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Design and development of an intelligent zone based master electronic control unit for power optimization in electric vehicles
The development of electric vehicles (EVs) has been incremental because EVs satisfy a significant demand for energy sources. Electronic control unit (ECU) is an important component that processes the electric signals received from various sensors for generating the control signals for the actuators. Automotive control systems were initially operated manually throughout the automotive revolution based on the responses of input signals received from ECUs and drivers. Most of the functions in EV are controlled by the ECU and every ECU consumes power at all times even if it is not in use. The larger power consumption of passive ECUs like adaptive lighting systems (ALS), automatic wiper systems (AWS) brake light systems (BLS), etc., affect the life of ECUs and the range of EVs. This article is primarily concerned with limiting power consumption by switching the power supply to the passive ECUs based on their requirements. Hence, to achieve the objective, the intelligent zone (i-zone) based master ECU is triggered to activate the slave ECUs. Designing suites including Proteus and KiCAD were used for designing the circuits including master as well as slave ECU. This prototype is built using three secondary ECUs such as ALS & AWS and BLS which are controlled using i-zone-based master ECU. The performance of this implemented design is evaluated, and it is discovered that almost 40% of the battery consumption is reduced. This i-zone-based master ECU and all its slave ECUs manage power while ensuring the safety and reliability of EVs.
Journal Article
Driving behavior analysis and classification by vehicle OBD data using machine learning
The transportation industry's focus on improving performance and reducing costs has driven the integration of IoT and machine learning technologies. The correlation between driving style and behavior with fuel consumption and emissions has highlighted the need to classify different driver's driving patterns. In response, vehicles now come equipped with sensors that gather a wide range of operational data. The proposed technique collects critical vehicle performance data, including speed, motor RPM, paddle position, determined motor load, and over 50 other parameters through the OBD interface. The OBD-II diagnostics protocol, the primary diagnostic process used by technicians, can acquire this information via the car's communication port. OBD-II protocol is used to acquire real-time data linked to the vehicle's operation. This data are used to collect engine operation-related characteristics and assist with fault detection. The proposed method uses machine learning techniques, such as SVM, AdaBoost, and Random Forest, to classify driver's behavior based on ten categories that include fuel consumption, steering stability, velocity stability, and braking patterns. The solution offers an effective means to study driving behavior and recommend corrective actions for efficient and safe driving. The proposed model offers a classification of ten driver classes based on fuel consumption, steering stability, velocity stability, and braking patterns. This research work uses data extracted from the engine's internal sensors via the OBD-II protocol, eliminating the need for additional sensors. The collected data are used to build a model that classifies driver's behavior and can be used to provide feedback to improve driving habits. Key driving events, such as high-speed braking, rapid acceleration, deceleration, and turning, are used to characterize individual drivers. Visualization techniques, such as line plots and correlation matrices, are used to compare drivers' performance. Time-series values of the sensor data are considered in the model. The supervised learning methods are employed to compare all driver classes. SVM, AdaBoost, and Random Forest algorithms are implemented with 99%, 99%, and 100% accuracy, respectively. The suggested model offers a practical approach to examining driving behavior and suggesting necessary measures to enhance driving safety and efficiency.
Journal Article
Optimization of CMCU with Code Sharing
The article proposes a method for reducing the number of LUT elements in the circuit of a compositional microprogram control unit (CMCU) with code sharing. The method is based on the two-fold encoding of operator linear chains (OLC). Each chain has a code as an element of the OLC set and as a class element of this set. This approach allows obtaining a two-level microinstruction addressing unit. The control memory of the CMCU is implemented in the embedded memory blocks. The article considers an example of synthesis and provides an analysis of the proposed method.
Journal Article
Network Coding Based Fault-Tolerant Dynamic Scheduling and Routing for In-Vehicle Networks
The development of autonomous vehicles brings new challenges for vehicle electronics as well as in-vehicle network (IVN) design. Functional safety is the uttermost requirement of future vehicles, which requires an exchange of a copious amount of safety-critical data through the in-vehicle network (IVN). To meet the high-reliability requirements of future vehicle applications in IVN, IEEE time-sensitive networking (TSN) proposes an active redundancy-based fault-tolerant mechanism called frame replication and elimination for reliability (FRER) in IEEE 802.1CB standard. In FRER, safety-critical data is transmitted via multiple disjoint paths between source and destination. In case one path fails, the safety-critical data can still be delivered to the destination via another path. The main drawback of FRER is that it is over-utilizing the available bandwidth of the network, which in turn reduces the number of schedulable flows. In this paper, the XOR network coding (XNC) technique is proposed as a new efficient spatial redundancy-based fault-tolerant mechanism for IVN. This work considered the strict time-scheduled transmissions for XNC and FRER. In this regard, three different dynamic scheduling and routing heuristics are developed and integrated with XNC techniques to increase the number of schedulable flows without degrading the reliability of the network. The experimental results show the efficacy of the XNC-Bottleneck heuristic which schedules almost 8.5% more flows in fix-time scheduling of the disjoint paths and 20% more flows in variable-time scheduling of the disjoint paths as compared to the FRER-Bottleneck heuristic.
Journal Article
Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies
This paper proposes entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments. Entropy balancing relies on a maximum entropy reweighting scheme that calibrates unit weights so that the reweighted treatment and control group satisfy a potentially large set of prespecified balance conditions that incorporate information about known sample moments. Entropy balancing thereby exactly adjusts inequalities in representation with respect to the first, second, and possibly higher moments of the covariate distributions. These balance improvements can reduce model dependence for the subsequent estimation of treatment effects. The method assures that balance improves on all covariate moments included in the reweighting. It also obviates the need for continual balance checking and iterative searching over propensity score models that may stochastically balance the covariate moments. We demonstrate the use of entropy balancing with Monte Carlo simulations and empirical applications.
Journal Article
Causal Inference without Balance Checking: Coarsened Exact Matching
We discuss a method for improving causal inferences called “Coarsened Exact Matching” (CEM), and the new “Monotonic Imbalance Bounding” (MIB) class of matching methods from which CEM is derived. We summarize what is known about CEM and MIB, derive and illustrate several new desirable statistical properties of CEM, and then propose a variety of useful extensions. We show that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use. We focus on the connection between theoretical properties and practical applications. We also make available easy-to-use open source software for R, Stata, and SPSS that implement all our suggestions.
Journal Article
Optimization of Parametrized Heat Fins Design Based on Thermal Simulation
Heat fins are used to improve thermal solutions in Electronic Control Units (ECU) as being the most common and cost-effective way to enable heat transfer and protect electronics side of the unit. Heat fins have limitations, including material, manufacturability, fins position, and available area size. Research was done to reveal existing solutions and their usability. Existing literature presents solutions with good effect in heat transfer, but this paper introduces solutions designed to be easily manufactured and cost-effective. Most of the solutions proposed by the literature with high thermal improvement are mostly fitting on prototypes. The case study proposed in the paper consists in a parametrized heat fins configurable design. The different solutions are introduced in a thermal simulation to highlight the efficiency of the heat transfer. Fins number and shape prove to be the most critical factors in heat transfer. However, thickness and shape are essential, as they affect the distance between fins and air flow.
Journal Article
Protected areas reduced poverty in Costa Rica and Thailand
As global efforts to protect ecosystems expand, the socioeconomic impact of protected areas on neighboring human communities continues to be a source of intense debate. The debate persists because previous studies do not directly measure socioeconomic outcomes and do not use appropriate comparison groups to account for potential confounders. We illustrate an approach using comprehensive national datasets and quasi-experimental matching methods. We estimate impacts of protected area systems on poverty in Costa Rica and Thailand and find that although communities near protected areas are indeed substantially poorer than national averages, an analysis based on comparison with appropriate controls does not support the hypothesis that these differences can be attributed to protected areas. In contrast, the results indicate that the net impact of ecosystem protection was to alleviate poverty.
Journal Article
Design and Development of Test and Control System for Hydraulic Steering Control Unit Based on Virtual Instrument
A system combined the function of Industrial Computer, PLC, virtual instruments, sensors and data acquisition was designed and developed for testing and controlling of hydraulic steering control unit, which can achieve automated data acquisition, data processing, test results judgment, false alarms and etc. with the development of computer and science. The software of test system was designed through LabVIEW, and by using module design method, error detection, modify and maintenance of the software turn to be simple and the software become more robust. The system also adopted shielding and filtering with combination of hardware and software to improve test accuracy which reduces test error caused by operators and improve test efficiency.
Journal Article