4.7 Article

Comparison of Three Electrochemical Energy Buffers Applied to a Hybrid Bus Powertrain With Simultaneous Optimal Sizing and Energy Management

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2013.2294675

关键词

Convex optimization; electrified vehicle; energy management strategy; energy storage; optimal sizing

资金

  1. Swedish Energy Agency
  2. Swedish Hybrid Vehicle Center

向作者/读者索取更多资源

This paper comparatively examines three different electrochemical energy storage systems (ESSs), i.e., a Li-ion battery pack, a supercapacitor pack, and a dual buffer, for a hybrid bus powertrain operated in Gothenburg, Sweden. Existing studies focus on comparing these ESSs, in terms of either general attributes (e.g., energy density and power density) or their implications to the fuel economy of hybrid vehicles with a heuristic/nonoptimal ESS size and power management strategy. This paper adds four original contributions to the related literature. First, the three ESSs are compared in a framework of simultaneous optimal ESS sizing and energy management, where the ESSs can serve the powertrain in the most cost-effective manner. Second, convex optimization is used to implement the framework, which allows the hybrid powertrain designers/integrators to rapidly and optimally perform integrated ESS selection, sizing, and power management. Third, both hybrid electric vehicle (HEV) and plug-in HEV (PHEV) scenarios for the powertrain are considered, in order to systematically examine how different the ESS requirements are for HEV and PHEV applications. Finally, a sensitivity analysis is carried out to evaluate how price variations of the onboard energy carriers affect the results and conclusions.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Engineering, Electrical & Electronic

Design and Comparative Analyses of Optimal Feedback Controllers for Hybrid Electric Vehicles

Maryam Razi, Nikolce Murgovski, Tomas McKelvey, Torsten Wik

Summary: This paper presents an adaptive equivalent consumption minimization strategy (ECMS) and a linear quadratic tracking (LQT) method for optimal power-split control of a combustion engine and an electric machine in a hybrid electric vehicle (HEV). The study models SOC constraints and proposes sub-optimal analytic solutions with convex objective functions. Additionally, the controllers' robustness to measurement noise is considered, with simulation results comparing the effectiveness of the two controllers.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

Article Engineering, Electrical & Electronic

Predictive Energy Management of Hybrid Electric Vehicles via Multi-Layer Control

Maryam Razi, Nikolce Murgovski, Tomas McKelvey, Torsten Wik

Summary: This paper introduces predictive energy management of hybrid electric vehicles using computationally efficient multi-layer control. It involves optimizing gear, engine, battery, and electric machine decisions, and proposes efficient computation methods. The approach aims to optimize driving performance, prolong battery life, and improve fuel efficiency.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

Article Engineering, Electrical & Electronic

Joint Component Sizing and Energy Management for Fuel Cell Hybrid Electric Trucks

Qian Xun, Nikolce Murgovski, Yujing Liu

Summary: This paper proposes a cost-effective way to design and operate fuel cell hybrid electric trucks (FCHETs) through sequential convex programming to minimize costs. The results show that the power rating of the electric machine is drastically reduced when the delivered power is satisfied in a probabilistic sense.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2022)

Article Engineering, Civil

Bilevel Optimization for Bunching Mitigation and Eco-Driving of Electric Bus Lines

Remi Lacombe, Sebastien Gros, Nikolce Murgovski, Balazs Kulcsar

Summary: Traditionally, the issues of bus bunching mitigation and vehicle energy management have been dealt with separately in the literature. This study presents a novel approach by formulating the optimal control problem for bus line eco-driving and regularity control as a smooth, multi-objective nonlinear program, enabling parallel computations and reducing communication loads between buses. By embedding this approach in a model predictive control, stochastic simulations show that the method achieves fast recoveries to regular headways and energy savings of up to 9.3% compared to traditional methods.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Article Engineering, Civil

Computationally Efficient Algorithm for Eco-Driving Over Long Look-Ahead Horizons

Ahad Hamednia, Nalin Kumar Sharma, Nikolce Murgovski, Jonas Fredriksson

Summary: This paper introduces a computationally efficient algorithm for eco-driving over long distances, which combines offline and online solutions to significantly reduce computational effort and achieve energy savings.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Article Engineering, Civil

Online Learning for Chance-Constrained Observer of Leading Heavy-Duty Vehicle Power Capability

Nalin Kumar Sharma, Nikolce Murgovski, Esteban R. Gelso

Summary: This paper proposes a stochastic observer for estimating the power capability of a preceding heavy-duty vehicle, using its speed measurement and road slope information. An online learning approach is used to solve a chance-constrained optimization problem considering uncertainties. The effectiveness of the proposed observer is demonstrated in case studies on real road topographies, showing its robustness against uncertainties.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Article Thermodynamics

Predictive energy management with engine switching control for hybrid electric vehicle via ADMM

Fei Ju, Nikolce Murgovski, Weichao Zhuang, Xiaosong Hu, Ziyou Song, Liangmo Wang

Summary: This paper addresses the energy management problem of a power-split hybrid electric vehicle (HEV) with planetary gear sets. A mixed-integer global optimal control problem is formulated, and convex modeling is presented to reformulate the problem as a two-step program. The alternating direction method of multipliers (ADMM) algorithm is employed to optimize the engine switching and battery power decisions. Simulation results show significant fuel savings and computational efficiency compared to heuristic and dynamic programming methods. An ADMM-MPC method is also developed for real-time control with promising results.

ENERGY (2023)

Article Engineering, Electrical & Electronic

Predictive Cruise Controller for Electric Vehicle to Save Energy and Extend Battery Lifetime

Fei Ju, Nikolce Murgovski, Weichao Zhuang, Qun Wang, Liangmo Wang

Summary: This paper designs a predictive cruise controller (EC) for electric vehicles to enhance energy efficiency and battery lifetime. Simulation results show that the proposed controller performs suboptimally compared to the globally optimal solution. An enhanced EC is developed for practical scenarios, achieving energy saving and battery life extension compared to the intelligent driving model.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2023)

Article Engineering, Electrical & Electronic

Optimal Thermal Management, Charging, and Eco-Driving of Battery Electric Vehicles

Ahad Hamednia, Nikolce Murgovski, Jonas Fredriksson, Jimmy Forsman, Mitra Pourabdollah, Viktor Larsson

Summary: This article explores optimal battery thermal management, charging, and eco-driving strategies for improving the grid-to-meter energy efficiency of battery electric vehicles (BEVs). An optimization problem is formulated to find the best trade-off between trip time and charging cost. The dynamics in driving and charging modes are modeled using different functions and decision-making is done in a spatial domain for driving and a temporal domain for charging. The proposed algorithm achieves a 44% reduction in trip time, including driving and charging times, compared to a case without active battery heating/cooling.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2023)

Article Engineering, Mechanical

Integrated Propulsion and Cabin-Cooling Management for Electric Vehicles

Fei Ju, Nikolce Murgovski, Weichao Zhuang, Liangmo Wang

Summary: This paper presents two nonlinear model predictive control methods for integrated propulsion and cabin-cooling management in electric vehicles. The proposed methods optimize system-level performance by minimizing battery energy consumption while maintaining cabin-cooling comfort. The results show that both methods offer significant energy benefits and maintain driving and thermal comfort. Additionally, the co-MPC method achieves comparable performance with reduced computation time compared to the joint MPC method.

ACTUATORS (2022)

Article Engineering, Civil

Distributed Eco-Driving Control of a Platoon of Electric Vehicles Through Riccati Recursion

Remi Lacombe, Sebastien Gros, Nikolce Murgovski, Balazs Kulcsar

Summary: This paper presents a distributed optimization procedure for the cooperative eco-driving control problem of a platoon of electric vehicles. Individual optimal trajectories are generated for each platoon member to account for heterogeneity and road slope. The proposed control strategy is privacy-preserving and can be deployed by any group of vehicles spontaneously while driving.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023)

Article Engineering, Civil

Numerical Strategies for Mixed-Integer Optimization of Power-Split and Gear Selection in Hybrid Electric Vehicles

Anand Ganesan, Sebastien Gros, Nikolce Murgovski

Summary: This paper presents numerical strategies for a computationally efficient energy management system that co-optimizes the power split and gear selection of a hybrid electric vehicle (HEV). The proposed strategies, namely Selective Relaxation Approach (SRA) and Round-n-Search Approach (RSA), are compared with two benchmark strategies using rule-based gear selection and dynamic programming. The results show that both SRA and RSA achieve significant cost reduction compared to the rule-based strategy and come close to the performance of the dynamic programming solution.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023)

Article Engineering, Electrical & Electronic

Stochastic Model Predictive Energy Management of Electric Trucks in Connected Traffic

Wei Du, Nikolce Murgovski, Fei Ju, Jingzhou Gao, Shengdun Zhao

Summary: This paper proposes a cost-effective power management strategy for dual electric machine coupling propulsion trucks using V2I communication data. A bilevel program is formulated where the high-level optimizes operation mode implicitly, and the low-level computes an explicit power distribution policy. Stochastic model predictive control (SMPC) strategy is employed at the high level, with position dependent stochastic velocity predictors developed using limited historical data. The proposed predictors are compared with a benchmark in simulations, showing a reduction in driving cost by 3.36% and 4.26% respectively.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2023)

Article Automation & Control Systems

Novel Results on Output-Feedback LQR Design

Adrian Ilka, Nikolce Murgovski

Summary: This article presents novel developments in output-feedback stabilization for linear time-invariant systems within the framework of linear quadratic regulator (LQR). The necessary and sufficient conditions for output-feedback stabilizability are derived, followed by the proposal of a novel iterative Newton's method and a computationally efficient modified approach. The proposed modified approach guarantees convergence from a stabilizing state feedback to a stabilizing output-feedback solution and successfully solves high-dimensional problems. Numerical examples demonstrate the effectiveness of the proposed methods.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2023)

Article Engineering, Electrical & Electronic

Real-Time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming

Toheed Ghandriz, Bengt Jacobson, Nikolce Murgovski, Peter Nilsson, Leo Laine

Summary: This paper proposes a real-time predictive energy management strategy for hybrid electric heavy vehicles, using a combination of model predictive control and sequential programming to optimize vehicle velocity and battery state of charge trajectories. By comparing the performance with two different sequential quadratic programs, it is found that the developed sequential linear program is faster and simpler in providing trajectories close to the best found by nonlinear programming.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

暂无数据