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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.

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.

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 off-line 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.

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.

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.

Traditional lighting systems suffer from the problem of low energy efficiency and low illumination quality due to its disappointing management. To address this issue, in this paper, a novel traffic-flow adaptive scheme of smart lighting systems is proposed on the basis of the cyber-physical cloud system. The cyber-physical cloud system consists of the digital twin and cyber-physical system. The operation of the lighting system is simulated in the counterpart twin system with the digital twin technology. The cyber-physical system realizes data collection, information interaction, analysis, and processing, as well as complex computation and remote control. The traffic adaptive scheme works according to the brightness sequence to improves the energy efficiency of the lighting system and provide higher illumination quality for drivers. Extensive simulation results verify the proposed control scheme could improve the energy efficiency of lighting systems.

This paper proposes an optimal car-following strategy for pure electric vehicles (EVs) with the aim of keeping an expected headway of the leader and reducing vehicle battery loss. In particular, a car-following system model is established. The primary task of the automatic vehicle is to follow the trajectory of the preceding car and maintain an expected headway. Then, the paper analyzes the powertrain of the electric vehicle. The loss of battery life over a period of time is proportional to the acceleration, so it takes the battery life into consideration. The Q-learning algorithm is conducted for the optimal car-following strategy using system data instead of system dynamics information. It utilizes reward function and greedy strategy to select actions to train the following vehicle to achieve car-following safety. When there is no collision in these two cars, acceleration is considered into reward function to reduce battery loss. Finally, it is verified by simulation that the proposed car-following strategy can keep good tracking, maintain the expected headway from the preceding vehicle, and reduce battery loss.

Recent years have seen growing interest in energy storage light rail where multi-module charging system is used to realize high power fast charging. In existing methods, each module supplies the same current to charge onboard super-capacitors. However, this current sharing method will increase maintenance frequency of the charging system and accelerate the modules aging speed, because it does not consider the different degradation degree among modules. In this paper, a lifetime-aware current control strategy is proposed for addressing the above issues. First, a data-driven remaining useful life (RUL) estimation algorithm based on a linear approximation model is proposed. Second, the currents supplied by all modules are allocated by a proportional current control strategy based on the RUL estimation algorithm. Experimental results show that the proposed method has the advantages of narrowing the degradation differences among modules and reducing the heat dissipation, compared to the current sharing mode.

Supercapacitor trams has been an emerging green and intelligent urban rail transportation system. Due to the high power demand of onboard supercapacitors, multiple charging modules are typically connected in parallel to charge the trams. However, The parallel connection of charging modules typically leads to the current imbalance and significant current ripples, which will degrade the reliability of the charging system. In this paper, we propose a cooperative charging approach for supercapacitor trams with ripple current suppression. In the proposed method, charging modules communicate with their neighboring nodes to synchronize the output current and achieve a constant phase difference. Then, a distributed phase-shift control method is proposed to diminish the ripple of the charging current. Experimental results show that the current imbalance can be nearly eliminated and the current ripple is reduced by 80.37% compared to the existing methods.

The constant current-constant voltage (CC-CV) charging is a classical method to charge a single supercapacitor cell. However, it typically causes voltage imbalance among cells when charging a series of cells, which results in the overcharging of some cells and the undercharging of the others. To address this issue, we propose a consensus control method for the CC-CV charging of supercapacitors. In this approach, each cell can be seen as a node in the graph topology to communicate with their neighbors. The objective is to synchronize cell voltages in the CC charging stage and avoid overcharging and undercharging of cells. Experiments are set up to verify the practicality and effectiveness of the proposed method. The results show that, compared with existing methods, the proposed method can synchronize cell voltages effectively in the CC-CV charging process.

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.

This paper presents a dual-band reflective metasurface operating at 5.8 GHz and 10 GHz for simultaneous communication and wireless power transfer (WPT). Based on the dual-polarization independent control structure element, the near-field focusing beam and the zero-order Bessel beam are obtained with different polarization at the two bands. Through full-wave simulation, the WPT performances of the two beam at the two bands are compared. The simulation results show that the area of the focusing aperture of near-field focusing is larger than the Bessel beam, but the side-lobe is smaller than the Bessel beam.

A subarray partition method of receiving antenna in microwave power transmission (MPT) is presented, that use different size of subarrays to lay out the receiving aperture. The purpose of the method is to homogenize the values of microwave power received from each subarray. Because the conversion efficiency of the rectifier connected to receiving antenna subarray is sensitive to the input microwave power, the method can avoid the loss of the efficiency caused by the input power mismatch between rectifier and receiving antenna subarray. The optimal layout design is achieved based on path search method, by using a specific set of subarray samples. The numerical simulation results show that the proposed method can satisfy the requirement of minimum power dispersion while ensuring there is no much reduction of the total reception efficiency.

In recent years, significant progress have been made in supercapacitor-based portable applications, where the switched-resistors circuit functions as a traditional cell balancing circuit. However, the existing charging control methods lack explicit consideration of the relatively high system temperature, leading to the decrease of the system life span and the serious thermal effect. In this paper, a temperature-suppressed charging method has been proposed to keep the system temperature at a low level during charging process. A proof is provided to demonstrate the effectiveness of this approach. Laboratory hardware setup has been completed and experimental results show that the average temperature of the system can be reduced by at least 1.2 °C compared with existing methods.

In this paper, a multi-uniform circular array (M-UCA) antenna is designed at 10GHz for generating multiple orbital angular momentum (OAM) vortex electromagnetic wave. The UCA antenna is constituted by 12 half wave dipoles as array elements and with a circular ground 1/4 wavelength behind the array. Different UCA antennas are designed to generate different OAM modes. The whole aperture sampling receiving (W ASR) scheme is adopted to receive the vortex electromagnetic wave. And the parameter mode percent (MP) which is newly defined by ourselves is used to evaluate the OAM mode spectrum. Both the transmission characteristics and OAM mode spectrum of the multiple OAM vortex electromagnetic wave generated by the M-UCA are discussed.

In this paper, an uniform circular antenna (UCA) at 10GHz is designed for generating the multiple orbital angular momentum (OAM) vortex electromagnetic wave. The excitation tolerance of both amplitude and phase of the array element are analyzed. First the characteristics of the OAM vortex electromagnetic (EM) wave including both electric field amplitude distribution and phase distribution are analyzed. Then the whole aperture sampling receiving (WASR) scheme is adopted for OAM mode performance analysis. And a parameter is defined as mode percent (MP) for estimating the performance or mode purity of the OAM vortex EM wave. All these analyses include both the cases of single OAM mode and multiple OAM mode. Finally some conclusions are presented about the UCA excitation design for OAM vortex EM wave generation and reception. This work can be beneficial to the UCA antenna design and OAM vortex generation and application.

We investigated the multiplexing transmission and demultiplexing reception properties of vortex electromagnetic wave with multiple orbital angular momentum (OAM) modes. In this paper, a uniform circular array (UCA) antenna model at lOGHz is designed for generating multiple OAM modes vortex electromagnetic wave, and both the transmission characteristics, i.e., the field distribution in the space, and the reception characteristics are analyzed. The whole aperture sampling receiving (WASR) scheme is used for the reception. And the radius of the reception aperture that influences the multiple OAM modes demultiplex reception effects is analyzed. Some numerical calculation simulation experiments are performed and a simple expression is defined as mode percent for measuring the reception effects.

A large number of serious traffic accidents occur at intersections due to the unsafe driving behaviors. In this paper, we propose a smartphone-based system to provide important information for driving behavior analysis at intersections. Our proposed system consists of two parts: (1) a deep convolutional neural network based model to detect traffic lights, crosswalks, and stop lines. (2) a Long Short-Term Memory (LSTM) neural network based model to estimate vehicle speed using accelerometer and gyroscope embedded in the smartphone. Important objects detection in traffic scenes and real time vehicle speed estimation are crucial for driving behavior analysis. We performed a thorough evaluation of our system, including analysis of the effectiveness of the proposed algorithm itself and the comparison with other methods as well. Our experiments exhibit the robustness of our system in various traffic scenarios.

This paper describes a latent space understandable network: Self- excited Generative Adversarial Network (Self- ExGAN), a novel self- excited structure based on adversarial learning. Compared with the conventional generative adversarial networks, SelfExGAN consists of three components, which are en- coder (E), generator (G), and discriminator (D). Different from other papers which directly apply reconstruction loss between encoder and generator, SelfExGAN introduces Nash equilibrium between these three parts in order to discover the correspondence between latent inputs and training data spontaneously. The most attractive point of SelfExGAN is that it can use the learned correspondence to guide G to generate homomorphic samples given different latent inputs in an unsupervised learning manner. We demonstrate the learning ability of SelfExGAN on MNIST, celebA, and SVHN datasets. Our code is available on https://github.com/LiamLYJ/SelfexGAN.

In this paper, we investigate a cognitive radio network design for the Two-way Relay (TWR) Channel, relying on the combination of Adaptive Quadrature amplitude modulation (AQAM) and network coding technique. More explicitly, we extend the AQAM in peer-to-peer transmission scenario to the downlink design of Decode-and-Forward Two-way Relaying (DF-TWR) scenario. On the basis of the bit-error-ratio (BER) bounds, joint power and rate adaptive QAM with network coding scheme is proposed for the purpose of improving the throughput of the relaying system. In addition, Network-coded-aided QAM suffers a modest SNR loss due to the direct current (DC) bias, which will be derived in detail in this paper. Simulation results indicate that our proposed adaptive Network-coded-aided QAM scheme hold the potential of improving the throughput of TWR network, compared to the scheme without adaptation.

The inverse discrete cosine transform (IDCT) is a significant component in today's JPEG and MPEG decoders. Of all the stages in the decoding process of a JPEG file, the IDCT is the most computationally intensive. Hence, we require fast and efficient implementations, either in software or hardware. Numerous individual designs for computing the ID-IDCT have been proposed. Our 2D-IDCT incorporates two of our ID-IDCT cores and a transpose network to provide a stall-free pipeline. In this paper, we describe a fast hardware implementation of a two-dimensional IDCT architecture that implements a variation of the modified Loeffler algorithm. This design is currently functionally verified, synthesized and tested on the Xilinx Virtex II FPGA. Our FPGA implementation has a throughput of over 800 M coefficients per second, implemented as an eight-wide pipeline with a clock frequency of 102 MHz. We suggest ideas to parallelize the design and further enhance performance. We also describe an ASIC design of the HDL model that operates at a clock frequency of 154 MHz using TSMC'S 0.18 mum CMOS technology. Our VHDL implementation is released as "open source "