This paper establishes the benefits of the proposed CT-CV charging at cell level and raises the possibility of extending it to the pack level by integrating it with battery management systems. [ABSTRACT FROM AUTHOR]

This paper investigates the role of artificial neural networks in enhancing the accuracy of instantaneous power estimation of electric vehicles’ batteries. In electric vehicles, a battery is used as a main or complementary bidirectional power source. To optimize the energy management of the vehicle, the power sourced or sinked by the battery must be estimated in real time under any condition. The power of the battery is a function of many variables including the current, the state of charge, the ambient temperature, and the state of health. This paper evaluates some existing equivalent circuit models for estimating the instantaneous power of electric vehicles' batteries and proposes new artificial neural network-based models to enhance the power estimation accuracy. The experimental data obtained by performing standardized electric vehicle tests using a 3.6-V/16.5-Ah lithium-ion battery cell and a 12.8-V/150-Ah lithium-ion battery pack are presented and used for models' evaluation. [ABSTRACT FROM AUTHOR]

This paper proposes data transmission method between primary and secondary of the flyback converter without additional communication circuit while simultaneously transferring power. In some application such as a battery charger, the data exchanges between the primary and secondary sides are necessary. In the conventional system, an additional line or wireless communication modules is used for data exchanges, thereby increasing the system and connector size. The proposed system, in comparison, does not use additional signal transceiver but instead exchanges data by simply alternating operation mode of the flyback converter, thus adding communication function while not increasing the volume of the terminal and overall system. The waveform of transformer voltage is used to count the number of resonant pulses, which is used for decoding and encoding the data packet. Bidirectional communication between primary and secondary sides is possible while power is transferred to the output using an appropriate communication protocol. This paper proposes data transmission method for both single output and the multioutput cases. Also, both half-duplex and full-duplex communication using the proposed method is explained. The experimental results are presented to verify the performance of the proposed communication method. [ABSTRACT FROM AUTHOR]

A plug-in hybrid electric vehicle (PHEV) uses an internal combustion engine to extend its cruising range, and to produce the electric power needed to be supplied to its electric motor when the charge level of the vehicle's battery becomes low and reaches a predetermined state of charge (SOC). This paper provides a better solution by replacing the internal combustion engine of a PHEV with a small-size photovoltaic (PV) module located on the roof of the PHEV, and a micro wind turbine located in front of the PHEV, behind the condenser of the air conditioning system. Thus, this study proposes a novel battery/PV/wind hybrid power source to be utilized in PHEVs. The power source equipped with vehicle-to-grid (V2G) technology is composed of a 19.2-kWh Lithium (Li)-ion battery used as the main energy storage device, and a PV module and a wind energy conversion system. A prototype of the battery/PV/wind hybrid power source has been constructed and utilized in a PHEV. Experimental verifications are presented that demonstrate utilizing the PV module and micro wind turbine adds 19.6 km to the cruising range of a PHEV with the weight of 1880 kg during two sunny days, and provides higher power efficiency (91.2%) and speed (121 km/h). Highly accurate dc-link voltage regulation and producing an appropriate three-phase stator current for the traction motor by using pulse width modulation technique are the other contributions of this paper. [ABSTRACT FROM AUTHOR]

In recent times, lithium iron phosphate (LFP) batteries have found wide usage in electric vehicles (EVs). Efficient utilization of the storage capacity of any battery used in EVs is heavily dependent on the accuracy of state of charge (SOC) estimation. The classical SOC-OCV (open circuit voltage) based approach for estimation of SOC is quite ineffective under rapid variation in operating condition of EVs. The impact of parameter variations and unknown disturbances due to widely varying environmental conditions and drive terrain, demands a robust observer for SOC estimation. In this regard, this paper proposes a novel SOC estimation technique using a nonlinear fractional-order unknown input observer (FOUIO). The scheme based on the combined framework of fractional calculus and state estimation with unknown input is able to meet the challenges of SOC estimation in EVs by decoupling the plant disturbance inputs from the state estimation process. The optimally designed FOUIO using a linear matrix inequality (LMI) based formulation has been analytically proved for convergence. The proposed FOUIO has been validated and compared with reported SOC estimation schemes for different drive profiles in terms of accuracy and convergence speed. The numerical simulations and hardware in loop (HIL) based experimental results confirm the effectiveness of the proposed FOUIO in estimating the SOC robustly under different scenarios of battery operation. [ABSTRACT FROM AUTHOR]

This paper proposes a flexible data-driven method for online estimating the State of Health (SOH) of Li-ion batteries in both charging and discharging modes. Based on comprehensive battery aging data analytics, a novel health indicator called voltage variance during equal time interval (VVETI) is extracted. The VVETI can be extracted during either charging or discharging mode. For online applications, the indicator is derived from a small segment of charge/discharging curves. Unlike existing methods, the indicator can be extracted in various voltage ranges which is highly flexible for application. Then, a hierarchical ensemble model of extreme learning machine (ELM) is proposed as the machine learning engine for accurate SOH estimation. The proposed method is tested on three open datasets and reports a very high accuracy (average RMSE below 0.5% under appropriate voltage ranges). [ABSTRACT FROM AUTHOR]

In order to keep a lithium-ion battery within optimal temperature range for excellent performance and long lifespan, it is necessary to have an effective control strategy for a battery thermal management system (BTMS) consisting of electric pump, cooling plate and radiator. In this paper, a control-oriented model for BTMS is established, and an intelligent model predictive control (IMPC) strategy is developed by integrating a neural network-based vehicle speed predictor and a target battery temperature adaptor based on Pareto boundaries. The strategy is applied to plug-in electric vehicles operating in electric vehicle mode. Results show its superiority in terms of battery temperature control, battery lifespan extension and energy saving. Under the new European driving cycle, average difference between the real-time battery temperature under the novel IMPC and its target temperature is 0.26 °C, and maximum temperature difference among modules is 1.03 °C. Moreover, compared with the on-off controller, model predictive control (MPC), and MPC with VSP, state of health under IMPC at the end of the driving cycle is 0.016%, 0.012%, and 0.008% higher, respectively. At this moment, the energy consumption of IMPC is 24.5% and 14.1% lower than that of the on-off controller and traditional MPC, respectively. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in intelligent battery management systems (BMSs). The current RUL prediction methods are mainly developed based on offline training, which are limited by sufficiency and reliability of available data. To address this problem, this paper presents a method for RUL prediction based on the capacity estimation and the Box-Cox transformation (BCT). Firstly, the effective aging features (AFs) are extracted from electrical and thermal characteristics of lithium-ion batteries and the variation in terms of the cyclic discharging voltage profiles. The random forest regression (RFR) is then employed to achieve dependable capacity estimation based on only one cell's degradation data for model training. Secondly, the BCT is exploited to transform the estimated capacity data and to construct a linear model between the transformed capacities and cycles. Next, the ridge regression algorithm (RRA) is adopted to identify the parameters of the linear model. Finally, the identified linear model based on the BCT is employed to predict the battery RUL, and the prediction uncertainties are investigated and the probability density function (PDF) is calculated through the Monte Carlo (MC) simulation. The experimental results demonstrate that the proposed method can not only estimate capacity with errors of less than 2%, but also accurately predict the battery RUL with the maximum error of 127 cycles and the maximum spans of 95% confidence of 37 cycles in the whole cycle life. [ABSTRACT FROM AUTHOR]

In this paper, we examine several different types of miniaturized plasma sources that have been developed for nonpropulsion applications, but could be useful for the advancement of electric propulsion. With the same or similar physical principles and often similar design solutions, such sources suggest useful pathways for modernization and integration of presently available well established as well as emerging miniaturized plasma sources into space thruster systems. Features related to miniaturization and optimization of the technological plasma sources will provide useful insights for consideration by the electric propulsion specialists. It is not the aim of this paper to show an entire spectrum of technological microplasma systems, but rather to outline possible future trends and perspectives for the miniaturized technological plasmas in relation to space micropropulsion systems. [ABSTRACT FROM AUTHOR]

This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate ( \textLiFePO_4) and lithium titanate ( \textLTO) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs’ battery management system (BMS). [ABSTRACT FROM PUBLISHER]

This paper presents a method for an online real-time electrochemical impedance spectroscopy (EIS) measurement of batteries using closed-loop control of power converter. Unlike the previously proposed method which allows the measurement of the ac impedance for a single frequency, the presented method in this paper allows for obtaining the EIS for a spectrum of frequencies by using the information included in a single perturbation cycle, or a few cycles of perturbation to obtain a more accurate EIS with a very wide frequency range. This will result in faster EIS measurement for a spectrum of frequencies and under the same battery operating conditions. The presented method utilizes closed-loop control operation for the EIS measurement functionality, which allows for better control of the output voltage and for upgrading the concept to be able to achieve no added perturbation ripple at the output of the system. The presented online real-time EIS measurement method utilizes a power converter with closed-loop control in order to create an output voltage step-function perturbation at a given frequency to generate battery voltage and current responses. By applying Fourier analysis to these responses, an EIS can be obtained for a range of frequencies equal or higher than the perturbation frequency of the step function. In addition, this paper presents a method to eliminate the added perturbation ripple when two or more power converters are used. The theoretical basis and experimental prototype results are provided to illustrate and validate the presented method. [ABSTRACT FROM PUBLISHER]

This paper presents the wideband system identification (WSI) technique, i.e., an online method to identify power impedances over a wide frequency range from which the corresponding parametric impedance can be calculated online as well. The WSI technique exploits an existing custom 25-kW power electronic converter on the top of its power conversion function, which serves as power amplifier of an existing power hardware-in-the-loop (PHiL) simulation setup. The PHiL simulation technique allows connecting a real device under test (DUT) with the real-time simulated rest of system (ROS) at power level. An interface algorithm (IA) on simulation side and a power amplifier (the 25-kW power electronic converter) connect ROS and DUT. This paper shows the impact of the uncertainties in the WSI chain on the accuracy of the impedance identification and highlights how the WSI technique can be combined with the damping impedance method IA to improve both accuracy and stability of the PHiL test bench. The application of the method is illustrated for the scenario of a PHiL test of a dc microgrid with a passive load. [ABSTRACT FROM PUBLISHER]

A hybrid method for the prediction of the remaining useful life (RUL) of Lithium-ion batteries considering error-correction is proposed in respect of capacity diving phenomenon in later capacity degradation. First, an improved empirical capacity degradation model is proposed based on our previous work, with the analytic expression further revised in this paper to enhance its fitting accuracy, parameter identifiability, and applicability to tracking algorithm. Unscented particle filter (UPF) algorithm is then implemented to obtain prognostic results with original error series. Next, to enhance the quality of original error data by reducing the local uncertainty, complete ensemble empirical mode decomposition (CEEMD) algorithm is utilized to reconstruct error series. The fundamental error evolution information is retained by selecting relatively highly correlated intrinsic mode functions (IMFs) of the decomposition results of original error series. Finally, after employing Gaussian process regression (GPR) algorithm, the prognostic error to correct the UPF-based prognostic result is obtained from the reconstructed error series. RUL prediction experiments for batteries with different working conditions have been conducted to verify the improved performance of the proposed model and the hybrid method, with mean absolute percentage errors of battery capacity degradation predicted less than 0.4%. [ABSTRACT FROM AUTHOR]

This paper proposes a battery charger (BC) for electric vehicles based on sinusoidal-ripple-current (SRC) method. The SRC method is used as an advanced charging-discharging method. On the one hand, the sinusoidal current variations alternately change output active power of the BC, negatively affect AC-side terminal current of the BC, and therefore, cause a power quality problem. On the other hand, the output active and reactive powers must be regulated at constant values without any variations for participation in vehicle-to-grid technology. Thus, a particular topology is suggested and then a control system is precisely designed in order to implement SRC charging-discharging method and adjust the output active and reactive powers for vehicle-to-grid technology without the power quality problem. SRC method can also be used in online electrochemical impedance spectroscopy (EIS) for state of charge and state of health estimation in battery management. The proposed control system can also generate the SRC signals, suitable for online EIS, in a controlled way unlike the recently proposed BCs. The simulation and experimental results demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Nowadays, electric vehicles such as cars and bicycles are increasing their popularity due to the rising environmental consciousness. The autonomy required by these means of transport has marked a significant and steady growth in the development of battery technologies. In this sense, it is crucial to estimate and prognosticate critical parameters of battery packs such as the State of Charge (SOC), the State of Maximum Power Available (SoMPA), and the Failure Time. All these indicators are relevant to determine if both the energy stored in the battery of electric vehicles and power specifications are sufficient to successfully complete a required route, avoiding battery preventive disconnection before arrival. In this regard, this paper presents a novel approach to estimate and prognosticate the SOC and SoMPA of Lithium-Ion batteries in the context of electromobility applications. The proposed method uses the formulation of an optimization problem to find an analytical relationship between the SOC and the SoMPA; whereas the battery pack is modeled in terms of both the polarization resistance and the SOC. Particle filtering algorithms are used to compute online estimates and prognostic results, while the characterization of the usage profile of the battery bank is achieved using probability-based models (Markov chains). The problem of battery monitoring for an electric bicycle is used as a case study to validate the proposed scheme, when driven in flat and sloped routes to generate different usage profiles. It is demonstrated that the proposed methodology allows to successfully prognosticate both SOC and SoMPA when the future discharge current profile is characterized in terms of probability-based models. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for lithium-ion battery electrode (LIBE) surface quality assurance. First, based on machine vision, an automatic optical inspection system is developed to check defects on LIBE. In addition, a background normalization algorithm is put forward to preprocess the large-scale LIBE with inhomogeneous thickness in uneven illumination. With the help of the auto-concentration compensation algorithm, flaws can be extracted precisely. Moreover, after characterizing the defects, features machine applied partiality parameter automatic adjustment method and partiality decision rules are exerted for defects accurate classification, which provides near-optimal performance and reduces the complexity of tuning parameters. The proposed method is computationally efficient and satisfies real-time online inspection requirement. Experimental results verify the effectiveness and performance of the proposed method according to the inspection speed and recognition rate. [ABSTRACT FROM AUTHOR]

Lithium-ion rechargeable batteries are widely used in various electronic products and equipment due to their immense benefits in power supplying. The exact remaining useful life (RUL) prediction of lithium-ion batteries has shown excellent achievements in preventing severe economic and security consequences incurred in failing to provide necessary power levels. Recently, the nonlinear-drifted fractional Brownian motion made quite a splash in RUL prediction, since its first hitting time distribution can be approximated by weak convergence theorem and time-space transformation. However, the previous RUL prediction methods based on fractional Brownian motion only considered current state measurement. In this paper, a prediction framework based on nonlinear-drifted fractional Brownian motion with multiple hidden state variables is put forward to estimate RUL. Specifically, all the parameters of nonlinear function are defined as specific hidden state variables of lithium-ion battery degradation model, and all the state measurements are used to posteriorly estimate the distribution of the multiple hidden state variables by unscented particle filter algorithm. Four sets of lithium-ion battery degradation data provided by NASA Ames Research Center are used to validate the proposed prediction framework. According to comparison study with other methods, the proposed prediction framework demonstrates greater precision in the RUL prediction. [ABSTRACT FROM AUTHOR]

This paper investigates the real-time estimation on the state-of-charge (SoC) and state-of-health (SoH) of lithium-ion (Li-ion) batteries for the purpose of achieving reliable, safe, and efficient use of batteries. Three terminal sliding-mode observers (TSMOs) are designed; each observer is used to estimate one variable of a Li-ion cell for developing a real-time SoC estimation algorithm. To estimate the SoH, two additional TSMOs are subsequently presented. Finally, a set of complete estimation algorithms for SoC and SoH are formulated. The output injection signals of the proposed TSMOs are designed to be continuous. This can attenuate chattering that exists in the traditional sliding-mode observers and simplify the estimation algorithms. The main advantage of the proposed algorithms is eliminating the low-pass filter in the estimation algorithms. Therefore, higher estimation accuracy and faster response speed are obtained. The proposed methods are tested and evaluated using the acquired dynamic stress test and federal urban driving schedule test data, which demonstrate the effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

The thermal effect has a significant impact on the performance of a lithium-ion battery. Thus, modeling the thermal process, which always involves unknown boundary heat exchange, is significant to battery management. Two critical issues should be addressed for the thermal process modeling: 1) the nominal model, which is constructed offline, can be updated efficiently to compensate for any online disturbances; and 2) the influence of previous and recent spatiotemporal dynamics may be varying and should be handled properly. Bearing these in mind, in this paper, a spatial correlation-based incremental learning technique is designed for spatiotemporal modeling. First, the incremental learning technique is developed to update the dominant spatial basis functions of the nominal model, which is constructed by a time/space separation-based method. Then, a forgetting factor is incorporated into the incremental learning technique to handle time-varying dynamics. Additionally, the popular approximator, that is, the radial basis function neural network, is utilized to identify the low-dimensional temporal model. Simulations and experiments on a pouch type battery with boundary heat exchange have demonstrated the accuracy and efficiency of the proposed modeling method. [ABSTRACT FROM AUTHOR]

Success of electric mobility and connected future depends on advanced high capacity Lithium-ion batteries and a tailored battery management system that keeps track of battery health and safety to optimize the performance. The drive for advanced batteries is being pursued on one front via the development of novel chemistry, for example, blended composite cathodes to achieve enhanced performance and life. On the other front, the need for optimal battery performance via operational control viz. a robust Battery Management System (BMS) that accurately predicts state-of-charge (SOC), state-of-power (SOP) and state-of-health (SOH) in a computationally efficient way, has been lacking in many ways due to the reliance on simple and fast-to-compute models such as equivalent circuit models (ECM) that lack the accuracy needed for large battery packs. This paper reports an on-board reduced-order electrochemical thermal model (ROTM) based SOC and voltage estimation for a 12S1P configuration composite-cathode battery pack and demonstrates its practicality by implementing it on four different micro-controller units (MCU) (ATmega:2560&328, Infineon:TC275&TC297). We show that on-board ROTM based SOC and voltage prediction have better accuracy compared to the conventional ECM based methods under both static and dynamic load conditions. [ABSTRACT FROM AUTHOR]

This paper devises an innovative mesoscale electrothermal model for Li-ion batteries. This model manipulates the mesoscale calculation grid in finite element analysis as independent small cell sandwiches and establishes a lumped equivalent circuit model for each cell sandwich. Then, such electrical models are arranged in parallel to form a multilayer equivalent circuit to simulate electrical characteristics of a whole battery, through capturing the current and terminal voltage of each constituent cell sandwich. This modeling idea overcomes the entrenched disadvantage of heat generation models with lumped parameters, i.e., the unavailability of heat generation distribution inside a battery. Besides the current and terminal voltage, the temperature and state of charge dependent open-circuit voltage and entropy coefficient are incorporated into a Newman's heat generation model to estimate the heat generated in the calculation grid. The battery temperature distribution is eventually derived by solving the heat conduction equation with thermal conductivity as a function of the battery temperature. We leverage the developed electrothermal model to track the temperature evolution of an 18 650 Li-ion battery at different ambient temperatures and discharge rates, for the first time. Experimental results demonstrate that the electrothermal model can precisely emulate the battery thermal dynamics with an average error of 0.72 °C. Moreover, a comparative study shows that the proposed model outperforms common resistance-based thermal models that do not consider the heat generation distribution and the interdependence between the battery temperature and thermal conductivity. [ABSTRACT FROM AUTHOR]

Battery safety is one of the most crucial issues in the utilization of lithium-ion batteries (LiBs) for all-climate electric vehicles. Short circuit, overcharge, and overheat are three common field failures of LiBs. In this paper, online fault diagnosis for external short circuit (ESC) of LiB packs is investigated. The experiments are carried out to obtain and compare ESC characteristics of 18650-type NMC battery pack and single cell. Based on the analysis of experimental results, a two-step equivalent circuit model is established to describe the ESC process and an online model-based scheme is proposed to diagnose ESC faults of battery packs. The proposed scheme is evaluated by experimental data. The results show that it can effectively diagnose ESC faults in 3.5 s after their occurrences with the terminal voltage error less than 25 mV. The proposed scheme has shown great generalization ability. ESC faults of battery packs under different number of cells connected in series and unavailable current information can also be diagnosed at the terminal voltage error less than 48 and 60 mV, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a highly effective voltage cell equalization method for lithium-ion (Li-ion) battery management systems (BMSs) for several applications, such as nearly zero energy buildings, microgrids, and electric vehicles. The concept of the suggested method is the proper control of the charging current of each cell of a battery stack through the suitable regulation of the gate source of each cell's parallel-connected mosfet. Thus, the mosfet can act as a controllable current source for providing the proper equalization current according to the specific needs of each battery cell. The correct voltage level of the gate-source of each mosfet is determined by a cell equalization controller through the comparison of the performance of each cell of a battery stack with the healthier cell, which is detected by an aging estimation algorithm based on the evaluation of the internal resistor and the capacity fade of each cell. Therefore, smoother and faster cell equalization is achieved compared to the conventional dissipative method. The practicality and the high effectiveness of the proposed cell equalization technique have been experimentally validated. Several experimental results are presented to demonstrate the operational improvements of the proposed cell equalization technique against the conventional one. [ABSTRACT FROM AUTHOR]

Lithium iron phosphate battery packs are widely employed for energy storage in electrified vehicles and power grids. However, their flat voltage curves rendering the weakly observable state of charge are a critical stumbling block for charge equalization management. This paper focuses on the real-time active balancing of series-connected lithium iron phosphate batteries. In the absence of accurate in situ state information in the voltage plateau, a balancing current ratio (BCR) based algorithm is proposed for battery balancing. Then, BCR-based and voltage-based algorithms are fused, responsible for the balancing task within and beyond the voltage plateau, respectively. The balancing process is formulated as a batch-based run-to-run control problem, as the first time in the research area of battery management. The control algorithm acts in two timescales, including timewise control within each batch run and batchwise control at the end of each batch. Hardware-in-the-loop experiments demonstrate that the proposed balancing algorithm is able to release 97.1% of the theoretical capacity and can improve the capacity utilization by 5.7% from its benchmarking algorithm. Furthermore, the proposed algorithm can be coded in C language with the binary code in 118 328 bytes only and, thus, is readily implementable in real time. [ABSTRACT FROM AUTHOR]

In this paper, a battery balancing circuit is proposed for the series-connected lithium-ion battery cells based on the principle of synchronous rectification. The proposed balancing circuit, also referred to as an equalizer, mainly includes a buck–boost converter (BBC), a multiport half-bridge converter (MHBC), and a driving circuit. The MHBC is coupled with a multiwinding transformer. Compared with the conventional congeneric methods, the number of transformer windings is almost reduced by half, leading to a more compact size and lower cost. Moreover, the secondary mosfets of the MHBC are synchronously driven by the primary half-bridge converter (HBC) without the need of additional isolated mosfet gate drivers and power supplies, dramatically reducing the circuit cost and complexity. In addition, the proposed equalizer can realize the module-to-multi-cell (M2MC) equalization under severe imbalance conditions to improve the balancing speed, and work at the any-cell-to-any-cell (AC2AC) self-balancing mode under slight imbalance conditions to improve the balancing efficiency and effectiveness, which achieves a good trade-off between balancing speed and effectiveness. The expressions of the equalization current and efficiency are derived and verified by experimental results. The proposed equalizer is experimentally evaluated for a string with 12 series-connected lithium-ion cells. [ABSTRACT FROM AUTHOR]

This paper studies a multilevel multiphase dc–ac conversion system configured by a neutral-point-clamped converter fed by multiple battery packs connected in series. A virtual-vector modulation is selected and a state-of-charge (SoC) balancing control is designed to be able to employ the full battery bank capacity, even under different battery initial SoC values or different battery nominal capacities. The SoC balancing among battery packs is accomplished through the multilevel converter operation in a lossless manner, by simply distributing the dc-to-ac power flow among the batteries according to their SoC. A simple average system model is also presented, which allows performing very fast system simulations over long periods of time and serves as a convenient tool to tune the compensator parameters. The satisfactory performance of the proposed system configuration and control, which can be applied with any number of levels and phases, has been verified through simulations and experiments in a four-level three-phase dc–ac converter fed by three lithium-ion battery packs. The results prove the feasibility and advantages of the proposed system configuration, which can be used to implement conversion systems with different specifications combining several instances of a standard battery pack and a standard power semiconductor device. [ABSTRACT FROM AUTHOR]

This paper proposes a novel error correction grey prediction model for degradation prediction of renewable energy storages. The proposed approach uses an error correction factor ψ to eliminate the inherent error of the original grey model (GM), and at the same time retain the original simplicity and fast prototyping. In addition, due to the uncertainty and complexity of failure mechanisms, a trigonometric residual modification is considered in order to well-describe the influence of operating conditions or cyclic fluctuation on the renewable energy storages. Two experimental case studies, including lithium-ion battery and fuel cell aging tests, are performed to validate the performance of the proposed method. In particular, the accuracy of the proposed method is investigated for different prediction horizon lengths, in order to further demonstrate its effectiveness and robustness. It is worth mentioning that the proposed method can ensure the accuracy of the remaining useful life estimation in the case of long-term forecasting, and thus, the maintenance management and corrective action of renewable energy storages can be scheduled earlier, leading to more effective cost minimization and risk mitigation. [ABSTRACT FROM AUTHOR]