This article deals with coupled, state, and parameter estimation for lithium-ion batteries described by an equivalent circuit model, including polarization dynamics. Since the model parameters depend ...on the battery state-of-charge (SoC) and temperature operating point, as well as on the battery state-of-health, all states and parameters need to be estimated simultaneously for an accurate overall estimation during the battery lifetime. The proposed estimation algorithm is structured in two timescales: 1) slow-scale, sigma-point Kalman filter (KF)-based estimation of battery capacity and 2) fast-scale, dual-extended KF-based estimation of SoC and model parameters. A particular emphasis is on the adaptive parameterization of SoC and capacity estimators, which provides robust coupling between two timescales and ensures favorable convergence and robust capacity tracking in conditions of SoC and model parameters' estimation errors. In support of estimation accuracy analysis, an algebraic observability analysis of impedance parameters is conducted. Also, by introducing an observability index calculated in each simulation timestep, a comparison of degrees of observability of different impedance parameter subsets is allowed for. The proposed estimation algorithm is verified both by simulation and experimentally for an electric scooter Li-NMC battery pack.
This article presents the design of a model predictive control (MPC) strategy for power flow management of a plug-in hybrid electric vehicle (PHEV), given in P2 parallel configuration and operating ...in a charge sustaining (CS) mode. The strategy relies on vehicle velocity prediction, a backward-looking (BWD) powertrain prediction model extended with transient loss effects, and dynamic programming (DP)-based control variable optimization on receding time horizon. The extended BWD (EXT-BWD) model accounts for power and torque losses occurring during powertrain transients, such as those related to gear shifting, engine-ON events, and dog clutch synchronization. The DP-minimized MPC cost function reflects the fuel consumption over the receding time horizon and the remaining trip time window. The latter allows for specifying the battery state-of-charge (SoC) directly at the end of driving cycle and avoiding tuning of cost function weighting coefficients that are generally dependent on driving cycle. The proposed MPC strategy is first verified against the global benchmark obtained by applying offline DP optimization over the full driving cycle. The MPC strategy is then compared with the previously developed, equivalent consumption minimization strategy (ECMS), given in regular and adaptive forms. The verification results indicate that the proposed MPC strategy is closely approaching the DP benchmark and provides overly consistent fuel savings when compared to both forms of ECMS.
This paper proposes a DP(dynamic programming)-based optimisation method of charging an EV (electric vehicle) fleet modelled as a single, so-called aggregate battery. The main advantage of the ...approach is that it provides a globally optimal solution, with a relatively non-excessive computational load owing to a low order of the aggregate battery model. The method is illustrated through a case study of an isolated, hypothetically electrified delivery truck transport system charged from both grid and RES (renewable energy sources). Two scenarios of energy production from RES (with and without excess in RES production), along with several electricity price models are studied. The DP optimisation results are compared with the results obtained by an existing heuristic charging algorithm used in EnergyPLAN software to illustrate the DP algorithm advantages in minimising the charging energy cost and satisfying the aggregate battery charge sustaining conditions. The proposed DP optimisation method can be used in various energy planning studies, as well as a core of the supervisory/aggregator level of hierarchical EV fleet charging strategies.
•EV (Electric vehicle) fleet is modelled through aggregate battery approach.•A dynamic programming-based method of EV fleet charging optimization is proposed.•The method provides globally optimal solution while satisfying charging constraints.•The method is validated against an existing heuristic charging approach.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
In order to increase the driving range of battery electric vehicles, while maintaining a high level of thermal comfort inside the passenger cabin, it is necessary to design an energy management ...system which optimally synthesizes multiple control actions of heating, ventilation and air-conditioning (HVAC) system. To gain an insight into optimal control actions and set a control benchmark, the paper first proposes an algorithm of dynamic programming (DP)-based optimisation of HVAC control variables, which minimises the conflicting criteria of passenger thermal comfort and HVAC efficiency. Next, a hierarchical structure of thermal comfort control system is proposed, which consists of optimised low-level feedback controllers, optimisation-based control allocation algorithm that sets references for the low-level controllers, and a superimposed cabin temperature controller that commands the cooling capacity to the allocation algorithm. Finally, the overall control system is verified by simulation for cool-down scenario, and the simulation results are compared with the DP benchmark. The results show that the control system behaviour can approach the DP benchmark if the superimposed controller bandwidth is tuned along with the allocation cost function weighting coefficients, where a fast controller tuning relates to better thermal comfort while a slow tuning results in improved efficiency.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The paper proposes a power flow control strategy for a P2 parallel plug-in hybrid electric vehicle (PHEV) which takes into account torque and power losses related to engine-on and gear shift ...transients. An extended backward-looking (EXT-BWD) model is proposed to account for the transient losses, while the control strategy combines a rule-based controller with an equivalent consumption minimization strategy. To describe the transient losses, the EXT-BWD model includes additional state variables related to engine on/off flag and gear ratio in the previous time step. To establish a performance benchmark for control strategy verification, a dynamic programming-based control variable optimization framework is established based on the EXT-BWD model. The proposed control strategy is demonstrated to improve the fuel efficiency and drivability compared to the original control strategy while retaining comparable computational efficiency.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Modern electric vehicle heating, ventilation, and air-conditioning (HVAC) systems operate in more efficient heat pump mode, thus, improving the driving range under cold ambient conditions. Coupling ...those HVAC systems with novel heating technologies such as infrared heating panels (IRP) results in a complex system with multiple actuators, which needs to be optimally coordinated to maximise the efficiency and comfort. The paper presents a multi-objective genetic algorithm-based control input allocation method, which relies on a multi-physical HVAC model and a CFD-evaluated cabin airflow distribution model implemented in Dymola. The considered control inputs include the cabin inlet air temperature, blower and radiator fan air mass flows, secondary coolant loop pump speeds, and IRP control settings. The optimisation objective is to minimise total electric power consumption and thermal comfort described by predictive mean vote (PMV) index. Optimisation results indicate that HVAC and IRP controls are effectively decoupled, and that a significant reduction of power consumption (typically from 20% to 30%) can be achieved using IRPs while maintaining the same level of thermal comfort. The previously proposed hierarchical HVAC control strategy is parameterised and extended with a PMV-based controller acting via IRP control inputs. The performance is verified through simulations in a heat-up scenario, and the power consumption reduction potential is analysed for different cabin air temperature setpoints.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The development of a vehicle equipped with a step-ratio automatic transmission (AT) requires manual calibration to enhance the shift quality. This involves labor-intensive iterations in a prototype ...vehicle to balance shift time, smoothness, and energy for every realizable gear combination. An improved shift control method is desired to deliver optimized shift quality with reduced calibration needs. This article proposes a hierarchical optimal control strategy for AT upshift aimed at optimizing shift time, comfort, and thermal loss during the inertia phase with minimal calibration effort for faster product development. The strategy consists of a static model-based predictive controller coupled with an optimal control allocation algorithm. The superimposed variable-gain feedback controller commands the AT input torque required to complete the inertia phase in the specified time. The optimal control allocation algorithm coordinates two clutches and the engine by solving a constrained quadratic programming problem to deliver the desired input torque, and minimize AT output torque tracking error and clutch power loss for shift comfort and thermal efficiency, respectively. The modulation of the off-going clutch allows fast, yet comfortable shifts as compared to the conventional shift control. The online performance of the control strategy is verified by realistic shift simulations and compared to the optimal profiles established through off-line control parameter optimization. The ease of shift calibration is demonstrated by setting different targets for shift time and output torque profile. The control robustness is verified against clutch torque and engine torque delivery errors.
This article deals with model predictive control (MPC) design for automatic transmission (AT) upshift inertia phase, which aims to optimally coordinate the actions of oncoming (ONC) and off-going ...(OFG) clutches and engine and to facilitate calibration. The designed MPC strategy accounts for clutch actuation dynamics and constraints, while setting the tradeoff between three key and conflicting shift quality criteria: comfort; duration; and efficiency. The shift comfort and duration are ensured by minimizing output shaft torque and ONC clutch slip speed tracking errors, and the shift efficiency is reflected in clutch energy loss minimization on a prediction horizon. This allows for the calibration of the MPC performance through setting the inertia phase duration, the output shaft torque reference, cost function weighting coefficients, and constraints, rather than optimizing the shift control profiles themselves. The MPC problem is formulated as a constrained quadratic programming problem and efficiently solved online by an interior-point solver. The proposed MPC strategy is applicable to other transmissions with multiple actuators, such as parallel hybrid transmissions. The MPC system is examined through nonlinear powertrain model simulations for one to three shift and its performance is compared with an offline, multiobjective optimization-based control strategy. The MPC design flexibility and ease of calibration are demonstrated for different shift comfort and duration targets, as well as cost function tuning, and robustness with respect to clutch actuation parameter uncertainties is examined.
•Bi-level optimisation is proposed for an Electric Vehicle (EV) fleet charging.•Inner loop includes dynamic programming-based optimisation of charging power.•Outer loop includes multi-objective ...genetic algorithm optimising battery charge.•Computationally efficient response surface-based transport demand model is used.•Bi-level optimisation is validated against single-level one for a delivery fleet.
The paper proposes a bi-level optimisation framework for Electric Vehicle (EV) fleet charging based on a realistic EV fleet model including a transport demand sub-model. The EV fleet is described by an aggregate battery model, which is parameterised by using recorded driving cycle data of a delivery vehicle fleet. The EV fleet model is used within the inner level of the bi-level optimisation framework, where the aggregate charging power is optimised by using the dynamic programming (DP) algorithm. At the superimposed optimisation level, the final State-of-Charge (SoC) values of individual EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of the bi-level optimisation algorithm, it is generally needed to recalculate the transport demand sub-model for the new set of final SoC values. In order to simplify this process, the transport demand is modelled by using a computationally efficient response surface method, which is based on naturalistic synthetic driving cycles and agent-based simulations of the EV model. When compared to the single-level charging optimisation approach, which assumes the final SoC values to be equal to 1 (full batteries on departure), the bi-level optimisation provides a degree of optimisation freedom more for more accurate techno-economic analyses of the integrated transport-energy system. The two approaches are compared through a simulation study of the particular delivery vehicle fleet transport-energy system.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The heating, ventilation and air conditioning (HVAC) system negatively affects the electric vehicle (EV) driving range, especially under cold ambient conditions. Modern HVAC systems based on the ...vapour-compression cycle can be rearranged to operate in the heat pump mode to improve the overall system efficiency compared to conventional electrical/resistive heaters. Since such an HVAC system is typically equipped with multiple actuators (compressor, pumps, fans, valves), with the majority of them being controlled in open loop, an optimisation-based control input allocation is necessary to achieve the highest efficiency. This paper presents a genetic algorithm optimisation-based HVAC control input allocation method, which utilises a multi-physical HVAC system model implemented in Dymola/Modelica. The considered control inputs include the cabin inlet air temperature reference, blower and radiator fan air mass flows and secondary coolant loop pumps’ speeds. The optimal allocation is subject to specified, target cabin air temperatures and heating power. Additional constraints include actuator hardware limits and safety functions, such as maintaining the superheat temperature at its reference level. The optimisation objective is to maximise the system efficiency defined by the coefficient of performance (COP). The optimised allocation maps are fitted by proper mathematical functions to facilitate the control strategy implementation and calibration. The overall control strategy consists of superimposed cabin air temperature controller that commands heating power, control input allocation functions, and low-level controllers that ensure cabin inlet air and superheat temperature regulation. The control system performance is verified through Dymola simulations for the heat pump mode in a heat-up scenario. Control input allocation map optimisation results are presented for air-conditioning (A/C) mode, as well.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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