Optimal Torque-Vectoring Control Strategy for Energy Efficiency and Vehicle Dynamic Improvement of Battery Electric Vehicles with Multiple Motors

Electric vehicles comprising multiple motors allow the individual wheel torque allocation, i.e. torque-vectoring. Powertrain configurations with multiple motors provide additional degree of freedom to improve system level efficiencies while ensuring handling performances and active safety. However, most of the works available on this topic do not simultaneously optimize both vehicle dynamic performance and energy efficiency while considering the real-time implementability of the controller. In this work, a new and systematic approach in designing, modeling, and simulating the main layers of a torque-vectoring control framework is introduced. The high level control combines the actions of an adaptive Linear Quadratic Regulator (A-LQR) and of a feedforward controller, to shape the steady-state and transient vehicle response by generating the reference yaw moment. A novel energy efficient torque allocation method is proposed as a low level controller. The torque is allocated on each wheel by solving a quadratic programming problem. The latter is solved in real-time to guarantee the desired yaw moment and the requested driver power demand while minimizing the system losses. The objective function of the quadratic problem accounts for the efficiency map of the electric machine as well as the dissipations due to tire slip phenomena. The torque-vectoring is evaluated in a co-simulation environment. Matlab/Simulink is used for the control strategy and VI-CarRealTime for the vehicle model and driver. The vehicle model represents a high performance pure electric SUV with four e-motors. The performance of the proposed controller is assessed using open loop maneuvers and in closed loop track lap scenarios. The results demonstrate that the proposed controller enhances the vehicle’s performance in terms of handling. Additionally, a significant improvement in energy saving in a wide range of lateral acceleration conditions is: presented. Moreover, the control strategy is validated using rapid control prototyping, thus guaranteeing a deterministic real-time implementation

A Full-fledge Simulation Framework for the Assessment of Connected Cars

Abstract Intelligent Transport Systems (ITS) have emerged as an integral part of smart cities, providing increased ease of mobility as well as efficiency and safety in vehicular traffic. Given its wide array of applications, ITS has also become a multidisciplinary field of work where vehicular communications, traffic control, ADAS (Advance Driver Assistance System) sensors, and vehicle dynamics have all to be accounted for. The study of such diverse aspects makes the evaluation of new ITS approaches, algorithms, and protocols not a small feat. For this reason, the availability of an effective, scalable, and comprehensive tool for the investigation and virtual validation of new ITS solutions is paramount. In this work, we present a simulation framework, called CoMoVe (Communication, Mobility, Vehicle dynamics), that effectively addresses the above need, as it enables the virtual validation of innovative solutions for vehicles that are both connected and equipped with ADAS sensors. Our framework encapsulates the important attributes of vehicle communication, road traffic, and dynamics into a single environment, by combining the strengths of different simulators. CoMoVe finds its use to evaluate the impact of vehicle connectivity, while imposing causality on vehicle dynamics and mobility. Such an assessment can greatly facilitate the development of control systems, algorithms, and protocols for real-world ITS

Allelic relationships of flowering time genes in chickpea

Flowering time and crop duration are the most important traits for adaptation of chickpea (Cicer arietinum L.) to different agro-climatic conditions. Early flowering and early maturity enhance adaptation of chickpea to short season environments. This study was conducted to establish allelic relationships of the early flowering genes of ICC 16641, ICC 16644 and ICCV 96029 with three known early flowering genes, efl-1 (ICCV 2), ppd or efl-2 (ICC 5810), and efl-3 (BGD 132). In all cases, late flowering was dominant to early-flowering. The results indicated that the efl-1 gene identified from ICCV 2 was also present in ICCV 96029, which has ICCV 2 as one of the parents in its pedigree. ICC 16641 and ICC 16644 had a common early flowering gene which was not allelic to other reported early flowering genes. The new early flowering gene was designated efl-4. In most of the crosses, days to flowering was positively correlated with days to maturity, number of pods per plant, number of seeds per plant and seed yield per plant and negatively correlated or had no correlation with 100-seed weight. The double-pod trait improved grain yield per plant in the crosses where it delayed maturity. The information on allelic relationships of early flowering genes and their effects on yield and yield components will be useful in chickpea breeding for desired phenology

A genome-scale integrated approach aids in genetic dissection of complex flowering time trait in chickpea

A combinatorial approach of candidate gene-based association analysis and genome-wide association study (GWAS) integrated with QTL mapping, differential gene expression profiling and molecular haplotyping was deployed in the present study for quantitative dissection of complex flowering time trait in chickpea. Candidate gene-based association mapping in a flowering time association panel (92 diverse desi and kabuli accessions) was performed by employing the genotyping information of 5724 SNPs discovered from 82 known flowering chickpea gene orthologs of Arabidopsis and legumes as well as 832 gene-encoding transcripts that are differentially expressed during flower development in chickpea. GWAS using both genome-wide GBS- and candidate gene-based genotyping data of 30,129 SNPs in a structured population of 92 sequenced accessions (with 200–250 kb LD decay) detected eight maximum effect genomic SNP loci (genes) associated (34 % combined PVE) with flowering time. Six flowering time-associated major genomic loci harbouring five robust QTLs mapped on a high-resolution intra-specific genetic linkage map were validated (11.6–27.3 % PVE at 5.4–11.7 LOD) further by traditional QTL mapping. The flower-specific expression, including differential up- and down-regulation (>three folds) of eight flowering time-associated genes (including six genes validated by QTL mapping) especially in early flowering than late flowering contrasting chickpea accessions/mapping individuals during flower development was evident. The gene haplotype-based LD mapping discovered diverse novel natural allelic variants and haplotypes in eight genes with high trait association potential (41 % combined PVE) for flowering time differentiation in cultivated and wild chickpea. Taken together, eight potential known/candidate flowering time-regulating genes [efl1 (early flowering 1), FLD (Flowering locus D), GI (GIGANTEA), Myb (Myeloblastosis), SFH3 (SEC14-like 3), bZIP (basic-leucine zipper), bHLH (basic helix-loop-helix) and SBP (SQUAMOSA promoter binding protein)], including novel markers, QTLs, alleles and haplotypes delineated by aforesaid genome-wide integrated approach have potential for marker-assisted genetic improvement and unravelling the domestication pattern of flowering time in chickpea

Energy management control strategies for Hybrid Electric Vehicles and benefits of Machine Learning in cooperative intelligent transportation systems

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Lateral Fissure-In-Ano - A Case Study

The word Parikartika comprises of two words, Pari (around) and Karthika (cutting pain), Kartanaavat Peeda is the main symptoms of Parikartika. Acharya’s have explained it as a one among the Vamana, Virechana and Bastivyapata. It can be equated with Fissure-In-Ano based on signs and symptoms. About 30-40% of population suffers from proctologic pathologies at least once in their lives, and anal fissure comprises of 10-15%. A 38 years male patient, who was businessman by profession came to Shalyatantra OPD with complaints of Pain at anal region with burning sensation after defecation since 20 days, bleeding during defecation since 20 days, feeling of mass per anal region since 20 days, hard stool since 25 days. In Chikitsa of Parikartika, Acharya’s have mentioned Madhura, Kashaya Rasa Sneha Yukta Dravyas in the form of Piccha Basti, Anuvasana Basti, Pichu, Varti and Lepa which pacifies Vata and Pitta Dosha’s. Hence present case study is planned with above said principle

Optimal Selection of Equivalence Factors for ECMS in Mild Hybrid Electric Vehicles

The increasing stringent emissions regulation over the years have shifted the focus of automotive industry towards more ef- ficient fuel economy solutions. One such solution is Hybrid electric architecture, which is able to improve the fuel economy and consequently cutting down emissions. A well known control strategy to solve optimization problem for energy management of Hybrid electric vehicles is ECMS (Equivalent Consumption Min- imization Strategy). Finding the best control parameters (equiv- alence factors) of this strategy may become quite involved. This paper proposes a method for the selection of the optimal equiv- alence factors, for charging and discharging, by applying ge- netic algorithm in the case of a P0 mild hybrid electric vehicle. This method is a systematic and deterministic way to guarantee an optimal solution with respect to the trial and error method. The proposed ECMS is compared to a technique available in literature, known as the shooting method, which relies only on one equivalence factor for discharging. It is demonstrated that the performance in terms of pollutant emissions are comparable. However, ECMS with GA always guarantees an optimal solution even in the case of heavy accessory load, when shooting method is not valid anymore, as it does not guarantee a charge sustaining condition

An ML-aided Reinforcement Learning Approach for Challenging Vehicle Maneuvers

The richness of information generated by today's vehicles fosters the development of data-driven decision-making models, with the additional capability to account for the context in which vehicles operate. In this work, we focus on Adaptive Cruise Control (ACC) in the case of such challenging vehicle maneuvers as cut-in and cut-out, and leverages Deep Reinforcement Learning (DRL) and vehicle connectivity to develop a data-driven cooperative ACC application. Our DRL framework accounts for all the relevant factors, namely, passengers' safety and comfort as well as efficient road capacity usage, and it properly weights them through a two-layer learning approach. We evaluate and compare the performance of the proposed scheme against existing alternatives through the CoMoVe framework, which realistically represents vehicle dynamics, communication and traffic. The results, obtained in different real-world scenarios, show that our solution provides excellent vehicle stability, passengers' comfort, and traffic efficiency, and highlight the crucial role that vehicle connectivity can play in ACC. Notably, our DRL scheme improves the road usage efficiency by being inside the desired range of headway in cut-out and cut-in scenarios for 69% and 78% (resp.) of the time, whereas alternatives respect the desired range only for 15% and 45% (resp.) of the time. We also validate the proposed solution through a hardware-in-the-loop implementation, and demonstrate that it achieves similar performance to that obtained through the CoMoVe framework