Search

An accurate battery capacity and state estimation method is one of the most significant and difficult techniques to ensure efficient and safe operation of the batteries for electric vehicles (EVs). Since capacity and state of charge (SoC) are strongly correlated, the SoC is hardly to be accurately estimated without knowing accurate battery capacity. Thus, a multiscale dual H infinity filter (HIF) has been proposed to estimate battery SoC and capacity in real time with dual timescales in response to slow-varying battery parameters and fast-varying battery state. The proposed method is first evaluated and verified using off-line experimental data and then compared with the single/multiscale dual Kalman filters (KFs). The results show that the proposed multiscale dual HIFs has better robustness and higher estimation accuracy than the single/multiscale dual KFs. To further validate the feasibility of the proposed method for EV applications, a lithium-ion battery-in-the-loop approach is applied to verify the stability and accuracy of the SoC estimation, and it is found that the SoC estimated from the proposed method can converge to the reference value gradually and be stabilized within 2%.

In this paper, a nonlinear control scheme is proposed for synchronous generators in a multimachine power system to damp out low-frequency oscillations and enhance the transient stability. The proposed controller is designed recursively to adapt some unknown stability sensitive parameters of synchronous generators. These unknown parameters, estimated through the adaptation laws with the inclusion of the projection operator, are incorporated into the controller to ensure the stability of whole power system with the formulation of control Lyapunov functions (CLFs). The convergence of different physical properties of synchronous generators, such as relative speed, terminal voltage, and rotor angle, is ensured from the negative definiteness or semidefiniteness of the derivative of CLFs. The performance of the proposed controller is evaluated through simulation results on a two-area multimachine power system in terms of robustness to estimate the unknown parameters and maintain the steady-state operation of power systems under different operating conditions. Simulation results are compared with an existing nonlinear adaptive backstepping controller and a conventional power system stabilizer, which illustrate the effectiveness of the proposed control scheme over the existing controllers.

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.

An adaptive H infinity filter approach is proposed to estimate the multistates including state of charge (SOC) and state of energy (SOE) for a lithium-ion battery pack. In the proposed approach, the covariance matching technique is used to adaptively update the covariance of system and observation noises and the recursive least square method is used to identify the battery model parameters in real time. The hardware-in-the-loop (HIL) platform for battery charge/discharge is set up to evaluate the accuracy and robustness of the SOC and the SOE estimation and compare the proposed approach with the multistate estimators using an extended Kalman filter and an H infinity filter. The experimental results indicate that the adaptive H infinity filter-based estimator is able to estimate the battery states in real time with the highest accuracy among the three filters.

Significant variance exists in the nominal capacity of lithium ion (Li-ion) pouch cells used for commercial electric vehicle battery packs. Accurate estimation of heat generation in such cells is critical for designing battery thermal management system. However, multi-physics models describing thermal behaviour of these cells are too complex whereas other numerical models discount the effect of cell capacity on heat generation. This paper proposes a new computational model based on artificial neural network (ANN) for estimating battery heat generation rate with cell nominal capacity as one of its key inputs along with ambient temperature, discharge rate and depth of discharge. A custom-designed calorimeter is utilised for experimentally generating the training dataset for the ANN. Problem of data scarcity is addressed analytically and virtual samples are produced via enthalpy formulation for battery heat generation. Subsequently, the model is trained using Levenberg–Marquardt algorithm. Results disclose that a three-layered feedforward ANN with one hidden layer having six neurons is optimum for this application. The architecture of the trained ANN for accurately simulating thermal behaviour of LiFePO4 pouch cells of the nominal capacities from 8 to 20 Ah under varied conditions is exemplified.

This paper proposes a nonlinear controller to mitigate subsynchronous control interaction (SSCI) in series-compensated doubly fed induction generator (DFIG)-based wind farms. The controller is designed based on partial feedback linearization (PFL) and the proposed design approach involves scrutinizing the partial feedback linearizability of the system. The stability of the internal dynamics, which is not transformed into linear autonomous subsystems by PFL, is also analyzed in the process of deriving the control laws. The frequency scanning method is used to evaluate the performance of the proposed PFL controller, and the performance is compared to that of a finely tuned conventional proportional integral controller. A grid-connected series-compensated 100-MW DFIG-based offshore wind farm is used to demonstrate the performance of the proposed scheme through the identification and mitigation of subsynchronous resonance. An analysis of the power system reveals that the resistance is negative across the entire subsynchronous frequency range, while the reactance becomes negative around 42 Hz. The proposed controller effectively mitigates SSCI, and it can be observed that it results in positive resistance and reactance values across the entire subsynchronous frequency range. Results from the eigenvalue (modal) analysis and electromagnetic transient simulation also confirm the results obtained from frequency scanning.

This paper proposes a new approach to design a robust adaptive backstepping excitation controller for multimachine power systems in order to reject external disturbances. The parameters which significantly affect the stability of power systems (also called stability sensitive parameters) are considered as unknown and the external disturbances are incorporated into the power system model. The proposed excitation controller is designed in such a way that it is adaptive to the unknown parameters and robust to external disturbances. The stability sensitive parameters are estimated through the adaptation laws and the convergences of these adaptation laws are obtained through the negative semi-definiteness of control Lyapunov functions (CLFs). The proposed controller not only provides robustness property against external disturbances but also overcomes the over-parameterization problem of stability sensitive parameters which usually appears in some conventional adaptive methods. Finally, the performance of the proposed controller is tested on a two-area four machine 11-bus power system by considering external disturbances under different scenarios and is compared to that of an existing nonlinear adaptive backstepping controller. Simulation results illustrate the robustness of the proposed controller over an existing one in terms of rejecting external disturbances.

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

Based on an adaptive backstepping approach, this study proposes a new coordinated control design where the coordination is done between the excitation controller of synchronous generators and steam-valve controller of turbine-governor systems in a multimachine power system by considering the critical parameters of both systems as unknown. This new coordinated control scheme can ensure the stability of multimachine power systems through the formulation of control Lyapunov functions (CLFs) and derive the adaptation laws to estimate the unknown parameters in the design process to prove the convergence of power systems using CLFs. The performances of the proposed control scheme are evaluated on a two-area four-machine 11-bus power system under different operating conditions such as three-phase short-circuit faults along with changes in loads. Simulation results are then compared with those of a similar controller where there is no coordination between the excitation and steam-valve controllers as well as with a coordinated sliding-mode controller. The results clearly show the effectiveness and superiority of the proposed control scheme.

In an electric vehicle (EV), thermal runaway, vibration or vehicle impact can lead to a potential failure of lithium-ion (Li-ion) battery packs due to their high sensitivity to ambient temperature, pressure and dynamic mechanical loads. Amongst several factors, safety and reliability of battery packs present the highest challenges to large scale electrification of public and private transportation sectors. This paper reviews mechanical design features that can address these issues. More than 75 sources including scientific and technical literature and particularly 43 US Patents are studied. The study illustrates through examples that simple mechanical features can be integrated into battery packaging design to minimise the probability of failure and mitigate the aforementioned safety risks. Furthermore, the key components of a robust battery pack have been closely studied and the materials have been identified to design these components and to meet their functional requirements. Strategic battery pack placement technique is also discussed using an example of Nissan LEAF battery packaging design. Finally, the disclosed design solutions described in this paper are compared with the Chevrolet Volt battery pack design to reveal the basic mechanical design requirements for a robust and reliable battery packaging system.

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.

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

This study presents the design of a robust active disturbance rejection (ADR) controller in order to improve low-voltage ride-through (LVRT) capability of wind farms connected with doubly fed induction generator (DFIG). The ADR controller is particularly effective in real-time estimation and mitigation of the total effect of various uncertainties against a wide range of parameter variations, model uncertainties and large disturbances. The performance evaluation of the designed controller is performed on an IEEE system under different test cases. The simulation results show that the proposed controller is robust against uncertainties in operating conditions and successfully improves the damping and voltage stability and thus the LVRT capability of DFIGs.

With the increasing penetration of doubly fed induction generators (DFIGs), the impact of the DFIG on transient stability attracts great attention. Transient stability is largely dominated by generator types in the power system, and the dynamic characteristics of DFIG wind turbines are different from that of the synchronous generators in the conventional power plants. The analysis of the transient stability on DFIG integrated power systems has become a very important issue. This paper reviews the current research on the issue from two aspects. One is to describe the methods to improve the fault ride through capability of DFIG wind turbines and the other is to analyse the impact of the DFIG wind farm on transient stability of power systems.

In this paper, a new charging strategy of lithium-polymer batteries (LiPBs) has been proposed based on the integration of Taguchi method (TM) and state of charge estimation. The TM is applied to search an optimal charging current pattern. An adaptive switching gain sliding mode observer (ASGSMO) is adopted to estimate the SOC which controls and terminates the charging process. The experimental results demonstrate that the proposed charging strategy can successfully charge the same types of LiPBs with different capacities and cycle life. The proposed charging strategy also provides much shorter charging time, narrower temperature variation and slightly higher energy efficiency than the equivalent constant current constant voltage charging method.

Based on a low cost multi-switched inductor balancing circuit (MSIBC), a fuzzy logic (FL) controller is proposed to improve the balancing performances of lithium-ion battery packs instead of an existing proportional-integral (PI) controller. In the proposed FL controller, a cell’s open circuit voltages (OCVs) and their differences in the pack are used as the inputs, and the output of the FL controller is the balancing current. The FL controller for the MSIBC has the advantage of maintaining high balancing currents over the existing PI controller in almost the entire balancing process for different lithium battery types. As a result, the proposed FL controller takes a much shorter time to achieve battery pack balancing, and thus more pack capacity can be recovered. This will help to improve the pack performance in electric vehicles and extend the serving time of the battery pack.

In this paper, a robust control scheme (RCS) for Steer-by-Wire (SbW) systems with partially known dynamics is proposed. It is shown that an SbW system can be represented by a nominal model and an unknown portion. A nominal feedback controller can then be used to stabilize the nominal model and a sliding mode compensator (SMC) is designed to remove the effects of both the unknown system dynamics and uncertain road conditions on the steering performance. For practical consideration, robust exact differentiator (RED) technique is utilized to estimate the derivatives of the position signals for controller design. It is further shown that the designed RCS is able to guarantee a robust steering performance against system and road uncertainties. The comparative experimental studies are given to verify the excellent performance of the proposed RCS for SbW systems.

This study presents the quantitative transient stability assessment and comparison for conventional synchronous generator (SG) and wind turbine generators (WTGs) by studying the impact of the fault clearing time, the grid coupling, the inertia constant, the generator terminal voltage sag and the slip on fault responses. The assessment and comparison is performed by evaluating the transient stability index through the modified transient energy function. Simulation results show WTGs exhibit different transient responses on the occurrence of faults as compared with conventional SG. This justifies the necessity of study in greater depth for reliable operation of conventional power systems with the integration of large wind power.

An adaptive gain sliding mode observer (AGSMO) for battery state of charge (SOC) estimation based on a combined battery equivalent circuit model (CBECM) is presented. The error convergence of the AGSMO for the SOC estimation is proved by Lyapunov stability theory. Comparing with conventional sliding mode observers for the SOC estimation, the AGSMO can minimise chattering levels and improve the accuracy by adaptively adjusting switching gains to compensate modelling errors. To design the AGSMO for the SOC estimation, the state equations of the CBECM are derived to capture dynamics of a battery. A lithium-polymer battery (LiPB) is used to conduct experiments for extracting parameters of the CBECM and verifying the effectiveness of the proposed AGSMO for the SOC estimation.

In this paper, the modeling of steer-by-wire (SbW) systems is further studied, and a sliding mode control scheme for the SbW systems with uncertain dynamics is developed. It is shown that an SbW system, from the steering motor to the steered front wheels, is equivalent to a second-order system. A sliding mode controller can then be designed based on the bound information of uncertain system parameters, uncertain self-aligning torque, and uncertain torque pulsation disturbances, in the sense that not only the strong robustness with respect to large and nonlinear system uncertainties can be obtained but also the front-wheel steering angle can converge to the handwheel reference angle asymptotically. Both the simulation and experimental results are presented in support of the excellent performance and effectiveness of the proposed scheme.

In this paper, a novel approach for battery state of charge (SOC) estimation in electric vehicles (EVs) based on an adaptive switching gain sliding mode observer (ASGSMO) has been presented. To design the ASGSMO for the SOC estimation, the state equations based on a battery equivalent circuit model (BECM) are derived to represent dynamic behaviours of a battery. Comparing with a conventional sliding mode observer, the ASGSMO has a capability of minimising chattering levels in the SOC estimation by using the self-adjusted switching gain while maintaining the characteristics of being able to compensate modelling errors caused by the parameter variations of the BECM. Lyapunov stability theory is adopted to prove the error convergence of the ASGSMO for the SOC estimation. The lithium-polymer battery (LiPB) is utilised to conduct experiments for determining the parameters of the BECM and verifying the effectiveness of the proposed ASGSMO in various discharge current profiles including EV driving conditions in both city and suburban.

Increasing wind power penetration into the grid justifies the requirement of the analysis of wind power dynamics, especially during transient faults. Quantitative transient stability (TS) assessment is required to provide deeper insight into the TS problems for speeding up the operational decision making process. This can be achieved by evaluating transient stability index (TSI) through the assessment of transient energy function. This paper carries out the quantitative insight of the impact of different generator technologies on the grid by comparatively studying the impacts of the fault clearing time, the grid coupling, the inertia constant, the generator terminal voltage sag and the slip on fault responses with the TSI between synchronous generators and wind turbine generators, such as squirrel cage induction generators and doubly fed induction generators.

A novel aggregated model for wind farms consisting of wind turbines equipped with doubly-fed induction generators (DFIGs) is proposed in this paper. In the proposed model, a mechanical torque compensating factor (MTCF) is integrated into a full aggregated wind farm model to deal with the nonlinearity of wind turbines in the partial load region and to make it behave as closely as possible to a complete model of the wind farm. The MTCF is initially constructed to approximate a Gaussian function by a fuzzy logic method and optimized on a trial and error basis to achieve less than 10% discrepancy between the proposed aggregated model and the complete model. Then, a large scale offshore wind farm comprising of 72 DFIG wind turbines is used to verify the effectiveness of the proposed aggregated model. The simulation results show that the proposed aggregated model approximates active power (Pe) and reactive power (Qe) at the point of common coupling more accurately than the full aggregated model by 8.7% and 12.5%, respectively, during normal operation while showing similar level of accuracy during grid disturbance. Computational time of the proposed aggregated model is slightly higher than that of the full aggregated model but much faster than the complete model by 90.3% during normal operation and 87% during grid disturbance.

Solar energy plays an important role in the global energy framework for future. Comparing with con- ventional generation systems using fossil fuels, the cost structure of photovoltaic (PV) systems is different: the capital cost is higher while the operation cost is negligible. Reliabilities of the PV system can also influence the cost for producing electricity. Investors, planners and regulators require deep insight into the return and cost of a PV pro- ject. A reliability based economical assessment of large- scale PV systems has been conducted utilizing Universal Generating Function (UGF) techniques. The reliability models of solar panel arrays, PV inverters and energy production units (EPUs) are represented as the corre- sponding UGFs. The expected energy production models for different PV system configurations have also been developed. The expected unit cost of electricity has been calculated to provide informative metrics for making optimal decisions. The proposed method has been applied to determine the PV system configuration which provides electricity for a water purification process.

This paper first presents a simplified single wheel model for underground mining electric vehicles with bounds of system uncertainties. Then, a fuzzy parameter tuning (FPT) technique is used to adjust the parameters in the sliding mode control (SMC) to improve system stability and robustness at an unexpected external disturbances and unbound system uncertainties. The comparison of the simulation results shows that the proposed control strategy has better performance than the SMCs without applying fuzzy tuning technique.

A novel pitch control design method is proposed for the doubly fed induction generator (DFIG) wind turbine (WT) using linear quadratic regulator (LQR). A seven-order model represents the DFIG WT which is linearized by truncated Taylor series expansion. A systematic approach is adopted to determine the weighting matrices in LQR design for the optimal solution. Simulations have been carried out to compare the performance of the proposed LQR pitch control method against a PI pitch control for small and large disturbances. It is shown that the proposed control method enhances low-voltage ride-through capability and improves system damping under large disturbances.

A H infinity observer based method is proposed to estimate the state of charge (SOC) for a series-connected battery pack for electric vehicles. In this method, an average virtual cell (AVC) model is defined and the SOC of the AVC model is estimated to represent the pack SOC when all terminal voltage differences (TVD) between each individual cell in the pack and the AVC are within a voltage value set in advance. The LiFePO 4 battery pack is utilized to conduct the experiments to verify the effectiveness of the proposed method.

In modern power electronic systems, DC-DC converter is one of the main controlled power sources for driving DC systems. But the inherent nonlinear and time-varying characteristics often result in some difficulties mostly related to the control issue. This paper presents a robust nonlinear adaptive controller design with a recursive methodology based on the pulse width modulation (PWM) to drive a DC-DC buck converter. The proposed controller is designed based on the dynamical model of the buck converter where all parameters within the model are assumed as unknown. These unknown parameters are estimated through the adaptation laws and the stability of these laws are ensured by formulating suitable control Lyapunov functions (CLFs) at different stages. The proposed control scheme also provides robustness against external disturbances as these disturbances are considered within the model. One of the main features of the proposed scheme is that it overcomes the over-parameterization problems of unknown parameters which usually appear in some conventional adaptive methods. Finally, the effectiveness of the proposed control scheme is verified through the simulation results and compared to that of an existing adaptive backstepping controller. Simulation results clearly indicate the performance improvement in terms of a faster output voltage tracking response.

In this paper we present an overview of the state-of-the-art on intelligent battery management systems for electric and hybrid electric vehicles. The focus is on mathematical principles, methods and practical implementations. The intelligent battery management systems aim at lengthening the lifetime of the battery pack and enhancing the safety of drivers of electric and hybrid electric vehicles. Three major research topics are covered in the paper, state of charge (SoC), state of health (SoH) of the battery pack, and the remaining driving range estimation.

Wind energy has been receiving more acceptance as a reproducible, resourceful and clean energy source since last decade. Wind power is not constant and may fluctuate below the rated wind power when the wind speed is lower than the rated speed. This fact affects the stability of the power system, to which the wind generators are connected. This is becoming more significant with the increasing penetration of wind energy systems. Pitch angle control has been one of the most common methods for smoothing output power fluctuations during below rated wind incidents. A fuzzy logic pitch angle controller is proposed in this paper for smoothing wind power fluctuations during below rated wind incidents beside traditional power regulations during above rated wind incidents. Two smoothing methods have been presented: the determination of the command output power based on the exponential moving average with a proper selection of correction factor by fuzzy reasoning and the dynamic selection of target output power according to the wind incident. Simulation results show the effectiveness of the proposed fuzzy logic pitch angle controller in smoothing output power fluctuations with significantly small drop of output power.

The sliding mode observer (SMO) for battery state of charge (SOC) estimation based on the improved battery equivalent circuit is presented. The convergence of the SMO is proved by Lyapunov stability theory. The advantage of the SMO is that the modelling errors caused by the variation parameters of circuit model can be compensated. Three sets of the testing data under different discharge current profiles generated by DUALFOIL battery simulation program are used to extract the circuit model parameters and to verify the effectiveness of the proposed SMO for the SOC estimation. It shows that the proposed SOC observer can provide robust tracking performance and accurate SOC estimation in electric vehicle driving conditions.

Size optimization of solar array and battery in a standalone photovoltaic (SPV) system is investigated. Based on the energy efficiency model, the loss of power supply probability (LPSP) of the SPV system is calculated for different size combinations of solar array and battery. For the desired LPSP at the given load demand, the optimal size combination is obtained at the minimum system cost. One case study is given to show the application of the method in Malaysian weather conditions.

This paper presents a new topology of uninterruptible power supply (UPS) by using a Z-source inverter, where a symmetrical LC network is employed to couple the main power circuit of an inverter to a battery bank. With this new topology, the proposed UPS can maintain the desired ac output voltage at the significant voltage drop of the battery bank with high efficiency, low harmonics, fast response, and good steady-state performance in comparison with traditional UPSs. The simulation and experimental results of a 3-kW UPS with the new topology confirm its validity.

As technology advances into nanometer territory, clock network layout plays an increasingly important role in determining circuit quality indicated by timing, power consumption, cost, power supply noise and tolerance to process variation. To alleviate the challenges to the existing routing algorithms due to the continuous increase of the problem size and the high-performance requirement, X-architecture has been proposed and applied to routing in that it can reduce wirelength and via counts, and thus improves the performance and routability compared with the conventional Manhattan routing. In this paper, we investigate zero skew clock routing using X-architecture based on an improved greedy matching algorithm (GMZSTX). The fitted Elmore delay model is employed to improve the accuracy over the Elmore delay model. The interactions among distance, delay balance and load balance are analyzed. Based on this analysis, an effective and efficient greedy matching scheme is suggested to reduce wire snaking and to get a more balanced clock tree. The proposed algorithm is simple and fast for practical applications. Experimental results on benchmark circuits show that our algorithm (GMZSTX) achieves a reduction of 8.15% in total wirelength, 30.19% in delay and 55.31% in CPU time on average compared with zero skew clock routing in the Manhattan plane (BB+DME-2, which means using the top-down balanced bipartition (BB) method [T.H. Chao, Y.C. Hsu, J.M. Ho, et al., Zero skew routing with minimum wirelength, IEEE Trans. Circuits Syst. II-Analog & Digital Signal Process 39 (11) (1992) 799-814] to generate the tree topology and using the Deferred-Merge Embedding (DME) algorithm [T.H. Chao, Y.C. Hsu, J.M. Ho, et al., Zero skew routing with minimum wirelength, IEEE Trans. Circuits Syst. II-Analog & Digital Signal Process 39 (11) (1992) 799-814] to embed the internal nodes), and reduces delay and CPU time by 17.44% and 62.21% on average over the BB+DME-4 method (which is similar to BB+DME-2, but routing in X-architecture). Our SPICE simulation further verifies the correctness of the resulting clock tree.

This paper reviews recent definitions of the state of charge (SOC) that are often used to estimate the battery residual available capacity (BRAC) for lead-acid batteries in electric vehicles (EVs) and identifies their shortcomings. Then, the state of available capacity (SOAC), instead of the SOC, is defined to denote the BRAC in EVs, which refers to the percentage of the battery available capacity (BAC) of the discharge current profile for the EV battery at the fully charged state. Based on the experimentation of different discharge current profiles, including theoretical current profiles and practical current profiles under EV driving cycles, the discharged and regenerative capacity distribution is proposed to describe discharge current profiles for the SOAC estimation. Because of the complexity and nonlinearity of the relationship between the SOAC and the capacity distribution at different temperatures, a neural network (NN) is applied to this SOAC estimation. Comparisons between the estimated SOACs by the NN and the calculated SOACs from the experimental data are used for verification. The results confirm that the proposed approach can provide an accurate and effective estimation of the BRAC for lead-acid batteries in EVs.

Lithium iron phosphate batteries (LiFePO4) are becoming one of the main power resources for electric vehicles (EVs), and the non-uniformity of cells in a battery pack has become the bottleneck to improve battery usable capacity. Many active balancing approaches are proposed to transfer charge among the cells to achieve the uniformity based on terminal voltage. This paper proposes a new balancing approach based on the battery state of charge (SOC) to equalize the cells in the LiFePO4 battery pack in charging process. The hybrid extended Kalman filter is adopted to dynamically estimate the cell SOCs which serve as a balancing criterion. A current sensor is added to detect balancing current for the SOC estimation. Simulations are carried out to compare the proposed approach with terminal voltage approaches and show that the proposed approach has charged more capacity into the pack in charging process.

This paper presents a nonlinear robust adaptive excitation controller design for a simple power system model where a synchronous generator is connected to an infinite bus. The proposed controller is designed to obtain the adaption laws for estimating critical parameters of synchronous generators which are considered as unknown while providing the robustness against the bounded external disturbances. The convergence of different physical quantities of a single machine infinite bus (SMIB) system, with the proposed control scheme, is ensured through the negative definiteness of the derivative of Lyapunov functions. The effects of external disturbances are considered during formulation of Lyapunov function and thus, the proposed excitation controller can ensure the stability of the SMIB system under the variation of critical parameters as well as external disturbances including noises. Finally, the performance of the proposed scheme is investigated with the inclusion of external disturbances in the SMIB system and its superiority is demonstrated through the comparison with an existing robust adaptive excitation controller. Simulation results show that the proposed scheme provides faster responses of physical quantities than the existing controller.

This paper presents a nonlinear adaptive excitation control scheme to enhance the dynamic stability of multimachine power systems. The proposed controller is designed based on the adaptive backstepping technique where the mechanical power input to the generators and the damping coefficient of each generator are considered as unknown. These unknown quantities are estimated through the adaption laws. The adaption laws are obtained from the formulation of Lyapunov functions which guarantee the convergence of different physical quantities of generators such as the relative speed, terminal voltage, and electrical power output. The proposed scheme is evaluated by applying a three-phase short-circuit fault at one of the key transmission lines in an 11-bus test power system and compared with an existing backstepping controller and conventional power system stabilizer (CPSS). Simulation results show that the proposed scheme is much more effective than existing controllers.

This study compares two battery equivalent circuits for state of charge (SOC) estimation in electric vehicles (EVs), namely the RC circuit and Thevenin circuit. The state space representations of each circuit are used to develop the adaptive gain sliding mode observers (AGSMO) for the SOC estimations, which are compared with the true SOC. The results show that the Thevenin circuit provides more accurate SOC estimation than the RC circuit. A lithium-ion battery is chosen for experimental verification under constant current discharge and variable current discharge based on EV driving cycles.

Cost and safety are always the most important factors for personnel transport and mining vehicles in mining industry. That is why the development of underground mining electrical vehicles (UMEVs) becomes more and more attractive in undergrounds mines. However, a stable, reliable and robust controller for the UMEV is also a critical condition due to the safety requirements. Sliding Mode Control (SMC) is able to deal with system uncertainties and external disturbances, but it cannot achieve smooth performance during the transition between different subspaces in the state space. This paper first presents the modeling of an UMEV for acceleration, braking and speed maintenance, and a global fuzzy model will be developed based on that. Then, a Fuzzy Sliding Mode Controller (FSMC) of the UMEV will be proposed. The comparison of the simulation results for both the proposed FSMC controller and an SMC controller shows that the proposed FSMC controller has a better performance when the system switches in different subspaces in the state space.

In this paper, the design of a nonlinear excitation control of a synchronous generator is presented where the generator is connected to a single machine infinite bus (SMIB) system. An adaptive backstepping method is used to design the excitation controller with an objective of enhancing the overall dynamic stability of the SMIB system under different contingencies. In this paper, two types of contingencies are considered- i) unknown parameters and physical quantities during the controller design process and ii) controller performance evaluation under different system configurations such as three-phase short circuit faults. The adaption law, which is mainly based on the formulation of Lyapunov function, is used to estimate the unknown parameters which guarantee the convergence of different physical quantities of synchronous generators, e.g., the relative speed, terminal voltage, etc. The effectiveness of the proposed scheme is evaluated under different system configurations as mentioned in the second contingency and compared to that of an existing adaptive backstepping controller and a conventional power system stabilizer (PSS). Simulation results demonstrate the superiority of the proposed control scheme over the existing controllers.

Steam-valving and excitation systems play an important role to maintain the transient stability of power systems with synchronous generators when power systems are subjected to large disturbances and sudden load changes. This paper presents a nonlinear adaptive backstepping approach for controlling excitation and steam-valving systems of synchronous generators. In this paper, the proposed excitation and steam-valving controllers are designed in a coordinated manner so that they can work under several and most severe operating conditions. Both excitation and steam-valving controllers are designed by considering some critical parameters as unknown. The effectiveness of the proposed coordinated control scheme is evaluated on a single machine infinite bus system under different operating conditions such as load changes and three-phase short circuit faults at the generator terminal. Finally, performance of the proposed scheme is compared to that of a similar nonlinear adaptive backstepping excitation controller without any coordination and simulation results demonstrate the superiority of the proposed one.

This paper presents a new estimation approach of residual available capacity for lead acid batteries in electric vehicles (EVs). The essence of this approach is to model lead acid batteries in EVs by using a neural network (NN) with the specially defined output and the proposed inputs. The inputs are the battery surface temperature and the discharged and regenerative capacity distribution to describe the discharge current profiles of lead acid batteries during EV operations. The output is the state of available capacity (SOAC) representing battery residual available capacity (BRAC). Then, SOACs of lead-acid batteries in EVs are experimentally investigated under different EV discharge current profiles in the presence of various battery surface temperatures. The corresponding data are recorded to train and verify the NN. The results indicate that the proposed NN can provide accurate and effective BRAC estimation for lead-acid batteries in EVs.

This paper presents an improved battery pack equalizer (BPE) based on a DC/DC converter using fuzzy logic controller (FLC). This BPE can be designed without the necessity of a cell model in the pack. It is simple and robust. The simulation results have demonstrated that the BPE with the FLC has better performance than the BPE with the PI controller in terms of the balancing speed and efficiency.

This paper presents a new estimation approach for the battery residual capacity (BRC) indicator in electric vehicles (EVs). The key of this approach is to model the EV battery by using a neural network (NN) with a newly defined output and newly proposed inputs. The inputs are the discharged and regenerative capacity distribution and the temperature. The output is the state of available capacity (SOAC) which represents the BRC. Various SOACs of the nickel-metal hydride (Ni-MH) battery are experimentally investigated under different EV discharge current profiles and temperatures. The corresponding data are recorded to train and verify the proposed NN. The results indicate that the NN can provide an accurate and effective estimation of the BRC. Moreover, this NN can be easily implemented as the BRC indicator or estimator for EVs by using a low-cost microcontroller.

This paper describes a new approach to estimate accurately the battery residual capacity (BRC) of the nickel–metal hydride (Ni–MH) battery for modern electric vehicles (EVs). The key to this approach is to model the Ni–MH battery in EVs by using the adaptive neuro-fuzzy inference system (ANFIS) with newly defined inputs and output. The inputs are the temperature and the discharged capacity distribution describing the discharge current profile, while the output is the state of available capacity (SOAC) representing the BRC. The estimated SOAC from ANFIS model and the measured SOAC from experiments are compared, and the results confirm that the proposed approach can provide an accurate estimation of the SOAC under variable discharge currents.

This paper proposes and implements a new method for the estimation of the battery residual capacity (BRC) for electric vehicles (EVs). The key of the proposed method is to model the EV battery by using the adaptive neuro-fuzzy inference system. Different operating profiles of the EV battery are investigated including the constant current discharge and the random current discharge as well as the standard EV driving cycles in Europe, the US, and Japan. The estimated BRCs are directly compared with the actual BRCs, verifying the accuracy and effectiveness of the proposed modeling method. Moreover, this method can be easily implemented by a low-cost microcontroller and can readily be extended to the estimation of the BRC for other types of EV batteries.

The ability to calculate the battery available capacity (BAC) for electric vehicles (EVs) is very important. Knowing the BAC and, thus, the driving range cannot only prevent EVs from being stranding on the road but also optimize the utilization of the battery energy storage in EVs. In order to determine the BAC, this paper presents a new neural network (NN) model of the lead–acid battery, based on the battery discharge current and temperature. Comparisons between the calculated BAC from the NN model and the measured BAC from experiments show good agreement. Furthermore, this new approach can readily be extended to the calculation of the BAC for other types of batteries.

Non-uniformity of Lithium-ion cells in a battery pack is inevitable and has become the bottleneck to the pack capacity, especially in the fast charging process. Therefore, a balancing approach is essentially required. This paper proposes an active online cell balancing approach in a fast charging process using the state of charge (SOC) as balancing criterion. The goal of this approach is to complete pack balancing within the limited charging time. An adaptive extended Kalman filter (AEKF) is applied to estimate the pack cell SOC during the charging process to obtain accurate results under modeling errors and measurement noises. To implement the proposed AEKF, only one additional current sensor is required to obtain the current of each cell required for the SOC estimation. An experimental platform is established to verify the effectiveness of the proposed approach. The results show that the proposed balancing approach with the SOC as a balancing criterion can overcome the challenges of non-uniformity and flat voltage plateau and charge more capacity into a LiFePO4 battery pack than those with the terminal voltage as a balancing criterion in the fast charging process.