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About this book

This book focuses on one of the most important aspects of electrical propulsion systems – the creation of highly reliable safety-critical traction electrical drives. It discusses the methods and models for analysis and optimization of reliability and fault tolerance indices, based on which, it proposes and assesses methods for improving the availability, fault tolerance and performance of traction electric drives.

Table of Contents


Chapter 1. Choosing the Optimal Type of Safety-Critical Traction Drives for Arctic Ships Based on Estimated Operational Efficiency and Real Ice Navigation Conditions

This chapter presents the methodology and results of a comparative analysis of the operational efficiency of icebreaker ships with electrical and mechanical transmission of power to the propeller for the Arctic region. This study has been carried out based on statistical operational data and stochastic models. Our conclusion is founded on the suitability of one of the traction drives we compared and which are employed in ships designed for Arctic operating conditions.
Igor Bolvashenkov, Hans-Georg Herzog, Ilia Frenkel, Lev Khvatskin, Anatoly Lisnianski

Chapter 2. The Lz-Transform Method for the Reliability, Fault Tolerance and Operational Sustainability Assessment of Multi-power Source Traction Drives

This chapter focuses on a comparative analysis of the most important parameters of an icebreaker ship’s sustainable operations: the operational availability, power performance and power performance deficiency of the multi-state Multi-Power Source Traction Drive s of Amguema- and Norilsk-type Arctic icebreaker ships. These parameters, as was shown in Chap. 1, have a significant impact on the correct choice of the propulsive system of icebreaking vessels. The parameters’ evaluation was based on statistical operational data on Arctic icebreaker ships with diesel-electric or diesel-geared traction drive. The Lz-transform approach was used to arrive at a solution of that problem. This approach drastically simplifies the solution compared with the straightforward Markov method.
Igor Bolvashenkov, Hans-Georg Herzog, Ilia Frenkel, Lev Khvatskin, Anatoly Lisnianski

Chapter 3. The Two-Step Approach to the Selection of a Traction Motor for Electric Vehicles

This chapter presents the two-step comparative analysis of electrical traction machines. The stable permanent demand of electrical drives requires appropriate selection of electrical machines to meet specific requirements. Different vehicles, such as aircraft, cars, ships and trains are considered here. Each kind of electric vehicle needs an electrical traction machine with unique parameters. The results show that the selection of the appropriate electrical machine will depend on the type of vehicle. Furthermore, in most cases there is always more than one appropriate machine type. Thus, the type of electrical traction machine has to be defined for each vehicle and a comparative analysis is a crucial tool here.
Igor Bolvashenkov, Hans-Georg Herzog, Ilia Frenkel, Lev Khvatskin, Anatoly Lisnianski

Chapter 4. The Markov Reward Approach for Selecting a Traction Electric Motor Based on Reliability Features

This chapter presents the Markov reward approach to the comparative analysis of different types of traction electric motors for hybrid-electric propulsion systems, that is, multi-state systems (MSSs), for icebreaker ships operating in Arctic waters. The preliminary results show that there are several appropriate machine types and that it is therefore necessary to define in advance what kind of equipment would be the most appropriate in order to arrive at a proper decision on this matter. In this chapter, we discuss how we developed the Markov reward approach for computing the MSS’s average availability, average converted power and reliability-associated cost, all the while taking into consideration the vehicular operational conditions of electric motors. We also propose what, in our opinion, is the optimal type of traction electric motor.
Igor Bolvashenkov, Hans-Georg Herzog, Ilia Frenkel, Lev Khvatskin, Anatoly Lisnianski


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