Supercapacitors have been widely used in public transportation for the advantages of high power density and extremely long lifetime. Owing to the high-power charging requirements of supercapacitors, the multicharger cooperative charging method has been applied to suppress the current imbalance among chargers. However, the cooperative charging system is vulnerable to cyber attacks with false data injections, which degrades the reliability of the charging system. To address this challenge, in this article, a detection method for a false data injection attack (FDIA) is proposed for the cooperative charging system. First, we introduce the cooperative charging system and analyze the possible cyber attacks. Then, an FDIA detection method is proposed based on a Kalman filter and the time–frequency features of chargers’ currents. A three-charger laboratory platform is built to verify the effectiveness of the proposed method. Extensive experimental results show that the proposed method can detect the FDIA effectively when compared with the existing methods.

As the two classical power allocation methods in battery-supercapacitor hybrid energy storage systems, split-frequency methods and power-level methods have been developed separately for many years. In this article, we made an attempt to integrate the advantages of the two methods and proposed an adaptive frequency-split-based quantitative power allocation strategy. First, an adaptive power preallocation is quantitatively determined according to the state of charge (SoC) of batteries and supercapacitors. Then, a windowed fast Fourier transform (FFT)-based power spectrum calculation algorithm is designed to derive the power level corresponding to each sampling frequency. By mapping the preallocated power to the power spectrum, the split frequency is adaptively computed. Finally, the power allocation is implemented through a low-pass filter (LPF) with the derived split frequency. Extensive experiments verify that the proposed method provides an improved performance in suppressing the dc bus voltage fluctuations and protecting batteries when compared with existing methods.

For the remaining useful life prediction of lithium-ion batteries, the reliability of the features and the validity of the regression algorithm used to construct the prediction model are very important to the prediction results.For this reason, this paper proposes a prediction method based on AdaBoost-support vector regression.First, 9 features are extracted from the battery aging data, and the correlation between features and RUL is verified.Then, the random forest is used to select the extracted features to improve the reliability of the features.Finally, based on the selected features, the prediction model of RUL is established by using AdaBoost to optimize the support vector regression model.The validity of the proposed method is verified in NASA lithium battery data set.

Battery/ultracapacitor hybrid energy storage systems have been widely studied in electric vehicles to prolong batteries' cycle life, where the energy management strategy plays a vital role to distribute the load power in real time and to optimize the vehicle economy simultaneously. This paper proposes a hierarchical energy management strategy using sequential quadratic programming and neural network (NN) for a semi-active battery/ultracapacitor hybrid energy storage systems to minimize the battery degradation cost and the electricity cost. In the off-line optimization level, a cost function of battery degradation cost and electricity cost is constructed. Sequential quadratic programming is performed to determine the optimal current reference of ultracapacitors, i.e., the output of the NN. While the load current, velocity, acceleration, the last battery current and ultracapacitor SoC are selected as inputs to train NN until the parameters converge. In the on-line implementation level, the trained NN provides the quasi-optimal reference of ultracapacitor current based on the real-time data. Circuit simulation results on NEDC driving cycle reveal that the proposed strategy can guarantee the ultracapacitor state-of-charge limitations. When compared with the adaptive filter-based method and the near-optimal rule-based method from dynamic programming, a 10.1% and 5.4% reduction of battery degradation and a 9.5% and 3.1% reduction of total cost are achieved by the proposed strategy.

Predicting the discharge time of lithium-ion batteries is an important issue in battery management system. Accurate prediction can avoid accidents, thereby improving the safety of the entire system. In order to overcome the challenge such as the need to identify model parameters in related research, this paper proposes a new prediction method based on Peukert's law. Firstly, the Peukert constant is fitted by the experimentally measured discharge data, and then the experiment data are used to predict the battery discharge time and calculate prediction relative error. Secondly, according to the relative error between the actual battery discharge time and the predicted time, the current Peukert constant is evaluated. Thirdly, the optimal Peukert constant is obtained by minimizing the average relative error, it is also found that if the appropriate Peukert constant is determined, the prediction error can be significantly reduced.

Autonomous rail transit (ART) vehicle is a new type of urban rail transportation, which has good development prospects. It is powered by onboard supercapacitors, which are charged at midway stations. It requires short charging time and fast charging speed. Usually, multiple chargers are used in parallel for charging. However, this will cause an overshoot phenomenon during charging, and the overshoot of multiple chargers will be superimposed on the supercapacitor, affecting the stability and life of both supercapacitors and chargers. In this paper, we propose a predictive set point modulation charging method, which can reduce the system's overshoot and increase the reliability of the system. First, the state-space averaging method is used to establish the electronic physical model of the multicharger system. Secondly, a predictive set point modulation charging control method is designed, and the closed-loop model of the proposed charging system is developed using the buck diagram. The effectiveness of the proposed method is verified through extensive simulation and experiments. The experimental results show that compared with the classical design method, the proposed method can effectively suppress the current overshoot.

Battery balance methods are the key technology to ensure the safe and efficient operation of the energy storage systems. Nevertheless, convenient balance methods experience slow convergence and difficult to adapt to quick charging applications. To solve the problem, in this paper, an artificial potential field-based lithium-ion battery balance method is proposed. Firstly, a cyber-physical model of the battery equalization system is proposed, in which the physical layer models the circuit components and the cyber layer represents the communication topology between the batteries. Then the virtual force function is established by artificial potential field to attract the voltage and state-of-charge of each cell to nominal values. With a feedback control law, the charging current of the battery is reasonably distributed to realize the rapid balance among batteries. The experimental results verify the effectiveness and superiority of the proposed method.

The durability and efficiency of lithium batteries are impaired in low temperature environments, in which it is necessary to heat them to a suitable temperature before normal operation. Self-heating by discharging battery current is an efficient and economical method, whose discharge current rate dramatically affects the heating time and the capacity decay. In the paper, a warm-up strategy based on extremum seeking is proposed to obtain the optimal discharging current. Firstly, the models are constructed by analyzing the electrical and thermal characteristics of lithium battery. Then, the loss function is formulated by combining capacity fade and heating efficiency. The extremum seeking algorithm is utilized to calculate the optimal heating current based on the real-time internal resistance state. The proposed strategy realizes the balance between heating time and energy loss, and reduce the required energy for heating battery.

This paper proposes an adaptive power allocation strategy using artificial potential field with a compensator for hybrid energy storage systems in electric vehicles. In the power allocation level, a potential field is constructed to guarantee the state-of-charge limitations of supercapacitors. Virtual forces of this field are mapped as the allocation ratio of load power. The cutoff frequency is obtained by cutting the real-time load spectrum with the allocation ratio. In the control level, a feed-forward compensator is designed to compensate for load variations in advance which can counteract dc-link fluctuations. Experimental tests under different supercapacitor initial state-of-charges and different driving cycles evaluate the superiority of proposed methods. The artificial potential field strategy provides lower battery capacity loss with supercapacitors state-of-charge limitations guaranteed compared with existing real-time power allocation strategies, e.g., a more than 15% reduction of battery capacity loss in the urban driving cycle. The feed-forward compensator allows the hybrid energy output to meet the load requirements better.

Reconfigurable circuits have emerged as a promising cell balancing solution in supercapacitor echelon applications. However, the classical decentralized control method of reconfigurable supercapacitors results in high-voltage profiles of cells, which degrades the lifetime of supercapacitors. In this article, we propose a consensus-based cell balancing approach for reconfigurable supercapacitors, where the cell voltages are synchronized and maintained as low as possible to alleviate the aging of supercapacitors. The switched systems theory and graph theory are utilized to characterize the cyber-physical model of reconfigurable supercapacitors. A consensus-based tracking control is designed to balance cell voltages. It is rigorously proved that the proposed cell balancing method can guarantee cell voltages converge to the reference voltage. A laboratory hardware testbed has been built to verify the effectiveness of the proposed method. Extensive experiment results show that the proposed method can prolong the lifetime of cells considerably when compared with existing methods. Some practical issues of the proposed method are also discussed.

Current overshoot in the charging is an undesired behavior that accelerates the aging of both chargers and batteries. This phenomenon is even more severe in parallel-connected multicharger systems of supercapacitor trams, as the overshooting of each charger is stacked up in the charging current. To address this issue, in this article, we propose a current-overshoot-suppression charging method for supercapacitor trams. First, the system modeling of the charging system is developed and the motivation of this article is highlighted. Then, a cooperative charging protocol is designed to achieve the current balancing of chargers, and a set point modulation method is presented to adjust the set-point of chargers smoothly with the aim of suppressing the current overshoot. The implementation of the proposed method is illustrated with a physical case study. A laboratory testbed has been built to verify the effectiveness of the design. Experiment results show that the proposed method can suppress the current overshoot without prolonging the settling time.

For charging energy storage systems of urban light trams, multiple modules are required in parallel to jointly bear the charging current. However, the traditional parallel multi-module charging system faces the risk of current fluctuation and imbalance caused by false-data injection attacks, reducing the reliability. This paper proposes a detection technology of false-data injection attacks in supercapacitors charging process based on adaptive cooperative control. The controller can dynamically analyze current volatility of each module in the process of charging current volatility. When the controller detects the output current fluctuation caused by injection attack, it executes the adaptive current equalization algorithm, automatically adjusts the current output value to approximate the reference current, so as to eliminate or reduce the impact of injection attack on the system. The experimental results verify the anti-interference capability, feasibility and practicability of the proposed distributed flow sharing control method.

Electric vehicles suffer from significant driving range loss at subzero temperature environments due to reduced energy and power capability of Li-ion batteries. Therefore the battery must be heated to suitable operating temperature before the operation process begins. The effect of the pulse heating method based on the fuzzy logic control is investigated experimentally in this paper. Firstly, the pulse frequencies, amplitudes on cell temperature evolution are studied. Then the fuzzy logic control strategy is designed to obtain the variable pulse amplitude criterion based on the real-time internal resistance and temperature of the battery. The experiments are carried out under different battery initial values of state of charge with the same pulse frequency. Compared with the constant amplitude pulse preheating strategy, the proposed strategy can shorten the heating time by 2 to 4 minutes, and save the energy loss by 50 percent.

In the process of charging, the supercapacitor cell balancing mechanism should be introduced to avoid the overcharging. However, the current balancing methods will cause voltage drop effect due to inter resistance. In order to solve this challenge, this paper proposes a cooperative balance strategy for charging supercapacitor with the generalized extend state observer. Firstly, a model is constructed to incorporate the switched resistor circuit and cyber communication interactions. Then the observer is used to estimate the voltage of the equivalent capacitor of each cell, and the generalized extend state observer based on the consensus theory is used to balance the supercapacitor which can realize the robustness of the system to disturbance. An experimental test platform constructed verifies the effectiveness of the proposed cooperative balancing strategy method.

Ultracapacitors have recently received great attention for energy storage due to their small pollution, high power density, and long lifetime. In many applications, ultracapacitors need to be charged with a high current, where a multi-module charging system is typically adopted. Although the classical decentralized control method can control the charging process of ultracapacitors, there exists a problem that the charging current may be imbalanced among charging modules. In this paper, a cooperative cascade charging method is proposed for the multi-module charging system to reduce the current imbalance among charging modules. First, the state-space averaging method and graph theory are used to model the multiple-module charging system. Second, an effective cooperative cascade control is proposed, where the outer voltage loop stabilizes the output voltage to the desired voltage and the inner current loop guarantees the current of each charger to follow the target current. The block diagram is used to establish the closed-loop model of the charging system. In order to evaluate the proposed charging method, a laboratory prototype was established. Compared with the classical decentralized method, this method can effectively suppress the current imbalance, which is proved by simulation and experimental results.

The filter-based real-time energy management method has been proved practical and widely utilized in hybrid energy storage systems. However, the determination for the cutoff frequency of the energy-split filter is challenging. In this paper, an optimal filter-based energy management strategy is proposed for a battery/ultracapacitor electric vehicle to minimize the total energy consumption. A cost function of energy consumption for the cutoff frequency is established first. Considering the working condition of ultracapacitors, dynamic programming is adopted to obtain the optimal cutoff frequency series, i.e., the optimal energy distribution between batteries and ultracapacitors. Such an offline optimization process is carried out under different driving cycles, e.g., urban and highway road conditions. Optimization results are used to determine the optimal cutoff frequency of a real-time filter-based energy management strategy. Simulation results indicate that the proposed strategy can minimize the total energy consumption of the hybrid energy storage system with ultracapacitors state of charge limitations being guaranteed. Compared with the existing real-time energy management strategies, the energy consumption is reduced 23.85% under aggressive acceleration conditions and 7.08% under urban conditions by the proposed strategy.

For a pure electric car-following system, if the auto-following vehicle acts in an aggressive following manner, battery life fades evidently, since overcharging or over-discharging damage the cell irreversibly. In this regard, this paper proposed an artificial potential function for battery life extension. First, the electric vehicle physical model and an empirical lithium-ion battery model have established form real-world data measurement. The physical layer models car-following dynamics and the battery model describes the energy consumption. Second, with the perceptive of the battery life in a loss-minimal, optimize manner, the controller mathematically computes the optimal acceleration/deceleration value with the Lagrange multipliers method. Then using the Matlab curve fitting tool toolbox to fusion optimal acceleration data with potential function, thus the acceleration consistent rule is realized through the consistency of an artificial potential function. Finally, the control strategy is validated through a simulation test in Matlab/Simulink, and the results show that the proposed control strategy extends battery life while keeping good tracking ability.

Battery-supercapacitor hybrid energy storage systems typically suffer from bus voltage fluctuations under varying loads in electric vehicles. To address this issue, this paper proposes an improved feed-forward load compensation method for hybrid energy management system to suppress voltage fluctuations. First, an active buck-boost topology is considered, where a low-pass filter is applied to allocate load currents for batteries and supercapacitors. Then we design a feed-forward load compensator with the objective of suppressing the DC bus voltage fluctuations. Different from existing studies, the expressions of the compensator and the low-pass filter have been built analytically. NEDC driving cycle is applied to verify the effectiveness of the proposed method. Experimental results show that the proposed load compensator significantly reduces bus voltage fluctuations when compared with conventional methods.

It is expected, the growing demand for supercapacitor energy storage system (ESS) in which thousands of supercapacitor cells are connected as a stack for meeting the desired load power. The lifetime of ESS dramatically depends on the operating thermal effects of individual cells. In this paper, a temperature-suppression charging strategy is proposed for supercapacitor ESS to maximize its lifetime. First, the charging current and equivalent series resistance (ESR) construct the supercapacitor thermal model, and the ESR represents the degradation degree of the supercapacitor. Second, the maximization of system lifetime and equalization of unit degradation degree jointly form a convex optimization problem with the charging time constraint. The optimization problem is solved to obtain the equivalent charging current, which is realized through pulsewidth modulation(PWM) digital control. Simulation and experimental results showthat the proposed method has the advantages of reducing ESS operating temperature and maximizing its lifetime.

In the hybrid energy storage system of electric vehicles, the main objective is to guarantee that the dc-bus voltage tracks the desired set point quickly and accurately. However, it is typically difficult for existing methods to provide both short settling time and small overshoot, which results in significant dc-bus voltage fluctuations. To address this issue, in this paper, we propose a predictive-set-point-modulation-based energy management control strategy. A lead-compensator-based predictive set-point modulation method is designed and integrated in the voltage control loop to improve the response speed. Theoretical analysis is carried out for the energy management system, and an explicit expression of the control parameters is derived. Moreover, an adaptive cutoff-frequency-based power allocation approach is proposed to guarantee that supercapacitors (SCs) can provide sustainable power supply in the long term. A laboratory testbed is built to verify the effectiveness of the proposed method. Experiment results show that the proposed method provides a lower dc-bus voltage fluctuation, higher SC capacity utilization, and a better protection for batteries, when compared with the conventional method.