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A highly sensitive biosensor for detecting HPV 16 DNA was prepared based on Keggin-type polyoxometalate (SiW12)-grafted CdS quantum dots (SiW12@CdS QDs) and colloidal gold nanoparticles (Au NPs), which exhibited remarkable selectivity and sensitivity upon target DNA detection because of its excellent photoelectrochemical (PEC) response. Here, an enhanced photoelectronic response ability was achieved with the strong association of SiW12@CdS QDs by polyoxometalate modification, which was developed through a convenient hydrothermal process. Furthermore, on Au NP-modified indium tin oxide slides, a multiple-site tripodal DNA walker sensing platform coupled with T7 exonuclease was successfully fabricated with SiW12@CdS QDs/NP DNA as a probe for detecting HPV 16 DNA. Due to the remarkable conductivity of Au NPs, the photosensitivity of the as-prepared biosensor was improved in an I3−/I− solution and avoided the use of other regents toxic to living organisms. Finally, under optimized conditions, the as-prepared biosensor protocol demonstrated wide linear ranges (15–130 nM), with a limit of detection of 0.8 nM and high selectivity, stability, and reproducibility. Moreover, the proposed PEC biosensor platform offers a reliable pathway for detecting other biological molecules with nano-functional materials.

Considering the battery-failure-induced catastrophic events reported frequently, the early fault warning of batteries is essential to the safety of electric vehicles (EVs). Motivated by this, a novel data-driven method for early-stage battery-fault warning is proposed in this paper by the fusion of the short-text mining and the grey correlation. In particular, the short-text mining approach is exploited to identify the fault information recorded in the maintenance and service documents and further to analyze the categories of battery faults in EVs statistically. The grey correlation algorithm is employed to build the relevance between the vehicle states and typical battery faults, which contributes to extracting the key features of corresponding failures. A key fault-prediction model of electric buses based on big data is then established on the key feature variables. Different selections of kernel functions and hyperparameters are scrutinized to optimize the performance of warning. The proposed method is validated with real-world data acquired from electric buses in operation. Results suggest that the constructed prediction model can effectively predict the faults and carry out the desired early fault warning.

Proton exchange membrane fuel cells (PEMFC) are considered a promising solution for renewable energy application. To meet industrial requirements, the power source consisting of PEMFC is required to be power regulator to generate a stable and desired current and/or voltage under various working conditions. In this article, the adaptive sliding mode control integrating with the radial basis function neural network (RBFNN) approach for DC/DC buck converter-based PEMFC is presented to address perturbations from inner parameters as well as external disturbances in terms of power conditioning. Sliding mode control (SMC) and backstepping schemes are integrated to tackle the nonlinear and coupled outputs resulting in large control errors and slow response caused by PEMFC characteristics. To accurately estimate the parametric uncertainties and disturbance injections, such as buck converter parameter varying and PEMFC operation point changing, the RBFNN adaptive law is developed according to the defined Lyapunov and Gaussian functions overcoming the limitations of non-/linear parameter estimating. Simulations and experiments on the PEMFC power supply prototype governed by the DS1104 board are carried out. The comparative results indicate that the proposed RBFNN estimation associated with the backstepping SMC can reduce up to 7.5% overshoot and smooth PEMFC voltage and inductor current when disturbance changes in a voltage regulation experiment. Thus, the proposed method can regulate the current or voltage of a PEMFC power supply with robustness, adaptivity, and no chattering phenomenon.

This paper deals with the speed synchronization controller design for networked integrated motor-transmission (IMT) powertrains via controller area network (CAN). It is well known that, in current implementations, CAN has been widely used in the control system design of automotive powertrains. However, on the other hand, the application of CAN would not only lead to network-induced delays but also bring about protocol constrains, e.g., data package capability and utilization ratio limitation, which would deteriorate the system and make the controller design a challenging problem. This paper is to provide a co-design methodology that can cope with all these problems and ensure satisfactory control effect for the speed synchronization control of IMT powertrain systems. First, a networked IMT powertrain system using CAN as underlying network is presented and the dynamic model for the speed synchronization control is derived. Second, the network-induced delay model is introduced and improved considering data packet capability and utilization ratio limitation. The control-orient discrete-time model is also derived based on the improved delay model. Third, a co-design methodology using sliding mode controller and offline priority scheduling based on Lyapunov stability criterion is proposed. The results of simulations and tests show the effectiveness of the proposed co-design methodology.

Electric sanitation vehicles have increasingly been applied to cleaning work due to the requirement of air pollution control. The power distribution and energy management strategy (EMS) influence the vehicle’s performance a lot both in the aspects of cleaning effect and electricity consumption. Aiming to improve energy economy and ensure clean tasks, first, the electricity consumption percentages of the vehicle onboard devices are analyzed and the main contributors are clarified, and the power requirement model of the working motor is built based on experimental data. Second, a universal modeling method of garbage distribution on the road surface is proposed, which implements a nonlinear autoregressive neural network as the predictor. Third, an adaptive model predictive control (AMPC)-based EMS is proposed and verified. The results show the AMPC method can accurately predict the garbage density and the proposed EMS can approximate the energy consumption of the DP-based EMS with little deviation. Compared to conventional rule-based EMS, the AMPC-based EMS achieved a 15.5% decrease in energy consumption as well as a 14.6% decrease in working time.

In this paper, we report a compact wavelength-flattened directional coupler (WFDC) based chemical sensor featuring an incorporated subwavelength grating (SWG) structure for the mid-infrared (MIR). By incorporating a SWG structure into directional coupler (DC), the dispersion in DC can be engineered to allow broadband operation which is advantageous to extract spectroscopic information for MIR sensing analysis. Meanwhile, the Bragg reflection introduced by the SWG structure produces a sharp trough at the Bragg wavelength. This sharp trough is sensitive to the surrounding refractive index (RI) change caused by the existence of analytes. Therefore, high sensitivity can be achieved in a small footprint. Around fivefold enhancement in the operation bandwidth compared to conventional DC is achieved for 100% coupling efficiency in a 40 µm long WFDC experimentally. Detection of dichloromethane (CH2Cl2) in ethanol (C2H5OH) is investigated in a SWG-based WFDC sensor 136.8 µm long. Sensing performance is studied by 3D finite-difference time domain (FDTD) simulation while sensitivity is derived by computation. Both RI sensing and absorption sensing are examined. RI sensing reveals a sensitivity of −0.47% self-normalized transmitted power change per percentage of CH2Cl2 concentration while 0.12% change in the normalized total integrated output power is realized in the absorption sensing. As the first demonstration of the DC based sensor in the MIR, our device has the potential for tertiary mixture sensing by utilizing both changes in the real and imaginary part of RI. It can also be used as a broadband building block for MIR application such as spectroscopic sensing system.

This paper deals with the speed synchronization control of integrated motor–transmission (IMT) powertrain systems in pure electric vehicles (EVs) over a controller area network (CAN) subject to both network-induced delays and network congestion. A CAN has advantages over point-to-point communication; however, it imposes network-induced delays and network congestion into the control system, which can deteriorate the shifting quality and make system integration difficult. This paper presents a co-design scheme combining active period scheduling and discrete-time slip mode control (SMC) to deal with both network-induced delays and network congestion of the CAN, which improves the speed synchronization control for high shifting quality and prevents network congestion for the system’s integration. The results of simulations and hardware-in-loop experiments show the effectiveness of the proposed scheme, which can ensure satisfactory speed synchronization performance while significantly reducing the network’s utilization.

All-wheel-independent-drive electric vehicles (AWID-EVs) have considerable advantages in terms of energy optimization, drivability and driving safety due to the remarkable actuation flexibility of electric motors. However, in their current implementations, various real-time data in the vehicle control system are exchanged via a controller area network (CAN), which causes network congestion and network-induced delays. These problems could lead to systemic instability and make the system integration difficult. The goal of this paper is to provide a design methodology that can cope with all these challenges for the lateral motion control of AWID-EVs. Firstly, a continuous-time model of an AWID-EV is derived. Then an expression for determining upper and lower bounds on the delays caused by CAN is presented and with which a discrete-time model of the closed-loop CAN system is derived. An expression on the bandwidth utilization is introduced as well. Thirdly, a co-design based scheme combining a period-dependent linear quadratic regulator (LQR) and a dynamic period scheduler is designed for the resulting model and the stability criterion is also derived. The results of simulations and hard-in-loop (HIL) experiments show that the proposed methodology can effectively guarantee the stability of the vehicle lateral motion control while obviously declining the network congestion.