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This paper presents an offline state-of-health (SoH) estimation based on charge transfer resistance for high-power lithium-ion (Li-ion) batteries, such as lithium iron phosphate (LFP) batteries. As shown in the experimental results, the charge transfer resistance has a great aging change with battery degradation and good abilities against state-of-charge (SoC) drift and external resistance variation in the impedance parameter set of a single-time-constant equivalent circuit model (ECM), including ohmic resistance, charge transfer resistance, double-layer capacitance, and time constant, for SoH estimation. A fast and efficient three-point (TP) impedance extraction method is also proposed in this paper for accurately extracting the charge transfer resistance in offline SoH estimation. The results of long-term cycling test demonstrate that the TP impedance extraction method can successfully indicate the SoH of LFP batteries with low estimation error of 6.1%. [ABSTRACT FROM PUBLISHER]

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]

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]

Electric vehicles based on high-energy lithium-ion batteries often exhibit a substantial loss in performance at subzero temperatures: Due to slower electrochemical kinetics, the internal resistances of the batteries rise and diminish available power and capacity. Hybrid energy storage systems (HESSs) can be used to overcome these weaknesses. In this paper, the performance of two HESSs, combining a high-energy lithium-ion battery with either a high-power lithium-ion battery or a lithium-ion capacitor, has been investigated experimentally for a driving scenario at various temperatures. Both configurations enable driving at -20 °C, which was not possible without hybridization. The HESS using the high-power lithium-ion battery provides a substantially higher driving range due to its higher energy density. An analysis of different operating strategies has helped to maximize the driving range: Discharging the high-energy battery with a constant current and keeping the high-power cell at a higher state of charge (SoC) extend the driving duration, as the requested driving power can still be provided at a lower SoC of the high-energy battery. In addition to the HESSs, two energy storage systems without hybridization, consisting of different generations of high-energy lithium-ion cells, have been examined to disclose improvements in battery technology. These improvements narrow the benefits of HESSs, as the high-energy batteries have become less reliant on the support of an additional high-power device. Although HESSs lose importance for current lithium-ion battery systems, they can be a valuable option for next-generation lithium batteries, which are expected to provide higher energy densities but exhibit reduced rate capability. [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]

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]

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]

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]

Generalized spatial modulation (GenSM) aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) has recently received substantial academic attention. However, due to the insufficient exploitation of the transmitter's knowledge of the channel state information, the achievable rates of state-of-the-art GenSM-aided mmWave MIMO systems are far from being optimal. Against this background, a novel analog precoding scheme is proposed in this paper to improve the spectral efficiency (SE) of conventional GenSM-aided mmWave MIMOs. More specifically, we firstly manage to lower-bound the achievable SE of GenSM-aided mmWave MIMO with a closed-form expression. Second, by exploiting this lower bound as a cost function, a low-complexity iterative algorithm is proposed to design the analog precoder for SE maximization. Finally, numerical simulations are conducted to substantiate the superior performance of the proposed design with respect to state-of-the-art GenSM-aided mmWave MIMO schemes. [ABSTRACT FROM PUBLISHER]

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]

The online estimation of battery state-of-health (SOH) is an ever significant issue for the intelligent energy management of the autonomous electric vehicles. Machine-learning based approaches are promising for the online SOH estimation. This paper proposes a machine-learning based algorithm for the online SOH estimation of Li-ion battery. A predictive diagnosis model used in the algorithm is established based on support vector machine (SVM). The support vectors, which reflects the intrinsic characteristics of the Li-ion battery, are determined from the charging data of fresh cells. Furthermore, the coefficients of the SVMs for cells at different SOH are identified once the support vectors are determined. The algorithm functions by comparing partial charging curves with the stored SVMs. Similarity factor is defined after comparison to quantify the SOH of the data under evaluation. The operation of the algorithm only requires partial charging curves, e.g., 15 min charging curves, making fast on-board diagnosis of battery SOH into reality. The partial charging curves can be intercepted from a wide range of voltage section, thereby relieving the pain that there is little chance that the driver charges the battery pack from a predefined state-of-charge. Train, validation, and test are conducted for two commercial Li-ion batteries with Li(NiCoMn)1/3O2 cathode and graphite anode, indicating that the algorithm can estimate the battery SOH with less than 2% error for 80% of all the cases, and less than 3% error for 95% of all the cases. [ABSTRACT FROM AUTHOR]

Real-time battery state-of-charge (SOC) estimation is critical in many applications. The extended Kalman filter (EKF) has been successfully deployed in SOC estimation allowing real-time SOC monitoring. However, modeling inaccuracies, measurement faults, and wrong initialization can cause the estimation algorithm to diverge. The precise knowledge of statistical information about process and measurements noise is crucial for accurate system modeling and estimation. This paper presents a novel SOC estimation approach based on maximum-likelihood estimation (MLE). The process and measurements models are transformed to an error state propagation system where the innovation covariance is utilized to maximize the likelihood of the multivariate innovation distribution with respect to process and measurement covariances. The MLE formulation allows the estimation of the process and measurement noise covariance magnitudes, which are used to obtain an optimal SOC estimate. The proposed method is validated experimentally using a number of Li-ion battery cells under various testing conditions. The estimation performance is compared with that of the conventional EKF technique as well as previously published results based on autocovariance least-squares measurements noise estimation. The results indicate an enhanced performance for the new algorithm over the traditional EKF across all conducted tests. [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]

This paper presents coordinated power management strategies for a hybrid electric boat (HEB) based on dynamic load's power distribution approach using diesel generators and batteries. Two variable-speed diesel generator sets connecting to dc-bus via fully controlled rectifiers serve as the main power supply source; one lithium-battery pack linked to dc-bus through a bidirectional DC/DC converter serves as an auxiliary power supply source. The power requirement of the thrusters and other onboard equipment is represented by a load power profile. The main contribution of this paper is focused on the load power sharing of the HEB between the two variable-speed diesel generators and the lithium-battery pack, through the fluctuations extracted from load power and their assignation to the batteries. Another important issue is that when the total load demand is low, one of the diesel generator set will be switched off to improve the diesel-fuel efficiency. The output powers of the diesel generators and the batteries are controlled based on the power distribution strategy via the converters controlling. The performances of the proposed hybrid power system control are evaluated through simulations in MATALB/Simulink, and through reduced-scale experimental tests carried out for a specific driving cycle of the HEB. [ABSTRACT FROM PUBLISHER]

This paper studies wireless charging of lithium-ion batteries for electric vehicles. The charging profile mandates a constant-current (CC) charging for a discharged battery until the battery voltage reaches the cutoff voltage at rated power. The charging continues at the cutoff voltage with a constant-voltage (CV) charging at a power level down to 3% of the rated power in order to fully charge the battery. An inductive-power-transfer (IPT) converter should be designed with minimal number of stages to achieve high efficiency. However, high efficiency for such a wide load range is difficult to achieve. Moreover, the efficiency-to-load relationship is distinctly different for CC and CV charging operations, posing difficulties for the single-stage design. This paper describes the design of a single-stage IPT converter that complies with the battery charging profile and, at the same time, achieves optimal efficiency. Design optimization includes soft switching for the entire battery load range, efficiency optimization for CC and CV modes of operation, and system efficiency optimization for the whole battery charging profile. Measured results of two experimental IPT battery chargers are presented for illustration and verification. [ABSTRACT FROM PUBLISHER]

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]

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]

The performance of lithium-ion batteries is contingent on the operational temperature and applied current rate. Especially in electric vehicle application, repeated heavy-duty drive cycles impose intense thermal stress on batteries, resulting in safety concerns. This paper explains the electrochemistry within the battery and analyzes the dependence of battery internal parameters on temperature and current rate based on their electrochemical nature. In addition, this interdependence is used in the equivalent circuit model to predict the battery behavior under different drive cycles. Finally, the behavior under different drive cycles is analyzed to improve the battery performance in electric vehicles. [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]

Simulation techniques are extensively used in vehicle performance evaluations, particularly for advanced vehicles such as hybrid electric vehicles (HEVs), plug-in HEVs, and battery electric vehicles. It is necessary that the parameters used in simulation models are estimated properly so that the simulations can produce results that are close to real-world behaviors. This paper suggests methodologies for estimating parameters for a lithium-ion (Li-ion) battery model by analyzing test data obtained from chassis dynamometer tests. A representative model based on a first-order equivalent circuit is used for the battery, and four main parameters of the model—source voltage, internal resistance, polarization resistance, and polarization capacitance—are obtained by characterizing the test results. The model is validated with the test data by applying the estimated parameters in the model, and the validation results show that the battery output voltage is calculated by the simulation model very well; 70% of simulations produce the output voltage within 1% of the root-mean-square error, as compared with the test data. Although the methodology cannot replace a conventional process that estimates the battery parameters from dedicated tests, the approach would be very feasible and save the effort and time needed to develop simulation models for Li-ion batteries. [ABSTRACT FROM PUBLISHER]

Accurate battery state-of-charge (SOC) estimation in real time is desired in many applications. Among other methods, the extended Kalman filter (EKF) allows for high-accuracy real-time tracking of the SOC. However, an accurate SOC model is needed to guarantee convergence. Additionally, knowledge of the statistics of the process noise and the measurement noise is needed for high-accuracy SOC estimation. In this paper, two methods, namely, the multiple-model EKF (MM-EKF) and the autocovariance least squares technique, are proposed for estimating the SOC of lithium-ion (Li-ion) battery cells. The first method has the advantage of minimizing the EKF algorithm's dependence on the correct assumptions of the measurement's noise statistics, thus, minimizing the impact of model mismatch. The MM-EKF assumes a number of hypotheses for the unknown measurement noise covariance. An EKF is assigned for each assumed measurement noise covariance. The SOC estimate is then obtained by probabilistically summing up the estimates of the hypothesized EKFs. On the other hand, the second method assumes that the measurement noise is unknown and determines its value from the statistics of the EKF. Given an initial and possibly wrong assumption of the measurement noise covariance, the method accounts for possible correlation in the measurement innovations. The estimated measurement noise covariance is subsequently used to obtain an optimal SOC estimate. The proposed methods are evaluated and compared with the conventional EKF method on experimental test data obtained from a 3.6-V Li-ion battery cell. [ABSTRACT FROM PUBLISHER]

Lithium-ion (Li-ion) batteries widely used in electric vehicles (EVs) and hybrid EVs (HEVs) are insufficient for vehicle use after they have degraded to 70% to 80% of their original capacity. Battery lifespan is a large consideration when designing battery packs for EVs/HEVs. Aging mechanisms, such as metal dissolution, growth of the passivated surface film layer on the electrodes, and loss of both recyclable lithium ions, affect the longevity of the Li-ion battery at high-temperature operations. Even vehicle maneuvers at low temperatures (T < \mbox0\ ^\circ\mboxC) contribute to battery lifetime degradation, owing to the anode electrode vulnerability to other degradation mechanisms such as lithium plating. Nowadays, only a few battery thermal management schemes have properly considered low-temperature degradation. This is due to the lack of studies on aging of Li-ion batteries at subzero temperature. This paper investigates how load cycle and calendar life properties affect the lifetime and aging processes of Li-ion cells at low temperatures. Accelerated aging tests were used to determine the effect of the ambient temperature on the performance of three 100-Ah LiFeMnP04 Li-ion cells. Two of them were aged through a normalized driving cycle at two temperature tests ( -\mbox20\ ^\circ\mboxC and 25 °C). The calendar test was carried out on one single battery at –20 °C and mid-range of state of charge (50%). Their capacities were continuously measured every two or three days. An aging model is developed and added to a preliminary single-cell electrothermal model to establish, in future works, a thermal strategy capable of predicting how the cell ages. This aging model was then validated by comparing its predictions with the aging data obtained from a cycling test at 0 °C. [ABSTRACT FROM PUBLISHER]

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]

Remaining useful life (RUL) prediction of lithium-ion batteries can assess the battery reliability to determine the advent of failure and mitigate battery risk. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning the long-term dependencies among the capacity degradations. This paper investigates deep-learning-enabled battery RUL prediction. The long short-term memory (LSTM) recurrent neural network (RNN) is employed to learn the long-term dependencies among the degraded capacities of lithium-ion batteries. The LSTM RNN is adaptively optimized using the resilient mean square back-propagation method, and a dropout technique is used to address the overfitting problem. The developed LSTM RNN is able to capture the underlying long-term dependencies among the degraded capacities and construct an explicitly capacity-oriented RUL predictor, whose long-term learning performance is contrasted to the support vector machine model, the particle filter model, and the simple RNN model. Monte Carlo simulation is combined to generate a probabilistic RUL prediction. Experimental data from multiple lithium-ion cells at two different temperatures is deployed for model construction, verification, and comparison. The developed method is able to predict the battery's RUL independent of offline training data, and when some offline data is available, the RUL can be predicted earlier than in the traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, we present an innovative and precise way to calculate the available capacity in a battery. This quantity is essential to assess the ageing process during real use or ageing tests. Classical methods for measuring the available capacity in a battery are very dependent of impedance and relaxation state of the battery. Consequently, these methods are not suitable to quantify reversible and irreversible capacity losses occurring on batteries. We propose an indirect measure of available capacity that reduces the distortion caused by battery relaxation and impedance changes. This new method provides more accurate results allowing to distinguish reversible from irreversible part of capacity losses. The obtained results on calendar ageing tests are used in a second part to model both self-discharge and capacity fade in a global approach by using the generalized Eyring relationship. [ABSTRACT FROM PUBLISHER]

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]

The conventional state-of-charge (SoC) estimation methods based on the equivalent circuit model (ECM) integrate all state variables into one augmented state vector. However, the correlations between RC voltages and SoC degrade the stability and accuracy of the estimates. To address this problem, this paper presents an adaptive SoC estimation method based on the split battery model, which divides the conventional augmented battery model into two submodels: the RC voltage submodel and the SoC submodel. Hence, the cross interference between RC voltages and SoC is reduced, which effectively reduces the oscillation in the estimation and improves the estimation accuracy. In addition, the adaptive algorithm is applied on the SoC submodel to improve the system robustness to noise disturbances. A case of a second-order ECM is analyzed and two types of Lithium-ion batteries are employed to verify the universality of the proposed method. Experimental results show that the undesired oscillation is eliminated during the convergence stage and the maximum SoC error is within 1% over a wide SoC range. [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]

Accurate estimation of state-of-charge (SoC) is vital to safe operation and efficient management of lithium-ion batteries. Currently, the existing SoC estimation methods can accurately estimate the SoC in a certain operation condition, but in uncertain operating environments, such as unforeseen road conditions and aging related effects, they may be unreliable or even divergent. This is due to the fact that the characteristics of lithium-ion batteries vary under different operation conditions and the adoption of constant parameters in battery model, which are identified offline, will affect the SoC estimation accuracy. In this paper, the joint SoC estimation method is proposed, where battery model parameters are estimated online using the H-infinity filter, while the SoC are estimated using the unscented Kalman filter. Then, the proposed method is compared with the SoC estimation methods with constant battery model parameters under different dynamic load profiles and operation temperatures. It shows that the proposed joint SoC estimation method possesses high accuracy, fast convergence, excellent robustness and adaptability. [ABSTRACT FROM PUBLISHER]

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]

Current consumption measurements are useful in a wide variety of applications, including power monitoring and fault detection within a lithium-ion battery management system (BMS). This measurement is typically taken using either a shunt resistor or a Hall-effect current sensor. Although both methods have achieved accurate current measurements, shunt resistors have inherent power loss and require isolation circuitry, and Hall-effect sensors are generally expensive. This paper explores a novel alternative to sensing battery current by measuring terminal voltages and cell temperatures and using an unknown input observer to estimate the battery current. An accurate model of a LiFePO4 cell is created, validated, and then used to characterize a model of the proposed current estimation technique. Finally, the current estimation technique is implemented in hardware and tested in an online BMS environment. Results show that the current estimation technique is sufficiently accurate for a variety of applications, including fault detection and power profiling. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive parameter identification technique is proposed for lithium-ion batteries. The proposed strategy capitalizes on the power of adaptive control theory to attain robustness to parameter variation. Therefore, accurate state-of-charge (SOC) and state-of-health (SOH) estimation is obtained since they are directly correlated to the battery's parameters. Unlike many estimation procedures, the proposed estimator's convergence and stability are guaranteed by Lyapunov's direct method. In addition, temperature variations introduce a drift on the battery's parameters, which reduces the estimation accuracy. Therefore, a postcompensation methodology is proposed using surface temperature to cope with these effects. The effectiveness of the proposed estimation scheme is validated through a set of experiments under different temperatures. [ABSTRACT FROM PUBLISHER]

This paper presents the ultracapacitors (U) and fuel-cell/lithium-battery connection with an original energy management method for hybrid electric vehicle (HEV) applications. The proposed method is focused on the frequency approach to meet the load energy requirement. The ultracapacitors are connected to the dc link through a buck–boost converter, and the fuel cell is connected to the dc link via a boost converter for the first topology. In the second topology, the lithium battery is connected to the dc link without a converter to avoid the dc-link voltage control. An asynchronous machine is used like the traction motor; it is related to the dc link through a dc/ac converter (inverter). The main contribution of this paper is focused on HEV energy management according to the dynamics (frequency) of the hybrid sources using polynomial correctors. The performances of the proposed method are evaluated through some simulations and the experimental tests, using the New European Driving Cycle (NEDC). This study is extended to an aggressive test cycle, such as the U.S. driving cycle (USDC), to understand the system response and the control performances. [ABSTRACT FROM PUBLISHER]

This paper studies the charging strategies of an Li-ion battery with a focus on the battery's power loss model. It is found that when the temperature is assumed constant, constant current/constant voltage (CC/CV) is near optimal for energy loss minimization. However, when the temperature is considered as a state variable, an optimal profile different from CC/CV is found. This new profile can be used to warm-up batteries during cold days, hence reducing the need for existing battery heating systems. As a result of the elevated battery temperature, significant energy savings can be achieved if the vehicle is used soon after the charging process ends. In this paper, the form of the optimization problem resembles a classic form, with the peculiarity of being highly nonlinear and time varying. The charging strategies resulting from this study will have significant impact on how plug-in hybrid electric vehicles (PHEVs) are charged and utilized. [ABSTRACT FROM PUBLISHER]

This paper presents the development of the electrical aspects of a Li-ion battery model that includes charge extraction due to current, battery capacity, effect of internal resistance, and thermal effects beyond only temperature rise due to power lost. Thermal models that represent temperature rise in the core and crust of each individual cell and a rate factor function that corrects the amount of charge extracted are developed to improve the accuracy of the battery characteristics. In addition, a predictive feature has been developed for this model so that it can predict the battery output characteristics over the selected operating range of temperatures and batteries with the limited amount of input data. In the end, a nine-cell stacking model is proposed and analyzed for its effect for different cooling methods (series and parallel cooling configurations). The simulation results show that the characteristics of the proposed model compared well with the published data. [ABSTRACT FROM PUBLISHER]

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]

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]

Batteries play a key role in achieving the target of universal access to reliable affordable energy. Despite their relevant importance, many challenges remain unsolved with regard to the characterization and management of batteries. One of the major issues in any battery application is the estimation of the state-of-charge (SoC). SoC, which is expressed as a percentage, indicates the amount of energy available in a battery. An accurate SoC estimation under realistic conditions improves battery performance, reliability, and lifetime. This paper proposes an SoC estimation method based on a new hybrid model that combines multivariate adaptive regression splines (MARS) and particle swarm optimization (PSO). The proposed hybrid PSO–MARS-based model uses data obtained from a high-power load profile (dynamic stress test) specified by the United States Advanced Battery Consortium (USABC). The results provide comparable accuracy to other more sophisticated techniques but at a lower computational cost. [ABSTRACT FROM PUBLISHER]

In hybrid vehicles, lithium-ion cells constituting a battery pack are frequently used to provide and recover high power to assist the vehicle's internal combustion engine (ICE) powertrain. This usage is more present in mild hybrid applications where the battery does not have long discharge time. Under such conditions, the pack's series resistance RS proved to be an important parameter to monitor since its evolution depends on the cell's characteristics (manufacturing tolerance, temperature, etc.). This resistance, which is monitored by the battery management system (BMS), reflecting the available power level in the cell can be used as an indicator to enhance the security of the battery pack. Its evolution can be used to quantify its aging (state of health: SoH). This paper presents an online approach to identify the cell's series resistance based on a direct estimation of RS. This parameter can be usually identified through the voltage drop occurring across the cell caused by a high current variation profile (mild hybrid conditions). These estimated values are then filtered with an “exponential moving average” method to limit the measurement noise effect. This approach provides good results for mild hybrid conditions, while minimizing the computing power required. [ABSTRACT FROM PUBLISHER]

This paper presents a robust sliding-mode observer (RSMO) for state-of-charge (SOC) estimation of a lithium-polymer battery (LiPB) in electric vehicles (EVs). A radial basis function (RBF) neural network (NN) is employed to adaptively learn an upper bound of system uncertainty. The switching gain of the RSMO is adjusted based on the learned upper bound to achieve asymptotic error convergence of the SOC estimation. A battery equivalent circuit model (BECM) is constructed for battery modeling, and its BECM is identified in real time by using a forgetting-factor recursive least squares (FFRLS) algorithm. The experiments under the discharge current profiles based on EV driving cycles are conducted on the LiPB to validate the effectiveness and accuracy of the proposed framework for the SOC estimation. [ABSTRACT FROM AUTHOR]

Support vector regression (SVR) algorithms have been applied to the identification of many nonlinear dynamic systems due to their excellent approximation and generalization capability. However, the standard SVR algorithm involves an iterative optimization process, which is often computationally expensive and inefficient. For applications such as the battery state-of-health (SOH) monitoring, where the identification algorithm needs to be applied repeatedly for multiple cells because of the variation in model dynamics (due to battery aging and cell-to-cell difference), the computational burden could pose difficulties for real-time or onboard implementation. In this paper, the battery V-Q curve identification problem for SOH monitoring is studied. Based on experimental battery aging data, we develop a model parametrization and adaptation framework utilizing the simple structure of SVR representation with determined support vectors (SVs) so that the model parameters can be estimated in real time. Through mathematical analysis and simulations using a mechanistic battery aging model, it is shown that the SVs of the battery models stay invariant, even when the batteries age or vary. The invariance of the SVs is verified using experimental aging data. Consequently, the resulting model for the battery V-Q curve can be directly incorporated into the battery management system (BMS) and adapted online for SOH monitoring. Moreover, the general characteristics of the data that could maintain the SVR invariance are identified. The proposed automated model parametrization process (via an optimization algorithm) can be extended to nonlinear dynamic systems with the given properties. [ABSTRACT FROM PUBLISHER]

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]

Lithium-ion batteries have emerged as the state-of-the-art energy storage for portable electronics, electrified vehicles, and smart grids. An enabling Battery Management System holds the key for efficient and reliable system operation, in which State-of-Charge (SOC) estimation and State-of-Health (SOH) monitoring are of particular importance. In this paper, an SOC and SOH co-estimation scheme is proposed based on the fractional-order calculus. First, a fractional-order equivalent circuit model is established and parameterized using a Hybrid Genetic Algorithm/Particle Swarm Optimization method. This model is capable of predicting the voltage response with a root-mean-squared error less than 10 mV under various driving-cycle-based tests. Comparative studies show that it improves the modeling accuracy appreciably from its second- and third-order counterparts. Then, a dual fractional-order extended Kalman filter is put forward to realize simultaneous SOC and SOH estimation. Extensive experimental results show that the maximum steady-state errors of SOC and SOH estimation can be achieved within 1%, in the presence of initial deviation, noise, and disturbance. The resilience of the co-estimation scheme against battery aging is also verified through experimentation. [ABSTRACT FROM AUTHOR]

Model-based observers are widely applied in state-of-charge (SOC) estimation. The existing model-based observers can achieve high precision in theory, but the estimation precision is influenced by many factors in practical application. The impact of the possible error sources on the SOC estimation needs to be clarified in order to enhance the practical estimation accuracy. In this paper, the effect of five error sources in the model-based SOC observer is analyzed, including the initial error, the capacity error, the current measurement error, the voltage measurement error, and the model prediction error. Both theoretical derivation and simulation analysis are implemented. The results show that the initial SOC error converges to zero during the iteration, while the errors of the capacity, current, voltage, and model cause the SOC estimation error in different extents. Quantitative comparisons of these different error sources are carried out in order to determine the critical factors of the SOC estimation. The SOC estimation accuracy is mainly limited by the model precision under the current hardware and software conditions of the battery management system, indicating a possible improving direction of the SOC estimation. [ABSTRACT FROM AUTHOR]

Cell voltage equalization is inevitable to ensure years of safe operation of series-connected energy storage cells, such as lithium-ion batteries and supercapacitors (SCs). Although various kinds of cell voltage equalizers have been proposed, most equalizer topologies require multiple switches and/or a multiwinding transformer, resulting in complex circuitry and poor modularity. In this paper, a single-switch single-transformer equalizer using a forward–flyback resonant inverter (FFRI) with a voltage multiplier is proposed. The required switch count of the proposed equalizer is the minimum without impairing modularity due to the single-switch circuitry with no need for a multiwinding transformer. An experimental equalization test performed for eight SCs connected in series successfully demonstrated the equalization performance of the proposed equalizer. The FFRI can be extended as a “resonant input cell,” and by combining one of resonant input cells and a voltage multiplier, a single-switch resonant equalization charger that is basically a charger with an equalization function is also derived. An experimental charging test using the resonant equalization charger was also performed, and its equalization-charging performance was demonstrated. [ABSTRACT FROM AUTHOR]

This paper presents the design guideline of a bidirectional isolated Ćuk converter with an active-snubber circuit, which is used as a cell level tester for multiple automotive dc energy storage devices (ESDs), such as ultracapacitors, Li-ion batteries, and NiMH batteries. The converter interfaces those automotive dc ESD cells with 24-V dc systems, and it realizes bidirectional power flow with two main switches. Based on the ratings of those dc ESD cells, the designed converter operates at a very low voltage range, i.e., from 1.5 to 6 V, with current as high as 300 A. The detailed operation principle and major features, such as soft switching and energy recovery, are analyzed under continuous current mode. Based on the analysis, the design guideline of the dc–dc converter is presented. A 1.2-kW laboratory prototype has been built and tested with full power rating. Both simulation and experimental results are presented to verify the analysis. [ABSTRACT FROM PUBLISHER]

Battery charging is growing in importance as it has direct influence on battery performance and safety. This paper develops a generalized online estimation method for a charge-polarization-voltage-based resistance–capacitance (RC) circuit model to simulate the charging behavior of Li-ion batteries. The effects of charging current, initial state of charge (SOC), initial polarization state, and aging on the charge polarization voltage are quantitatively analyzed in both time and SOC domains. It is demonstrated that the charge polarization voltage is nonlinearly related to these impact factors. In the SOC domain, the change in the charge polarization voltage is also analyzed with the gradient analytical method, and the relations between current, polarization voltage amplitude at the inflection point, and SOC are quantitatively established. This can be used to estimate battery SOC and polarization voltage accurately. A constant-polarization-based fuzzy-control charging method is proposed to adapt charging current acceptance with battery SOC stages. Experimental results demonstrate that the proposed charging method significantly shortens charging time with no obvious temperature rise compared with the traditional constant-current–constant-voltage charging method. [ABSTRACT FROM AUTHOR]

State-of-health estimates of batteries are essential for onboard electric vehicles in order to provide safe, reliable, and cost-effective battery operation. This paper suggests a method to estimate the 10-s discharge resistance, which is an established battery figure of merit from laboratory testing, without performing the laboratory test. Instead, a state-of-health estimate of batteries is obtained using data directly from their operational use, e.g., onboard electric vehicles. It is shown that simple dynamical battery models, based on a current input and a voltage output, with model parameters dependent on temperature and state of charge, can be derived using AutoRegressive with eXogenous input models, whose order can be adjusted to describe the complex battery behavior. Then, the 10-s discharge resistance can be conveniently computed from the identified model parameters. Moreover, the uncertainty of the estimated resistance values is provided by the method. The suggested method is validated with usage data from emulated electric vehicle operation of an automotive lithium-ion battery cell. The resistance values are estimated accurately for a state-of-charge and temperature range spanning typical electric vehicle operating conditions. The identification of the model parameters and the resistance computation are very fast, rendering the method suitable for onboard application. [ABSTRACT FROM PUBLISHER]