Engine On/Off Control for the Energy Management of a Serial Hybrid Electric Bus via Convex Optimization

Elbert, Philipp; Nüesch, Tobias; Ritter, Andreas; Murgovski, Nikolce; Guzzella, Lino

doi:10.1109/tvt.2014.2304137

Cited by 114 publications

(68 citation statements)

References 12 publications

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“…The symbol stands for optimal, and ≡ stands for identity of the trajectory. On the other hand, solving the convex problem for the optimal values of u g and u e yields the optimal value of s out ≡ s [30,34]. Therefore, if s in ≡ s , then s out ≡ s in ≡ s .…”

Section: Iterative Algorithmmentioning

confidence: 99%

“…To ensure convergence towards s , damping is introduced by including an update law for the equivalence factor s (j) in before step 2. The update law used here is taken from [30], which is:…”

Section: Step 4: Check For Convergencementioning

confidence: 99%

“…It is seen as an alternative method for the optimization of the power flows in HEVs with the advantage of being computationally more efficient than for example DP or indirect methods such as PMP are. Convex optimization has already been employed for dimensioning of powertrains [28][29][30], energy efficiency analysis [31] and online control of HEVs [32,33]. However, inherently discrete decision variables, such as the engine on/off decision or the gear decision, are typically determined using heuristic approximations.…”

Section: Introductionmentioning

confidence: 99%

“…A more efficient approach consists of splitting up the problem into two parts, where the discrete decision variables are optimized prior to the convex optimization [33] with the possibility to iterate between the two steps [34]. Based on this methodology, the authors of [30] showed that the optimal engine on/off strategy can be calculated for a serial HEV.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

et al. 2014

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This paper presents a novel method to solve the energy management problem for hybrid electric vehicles (HEVs) with engine start and gearshift costs. The method is based on a combination of deterministic dynamic programming (DP) and convex optimization. As demonstrated in a case study, the method yields globally optimal results while returning the solution in much less time than the conventional DP method. In addition, the proposed method handles state constraints, which allows for the application to scenarios where the battery state of charge (SOC) reaches its boundaries.

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Section: Iterative Algorithmmentioning

confidence: 99%

Section: Step 4: Check For Convergencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

et al. 2014

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“…The proposed solution strategies can be divided into so-called offline and online solution strategies [2,3]. Offline solution strategies have been developed based on, e.g., dynamic programming (DP; see, e.g., [4][5][6]), Pontryagin's minimum principle (PMP; see, e.g., [7][8][9][10]) or convex optimization (see, e.g., [11,12]). The offline solution strategies require all disturbances to be known (e.g., the driving cycle) so that the global optimal solution can be computed and can therefore not be implemented in real time.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

et al. 2017

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Abstract:In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model.

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Combined Design and Control Optimization of Hybrid Vehicles

Murgovski¹,

Hu²,

Johannesson³

et al. 2015

Handbook of Clean Energy Systems

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Hybrid vehicles play an important role in reducing energy consumption and pollutant emissions of ground transportation. The increased mechatronic system complexity, however, results in a heavy challenge for efficient component sizing and power coordination among multiple power sources. This chapter presents a convex programming framework for the combined design and control optimization of hybrid vehicles. An instructive and straightforward case study of design and energy control optimization for a fuel cell/supercapacitor hybrid bus is delineated to demonstrate the effectiveness and the computational advantage of the convex programming methodology. Convex modeling of key components in the fuel cell/supercapactior hybrid powertrain is introduced, while a pseudo code in CVX is also provided to elucidate how to practically implement the convex optimization. The generalization, applicability, and validity of the convex optimization framework

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Engine On/Off Control for the Energy Management of a Serial Hybrid Electric Bus via Convex Optimization

Cited by 114 publications

References 12 publications

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

Combined Design and Control Optimization of Hybrid Vehicles

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