• Energy Research

Optimal thermal management system design is critical for power electronic converters to ensure the reliability of power semiconductor switches. Medium power density inverter systems are often air-cooled to ensure an efficient and cost-effective thermal management solution. In addition, using heat pipes as the heat transfer medium between the heat sources and the heat sink can provide lower volume for the entire inverter. This paper investigates the effectiveness of Teaching Learning Based Optimization (TLBO) for finding the optimal forced-air heat sink with heat pipe cooling system to achieve higher fan efficiency and lower inverter packaging volume. The optimal design is found utilizing commercially available fans and heat pipes. The TLBO design optimization is also compared to the highly implemented Particle Swarm Optimization (PSO) and it is found that TLBO uses 20 times fewer iterations than PSO to converge, and that the TLBO results are more robust for different design constraints.

Abstract An online simulation framework is developed in this article to evaluate the performance of a multi-mode electrified powertrain equipped with more than one power source. An electrically variable transmission with two planetary gear-set has been chosen as the centerpiece of the powertrain considering the versatility and prospects of such transmissions. A novel architecture topology of the aforementioned class of transmission is selected through rigorous screening process whose workflow is presented here with brevity. The article systematically delineates the steps for deriving dynamics associated with all the feasible operating modes facilitated by the selected topology. The dynamics associated with all the feasible mode-shift events are also heeded judiciously. One of the legitimate concern of multi-mode transmission is its proclivity to contribute discontinuity of power-flow downstream of the powertrain. Mode-shift events can be predominantly held responsible for engendering such discontinuity. Many scholars in literature have substantiated the advent of dynamic coordinated control as a technique for ameliorating such discontinuity. Hence, a system-level coordinated control is employed within the energy management system (equivalent consumption minimization strategy), which governs the mode schedule of the multi-mode powertrain in real-time simulation. Simulation results corroborate the effect of coordinated control on the equivalent consumption minimization strategy in generating optimal mode schedule.

Model-based state-of-charge (SOC) and state-of-health (SOH) estimation for lithium-ion batteries has been widely applied in electrified vehicles, while the SOC and SOH estimators are highly coupled and nonlinear in conventional techniques. This leads to a bulky design of observer network and complicates the stability analyses. In this article, a new reduced-decoupling SOC and SOH coestimation algorithm based on convex optimization is proposed. This scheme estimates the battery SOC from the battery model and does not require the classic Coulomb-counting method. Therefore, it can decouple the capacity estimation from the SOC estimator and reduce the strong interaction existing in conventional coestimation methods. Besides, all state variables can be solved together by one estimator, which is straightforward and avoids the complicated observer network. Owing to the decoupling design, the stability of the proposed method becomes more intuitive and can be always guaranteed according to the convexity analysis without using other stabilizing approaches. In consequence, a weak-interaction and robust coestimation algorithm of SOC and SOH can be realized by the proposed technique. The experiments on a 5.4-Ah lithium polymer battery are implemented to validate the feasibility of the algorithm.

LLC converters benefit from soft switching and sinusoidal currents over dual active bridge (DAB) converters. The design process of an LLC converter involves the selection of resonant tank inductor, capacitor, magnetizing inductance, resonant frequency, and transformer turns ratio for proper operation within the desired range of voltages and power. However, the design and control of frequency-modulated LLC converters in wide voltage range applications is challenging due to the wide range of switching frequencies. Topology morphing control is an established technique utilized for countering the challenges of wide voltage range LLC operation. This work provides a design framework for an LLC converter with topology morphing for wide voltage range applications. The proposed design framework uses time domain analysis and a power loss model to evaluate the optimal converter parameters for efficiency maximization over the entire voltage range. Methodology of implementing online topology morphing with closed-loop control in a digital signal processor (DSP) considering an on-board battery charger (OBC) application is also provided. The design optimization process and control methodology are validated through a 300–700 V input, 250–450 V output, 3.3 kW hardware demonstrator. An experimental peak efficiency of 97.72% is achieved compared to a calculated 97.63% efficiency, proving the accuracy of the analytical model. Time weighted averaged efficiency above 96.7% is observed over the entire voltage range.

Among the hybrid electric vehicle categories, the multimode power-split allows to fully exploit the advantages related to the powertrain electrification. However, together with the increased flexibility, it comes with greater difficulty in defining an effective control strategy, both in terms of predicted fuel consumption and computational cost. To overcome the limits of the most diffused energy management strategies, slope-weighted energy-based rapid control analysis (SERCA) has been recently proposed. Nevertheless, so far, the algorithm has been applied to powertrains characterized by two operative modes solely. In this paper, we first present the inconsistency of SERCA applied to the whole set of multimode power-split arrangements. Subsequently, after correlating this divergence to the mode selection process, to overcome this draft, we introduce a novel strategy called SERCA+. This algorithm is proven to be robust and to achieve results close to the optimum benchmark with an insignificant increase in computational cost. Therefore, SERCA+ could potentially find application in design methodologies for multimode power-split HEVs to accelerate the overall vehicle design process.

Lithium-Ion battery packs are an essential component for electric vehicles (EVs). These packs are configured from hundreds of series and parallel connected cells to provide the necessary power and energy for the vehicle. An accurate, adaptable battery management system (BMS) is essential to monitor and control such a large number of cells. Series and parallel connected cells also experience different production and operational conditions, which makes it challenging for the BMS to ensure the safe operation of each individual cell. The main functions of the BMS include battery state estimation, cell balancing, thermal management, and fault diagnosis. Robust estimation of the state of charge (SOC) is crucial for providing the driver with an accurate indication of the remaining range. This paper presents the state of art of battery pack SOC estimation methods along with the impact of cell inconsistency on pack performance and SOC estimation. Cell balancing methods, which are necessary due to cell inconsistencies, are discussed as well. Four categories of pack SOC estimation methods are presented, including individual cell, lumped cell, reference cell, and mean cell and difference estimation methods. The SOC estimation methods are compared in terms of algorithm type, computational load, and engineering effort to help practitioners decide which method best fits their application.

This paper investigates the economic viability of replacing the high-voltage battery pack of a power-split hybrid electric vehicle (HEV) and a plug-in hybrid electric vehicle (PHEV) by estimating the impact of battery ageing on the fuel economy, drivability capability and electric range. The HEV is modelled first, an optimal energy management strategy based on dynamic programming is then implemented, and experimental characterization data for the battery cell is presented. The batteries are tested to a heavily aged state, with up to an 84% loss of capacity. The battery pack payback period is estimated by assessing the vehicle operative costs in terms of fuel and electricity as obtained through numerical simulations as a function of battery ageing. Replacing the battery pack at the conventional end-of-life limit of 80% residual capacity is suggested not to be convenient from an economic standpoint for both the HEV and the PHEV. On the other hand, acceptable payback periods (i.e. 2 to 5 years) can be achieved for the battery pack when being replaced at 20% to 40% residual capacity. The proposed methodology can be implemented to advise an HEV or PHEV user regarding the benefit of replacing the battery pack due to excessive ageing.