Search Results (558)

Laboratory-based certification cycles systematically underestimate real-world fuel consumption and CO₂ emissions. On-board diagnostics (OBD-II) telemetry offers a low-cost alternative, yet most published approaches rely on mass air flow (MAF) sensors absent from many modern vehicles. This study validates a speed-density air-mass estimation method on a naturally aspirated RON 95 gasoline passenger car (1368 cm³, Euro 6) across seven drive cycles recorded over three measurement days in northwestern Türkiye, covering 609.6 km of highway, urban, and mixed conditions. Instantaneous air mass flow was estimated from four standard OBD-II PIDs—manifold absolute pressure, engine speed, intake air temperature, and fuel trim corrections—using the ideal gas law applied to actual engine displacement. Results were validated against pump-measured fill-up volumes. The speed-density model achieved errors of −3.6% to +4.3% across individual segments (combined error: −0.5%), outperforming the vehicle’s onboard trip computer, which exhibited errors of −10.6% to +14.6%. Derived CO₂ intensities ranged from 125.0 to 166.4 g/km, with a combined average of 147.2 g/km (pump reference: 147.9 g/km). Urban driving produced approximately 15% higher specific emissions than highway driving. These results demonstrate that a physics-based speed-density model can achieve within ±5% trip-level accuracy across diverse real-world conditions without machine learning, bespoke calibration, or a physical MAF sensor. Full article

To address the poor adaptability to diverse driving cycles and the imbalance between optimization performance and computational efficiency in existing energy management strategies (EMSs) for plug-in hybrid electric vehicles (PHEVs), this paper proposes a lightweight intelligent EMS (IEMS) with adaptive rule-parameter updating. The key contributions lie in constructing an optimized rule library using parameter optimization, and developing an online adaptive updating mechanism for rule parameters combined with driving cycle prediction, realizing dynamic self-adjustment of energy management rules. The results show that compared with the rule-based EMS (RBEMS), the strategy reduces energy consumption by 9.09%, 10.85% and 9.25% under NEDC, WLTC and real-world test cycles, respectively, with drastically lower computation times than dynamic programming (DP). The proposed IEMS can effectively balance fuel economy, driving cycle adaptability and computational efficiency. Full article

This paper presents the design and implementation of a field-programmable gate array (FPGA)-based System-on-Programmable-Chip (SoPC) architecture for sensorless speed control of permanent magnet synchronous motor (PMSM) drives. To enable real-time execution of the computationally intensive estimation stage, a parallelized Unscented Kalman Filter (UKF) is proposed for the joint estimation of rotor speed, position, and load torque. Unlike traditional sequential processor-based UKF implementations, the proposed parallel architecture simplifies the iterative process and significantly reduces computational latency and hardware resource utilization while preserving high estimation fidelity. This transformation reduces the number of sequential dependency stages within one estimation cycle and enables simultaneous execution of matrix operations using dedicated FPGA resources, thereby decreasing effective iteration latency. The complete control system comprises current regulators, a coordinate transformation module, a proportional–integral (PI) speed controller, and auxiliary functional blocks—all fully integrated within a single SoPC. The UKF estimator and control components are described using a hardware description language (HDL), enabling efficient hardware-level parallelism and real-time execution. The proposed system is validated through co-simulation and experimental verification on a Xilinx ZCU102 platform driving an inverter-fed PMSM. The results confirm correct real-time operation of the proposed architecture and demonstrate its feasibility for FPGA-based sensorless motor drive implementation. A detailed quantitative comparison with a fully sequential FPGA-based UKF implementation is identified as future work to further substantiate the reported latency reduction. Full article

Underwater gliders are suited for long-duration oceanographic observation, but their endurance is bounded by onboard energy capacity. An overlooked source of energy loss is the attitude control system, which repeatedly repositions the internal moving mass to hold the desired pitch angle throughout each gliding cycle. Conventional PID and manually tuned fuzzy controllers continue driving the actuator after pitch convergence and adapt poorly to nonlinear buoyancy variations at depth. To address this, we propose an ANFIS (Adaptive Neuro-Fuzzy Inference System)-based pitch control strategy for a 1000 m-class underwater glider. A nonlinear 6-DOF dynamic simulator incorporating experimentally derived power models for the buoyancy engine and attitude controller was validated up to 100 bar. A 13-rule Sugeno-type fuzzy inference system was optimized through ANFIS hybrid learning using approximately 5500 samples from PID steady-state data. Simulation results show energy savings of 57.05% over PID and 4.98% over a manually tuned fuzzy controller, with no degradation in tracking accuracy. Sea trials confirm a reduction in moving mass displacement under real disturbance conditions, providing qualitative evidence consistent with the simulation results. Further quantitative validation of the energy reduction effect through free-running sea trials remains as future work. Full article

Addressing the dual challenges of green agricultural transformation and the national carbon neutrality targets, the traditional pest control systems in the mulberry plantations of Nantong, Jiangsu Province, face concurrent problems, including excessive pesticide use, high direct carbon emissions, and low economic returns. This study establishes a comprehensive evaluation framework integrating technical, environmental, and economic dimensions. Utilizing a lightweight mobile sensing system, this research enables the early identification of white powdery mildew on mulberry trees and facilitates precise spatial pesticide management. Unlike traditional life cycle assessment (LCA) studies that rely on static data, this case study uses real-time field monitoring data as dynamic input to drive the standardized life cycle assessment model. In this pilot-scale validation (n = 3 pairs, one growing season), the proposed model reduced pesticide usage by an average of 28.7% (±3.1%), achieved a carbon emission reduction of 23.1 (±2.7) g/m², and increased net income by 0.199 (±0.018) yuan/m². Precision pest control driven by mobile sensing effectively enhances the synergy between ecological and economic benefits in specialty crop systems. Consequently, the study proposes a data-driven framework that shows promise for pesticide–carbon–income synergy, pending further validation across more sites and seasons. Full article

End-to-end autonomous driving remains fragile in long-tail scenarios, while incorporating vision-language models (VLMs) introduces substantial deliberation latency that cannot interfere with the real-time planning loop. We present ThinkDrive, a dual-process driving framework designed under explicit real-time queuing constraints. The framework contains four coordinated components. First, a Scene Complexity Estimator regulates System-2 activation through a trigger cool-down mechanism, allowing at most one asynchronous request every $\lceil L_2/\Delta t\rceil$ frames and thereby preventing queue saturation under a System-2 latency of $L_2=565$ ms. Second, a multi-modal System-1 planner generates $K_1=5$ candidate trajectories within 44 ms and is trained with winner-takes-all imitation learning together with explicit score supervision. Third, a two-stage Causal-CoT module uses the VLM to identify risk agents and predict a preferred spatial goal $G_{\mathrm{VLM}}$, after which a single batched scm_rollout selects the safest candidate and extracts its endpoint as a world-coordinate goal anchor ${\mathbf{g}}_{S2}$. Fourth, a Goal-Anchor Replanning module transforms ${\mathbf{g}}_{S2}$ into the current ego frame and selects the candidate whose waypoint at the remaining time horizon is closest to the transformed goal. This design avoids coordinate-space mixing, mitigates bias caused by mismatched temporal horizons, and prevents semantic instability across replanning cycles. On nuPlan test14-hard, ThinkDrive with InternVL2-8B and a 6.8% trigger rate achieves 74.9 PDMs, outperforming AdaThinkDrive at 73.1 while maintaining a nominal latency of 44 ms. Full article

To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network is employed to predict future driving conditions, providing preview information for the MPC. Subsequently, a DP-MPC cooperative architecture is constructed, which invokes DP to solve for local optimal solutions during the receding horizon optimization process and incorporates linear reference SOC trajectory planning to approximate the global optimum. Simulation results under the WLTC driving cycle demonstrate that the fuel consumption of the proposed strategy is 2.311 L/100 km, representing a 33.2% reduction in pure fuel consumption compared to the rule-based (RB) strategy, and a 16.3% reduction in equivalent fuel consumption (including electricity converted to fuel based on the engine’s generation efficiency), while achieving 96.31% of the fuel economy of the global optimal DP strategy. The study validates that this method significantly improves fuel economy while guaranteeing real-time performance. Full article

The real driving emission (RDE) test is going to be a necessary and effective evaluation method in the next-stage heavy-duty vehicle (HDV) emission standards, the rulemaking of which is under way worldwide (e.g., EPA 2027, Euro 7 and China 7). In this work, a time-based method (TBM) was proposed for future HDV RDE calculation. In TBM, cold-start and hot-run emissions are evaluated separately with moving average windows, yet no type-approval test results are needed so that it can also be used as a remote monitoring algorithm. This study analyzes the emissions of NO_x. The value of 0.1 times maximum engine power is utilized to determine the cold-start window, while a 2-bin window structure is adopted for hot-run analysis. In order to further illustrate and validate this method, 16,629.4 h of remote monitoring data with a sampling rate of 1 Hz from 36 China 6 HDVs and 4 different months were analyzed for driving and NO_x emission characteristics with TBM. The average duration of the 21,466 trips analyzed in this work was found to be 0.68 h, and the average ratio of trip work to WHTC (world harmonized transient driving cycle) work was around 1.38, indicating that lower duration and work requirements are needed in future RDE test. Moreover, the average cold-start length was approximately 912.4 s (15.2 min), and long cold starts could be found in cases with low ambient temperatures, low driving speeds and frequent stops. As for hot-run analysis, the proportion of Bin 1 (low-load windows) and Bin 2 (high-load windows) is directly related to the driving scenarios. The calculation results of TBM are comparable to the 2-bin method in EPA 2027. In addition, the optimization of NO_x emissions under cold start and idle conditions are challenging for future HDV updates. Full article

Coagulation-based technologies are increasingly recognized as key for controlling fluoride and hexavalent chromium in urban water and wastewater. Combined geogenic and industrial sources often drive chronic exposure and create an underrecognized public health burden. This review synthesizes current knowledge on the occurrence, speciation, and toxicology of F⁻ and Cr(VI) in urban systems, links regulatory targets to health outcomes, and critically examines conventional, advanced, and electrochemical coagulation processes for their removal under realistic water-quality conditions. Mechanistic sections describe how aluminum-, iron-, magnesium- and zirconium-based coagulants, including pre-polymerized and composite formulations (e.g., IPC-type coagulants, PSiFAC-Mg, ZrCl₄), remove fluoride via Al–F complexation, Al–F–OH co-precipitation, ion exchange, and sweep flocculation, while Cr(VI) control relies on Fe(II)-mediated reduction to Cr(III), followed by adsorption and co-precipitation with metal hydroxides. The review assesses how water chemistry and operating conditions affect single- and multi-contaminant removal, highlighting competition among fluoride, Cr(VI), nutrients, and other oxyanions. Performance data from bench-, pilot-, and selected full-scale studies show that optimized coagulation and electrocoagulation can substantially reduce fluoride and Cr(VI) (to drinking-water-relevant levels) in diverse urban waters, but also reveal persistent issues of sludge generation and stability, residual metals, process robustness, and cost. The review identifies priorities, including long-term urban-scale assessments, low-toxicity green coagulants, life-cycle and health impact assessments, and real-time coagulation control for fluoride and Cr(VI). Full article

This paper aims to improve the energy autonomy and the mechanical performances of an on-board drive chain for robotics. The energy autonomy improvement is performed by reducing electrical losses in the inverter. Electrical losses are reduced by decreasing the number of switching cycles per period of the inverter’s power semiconductor switches, while maintaining a low Total Harmonic Distortion (THD). These improvements are expected thanks to a new control strategy called Pre-Calculated Pulse Width Modulation (PC PWM). The principle of this new control strategy is that all the symmetries of an ideal three-phase voltage system are assigned to the real output voltage of the inverter. Then the switching instants of the inverter’s switches are determined off line, by means of Fourier’s analysis, so that the maximum number of successive harmonics is zeroed. This allows the optimal switching sequence to be predefined, thereby reducing unnecessary commutations of the power switches. The performance of the new method (PC PWM) is evaluated through detailed simulation studies and compared with the conventional method called Sinusoidal Pulse Width Modulation (SPWM). The simulation results show that despite the reduction in the number of commutations per period, the performance of the actuation chain has been significantly improved with PC-PWM (new technique). Indeed, for the same mechanical load, the PC-PWM method allows for a lower current, a shorter transient response time and a lower torque ripple than the SPWM method. Full article

Thermal management is a key factor for the efficiency, performance, and reliability of battery electric vehicles (BEVs), particularly in systems with strongly coupled components and heterogeneous thermal dynamics. This study proposes a centralized nonlinear model predictive control (NMPC) strategy for component cooling in BEVs, designed to maintain temperatures within optimal ranges while minimizing energy consumption and respecting actuator constraints. A reduced-order physics-based model is developed in MATLAB/Simulink R2024b, and the NMPC is implemented using CasADi, incorporating coolant temperatures as stabilizing states and a systematic parametrization of sampling time, prediction horizon, and weighting factors. The considered thermal management system consists of hydraulically coupled subsystems with different overall time constants, for which a single-horizon NMPC formulation is applied. Simulation results show that the proposed controller accurately tracks thermal dynamics across components with varying inertia and effectively captures cross-coupling effects. Sensitivity analyses indicate that variations in sampling time and prediction horizon have a limited impact on temperature trajectories and energy consumption, demonstrating robustness and real-time applicability. Compared to a rule-based controller, the NMPC achieves up to 30% reduction in energy consumption depending on ambient conditions and driving cycles, while improving temperature regulation, particularly for the high-voltage battery, with up to 2 K lower peak temperatures and a more balanced temperature distribution. These findings demonstrate that centralized NMPC is a suitable and efficient approach for thermal management in directly coupled BEV subsystems with heterogeneous dynamics. Full article

Hardware-in-the-Loop (HiL) driving simulators are increasingly adopted in vehicle development to improve efficiency, flexibility, and repeatability across the product life cycle. Their implementation, however, remains challenging, as the integration of real vehicle components into simulation environments significantly increases system complexity and requires the coherent interaction of real hardware, actuation subsystems, and numerical models representing non-physical components. This paper addresses these challenges through the development of a remotely controlled HiL test rig for the braking system, focusing on its integration with a driver’s station in a driving simulator. The role of braking systems within HiL simulators is first discussed, highlighting their relevance for early development, debugging, and calibration activities. An exemplary development pipeline is then presented, introducing a modular and scalable software architecture implemented in MATLAB/Simulink to manage remote brake actuation and force feedback. The performance of the proposed actuation system is experimentally evaluated and discussed, including its integration with a commercial force-feedback device. The results demonstrate the feasibility and effectiveness of the proposed framework, showing concrete benefits in respect of development efficiency and industrial applicability. Full article

To address the challenge of balancing regenerative braking efficiency and braking safety in rear-wheel-drive electric motorcycles, this study proposes a genetic algorithm-based electromechanical hybrid fuzzy braking control strategy. First, a three-dimensional fuzzy controller is designed with braking force, motorcycle speed, and battery state of charge (SOC) as input variables to adjust the regenerative braking ratio in real-time. To further improve the fuzzy logic, which typically relies on engineering experience, a genetic algorithm (GA) is employed to optimize the controller’s parameter space. Co-simulation results using BikeSim 2013.1 and MATLAB/Simulink R2022a demonstrate that, under WMTC and NEDC standard driving cycles, the proposed GA-optimized fuzzy control system increases energy recovery rates by 6.59% and 11.65%, respectively, compared with the unoptimized fuzzy control strategy. Full article

Proton exchange membrane fuel cell vehicles (PEMFCVs) have attracted widespread attention in recent years. However, there are many challenges existing in the development, such as the durability and economy of the fuel cell system (FCS). In this investigation, a fuzzy energy management strategy based on Grey Bernoulli Prediction (FEMS-GBP) is proposed to mitigate these two issues. Grey Bernoulli Prediction (GBP) is used to predict the FCS short-term future power demand with a low calculation amount, which is suitable for real-time on-board applications in PEMFCVs. Therefore, FEMS-GBP can proactively adjust FCS output power to reduce large load change times during PEMFCV operation, thereby improving FCS durability. Fuzzy control is employed to accomplish the energy management task between the FCS and the battery for better fuel economy. Numerical simulations and experiments under different vehicle driving cycles are carried out to evaluate the performance of FEMS-GBP. By comparing it with two other conventional energy management strategies, FEMS-GBP is demonstrated to be feasible and effective, as it achieves favorable performance in balancing durability and economy, especially under practical driving conditions. Full article

To address the limited accuracy of power demand prediction for hybrid electric vehicles under complex and dynamic driving conditions, this paper proposes a hybrid prediction approach based on the cascade correction of Long Short-Term Memory networks and Least Squares Support Vector Machines (LSTM-LSSVM-CC). The proposed method adopts a stage-wise modeling framework that exploits the least-squares optimality of LSSVM for low-frequency steady-state signals and the dynamic compensation capability of LSTM for high-frequency non-stationary residuals, thereby achieving complementary feature representation in the frequency domain. Specifically, an LSSVM is first used to construct a baseline regression model that captures stationary components, followed by an LSTM network that performs deep temporal modeling of the residual sequence to correct nonlinear prediction errors. Extensive experiments conducted on three standard driving cycles—CLTC-P, WLTP, and UDDS—demonstrate that the proposed model consistently outperforms conventional methods including LSSVM, RNN, ELMAN, and Random Forest in multi-step predictions, achieving an average RMSE reduction of 28–52% and maintaining correlation coefficients (R²) between 0.87 and 0.99. Particularly under highly dynamic and abrupt load conditions, the model exhibits superior real-time performance and stability while significantly mitigating cumulative prediction errors. These results demonstrate that the proposed LSTM-LSSVM-CC model achieves robust modeling performance of non-stationary time series while balancing prediction accuracy and computational efficiency, providing an effective technical foundation for hybrid vehicle energy management optimization and offering a transferable theoretical framework for time-series prediction in complex systems. Full article