Robustness of ECMS-based Optimal Control in Parallel Hybrid Vehicles
|
|
- Julian Robbins
- 5 years ago
- Views:
Transcription
1 7th IFAC Symposium on Advances in Automotive Control The International Federation of Automatic Control Robustness of ECMS-based Optimal Control in Parallel Hybrid Vehicles Chris Manzie Olivier Grondin, Antonio Sciarretta and Gianluca Zito Department of Mechanical Engineering, The University of Melbourne, Victoria, Australia 31 ( Control, Signals and Systems Department, IFP Energies Nouvelles, 1 et 4, avenue de Bois-Preau, Rueil-Malmaison, France 9 Abstract: Control algorithms for hybrid vehicles have undergone extensive development leading to near-optimal techniques being employed and demonstrated over the previous decade. The different optimal controllers are inevitably linked through the assumed knowledge of the powertrain. For alternative fuelled engines, this assumed knowledge becomes circumspect due to composition variability, leading to uncertainty in the models used by the hybrid powertrain controller. The robustness of a map-based optimal controller using an equivalent charge maintenance strategy (ECMS) is consequently investigated in terms of theoretical fuel consumption losses under certain restrictive assumptions. The potential real world impact of variable composition fuels in hybrid powertrains is also assessed through two case studies involving significantly different prototype flex fuel hybrid vehicles. Keywords: Hybrid vehicles, flex-fuel, optimal control, robustness. 1. INTRODUCTION The ability of a hybrid vehicle to maximise fuel economy is indelibly linked to the control strategy used to select the utility of the combustion engine and the electric motor. Initial work in this regard utilised rule-based control strategies (Buntin and Howze, 1995; Salman et al., ; Schouten et al., 22), while the integration of optimisation (Borhan et al., 212) and optimal control methodologies (Delprat et al., 24; Sciarretta et al., 24; Wei et al., 27; Serrao et al., 29) have led to improved fuel economy, particularly in the knowledge of part (Manzie et al., 212) or all (Back et al., 22, 24) of the future driving conditions. These optimal control techniques have been readily extended to cope with additional issues facing hybrid electric vehicles including battery charging and catalyst temperature (Serrao et al., 211), while a lack of knowledge about the drive cycle has been partially addressed through the incorporation of speed (Kim et al., 29) or state-of-charge (Musardo et al., 25) dependence on the equivalence factor, typically through the integration of a proportional feedback term. Common to the optimal control approaches is the need to inform the controller of certain characteristics about the engine and motor being controlled. The required information is typically quasi-steady, and includes e.g. fuel consumption maps in the case of ECMS (Sciarretta et al., 24; Stockar et al., 211) or efficiency contours under The authors acknowledge the support of the Australian Research Council through FT11324, as well as support provided by IFP Energies Nouvelles and the ACART centre at the University of Melbourne. other proposed schemes (Manzie et al., 212; Zhang et al., 211). Aside from powertrain electrification, there is also significant interest in the use of alternatives to gasoline and diesel to aid in fuel security and improve tailpipe CO 2. Fuels such as liquefied petroleum gas (LPG), compressed natural gas (CNG) and gasoline-ethanol blends have attracted different levels of regional interest depending on local availability and distribution networks. However, the composition of all these fuels in an engine is subject to high variability. For instance, LPG can vary from almost pure propane to equal proportions of propane and butane; while ethanol engines may encounter fuel blends ranging from E85 to E. On a vehicle with only an alternative-fuel internal combustion engine, this compositional variation leads to a detuning of the engine calibration to ensure sufficient robustness for all possible fuels that will be encountered. If the alternative fuelled engine is integrated into a hybrid vehicle, there is another level of consideration as the aforementioned model based controllers all rely on quasisteady maps that are potentially incorrect. Given the possible CO 2 advantages afforded by marrying hybrid powertrains with an alternative fuel, the robustness of the existing algorithms, and indeed their ability to deliver close to optimal performance in the presence of map uncertainty, is an open question. This paper sets out to analyse the general problem of using uncertain fuel models in an ECMS controller. The degradation in performance is then quantified for two prototype flex-fuel hybrid vehicles developed to run on ethanol blends ranging from E5 to E /213 IFAC / JP
2 2. LOCAL ROBUSTNESS ANALYSIS OF ECMS Quasi-steady models for fuel and charge consumption, m f and q respectively, used in a general Hamiltonian-based controller design for a hybrid powertrain with control inputs u are described in the following general form: m f = f(t, u)dt (1) q = g(t, u)dt (2) Given the quasi-steady assumption, the control variables are the engine and motor operating points. In a Pontryagin s Minimum Principle-based controller such as described in (Sciarretta et al., 24), the resulting Hamiltonian and optimal equivalent charge management strategy given an allowable input range contained in the (potentially time varying) set U t may then be described as H(f, g, u, t) = f(t, u) + s(t)g(t, u) (3) u (t) = arg min H(f, g, u, t) u U t (4) Note that s is termed the equivalence factor as it represents the fuel-electricity equivalence. If the drive cycle is known a priori, s(t) := s can be determined numerically as the constant that drives battery state of charge at the end of the cycle to a desired level. Assumption 1 The values of u (t) form dense subsets of the allowable input range, u Ut U t containing the point of maximum engine efficiency, η peak, and zero engine torque. Furthermore, the average engine efficiency within the subset containing η peak is given by η av = (1 γ)η peak, where γ is a small positive number. Ideally, the optimal inputs will be clustered around regions of high engine efficiency whenever the engine is on. The density of the inputs in the efficiency space will be influenced by the vehicles torque demand arising from the drive cycle as well as the presence and severity of any operational constraints placed on the controller. The value of γ will be affected both by the density of the inputs and the gradients in both the fuel and electrical usage maps used by the controller, f(t, u) and g(t, u). Now consider that the quasi-steady fuel consumption map used in the controller is perturbed by a static map, f(t, u), i.e. the map used by the controller is f(t, u) = f(t, u) + f(t, u). Uncertainties in the electrical path and driveline of hybrid vehicles are much less common, and so perturbations to g(t, u) are not considered here. The resulting (assumed optimal) control, ũ, obtained using the ECMS strategy is described by H( f, g, u, t) = f(t, u) + s(t)g(t, u) (5) ũ (t) = arg min u U t H( f, g, u, t) (6) The following assumption is now applied for all three maps relevant to the performance of the ECMS controller. Assumption 2 The quasi-steady maps are sufficiently well approximated at each speed by the following equations: f(u, t) = 1 2 a ( (t))u 2 + b ( (t))u + c ( (t)) (7) g(u, t) = 1 2 a 1(N m (t))u 2 + b 1 (N m (t))u + c 1 (N m (t)) (8) f(u, t) = 1 2 a 2( (t))u 2 + b 2 ( (t))u + c 2 ( (t)) (9) The assumption of quadratic maps in practice restricts discussion to a local analysis, albeit in conjunction with Assumption 1 this will be about the point of maximum engine efficiency. Allowing higher order polynomials to represent f invokes the potential for discontinuities in the arg min H operations, that are not readily analysed. Also, note that the coefficients of f and g will ensure that one function is increasing in u while the other is decreasing. For ease of notation, the speed-dependence of the coefficients in (7) - (9) is omitted in the following discussion. The following additional assumptions are now placed on the perturbation map and drive cycle. Assumption 3 The parameters of the perturbation map a 2, b 2 and c 2 are small. This is in keeping with the local nature of the analysis. Assumption 4 The drive cycle is of length T and completely known a priori. This allows constant equivalence factors, s (f, g) and s (f + f, g) to be used in the analysis. These optimal equivalence factors may be charge sustaining in the case of parallel hybrid operation, but optimal charge depletion for plug-in hybrid vehicles is also possible. Theorem 1. Under Assumptions 1-4, using an ECMS strategy with an incorrect fuel map parameterised by a 2, b 2 and c 2 will lead to a fuel consumption penalty on a known drive cycle of: m(a 2,b 2, c 2, γ) = (1 γ) T (O(a 2 ) + O(b 2 ) + O(c 2 )) κ(t)dt (1) where κ(t) = 1 if the engine is on and κ(t) = otherwise. Proof. Following from Assumption 2 the Hamiltonians for the true and the assumed systems are respectively: H(f, g, t) = f(u, t) + s g(u, t) (11) = 1 2 (a + s a 1 )u 2 + (b + s b 1 )u + (c + s c 1 ) H( f, g, t) = 1 2 (a + s a 1 + a 2 )u 2 (12) + (b + s b 1 + b 2 )u + (c + s c 1 + c 2 ) (13) In the absence of any input or state constraints, the Hamiltonian-minimising controls calculated from (4) and (6) are given by u min (t) = b + b 1 s a + a 1 s (14) ũ min (t) = b + b 1 s + b 2 a + a 1 s + a 2 (15) The resulting optimal constrained controls are then obtained by projecting these into the allowable sets, i.e.: 128
3 u (t) = proj Ut u min (t) (16) ũ (t) = proj Ut ũ min (17) As both s and s are constant by Assumption 4, in the case of parallel and series hybrids they will be charge sustaining by definition. Hence there is no need for state of charge correction in calculating the increase in fuel used over the drive cycle by adopting the suboptimal policy ũ (t). The fuel penalty for using incorrect map, m f, is subsequently given by: m f := f(ũ (t)) f(u (t))dt (18) Defining κ(t) = 1 if the engine is on and κ(t) = otherwise, then from Assumption 1 it follows that the fuel penalty can be expressed as: [ ( ) 2 ( a κ(t) b + b 1 s + b 2 b + b 1 s ) ] 2 m f = 2 a + a 1 s + a 2 [ b + b 1 s + b κ(t) a + a 1 s b + b 1 s + b 2 a + a 1 s + a 2 a + a 1 s ] dt (19) While (23) shows an explicit dependence on the curvature and gradient of the map perturbation, there is only an implicit dependence on c 2 and γ that arises through the different equivalent factors, s and s. This is made explicit through consideration of the engine efficiency, η(, u) := ku f(u). If the efficiency gradients are relatively constant with distance from η peak, from Assumption 1 the averaged operating point within Ut can be related to the maximum efficiency operating point, u p according to: ( ) η u = (1 γ) u p (2) u The maximum efficiency point, u p, can be found by applying Assumption 2 with dη du =. The left hand side of (2) is obtained (14) and (15) for each case, and hence the following solutions for it s and s are obtained: s = b a + (1 γ) η u a c b 1 a + (1 γ) η u a (21) 1 c s = (b + b 2 ) a + a 2 + (1 γ) η u (a + a 2 ) c + c 2 b 1 a + a 2 + (1 γ) η u a 1 c + c 2 (22) Since m f (,, ), by substituting (21) and (22) into (19) and applying Assumption 3 provides the truncated Taylor series expression: m f (a 2,b 2, c 2, γ) = m f (,, ) + m f a 2 a 2 (,,) + m f b 2 b 2 + m f (,,) c 2 c 2 (23) (,,) u p The result of the theorem then follows directly. It is worth noting that for the case where a, c (i.e. the engine efficiency is constant for all torque), from (21) that s b b 1. It is readily shown that this equates to the known ECMS result for constant engine and motor efficiencies that s ηe η f during propulsion. Furthermore, in the event that the drive cycle is unknown and the equivalence factor is estimated online, the dependence on the f parameters may change as there are additional feedback dynamics involved. The actual performance degradation will be additionally influenced by both the structure of the s -estimator algorithm and the gains used within it. 3. OVERVIEW OF ENGINE AND SIMULATOR Two vehicles are used as real world case studies. The first vehicle (Vehicle A) is a parallel hybrid demonstrator developed at IFP Energies Nouvelles for flex fuel operation. On the electrical side it has a 37.7kW, 35Nm electric motor capable of 2, rpm. The battery pack consists of Li- Ion cells with 7.6 kwh capacity. This is relatively large, but reflects potential plug in capability being incorporated, although only charge sustaining operation is considered here. The battery state of charge is allowed to vary within the range 5 to.75, with a desired setpoint of.55. There is also an integrated starter-alternator enabling regenerative braking and engine-off capability. The second vehicle (Vehicle B) was also developed within IFP and has a more conventional electrical configuration for parallel hybrid operation, with a similar size motor but much smaller Li-Ion battery pack (42 kw and 1.5 kwh respectively). On the mechanical side, the engine used in Vehicle A is a naturally aspirated, 4-cylinder, litre spark ignition engine and a five speed automated transmission. On the other hand, Vehicle B has a turbocharged, 4-cylinder 2. litre spark ignition engine with slightly lower compression ratio. The key parameters of each vehicle are summarised in Table 1. Table 1. Case study vehicle summaries. Vehicle A Vehicle B Engine Type ET3J4 F4Rt Total displacement l 2 l Air charging Naturally aspirated Twin-scroll TC Compression ratio 11:1 1.55:1 Max engine power 65 rpm 15 rpm Max engine torque 133 rpm 3 rpm Plug-in capable Yes No Max motor power 37.7 kw 42 kw Battery energy 7.6 kwh 1.5 kwh Vehicle mass 17 kg 193 kg Static calibrations for the warm engines were performed on a dynamometer for ethanol blends ranging from E5 to E85. The difference in fuel flow rate maps for the two compositions, f := f E5 f E85, is shown in Figure 1 for different engine speeds. From these maps it appears that limiting the approximation of f to a polynomial in torque of order two for fixed engine speed is not unreasonable, and appears to be relevant across the entire torque range, with the possible exception of high torque and high speed operating points on the F4R engine. For further insight, the efficiency contour maps are shown in Figure 2 and 3. From these it is clear that despite a relatively linear characteristic for the f contours, 129
4 ! f (g/s) = rpm = rpm! f (g/s) = rpm = rpm Fig. 1. Difference in flow rate (g/s) between E5 and E85 fuels as a function of torque for fixed engine speeds for (left) ET3 and (right) F4R engines Fig. 2. Efficiency contours for the ET3 engine running on (left) E5 and (right) E85 fuel Fig. 3. Efficiency contours for the F4R engine running on (left) E5 and (right) E85 fuel the maximal efficiency for each composition is obtained under quite different operating points in each engine. For example, the ET3 engine operating on E5 has a degradation in efficiency past a torque of around 85Nm across all speed ranges, while under E85 the peak efficiency is obtained at the highest torques. Similarly, the efficiency peak for the F4R engine is at a medium torque and high speed running on E5, but this shifts to a medium speed and high torque when E85 fuel is used. The simulator used in this work is a version of the Hy- HiL environment developed within IFP Energies Nouvelles and used extensively in the development and testing of hybrid powertrain controllers, e.g. Del Mastro et al. (29); Chasse et al. (29a,b). It includes detailed electrical and mechanical modelling capability, with efficiencies of both paths realistically prescribed from experimental testing. To partially alleviate drive-cycle dependant results, several drive cycles were employed in the testing. Those chosen included two standard regulatory cycles, the well-known NEDC and the FTP cycles, and one more representative of real world driving, the World Harmonized Light Duty 55 5 Test Procedure (WLTP). The latter is mooted to become the regulatory cycle in several countries (Marotta, 212). 4. SIMULATION RESULTS 4.1 Parallel hybrid using an artificial map case Before considering the effect of the incorrect fuel blend information in the ECMS performance explicitly, the result of Theorem 1 was tested by using a quadratic approximation of f(u) derived from the ET3 engine running on E5 fuel at rpm. This curve was used to build a speedindependent map that could be used in the ECMS controller, with a peak in efficiency at approximately 96Nm of torque. This map was subjected to small perturbations in the coefficients of 1% the original value, leading to the efficiency peak shifting by less than 5Nm. Maintaining small perturbations is consistent with Assumption 3, and also ensures that the ECMS controller s operation is not significantly varied through encountering significantly different state constraints during operation on each fuel blend. To aid in isolating the map effect, some controller constraints, including limitations on gear changes etc, were relaxed during this phase of the testing. The simulated fuel consumption was corrected for state of charge deviations, and the subsequent fuel losses (expressed as a percentage) for different small perturbations over the NEDC are shown in Table 2. Table 2. Effect on NEDC corrected fuel economy of small perturbations on fuel map Perturbation m f Perturbation m f a 2 = 1%.6% a 2 = 1%.7% b 2 = 1% 1.6% b 2 = 1% 3.7% c 2 = 1% % c 2 = 1% 1.% a 2, b 2, c 2 = 1% 2.3% a 2, b 2, c 2 = 1% 5.2% These results suggest that the controller appears reasonably robust to small variations in any particular parameter, as the percentage of fuel losses are smaller than the variation in any coefficient. There is also a slight asymmetry in the results, with positive parameter perturbations leading to slightly larger observed fuel loss. This is a mapdependant characteristic influenced by the small changes of the map gradients in the regions of operation, and also influenced by the electrical map, g. Concurrent variations in the coefficients also lead to approximately linear perturbations in the fuel mass, although given the difficulty in compensating for state of charge variation at high precision, it is problematic to ascertain whether there are potential higher order effects that are being observed, or whether the deviations in multiple parameters have complementary effects. 4.2 Vehicle case studies Attention now turns to assessing the real world significance of fuel map perturbations. From Theorem 1, it is expected that small perturbations in the fuel maps will lead to proportional degradation in the fuel economy, with the level of proportionality dependent on the size of the perturbations in the quadratic surfaces. The total fuel economy degradation is also affected by the average operating efficiency 13
5 Table 3. Simulated fuel consumption using ET3 hybrid running with incorrect fuel maps and equivalence ratio Drive Actual Ass s Fuel m f cycle fuel fuel cons. NEDC E5 E E5 E % NEDC E85 E E85 E % FTP E5 E E5 E % FTP E85 E E85 E % WLTP E5 E E5 E % WLTP E85 E E85 E % in the constrained region of operation. To assess the likely implications for ethanol blended gasoline, simulations with the maps for E5 and E85 from Section 3 as both the true and assumed fuels are conducted for each of the two vehicles described in Section 3. The parameter variations in the fuel maps are engine speed dependant, although are larger in the case of Vehicle B as might be expected from Figure 3. Full controller constraints are also included explicitly, and play a role in determining the validity of Assumption 1. The imposed constraints are typical of those in real world implementation, and include limiting gear selection, consideration of alternator limitations and state of charge usage to ensure driveability and battery management requirements are maintained. However these are limited to constraints at the ECMS-controller level - due to the quasi-steady nature of the simulation environment, it is not possible to consider the impact of engine-level control constraints, for example knock limits, and so these are assumed to be already captured within the quasi-steady fuel consumption maps. Consistent with Theorem 1, complete knowledge of the drive cycle is assumed to enable the ECMS to use the optimal fuel-electricity equivalence factor, s, (i.e. one that ensures no battery state of deviations on completion of the cycle). The fuel maps and equivalence factor were synchronised, so that use of the incorrect fuel information involves both the matched (but incorrect) map and equivalence factor. This is more realistic and also means that only marginal state of charge correction is required in evaluating the fuel use. The results of the simulations over three drive cycles are given in Table 3 and Table 4 for Vehicles A and B respectively. For Vehicle A, it is clear that the fuel penalty incurred is not significant with no combination of drive cycle and incorrect information yielding greater than 1.5% worse economy. To investigate further it is useful to consider the selected engine operating points over one of the drive cycles. Figure 4 show the engine speed and torque over the WLTP cycle, superimposed on the efficiency contours for E5 operation, when the engine is running on E5 fuel but the ECMS controller has both the correct and incorrect fuel information. Table 4. Simulated fuel consumption using F4R hybrid running with incorrect fuel maps and equivalence ratio Drive Actual Ass s Fuel m f cycle fuel fuel cons. NEDC E5 E E5 E % NEDC E85 E E85 E % FTP E5 E E5 E % FTP E85 E E85 E % WLTP E5 E E5 E % WLTP E85 E E85 E % Fig. 4. ET3 engine operating points superimposed on efficiency contours for the WLTP running on E5 fuel with the ECMS using (left) E5 and (right) E85 information only From this Figure, it is clear that Assumption 1 regarding the denseness of the operating points about the maximum efficiency point is not well supported, primarily due to the imposition of constraints on the ECMS controller invoked by the available hardware. The large overlap between the regions occupied by the engine operating points in Figure 4, coupled with the relatively flat efficiency contours in the occupied regions leads to similar fuel consumptions. In the context of Theorem 1, this implies the parameter γ is relatively large and therefore the fuel losses are expected to be reduced. To partially isolate the effect of the imposed controller constraints on Vehicle A, it is calculated the ECMS with correct fuel information results in an average engine efficiency for the WLTP cycle of 35.4%. As the peak engine efficiency is 38.2% for this engine running on E5 fuel, there appears to be close to 8% fuel penalty imposed by the constraints of the ECMS controller if the WLTP cycle is assumed to be sufficiently rich. The average operating efficiency degrades to only 35.1% when the wrong fuel information is supplied to the controller. Thus, in this case study the ECMS appears quite robust to the fuel uncertainty, although different constraint imposition made possible through changes to the hardware integration may lead to better fuel economy with the correct fuel information, and subsequently a more appreciable difference between the ECMS utilising different fuel maps. In the case of Vehicle B the fuel economy degradation is much more severe in two of the drive cycles when E5 fuel is used but the controller assumes E85 fuel is
6 present, with 8 and 12% losses incurred. The asymmetry in the results (i.e. there is no significant fuel economy degradation using an assumed fuel of E85 on any drive cycle) is predicted from the peak efficiency points. The low degradation in the case of the NEDC cycle is primarily due to the comparatively low number of operating points in the cycle not allowing a comprehensive exploitation of the degrees of freedom available to the controller, and is not entirely unexpected. The operating points for Vehicle B on the WLTP cycle using both correct and incorrect fuel information are shown in Figure 5, and appear to show a higher density of points in both cases. This in turn leads to greater fuel penalties when the wrong fuel composition is assumed Fig. 5. F4R engine operating points superimposed on efficiency contours for the WLTP running on E5 fuel with the ECMS using (left) E5 and (right) E85 information only 5. CONCLUSION The optimal control implementations of ECMS in hybrid powertrains appear to demonstrate a robustness to small perturbations in the maps. However, case studies performed on real vehicles exhibited larger perturbations and consequently the fuel consumption differences may become significant. Further work will consider how to characterise the likely losses for real vehicles, and the development of possible compensatory strategies. REFERENCES Back, M., Simons, M., and Kirschbaum, F. (22). Predictive control of drivetrains. In 15th Triennial IFAC World Congress. Back, M., Terwen, S., and Krebs, V. (24). Predictive powertrain control for hybrid electric vehicles. In IFAC Symposium on Advances in Automotive Control. Borhan, H., Vahidi, A., Phillips, A.M., Kuang, M.L., Kolmanovsky, I.V., and Di Cairano, S. (212). Mpc-based energy management of a power-split hybrid electric vehicle. IEEE Transactions on Control Systems Technology, 2(3), Buntin, D. and Howze, J. (1995). A switching logic controller for a hybrid electric/ice vehicle. In American Control Conference, volume 2, Chasse, A., Hafidi, G., Pognant-Gros, P., and Sciarretta, A. (29a). Supervisory control of hybrid powertrains: an experimental benchmark of offline optimization and online energy management. In E-COSM 9 - IFAC Workshop on Engine and Powertrain Control, Simulation and Modelling. Rueil-Malmaison, France. 5 Chasse, A., Pognant-Gros, P., and Sciarretta, A. (29b). Online implementation of an optimal supervisory control for a parallel hybrid powertrain. In SAE World Congress, Del Mastro, A., Chasse, A., Pognant-Gros, P., Corde, G., Perez, F., Gallo, F., and Hennequet, G. (29). Advanced Hybrid Vehicle Simulation: from Virtual to HyHiL test bench. In SAE World Congress, Delprat, S., Lauber, J., Cuerra, T.M., and Rimaux, J. (24). Control of a parallel hybrid powertrain: Optimal control. IEEE Transactions on Vehicular Technology, 53(3), Kim, T., Manzie, C., and Sharma, R. (29). Model predictive control of velocity and torque split in a parallel hybrid vehicle. In IEEE International Conference on Systems, Man, and Cybernetics. Manzie, C., Kim, T., and Sharma, R. (212). Optimal use of telemetry by parallel hybrid vehicles in urban driving. Transportation Research Part C, 25, Marotta, A. (212). The WLTP from a EU perspective. In Ulysses Workshop. Ispra, Italy. Musardo, C., Rizzoni, G., Guezennec, Y., and Staccia, B. (25). A-ECMS: An adaptive algorithm for hybrid electric vehicle energy management. European Journal of Control, 11(4-5), Salman, M., Schouten, N.J., and Kheir, N.A. (). Control strategies for parallel hybrid vehicles. In American Control Conference. Schouten, N., Salman, M., and Kheir, N. (22). Fuzzy logic control for parallel hybrid vehicles. IEEE Transactions on Control Systems Technology, 1, 46. Sciarretta, A., Back, M., and Guzzella, L. (24). Optimal control of parallel hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 12(3), Serrao, L., Onori, S., and Rizzoni, G. (29). ECMS as a realization of Pontryagin s minimum principle for HEV control. In American Control Conference, Serrao, L., Sciaretta, A., Grondin, O., Chasse, A., Creff, Y., di Domenico, D., Pognant-Gros, P., Querel, C., and Thibault, L. (211). Open Issues in Supervisory Control of Hybrid Electric Vehicles: A Unified Approach using Optimal Control Methods. In Int. Sci. Conf. on Hybrid and Electric Vehicles. Stockar, S., Marano, V., Canova, M., Rizzoni, G., and Guzzella, L. (211). Energy-optimal control of plugin hybrid electric vehicles for real world driving cycles. IEEE Transactions on Vehicular Technology, 6(7), Wei, X., Guzzella, L., Utkin, V.I., and Rizzoni, G. (27). Model-based fuel optimal control of hybrid electric vehicle using variable structure control systems. Journal of Dynamic Systems, Measurement, and Control, 129, Zhang, B., Mi, C., and Zhang, M. (211). Charge depleting control strategies and fuel optimization of blended mode plug-in hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 6(4),
State of Charge Management for Plug in Hybrid Electric Vehicles with Uncertain Distance to Recharge
State of Charge Management for Plug in Hybrid Electric Vehicles with Uncertain Distance to Recharge Chris Manzie Department of Mechanical Engineering The University of Melbourne Victoria 3010, Australia
More informationModel Predictive Control of Velocity and Torque Split in a Parallel Hybrid Vehicle
Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 2009 Model Predictive Control of Velocity and Torque Split in a Parallel Hybrid Vehicle
More informationFuel Economy Benefits of Look-ahead Capability in a Mild Hybrid Configuration
Proceedings of the 17th World Congress The International Federation of Automatic Control Fuel Economy Benefits of Look-ahead Capability in a Mild Hybrid Configuration Tae Soo Kim 1, Chris Manzie 1,2, Harry
More informationAdaptive Control of a Hybrid Powertrain with Map-based ECMS
Milano (Italy) August 8 - September, 11 Adaptive Control of a Hybrid Powertrain with Map-based ECMS Martin Sivertsson, Christofer Sundström, and Lars Eriksson Vehicular Systems, Dept. of Electrical Engineering,
More informationModeling and Control of Hybrid Electric Vehicles Tutorial Session
Modeling and Control of Hybrid Electric Vehicles Tutorial Session Ardalan Vahidi And Students: Ali Borhan, Chen Zhang, Dean Rotenberg Mechanical Engineering, Clemson University Clemson, South Carolina
More informationSupervisory Control of Plug-in Hybrid Electric Vehicle with Hybrid Dynamical System
Supervisory Control of Plug-in Hybrid Electric Vehicle with Hybrid Dynamical System Harpreetsingh Banvait, Jianghai Hu and Yaobin chen Abstract In this paper, a supervisory control of Plug-in Hybrid Electric
More informationStochastic Dynamic Programming based Energy Management of HEV s: an Experimental Validation
Preprints of the 19th World Congress The International Federation of Automatic Control Stochastic Dynamic Programming based Energy Management of HEV s: an Experimental Validation T. Leroy F. Vidal-Naquet
More informationAnalysis of Fuel Economy and Battery Life depending on the Types of HEV using Dynamic Programming
World Electric Vehicle Journal Vol. 6 - ISSN 2032-6653 - 2013 WEVA Page Page 0320 EVS27 Barcelona, Spain, November 17-20, 2013 Analysis of Fuel Economy and Battery Life depending on the Types of HEV using
More informationPerformance Evaluation of Electric Vehicles in Macau
Journal of Asian Electric Vehicles, Volume 12, Number 1, June 2014 Performance Evaluation of Electric Vehicles in Macau Tze Wood Ching 1, Wenlong Li 2, Tao Xu 3, and Shaojia Huang 4 1 Department of Electromechanical
More informationDirect Injection Ethanol Boosted Gasoline Engines: Biofuel Leveraging For Cost Effective Reduction of Oil Dependence and CO 2 Emissions
Direct Injection Ethanol Boosted Gasoline Engines: Biofuel Leveraging For Cost Effective Reduction of Oil Dependence and CO 2 Emissions D.R. Cohn* L. Bromberg* J.B. Heywood Massachusetts Institute of Technology
More informationValidation and Control Strategy to Reduce Fuel Consumption for RE-EV
Validation and Control Strategy to Reduce Fuel Consumption for RE-EV Wonbin Lee, Wonseok Choi, Hyunjong Ha, Jiho Yoo, Junbeom Wi, Jaewon Jung and Hyunsoo Kim School of Mechanical Engineering, Sungkyunkwan
More informationOPTIMAL POWER MANAGEMENT OF HYDROGEN FUEL CELL VEHICLES
OPTIMAL POWER MANAGEMENT OF HYDROGEN FUEL CELL VEHICLES Giuliano Premier Sustainable Environment Research Centre (SERC) Renewable Hydrogen Research & Demonstration Centre University of Glamorgan Baglan
More informationMODELING, VALIDATION AND ANALYSIS OF HMMWV XM1124 HYBRID POWERTRAIN
2014 NDIA GROUND VEHICLE SYSTEMS ENGINEERING AND TECHNOLOGY SYMPOSIUM POWER & MOBILITY (P&M) TECHNICAL SESSION AUGUST 12-14, 2014 - NOVI, MICHIGAN MODELING, VALIDATION AND ANALYSIS OF HMMWV XM1124 HYBRID
More informationDevelopment of Engine Clutch Control for Parallel Hybrid
EVS27 Barcelona, Spain, November 17-20, 2013 Development of Engine Clutch Control for Parallel Hybrid Vehicles Joonyoung Park 1 1 Hyundai Motor Company, 772-1, Jangduk, Hwaseong, Gyeonggi, 445-706, Korea,
More informationApproach for determining WLTPbased targets for the EU CO 2 Regulation for Light Duty Vehicles
Approach for determining WLTPbased targets for the EU CO 2 Regulation for Light Duty Vehicles Brussels, 17 May 2013 richard.smokers@tno.nl norbert.ligterink@tno.nl alessandro.marotta@jrc.ec.europa.eu Summary
More informationMECA0500: PLUG-IN HYBRID ELECTRIC VEHICLES. DESIGN AND CONTROL. Pierre Duysinx
MECA0500: PLUG-IN HYBRID ELECTRIC VEHICLES. DESIGN AND CONTROL Pierre Duysinx Research Center in Sustainable Automotive Technologies of University of Liege Academic Year 2017-2018 1 References R. Bosch.
More informationValidation of a simulation model for the assessment of CO 2 emissions of passenger cars under real-world conditions
Validation of a simulation model for the assessment of CO 2 emissions of passenger cars under real-world conditions The gap between real-world fuel consumption and manufacturers figures has been increasing
More informationConstruction of a Hybrid Electrical Racing Kart as a Student Project
Construction of a Hybrid Electrical Racing Kart as a Student Project Tobias Knoke, Tobias Schneider, Joachim Böcker Paderborn University Institute of Power Electronics and Electrical Drives 33095 Paderborn,
More informationFuzzy based Adaptive Control of Antilock Braking System
Fuzzy based Adaptive Control of Antilock Braking System Ujwal. P Krishna. S M.Tech Mechatronics, Asst. Professor, Mechatronics VIT University, Vellore, India VIT university, Vellore, India Abstract-ABS
More informationComparative analysis of forward-facing models vs backwardfacing models in powertrain component sizing
Comparative analysis of forward-facing models vs backwardfacing models in powertrain component sizing G Mohan, F Assadian, S Longo Department of Automotive Engineering, Cranfield University, United Kingdom
More informationA conceptual design of main components sizing for UMT PHEV powertrain
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS A conceptual design of main components sizing for UMT PHEV powertrain Related content - Development of a KT driving cycle for
More informationVehicle Performance. Pierre Duysinx. Research Center in Sustainable Automotive Technologies of University of Liege Academic Year
Vehicle Performance Pierre Duysinx Research Center in Sustainable Automotive Technologies of University of Liege Academic Year 2015-2016 1 Lesson 4: Fuel consumption and emissions 2 Outline FUEL CONSUMPTION
More informationMODELING SUSPENSION DAMPER MODULES USING LS-DYNA
MODELING SUSPENSION DAMPER MODULES USING LS-DYNA Jason J. Tao Delphi Automotive Systems Energy & Chassis Systems Division 435 Cincinnati Street Dayton, OH 4548 Telephone: (937) 455-6298 E-mail: Jason.J.Tao@Delphiauto.com
More informationMulti Body Dynamic Analysis of Slider Crank Mechanism to Study the effect of Cylinder Offset
Multi Body Dynamic Analysis of Slider Crank Mechanism to Study the effect of Cylinder Offset Vikas Kumar Agarwal Deputy Manager Mahindra Two Wheelers Ltd. MIDC Chinchwad Pune 411019 India Abbreviations:
More informationSupercapacitors For Load-Levelling In Hybrid Vehicles
Supercapacitors For Load-Levelling In Hybrid Vehicles G.L. Paul cap-xx Pty. Ltd., Villawood NSW, 2163 Australia A.M. Vassallo CSIRO Division of Coal & Energy Technology, North Ryde NSW, 2113 Australia
More informationSystem Analysis of the Diesel Parallel Hybrid Vehicle Powertrain
System Analysis of the Diesel Parallel Hybrid Vehicle Powertrain Kitae Yeom and Choongsik Bae Korea Advanced Institute of Science and Technology ABSTRACT The automotive industries are recently developing
More informationSwitching Control for Smooth Mode Changes in Hybrid Electric Vehicles
Switching Control for Smooth Mode Changes in Hybrid Electric Vehicles Kerem Koprubasi (1), Eric Westervelt (2), Giorgio Rizzoni (3) (1) PhD Student, (2) Assistant Professor, (3) Professor Department of
More informationUsing Trip Information for PHEV Fuel Consumption Minimization
Using Trip Information for PHEV Fuel Consumption Minimization 27 th International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium (EVS27) Barcelona, Nov. 17-20, 2013 Dominik Karbowski, Vivien
More informationA flywheel energy storage system for an isolated micro-grid
International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) A flywheel energy storage system for an isolated micro-grid Venkata Mahendra Chimmili Studying B.Tech 4th year in department of
More informationReal-world to Lab Robust measurement requirements for future vehicle powertrains
Real-world to Lab Robust measurement requirements for future vehicle powertrains Andrew Lewis, Edward Chappell, Richard Burke, Sam Akehurst, Simon Pickering University of Bath Simon Regitz, David R Rogers
More informationINTELLIGENT ENERGY MANAGEMENT IN A TWO POWER-BUS VEHICLE SYSTEM
2011 NDIA GROUND VEHICLE SYSTEMS ENGINEERING AND TECHNOLOGY SYMPOSIUM MODELING & SIMULATION, TESTING AND VALIDATION (MSTV) MINI-SYMPOSIUM AUGUST 9-11 DEARBORN, MICHIGAN INTELLIGENT ENERGY MANAGEMENT IN
More informationScania presents the bus of the future: Innovative hybrid concept from Scania improves fuel-efficiency by at least 25%
PRESS info P07502EN / Per-Erik Nordström 21 May 2007 Scania presents the bus of the future: Innovative hybrid concept from Scania improves fuel-efficiency by at least 25% Scania presents a unique hybrid-electric
More informationPHEV Control Strategy Optimization Using MATLAB Distributed Computing: From Pattern to Tuning
PHEV Control Strategy Optimization Using MATLAB Distributed Computing: From Pattern to Tuning MathWorks Automotive Conference 3 June, 2008 S. Pagerit, D. Karbowski, S. Bittner, A. Rousseau, P. Sharer Argonne
More informationConsideration on the Implications of the WLTC - (Worldwide Harmonized Light-Duty Test Cycle) for a Middle Class Car
Consideration on the Implications of the WLTC - (Worldwide Harmonized Light-Duty Test Cycle) for a Middle Class Car Adrian Răzvan Sibiceanu 1,2, Adrian Iorga 1, Viorel Nicolae 1, Florian Ivan 1 1 University
More informationFuel Consumption, Exhaust Emission and Vehicle Performance Simulations of a Series-Hybrid Electric Non-Automotive Vehicle
2017 Published in 5th International Symposium on Innovative Technologies in Engineering and Science 29-30 September 2017 (ISITES2017 Baku - Azerbaijan) Fuel Consumption, Exhaust Emission and Vehicle Performance
More informationAUTONOMIE [2] is used in collaboration with an optimization algorithm developed by MathWorks.
Impact of Fuel Cell System Design Used in Series Fuel Cell HEV on Net Present Value (NPV) Jason Kwon, Xiaohua Wang, Rajesh K. Ahluwalia, Aymeric Rousseau Argonne National Laboratory jkwon@anl.gov Abstract
More informationPerodua Myvi engine fuel consumption map and fuel economy vehicle simulation on the drive cycles based on Malaysian roads
Perodua Myvi engine fuel consumption map and fuel economy vehicle simulation on the drive cycles based on Malaysian roads Muhammad Iftishah Ramdan 1,* 1 School of Mechanical Engineering, Universiti Sains
More informationParallel Hybrid (Boosted) Range Extender Powertrain
World Electric Vehicle Journal Vol. 4 - ISSN 232-6653 - 21 WEVA Page622 EVS25 Shenzhen, China, Nov 5-9, 21 Parallel Hybrid (Boosted) Range Extender Powertrain Patrick Debal 1, Saphir Faid 1, and Steven
More informationAn Experimental System for Battery Management Algorithm Development
An Experimental System for Battery Management Algorithm evelopment Jonas Hellgren, Lei Feng, Björn Andersson and Ricard Blanc Volvo Technology, Göteborg, Sweden E-mail: {jonas.hellgren, lei.feng, bjorn.bj.andersson,
More informationResearch in use of fuel conversion adapters in automobiles running on bioethanol and gasoline mixtures
Agronomy Research 11 (1), 205 214, 2013 Research in use of fuel conversion adapters in automobiles running on bioethanol and gasoline mixtures V. Pirs * and M. Gailis Motor Vehicle Institute, Faculty of
More informationDesign Modeling and Simulation of Supervisor Control for Hybrid Power System
2013 First International Conference on Artificial Intelligence, Modelling & Simulation Design Modeling and Simulation of Supervisor Control for Hybrid Power System Vivek Venkobarao Bangalore Karnataka
More informationPredictive Control Strategies using Simulink
Example slide Predictive Control Strategies using Simulink Kiran Ravindran, Ashwini Athreya, HEV-SW, EE/MBRDI March 2014 Project Overview 2 Predictive Control Strategies using Simulink Kiran Ravindran
More informationApplication of the SuperGen Electro-Mechanical Supercharger to Miller-Cycle Gasoline Turbocharged Engines
Application of the SuperGen Electro-Mechanical Supercharger to Miller-Cycle Gasoline Turbocharged Engines A. H. Guzel, J. Martin North American GT Conference 2017 11/14/2017 1 Overview Program Goal & Technology
More informationThe MathWorks Crossover to Model-Based Design
The MathWorks Crossover to Model-Based Design The Ohio State University Kerem Koprubasi, Ph.D. Candidate Mechanical Engineering The 2008 Challenge X Competition Benefits of MathWorks Tools Model-based
More informationResearch Report. FD807 Electric Vehicle Component Sizing vs. Vehicle Structural Weight Report
RD.9/175.3 Ricardo plc 9 1 FD7 Electric Vehicle Component Sizing vs. Vehicle Structural Weight Report Research Report Conducted by Ricardo for The Aluminum Association 9 - RD.9/175.3 Ricardo plc 9 2 Scope
More informationCorrection of test cycle tolerances: assessing the impact on CO 2 results. J. Pavlovic, A. Marotta, B. Ciuffo
Correction of test cycle tolerances: assessing the impact on CO 2 results J. Pavlovic, A. Marotta, B. Ciuffo WLTP 2 nd Act November 10, 2016 Agenda Flexibilities of test cycle and laboratory procedures
More informationinter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering August 2000, Nice, FRANCE
Copyright SFA - InterNoise 2000 1 inter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering 27-30 August 2000, Nice, FRANCE I-INCE Classification: 0.0 EFFECTS OF TRANSVERSE
More informationInvestigation in to the Application of PLS in MPC Schemes
Ian David Lockhart Bogle and Michael Fairweather (Editors), Proceedings of the 22nd European Symposium on Computer Aided Process Engineering, 17-20 June 2012, London. 2012 Elsevier B.V. All rights reserved
More informationSIL, HIL, and Vehicle Fuel Economy Analysis of a Pre- Transmission Parallel PHEV
EVS27 Barcelona, Spain, November 17-20, 2013 SIL, HIL, and Vehicle Fuel Economy Analysis of a Pre- Transmission Parallel PHEV Jonathan D. Moore and G. Marshall Molen Mississippi State University Jdm833@msstate.edu
More informationProject Summary Fuzzy Logic Control of Electric Motors and Motor Drives: Feasibility Study
EPA United States Air and Energy Engineering Environmental Protection Research Laboratory Agency Research Triangle Park, NC 277 Research and Development EPA/600/SR-95/75 April 996 Project Summary Fuzzy
More informationOptimising Aeristech FETT (Fully Electric Turbocharger Technology) for Future Gasoline Engine Requirements
Optimising Aeristech FETT (Fully Electric Turbocharger Technology) for Future Gasoline Engine Requirements Dr Sam Akehurst, Dr Nic Zhang 25 th April 2017 1 Contents Introduction to the Fully Electric Turbocharging
More informationRotorcraft Gearbox Foundation Design by a Network of Optimizations
13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference 13-15 September 2010, Fort Worth, Texas AIAA 2010-9310 Rotorcraft Gearbox Foundation Design by a Network of Optimizations Geng Zhang 1
More informationSTUDY OF ENERGETIC BALANCE OF REGENERATIVE ELECTRIC VEHICLE IN A CITY DRIVING CYCLE
ENGINEERING FOR RURAL DEVELOPMENT Jelgava, 24.-25.5.212. STUDY OF ENERGETIC BALANCE OF REGENERATIVE ELECTRIC VEHICLE IN A CITY DRIVING CYCLE Vitalijs Osadcuks, Aldis Pecka, Raimunds Selegovskis, Liene
More informationDesign an Energy Management Strategy for a Parallel Hybrid Electric Vehicle
Journal of Asian Electric Vehicles, Volume 13, Number 1, June 215 Design an Energy Management Strategy for a Parallel Hybrid Electric Vehicle Seyyed Ghaffar Nabavi School of Electrical Engineering, Tarbiat
More informationSupport for the revision of the CO 2 Regulation for light duty vehicles
Support for the revision of the CO 2 Regulation for light duty vehicles and #3 for - No, Maarten Verbeek, Jordy Spreen ICCT-workshop, Brussels, April 27, 2012 Objectives of projects Assist European Commission
More informationDesign & Development of Regenerative Braking System at Rear Axle
International Journal of Advanced Mechanical Engineering. ISSN 2250-3234 Volume 8, Number 2 (2018), pp. 165-172 Research India Publications http://www.ripublication.com Design & Development of Regenerative
More informationModel-Based Design and Hardware-in-the-Loop Simulation for Clean Vehicles Bo Chen, Ph.D.
Model-Based Design and Hardware-in-the-Loop Simulation for Clean Vehicles Bo Chen, Ph.D. Dave House Associate Professor of Mechanical Engineering and Electrical Engineering Department of Mechanical Engineering
More informationImprovement of Vehicle Dynamics by Right-and-Left Torque Vectoring System in Various Drivetrains x
Improvement of Vehicle Dynamics by Right-and-Left Torque Vectoring System in Various Drivetrains x Kaoru SAWASE* Yuichi USHIRODA* Abstract This paper describes the verification by calculation of vehicle
More informationContents. Figures. iii
Contents Executive Summary... 1 Introduction... 2 Objective... 2 Approach... 2 Sizing of Fuel Cell Electric Vehicles... 3 Assumptions... 5 Sizing Results... 7 Results: Midsize FC HEV and FC PHEV... 8 Contribution
More informationAnalysis and evaluation of a tyre model through test data obtained using the IMMa tyre test bench
Vehicle System Dynamics Vol. 43, Supplement, 2005, 241 252 Analysis and evaluation of a tyre model through test data obtained using the IMMa tyre test bench A. ORTIZ*, J.A. CABRERA, J. CASTILLO and A.
More informationVehicle Scrappage and Gasoline Policy. Online Appendix. Alternative First Stage and Reduced Form Specifications
Vehicle Scrappage and Gasoline Policy By Mark R. Jacobsen and Arthur A. van Benthem Online Appendix Appendix A Alternative First Stage and Reduced Form Specifications Reduced Form Using MPG Quartiles The
More informationMORSE: MOdel-based Real-time Systems Engineering. Reducing physical testing in the calibration of diagnostic and driveabilty features
MORSE: MOdel-based Real-time Systems Engineering Reducing physical testing in the calibration of diagnostic and driveabilty features Mike Dempsey Claytex Future Powertrain Conference 2017 MORSE project
More informationA Simple Approach for Hybrid Transmissions Efficiency
A Simple Approach for Hybrid Transmissions Efficiency FRANCESCO BOTTIGLIONE Dipartimento di Meccanica, Matematica e Management Politecnico di Bari Viale Japigia 182, Bari ITALY f.bottiglione@poliba.it
More informationDevelopment of Bi-Fuel Systems for Satisfying CNG Fuel Properties
Keihin Technical Review Vol.6 (2017) Technical Paper Development of Bi-Fuel Systems for Satisfying Fuel Properties Takayuki SHIMATSU *1 Key Words:, NGV, Bi-fuel add-on system, Fuel properties 1. Introduction
More informationOptimal Predictive Control for Connected HEV AMAA Brussels September 22 nd -23 rd 2016
Optimal Predictive Control for Connected HEV AMAA Brussels September 22 nd -23 rd 2016 Hamza I.H. AZAMI Toulouse - France www.continental-corporation.com Powertrain Technology Innovation Optimal Predictive
More informationRotor Position Detection of CPPM Belt Starter Generator with Trapezoidal Back EMF using Six Hall Sensors
Journal of Magnetics 21(2), 173-178 (2016) ISSN (Print) 1226-1750 ISSN (Online) 2233-6656 http://dx.doi.org/10.4283/jmag.2016.21.2.173 Rotor Position Detection of CPPM Belt Starter Generator with Trapezoidal
More informationDevelopment of Two-stage Electric Turbocharging system for Automobiles
Development of Two-stage Electric Turbocharging system for Automobiles 71 BYEONGIL AN *1 NAOMICHI SHIBATA *2 HIROSHI SUZUKI *3 MOTOKI EBISU *1 Engine downsizing using supercharging is progressing to cope
More informationLong Transfer Lines Enabling Large Separations between Compressor and Coldhead for High- Frequency Acoustic-Stirling ( Pulse-Tube ) Coolers
Long Transfer Lines Enabling Large Separations between Compressor and Coldhead for High- Frequency Acoustic-Stirling ( Pulse-Tube ) Coolers P. S. Spoor and J. A. Corey CFIC-Qdrive Troy, NY 12180 ABSTRACT
More informationVehicle calibration optimization using a dynamic test bed with real time vehicle simulation
Vehicle calibration optimization using a dynamic test bed with real time vehicle simulation G. Burette 1, F. Perez 2, K. Bansal 2 1: D2T Powertrain Engineering Direction Ingénierie du GMP Technopôle du
More informationNumerical Investigation of Diesel Engine Characteristics During Control System Development
Numerical Investigation of Diesel Engine Characteristics During Control System Development Aleksandr Aleksandrovich Kudryavtsev, Aleksandr Gavriilovich Kuznetsov Sergey Viktorovich Kharitonov and Dmitriy
More informationLow Carbon Technology Project Workstream 8 Vehicle Dynamics and Traction control for Maximum Energy Recovery
Low Carbon Technology Project Workstream 8 Vehicle Dynamics and Traction control for Maximum Energy Recovery Phil Barber CENEX Technical review 19 th May 2011 Overview of WS8 Workstream 8 was set up to
More informationHybrid Architectures for Automated Transmission Systems
1 / 5 Hybrid Architectures for Automated Transmission Systems - add-on and integrated solutions - Dierk REITZ, Uwe WAGNER, Reinhard BERGER LuK GmbH & Co. ohg Bussmatten 2, 77815 Bühl, Germany (E-Mail:
More informationInstantaneous Minimum Fuel Consumption Control for Parallel Hybrid Hydraulic Excavator
Send Orders for Reprints to reprints@benthamscience.ae The Open Mechanical Engineering Journal, 2015, 9, 181-188 181 Open Access Instantaneous Minimum Fuel Consumption Control for Parallel Hybrid Hydraulic
More informationDYNAMIC BEHAVIOUR OF SINGLE-PHASE INDUCTION GENERATORS DURING DISCONNECTION AND RECONNECTION TO THE GRID
DYNAMIC BEHAVIOUR OF SINGLE-PHASE INDUCTION GENERATORS DURING DISCONNECTION AND RECONNECTION TO THE GRID J.Ramachandran 1 G.A. Putrus 2 1 Faculty of Engineering and Computing, Coventry University, UK j.ramachandran@coventry.ac.uk
More informationInvestigation of CO 2 emissions in usage phase due to an electric vehicle - Study of battery degradation impact on emissions -
EVS27 Barcelona, Spain, November 17 -, 13 Investigation of CO 2 emissions in usage phase due to an electric vehicle - Study of battery degradation impact on emissions - Abstract Tetsuya Niikuni, Kenichiroh
More informationDevelopment of a Plug-In HEV Based on Novel Compound Power-Split Transmission
Page WEVJ7-66 EVS8 KINEX, Korea, May 3-6, 5 velopment of a Plug-In HEV Based on Novel Compound Power-Split ransmission ong Zhang, Chen Wang,, Zhiguo Zhao, Wentai Zhou, Corun CHS echnology Co., Ltd., NO.888
More informationEvaluation of Ethanol Blends for PHEVs using Engine-in-the-Loop
Evaluation of Ethanol Blends for PHEVs using Engine-in-the-Loop Neeraj Shidore, Andrew Ickes, Thomas Wallner, Aymeric Rousseau, Mehrdad Ehsani* Argonne National Laboratory, Texas A&M University* nshidore@anl.gov
More informationThe effect of road profile on passenger car emissions
Transport and Air Pollution, 5 th Int. Sci. Symp., Avignon, France, June The effect of road profile on passenger car emissions Abstract Leonid TARTAKOVSKY*, Marcel GUTMAN*, Yuri ALEINIKOV*, Mark VEINBLAT*,
More informationOnboard Plasmatron Generation of Hydrogen Rich Gas for Diesel Engine Exhaust Aftertreatment and Other Applications.
PSFC/JA-02-30 Onboard Plasmatron Generation of Hydrogen Rich Gas for Diesel Engine Exhaust Aftertreatment and Other Applications L. Bromberg 1, D.R. Cohn 1, J. Heywood 2, A. Rabinovich 1 December 11, 2002
More informationFuel Economy Potential of Advanced Configurations from 2010 to 2045
Fuel Economy Potential of Advanced Configurations from 2010 to 2045 IFP HEV Conference November, 2008 Aymeric Rousseau Argonne National Laboratory Sponsored by Lee Slezak U.S. DOE Evaluate Vehicle Fuel
More informationImpact of Drive Cycles on PHEV Component Requirements
Paper Number Impact of Drive Cycles on PHEV Component Requirements Copyright 2008 SAE International J. Kwon, J. Kim, E. Fallas, S. Pagerit, and A. Rousseau Argonne National Laboratory ABSTRACT Plug-in
More informationMeasurement methods for skid resistance of road surfaces
Measurement methods for skid resistance of road surfaces Presented by Martin Greene (TRL) and Veronique Cerezo (IFSTTAR) 11 October 2016 Background and requirements for Common Scale 1 Background Measurement
More information837. Dynamics of hybrid PM/EM electromagnetic valve in SI engines
837. Dynamics of hybrid PM/EM electromagnetic valve in SI engines Yaojung Shiao 1, Ly Vinh Dat 2 Department of Vehicle Engineering, National Taipei University of Technology, Taipei, Taiwan, R. O. C. E-mail:
More informationPressure and Flow Based Control of a Turbocharged Diesel Engine Air-path System Equipped with Dual-Loop EGR and VGT*
2014 American Control Conference (ACC) June 4-6, 2014. Portland, Oregon, USA Pressure and Flow Based Control of a Turbocharged Diesel Engine Air-path System Equipped with Dual-Loop EGR and VGT* Sooyoung
More informationExperimental Analysis of Utilization of Heat Using Methanol - Diesel Blended Fuel in Four Stroke Single Cylinder Water Cooled Diesel Engine
Experimental Analysis of Utilization of Heat Using Methanol - Diesel Blended Fuel in Four Stroke Single Cylinder Water Cooled Diesel Engine T. Singha 1, S. Sakhari 1, T. Sarkar 1, P. Das 1, A. Dutta 1,
More informationImprovements of Existing Overhead Lines for 180km/h operation of the Tilting Train
Improvements of Existing Overhead Lines for 180km/h operation of the Tilting Train K. Lee, Y.H. Cho, Y. Park, S. Kwon Korea Railroad Research Institute, Uiwang-City, Korea Abstract The purpose of this
More informationComparison between Optimized Passive Vehicle Suspension System and Semi Active Fuzzy Logic Controlled Suspension System Regarding Ride and Handling
Comparison between Optimized Passive Vehicle Suspension System and Semi Active Fuzzy Logic Controlled Suspension System Regarding Ride and Handling Mehrdad N. Khajavi, and Vahid Abdollahi Abstract The
More informationInternational Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 ISSN
ISSN 9-5518 970 College of Engineering Trivandrum Department of Mechanical Engineering arundanam@gmail.com, arjunjk91@gmail.com Abstract This paper investigates the performance of a shock tube with air
More informationVehicle retail price estimation
Vehicle retail price estimation Table of contents This document has changed from version 2c of March 2007 with regard to the Diesel vehicle price estimation 1 Main price assumptions for components and
More informationEVALUATION OF CURRENT AND FUTURE ATKINSON ENGINE TECHNOLOGIES
EVALUATION OF CURRENT AND FUTURE ATKINSON ENGINE TECHNOLOGIES 2 nd CRC Advanced Fuel and Engine Efficiency Workshop 11/2/2016 Charles Schenk, U.S. EPA Developmental data: internal EPA use only 1 Background
More informationHigh performance and low CO 2 from a Flybrid mechanical kinetic energy recovery system
High performance and low CO 2 from a Flybrid mechanical kinetic energy recovery system A J Deakin Torotrak Group PLC. UK Abstract Development of the Flybrid Kinetic Energy Recovery System (KERS) has been
More informationFive Cool Things You Can Do With Powertrain Blockset The MathWorks, Inc. 1
Five Cool Things You Can Do With Powertrain Blockset Mike Sasena, PhD Automotive Product Manager 2017 The MathWorks, Inc. 1 FTP75 Simulation 2 Powertrain Blockset Value Proposition Perform fuel economy
More informationMODELICA LIBRARY FOR SIMULATING ENERGY CONSUMPTION OF AUXILIARY UNITS IN HEAVY VEHICLES 1.
MODELICA LIBRARY FOR SIMULATING ENERGY CONSUMPTION OF AUXILIARY UNITS IN HEAVY VEHICLES 1 Niklas Pettersson a,b, Karl Henrik Johansson b a Scania CV AB, Södertälje, Sweden b Department of Signals, Sensors
More informationApplication of claw-back
Application of claw-back A report for Vector Dr. Tom Hird Daniel Young June 2012 Table of Contents 1. Introduction 1 2. How to determine the claw-back amount 2 2.1. Allowance for lower amount of claw-back
More informationAN OPTIMAL PROFILE AND LEAD MODIFICATION IN CYLINDRICAL GEAR TOOTH BY REDUCING THE LOAD DISTRIBUTION FACTOR
AN OPTIMAL PROFILE AND LEAD MODIFICATION IN CYLINDRICAL GEAR TOOTH BY REDUCING THE LOAD DISTRIBUTION FACTOR Balasubramanian Narayanan Department of Production Engineering, Sathyabama University, Chennai,
More informationCITY OF MINNEAPOLIS GREEN FLEET POLICY
CITY OF MINNEAPOLIS GREEN FLEET POLICY TABLE OF CONTENTS I. Introduction Purpose & Objectives Oversight: The Green Fleet Team II. Establishing a Baseline for Inventory III. Implementation Strategies Optimize
More informationHigh-effciency operation of a HYBRID ELECTRIC VEHICLE STARTER/GENERATOR over road profiles.
Content Appeared in the May / June 2003 IEEE Industry Applications (Vol. 9, No. 3. ISSN 1077-2618) High-effciency operation of a HYBRID ELECTRIC VEHICLE STARTER/GENERATOR over road profiles. BY RAYMOND
More informationHardware-in-the-loop simulation of regenerative braking for a hybrid electric vehicle
855 Hardware-in-the-loop simulation of regenerative braking for a hybrid electric vehicle HYeoand HKim* School of Mechanical Engineering, Sungkyunkwan University, Suwon, South Korea Abstract: A regenerative
More informationIC Engine Control - the Challenge of Downsizing
IC Engine Control - the Challenge of Downsizing Dariusz Cieslar* 2nd Workshop on Control of Uncertain Systems: Modelling, Approximation, and Design Department of Engineering, University of Cambridge 23-24/9/2013
More informationControl of Charge Dilution in Turbocharged CIDI Engines via Exhaust Valve Timing
Control of Charge Dilution in Turbocharged CIDI Engines via Exhaust Valve Timing Anna Stefanopoulou, Hakan Yilmaz, David Rausen University of Michigan, Ann Arbor Extended Summary ABSTRACT Stringent NOx
More information