Adaptive convex loss mappings for enhanced loss assessment in asynchronous drives

Control topologies in electric drive applications commonly aim at minimizing the dissipated power in the system to guarantee energy-efficient operation. Especially in vehicle electrification, loss minimization is the main objective in the supervisory control loops as this is directly related to the range of the vehicle. Advanced drive systems are characterized by an elevated complexity but require nevertheless a real-time control strategy to be implemented. Appropriate model abstraction, enabling real-time viability with a reliable system representation, is found in convex mapping procedures of the dissipated power in the drive components. These reduced-order models are generally obtained based on model information solely. This paper proposes a methodology to recursively enhance the reliability of the convex loss approximations. An instantaneous power flow estimation is assessed based on a unification of model expectations and sensor data. Using this information, a proper adaptation to the underlying convex loss coefficients is then determined. The methodology is validated in simulation for an electric drive on three different case studies. The algorithm is furthermore applied on actual experimental data of an asynchronous drive for validation purposes. Preliminary results demonstrate that the error on the loss assessment is reduced by 55.7%-89.0%. Adaptive convex loss mappings can, therefore, be consulted in practical control structures to ameliorate the reliability of loss minimization control schemes, while still maintaining a computationally efficient format

Optimal torque actuations of an electric drivetrain using convex optimized power flows

This paper presents a methodology for the determination of the optimal torque set points actuated to a pair of induction machines which is part of a battery-converter-induction machine subsystem that on his turn is connected to a variable input load. The torque values of this large scale mechatronic system are determined by convex optimization of a general loss function incorporating switching losses, conduction losses, resistor losses, and mechanical friction of the considered drivetrain. Behavioral physics-based models are used to acquire the power flows and corresponding losses are calculated. The dynamic optimization formulation is casted to a convex minimization problem for the computationally efficient assessment of the torque set points. Results show that the proposed method is able to determine in a computationally efficient way the torque set points in this drivetrain for variable input loads

Computationally efficient modeling for assessing the energy efficiency of electric drivetrains using convex formulations

High-fidelity models capturing the dynamical behavior can be engaged for the analysis of complex mechatronic systems. Determining the optimal control parameters and design characteristics of such systems necessitates solving multiple interconnected models acting on their respective physical domains and time scales. In this paper, high-fidelity physics-based models are constructed for several electrical subsystems. Loss mechanisms in the various components are inferred because these are key when performing optimal design and control in terms of energy-efficient conversion from power source to actuation. The complexity of the analyzed models is then reduced by introducing convex approximations for the occurring dissipation during power transfers, allowing abstracting the complicated dynamic behavior into a tractable convex formulation, specifically suited for time-efficient numerical simulation. The effectiveness of the strategy is demonstrated on a case study originating from the field of all-electric vehicles, embodying a series interconnection of a battery stack, a buck-boost converter, a voltage source inverter, and an asynchronous electric motor. Results show that the dynamic simulation of the proposed system, composed of multiple time scales, can be reliably computed using the composed convex mappings, hereby reducing the computational time approximately by a factor 461, compromising only 1.8% accuracy regarding energy consumption assessment. The introduced convex formulation can therefore constitute the foundation for optimal control and design of complex mechatronic drives

Towards optimal exploitation of all-electric dual drive powertrains in smart e-motion systems

Government institutions and industrial partners are aspiring green alternatives for contemporary transportation systems or industrial processes. All-electric drivetrains demonstrate interesting properties in this perspective, as direct harmful emissions in the atmosphere are eliminated. Optimal exploitation of the associated possibilities requires filling the gaps in state-of-the-art technology in terms of topology design, energy-efficient control strategies and supervisory power flow management agents. First, the design problem is reformulated and tackled using an evolutionary leading to 99,3% less pronounced design time requirements when benchmarked against traditional approaches. Dedicated approximate dynamic programming techniques furthermore reduce the overall operational cost of an isolated drive by up to 57,3%. At the system level, automated regression techniques are engaged to cast the power dissipation of the subsystems into efficient dissipation models. An intelligent supervisory dynamic programming agent, optimizing the power flow paths in the dual drive topology, provides range extensions of approximately 16%. Combining the proposed strategies might thus pave the way for a deeper integration of all-electric vehicles in contemporary society and consequently a more sustainable transportation system

Moving the customer experience field forward : introducing the Touchpoints, Context, Qualities (TCQ) nomenclature

In response to initial voices that put the customer experience (management) (CX(M)) movement into question, this article aims to introduce a formal nomenclature to push the CX(M) field toward a more mature state. First, drawing from an inductive analysis of 143 CX(M) papers, the authors identify 12 basic CX components that aggregate into three overarching building blocks-touchpoints (T, i.e., points of interaction between the customer and brand/firm), context (C, i.e., situationally available resources internal and/or external to the customer), and qualities (Q, i.e., attributes that reflect the nature of customer responses and reactions to interactions with the brand/firm). The TCQ nomenclature offers a language to make CX actionable, moving beyond the breadth of the current definition and frameworks by disentangling CX into small bite-sized chunks (i.e., the CX components) that any academic and practitioner, regardless of their discipline, may understand and use to discuss and manage CX. Second, using the TCQ nomenclature, the authors assess the current state of the CX(M) literature and identify mature (e.g., firm-controlled touchpoints and cognitive and emotional qualities associated with CX) and underdeveloped (e.g., nonfirm controlled touchpoints and the market and environmental context in which CX emerges) areas ripe for future research. In addition, they also provide a set of recommendations to strengthen the methodological rigor of the field. Third, the TCQ nomenclature may support managers in auditing their current CXM practices and/or serve as a basis for CX design and innovation