Johannes Ritzmann

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Ritzmann

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Johannes

ORCID

0000-0001-9628-894X

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Publications 1 - 10 of 10

A control strategy for cylinder deactivation
Ritzmann, Johannes; Zsiga, Norbert; Peterhans, Christian; et al. (2020)
Control Engineering Practice
Cylinder deactivation is an effective measure to reduce the fuel consumption of internal combustion engines. This paper presents a control strategy for the cylinder deactivation (CDA) process and the cylinder reactivation (CRA) process, based on the inversion of a control-oriented discrete-event model. The focus of this paper lies on quantity-controlled stoichiometrically-operated engines with different layouts. Nevertheless, the main results are transferable to other engines, including quality-controlled engines. Two major aspects of CDA and CRA are considered in detail in this paper. The first aspect is the cycle-averaged torque during the CDA and CRA. Due to the reciprocating behaviour of the engine, an unavoidable discontinuity in the cycle-averaged torque is identified, irrespective of the type of engine. The amplitude of this torque ripple depends on the duration of the CDA or CRA and can be quantified a priori. A trade-off between the torque ripple amplitude and the CDA or CRA duration is identified. The second aspect is the amount of fuel consumed during the CDA and CRA. It is shown that to achieve a CDA or CRA with a limited torque ripple amplitude, some combustion cycles at unfavourable low-load conditions must occur. For example, ignition retardation might be used resulting in a significant portion of the fuel energy being lost. As a result an increase in the amount of fuel consumed during the CDA or CRA compared to that of continued operation in activated cylinder mode may arise. The conducted investigations show that this increase in fuel consumption is compensated after a few seconds of operation in the more fuel-efficient deactivated cylinder mode. © 2020 Elsevier
Low-Load Limit in a Diesel-Ignited Gas Engine
Hutter, Richard; Ritzmann, Johannes; Elbert, Philipp; et al. (2017)
Energies
The lean-burn capability of the Diesel-ignited gas engine combined with its potential for high efficiency and low CO 2 emissions makes this engine concept one of the most promising alternative fuel converters for passenger cars. Instead of using a spark plug, the ignition relies on the compression-ignited Diesel fuel providing ignition centers for the homogeneous air-gas mixture. In this study the amount of Diesel is reduced to the minimum amount required for the desired ignition. The low-load operation of such an engine is known to be challenging, as hydrocarbon (HC) emissions rise. The objective of this study is to develop optimal low-load operation strategies for the input variables equivalence ratio and exhaust gas recirculation (EGR) rate. A physical engine model helps to investigate three important limitations, namely maximum acceptable HC emissions, minimal CO 2 reduction, and minimal exhaust gas temperature. An important finding is the fact that the high HC emissions under low-load and lean conditions are a consequence of the inability to raise the gas equivalence ratio resulting in a poor flame propagation. The simulations on the various low-load strategies reveal the conflicting demand of lean combustion with low CO 2 emissions and stoichiometric operation with low HC emissions, as well as the minimal feasible dual-fuel load of 3.2 bar brake mean effective pressure.
Optimization Method for the Energy and Emissions Management of a Hybrid Electric Vehicle with an Exhaust Aftertreatment System
Ritzmann, Johannes; Lins, Georg; Onder, Christopher (2020)
IFAC-PapersOnLine ~ 21st IFAC World Congress
This paper presents a real-time optimization method to compute the fuel-optimal torque split, gear selection and engine on/off command for a Diesel hybrid electric vehicle equipped with an exhaust aftertreatment system. We aim to minimize the amount of fuel consumed, while achieving a charge-sustaining operation and keeping the tailpipe NOx emissions below the legislative limit. We simplify the full vehicle model to facilitate the formulation of a mixed-integer convex problem which is then solved using the proposed iterative convex optimization (ICO) algorithm. We validate the result by comparing it to the globally optimal solution computed using dynamic programming (DP). For the simple model, the ICO algorithm finds the same solution as the DP benchmark. The computation time was reduced from one week for the DP benchmark to 49 s for the ICO solution. By comparing the DP solution obtained on the full model with the ICO solution evaluated on the full model, we observe an offset in the solution due to model mismatch, but find that the ICO algorithm captures the qualitative trends of the optimal solution. The proposed algorithm is capable of solving the energy and emissions management problem in real-time, forming the basis for online optimal control.
Multi-Level Model Predictive Control for the Energy Management of Hybrid Electric Vehicles Including Thermal Derating
Machacek, David; Barhoumi, Kerim; Ritzmann, Johannes; et al. (2022)
IEEE Transactions on Vehicular Technology
This paper presents an online-capable controller based on model predictive control for the energy management system of a parallel hybrid electric vehicle, which is equipped with two electric motors (EM). Its task is to minimize the vehicle's fuel consumption along a predicted driving mission. If the fuel consumption is the only cost to be minimized, a frequent use of the electric components ensues. This can result in their overheating, which is prevented in practice by defining a maximum motor temperature, beyond which the motor's maximum capability is decreased drastically. This is referred to as thermal derating. A multi-level control structure is proposed that explicitly includes maximum temperature bounds on both EMs. The high-level controller is developed based on the model predictive control approach. The low-level controller is based on Pontryagin's maximum principle and is an extension of the equivalent consumption minimization strategy. To validate the multi-level control structure, three test scenarios of increasing difficulty are presented in simulation. They include thermal disturbances as well as driving mission mispredictions and are provided to demonstrate the robustness of the control algorithm. The proposed controller is able to recover 70% of the loss of optimality of a state-of-the-art predictive controller. A comparison to a dynamic programming optimization reveals close to optimal results.
Battery health target tracking for HEVs: Closed-loop control approach, simulation framework, and reference trajectory optimization
Widmer, Fabio; Ritter, Andreas; Ritzmann, Johannes; et al. (2023)
eTransportation
In this paper, we address the trade-off between primary energy consumption and battery wear for hybrid electric vehicles in an optimal manner, for which we provide three contributions: First, we suggest a control structure to track a battery lifetime target in a closed control loop by incorporating periodic measurements of the state of health. This feedback enables the energy management system to reliably meet the target lifetime in the presence of disturbances and model mismatch. We validate the control scheme in a case study featuring a battery-assisted trolley bus. In this case study, we show that without the proposed measurement feedback and in the presence of disturbances and model mismatch, the sub-optimal use of the battery can either result in an increase in energy consumption of up to 9% over the vehicle's lifetime or in a prematurely required battery replacement. Second, to speed up the necessary calculations, we devise an algorithm that is able to perform simulations of a complete vehicle lifetime in less than a minute. A comparison to a standard simulation approach shows that our approach is able to accurately calculate both energy consumption and battery degradation with an error of less than 1% on average, while the execution time is reduced by a factor of about 70000. Third, we numerically optimize the battery health trajectory over the vehicle lifetime. We show that, while a quadratic health trajectory leads to improved energy efficiency, for the specific vehicle and cell technology considered in our case study, a linear trajectory results in only a small energy penalty of 0.05% over the vehicle lifetime.
Model Predictive Supervisory Control for Integrated Emission Management of Diesel Engines
Ritzmann, Johannes; Peterhans, Christian; Chinellato, Oscar; et al. (2022)
Energies
In this work, a predictive supervisory controller is presented that optimizes the interaction between a diesel engine and its aftertreatment system (ATS). The fuel consumption is minimized while respecting an upper bound on the emitted tailpipe NOx mass. This is achieved by optimally balancing the fuel consumption, the engine-out NOx emissions, and the ATS heating. The proposed predictive supervisory controller employs a two-layer model predictive control structure and solves the optimal control problem using a direct method. Through experimental validation, the resulting controller was shown to reduce the fuel consumption by 1.1% at equivalent tailpipe NOx emissions for the nonroad transient cycle when compared to the operation with a fixed engine calibration. Further, the controller’s robustness to different missions, initial ATS temperatures, NOx limits, and mispredictions was demonstrated.
Optimal Integrated Emission Management through Variable Engine Calibration
Ritzmann, Johannes; Chinellato, Oscar; Hutter, Richard; et al. (2021)
Energies
In this work, the potential for improving the trade-off between fuel consumption and tailpipe NOx emissions through variable engine calibration (VEC) is demonstrated for both conventional and hybrid electric vehicles (HEV). First, a preoptimization procedure for the engine operation is proposed to address the challenge posed by the large number of engine control inputs. By excluding infeasible and suboptimal operation offline, an engine model is developed that can be evaluated efficiently during online optimization. Next, dynamic programming is used to find the optimal trade-off between fuel consumption and tailpipe NOx emissions for various vehicle configurations and driving missions. Simulation results show that for a conventional vehicle equipped with VEC and gear optimization run on the worldwide harmonized light vehicles test cycle (WLTC), the fuel consumption can be reduced by 5.4% at equivalent NOx emissions. At equivalent fuel consumption, the NOx emissions can be reduced by 80%. For an HEV, the introduction of VEC, in addition to the optimization of the torque split and the gear selection, drastically extended the achievable trade-off between fuel consumption and tailpipe NOx emissions in simulations. Most notably, the region with very low NOx emissions could only be reached with VEC.
Variable smoothing of optimal diesel engine calibration for improved performance and drivability during transient operation
Pandey, Varun; van Dooren, Stijn; Ritzmann, Johannes; et al. (2021)
International Journal of Engine Research
The model-based method to define the optimal calibration maps for important diesel engine parameters may involve three major steps. First, the engine speed and load domain – in which the engine is operated – are identified. Then, a global engine model is created, which can be used for offline simulations to estimate engine performance. Finally, optimal calibration maps are obtained by formulating and solving an optimisation problem, with the goal of minimising fuel consumption while meeting constraints on pollutant emissions. This last step in the calibration process usually involves smoothing of the maps in order to improve drivability. This article presents a method to trade off map smoothness, brake-specific fuel consumption and nitrogen oxide emissions. After calculating the optimal but potentially non-smooth calibration maps, a variation-based smoothing method is employed to obtain different levels of smoothness by adapting a single tuning parameter. The method was experimentally validated on a heavy-duty diesel engine, and the non-road transient cycle was used as a case study. The error between the reference and actual engine torque was used as a metric for drivability, and the error was found to decrease with increasing map smoothness. After having obtained this trade-off for various fixed levels of smoothness, a time-varying smoothness calibration was generated and tested. Experimental results showed that, with a time-varying smoothness strategy, nitrogen oxide emissions could be reduced by 4%, while achieving the same drivability and fuel consumption as in the case of a fixed smoothing strategy.
Predictive Supervisory Control of Powertrains
Ritzmann, Johannes (2022)
Fuel-Optimal Power Split and Gear Selection Strategies for a Hybrid Electric Vehicle
Ritzmann, Johannes; Christon, Andreas; Salazar Villalon, Mauro; et al. (2019)
SAE Technical Papers

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Johannes Ritzmann

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