**1. Introduction**

16 Induction Motor

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numbers (1937-1954), ISSN 0278-0046

(January 2010) page numbers (26-34), ISSN 1751-8660

(3242-3252 ), ISSN 0278-0046

(389-392), ISBN 978-0-201-82054-6

numbers (550-554), ISBN 978-0-471-14326-0

978-0-137-23785-2

This work addresses the problem of optimal selection of the flux level in induction motors used in electric vehicles (EVs). The basic function of a fully electric powertrain controller is to generate electric torque (force) which is required at any time by the driver. But, it is wellknown that the flux level used in a controller for induction motors offers an extra degree of freedom that can be used to maximise energy efficiency. The induction motor is an efficient motor when working close to its rated operating point (Zeraoulia, Benbouzid et al. 2006). However, at light loads the efficiency is greatly reduced when magnetization flux is maintained at nominal value. In induction motor drives for EVs, where real operation conditions are significantly different from rated conditions, the energy saving control is crucial for improving the running distance per charge.

Due to the widespread use of induction motors, its efficiency optimization gave rise to a large number of research publications (Bazzi & Krein 2010). Algorithms for real-time implementation of loss-minimization methods are vital for designing intelligent and optimized EV controllers. Standard methods for induction motor control, including fieldoriented control (FOC) or direct torque control (DTC), can be improved in efficiency by using loss minimization control. Basically, there are three different methods to improve the efficiency in induction motors: i) loss model based methods (which is considered in this work), ii) power measure based methods, also known as search controllers; and iii) hybrid controllers that combines the first two methods. The main goal of the present work is to investigate the potential benefits of loss minimization algorithms in EVs powered by induction motors. Accordingly, a detailed simulation case study will be provided which will show that, depending on the type of driving cycle, energy savings up to 12.5% can be achieved. The chapter is organized as follows: Section 2 reviews the basic concepts of rotor field oriented control (FOC). Section 3 introduces the loss minimization method based on a standard mathematical model of the induction motor and gives the value of the flux level

© 2012 Melo et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Melo et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

which maximizes the energy efficiency at given torque subject to voltage and currents limits. In Section 4 the developed EV non-causal simulation model (motor-to-wheel) is presented, while Section 5 includes the simulation results and its analysis for a set of standard driving cycles. Finally, Section 6 contains the main conclusions and some reference to future work.

Evaluation of an Energy Loss-Minimization Algorithm for EVs Based on Induction Motor 403

(3)

*dt* (4)

*dt* (5)

e slip r t tt (6)

*m qs r rr L i L R*

> *slip t t*

 

*r t*

 

 

The loss-minimization scheme demands the decrease or increase of the flux level depending on the torque. This means that the minimization algorithm selects the flux reference through the minimization of the copper and core losses while ensuring the desired torque requested by the driver. Different techniques for loss minimization in induction motor are presented in the literature (Bazzi & Krein 2010). Recently, (Lim & Nam 2004) proposed a LMA that features a major difference from previous works by taking into consideration the leakage inductance and the practical constrains on voltage and current in the high-speed region, which play a great role in EVs applications. This is an important difference from other works, like (Garcia et al., 1994), (Kioskeridis & Margaris, 1996), (Fernandez-Bernal et al., 2000), where leakage inductance are not considered (although similar motor loss models are included), leading to considerable result differences in the high-speed region. In addition, our work considers the optimization of both positive and negative torque generation with

The implemented method is based on the conventional induction motor model where the iron losses are represented by an equivalent resistance (Rm) modelling the iron losses, placed in parallel with the magnetizing inductance (Lm). A simplification is then considered, allowing a partial decoupling between Rm and Lm: the iron losses are represented by separated circuits with dependent voltage sources (Vdme and Vqme). Figure 2 shows the

Considering steady state analysis with low slip values (s) – rotor iron losses may be neglected –, the total motor losses (copper and iron ones) are given by (Lim & Nam 2004):

Since slip e r /

Knowing the instantaneous rotor speed r, one have:

**3. Loss minimization by selecting flux references** 

bounded constraints on both current and voltage.

complete equivalent model in the synchronous reference frame.

**3.1. The LMA method** 

slip is given by:

From figure 1:

 

 <sup>0</sup> slip slip 0 t t

> <sup>0</sup> r r0 t t *<sup>t</sup>*
