New Developments of PJSC «NIPTIEM» for Electric Vehicles: Traction Induction Motors for Trolleybus Motion

Выполнен сопоставительный анализ электромеханических характеристик пяти вариантов тяговых асинхронных двигателей для привода движения троллейбуса с различным объемом активных частей, материала клетки ротора, конфигурации зубцово-пазовой зоны сердечников. Технический уровень определен без учета и с учетом превышения температуры обмотки статора при работе в ездовом цикле. Дополнительно представлены показатели разработанного тягового двигателя троллейбуса повышенной грузоподъемности.The comparative analysis of the electromechanical characteristics of the five variants of traction induction motors for driving a trolleybus with different volume of active parts, the material of the rotor cage, the configuration of the tooth-slot core zone is performed. The technical level is determined without taking into account and taking into account the temperature rise of the stator winding during operation in the driving cycle. In addition, the characteristics of the developed traction motor of a trolley bus of increased payload capacity are presented

GROUND SEALING EQUIPMENT ON THE BASIS OF HYDRAULIC IMPACT DEVICES

A tamping – an effective way of soil compaction. At a tamping the ground is dabed at the expense of a tool impact energy. Hinged ground sealing the equipment to excavators hydrostamps on the basis of hydraulic impact devices are perspectiv. Data about ground sealing equipment on the basis of hydraulic impact devices are resulted. Power indicators (energy, capacity of individual blow) hydropneumatic impact devices depend on speed mobile part and its such design data, as weight, course size mobile part, pressure of gymnastics of gas of the pneumoaccumulator. Dependences of mass of loose ports and an impact energy from width of a dabed surface soil and contact voltages on a ground surface are presented

METHODS OF INVESTIGATION AND SIMULATION OF HYDROPNEUMEROUS MECHANISMS APPLIED IN THE DESTRUCTION OF FROZEN AND SCALING SOILS BY MINING AND CONSTRUCTION MACHINES

The article contains information on the identification of the main directions in the study and design of hydraulic shock mechanisms used in the destruction of strong and rocky soils by mining and construction machines. The program and algorithm for calculating and modeling the basic parameters of hammers are presented, and also the dependencies of the parameters of hydrostatic devices used as working bodies of road-building machines are given, depending on the parameters of the developed soil and the parameters of the base machine built in the Maple program

The technical- economic report on designing of bridge cranes

The technical- economic report on new technics is carried out by comparison of its indicators and characteristics with similar achievements in world and domestic practice. The great value for increase of overall performance of the bridge crane has perfection of existing mechanisms of cranes, introduction of new, more progressive constructive decisions. Formulas for definition of expenses for designing of new technics, the cost price, annual production rate of the new (modernised) machines are resulted

Computer modeling of the basic mechanisms of bridge cranes

The authors have proved that computer modeling of mechanisms, components and parts of bridge cranes is an important element in forming optimal design decisions of structures in systems of computer-aided engineering. There is presented an algorithm for designing bridge crane, there are identified functional dependences of energy characteristics of the mechanisms of load lifting and movement

Influence of key parametres of bridge crane’s mechanisms on its productivity

Productivity of bridge cranes depends on performance of mechanisms of moving and lifting loads of bridge cranes. The article contains data on influencing speeds of crane’s movement, cargo cart, speed of loads lifting, and also ways of crane’s movement, cargo cart, height of lifting load and other factors on its productivity. There are presented functional dependences of bridge cranes’ productivity on influencing factors

Approximate programming of magnetic memory elements for energy saving

International audienceThe high density of on-chip nonvolatile memory provided by memristive elements is highly desirable for many applications. However, it raises concerns about finding the best programming strategies to limit the energy consumption of such systems. Here, we highlight the case of magnetic memory, where several unconventional programming strategies can reduce energy consumption, especially for applications in neuromorphic computing. 1. STT-MTJ programming Magnetic Tunnel Junctions programmed through the Spin Transfer Torque effect (STT-MTJs), the basic cells of spin transfer torque magnetic RAM (STT-MRAM), feature fast programming, non-volatility and outstanding endurance. The behavior of such device is presented in Fig. 1 and is reminiscent of a binary bipolar memristor. The specificity of such devices appears through the stochastic nature of their programming. Indeed, under a programming pulse of given voltage í µí±‰ í µí±í µí±Ÿí µí±œí µí±” and duration ∆í µí±¡, the STT-MTJ has a non-100% probability í µí±ƒ(í µí±‰ í µí±í µí±Ÿí µí±œí µí±” , ∆í µí±¡) to switch state (Fig. 2). This effect has been extensively studied and the switching statistics can be described through comprehensive analytical models [1]. We consider a STT-MTJ corresponding to a 32nm technology. For any voltage amplitude, models allow us to derive the reduction of the programming bit error rate (ER) – the probability of failed switching – as the pulse duration (Fig. 3) and the programming energy (Fig. 4) increase. It appears that the pulse duration gives us an efficient handle to tune the ER. Different programming regimes can then be identified depending on targeted application and error rate. Starting from a programming regime for high-significance data, ensuring ER=10-10 , we evaluate the energy reduction that can be obtained by accepting an increase of the error rate (Fig. 5). 2. Disciplined approximate storage Given a programming voltage í µí±‰ í µí±í µí±Ÿí µí±œí µí±” =0.65V, allowing an increase of the ER to 10-2 grants a reduction of 62% of the energy expense. Considering the use of a STT-MRAM to store synaptic weights as floating point number in neural networks applications, strategies of disciplined approximate programming can be enforced to reduce the global energy expense [2]: identifying lower-significance part of the data i.e., least significant bits of the weight (LSB), and releasing them from low error-rate constraints. On Fig. 6, we expose the predicted energy reduction factor as a function of the number of bits labelled as lowly significant, and the error rate with which they are programmed. For instance, the reasonable choice of 26 LSB (half of the float significand) with ER=10-2 grants a 22% energy reduction when compared to a fully high-significance memory use. 3. MTJs as stochastic synapses Further increase of the ER above 10-2 corresponds to entering a regime of stochastic programming of the devices. This very low energy regime can be implemented to achieve memristive synapses with stochastic plasticity in hardware neural network [3,4]. In such applications, the devices state –thus the synapses conductance– evolves during the learning process according to a stochastic rule, with very high ER, possibly over 90%. In that case, the drastic reduction of the energy expense (86%) comes from exploiting the intrinsic device randomness as an essential feature of the system. In recent work [3], we highlighted that a STT-MTJ based hardware neural network has potential to achieve unsupervised learning by testing it against a task of vehicle counting (Fig. 7). 4. Sensibility to programming variability As can be seen on Fig. 4, the ER resilience to programming conditions variability (5% variability on the voltage are considered here) increases as the targeted error-rate increases. While releasing low error-rate constraints, systems then tends to become more robust to variability. Notably, neural networks based on stochastic STT-MTJs with ER=90% proved unchanged performances for devices variability up to 17% [3]. 5. Conclusion Deterministic programming strategies are associated to high energy cost and do not suit ideally bio-inspired applications. By contrast, strategies can be developed to consider higher ER programming for energy reduction