For example, powertrain models for hybrid road vehicles are often steady-state or quasi-steady-state descriptions, involving simplifications that neglect most dynamic effects. The type of mathematical description affects the computational methods used. The model structure, model boundaries and model timescales must therefore all be tailored carefully to the intended use. ![]() ![]() However, fast transients may have to be considered if the detailed performance or control of powertrain components, such as power-electronic converters, is of interest. For example, events on a millisecond timescale may be unimportant if the aim is to estimate the onboard hydrogen storage requirements for a specific journey. The timescale for the simulation model also must be considered carefully. Regenerative braking is allowed, with arrows indicating possible directions of power flow.įigure 1: Block diagram structure for dynamic model of hydrogen fuel-cell/battery-electric train.ĭepending on the system under investigation and the modelling objectives, model boundaries may be extended to include external energy supply and distribution systems, as would arise in discontinuous electrification schemes. Figure 1 is a simplified block diagram showing powertrain components, the longitudinal train model and the route model in the case of a fuel-cell/battery-electric unit. Powertrain component models must therefore be integrated with a model of the train dynamics, along with constraints such as allowable charge and discharge rates for battery packs, the maximum and minimum allowed battery charge state and conditions for efficient engine or fuel-cell stack operation. For powertrain control and energy management applications the dynamics of the train itself, together with the characteristics of the route such as gradients, curvature and speed restrictions, are vitally important. The longitudinal motion of a single train for a specific route is considered here, involving variables such as distance travelled, speed, acceleration, tractive force, power and energy used. Simulation time-scaling methods allow for a reference schedule of this kind to be adjusted to investigate possible performance enhancement. Where a hybrid design is being developed to replace an existing train type, such as a diesel multiple unit (DMU), a time history of distance versus time obtained from data recorded on the train on a specific route or from simulation, is potentially useful. This inverse simulation approach, first developed for aircraft flight dynamics applications, allows power requirements to be investigated for a given train schedule and is useful when considering energy management and control issues in hybrid powertrains. However, it is also possible to work backwards, using a time history of required speed or distance travelled as inputs to produce tractive force or power values as output. However, the conversion of existing diesel and electric multiple units provides one way forward, with several prototype systems of that kind now under development in the UK.Ĭonventional simulation models for the longitudinal motion of a train usually involve an input variable which is a time-history of power or tractive force, with output variables that may include records of acceleration, speed or distance travelled. The reason for the current interest in this is linked to the de-carbonisation of passenger rail services, especially for long routes in sparsely populated areas where traffic levels may not justify conventional electrification or partially electrified routes. The design of hybrid trains involving primary energy sources (e.g., diesel engines or hydrogen fuel cells), energy storage elements, (e.g., batteries, supercapacitors, or flywheels), transmission elements (e.g., electronic power converters) and drive elements (e.g., electric motors) provides relevant examples. ![]() Models should have an adequate level of accuracy in terms of their predictions, compared with data from the corresponding real system, and must also be capable of being used effectively and conveniently for the application at hand. Some attitudes to simulation, such as ‘this is how we always do it in our models’ or ‘it is a standard approach so it must be right’ are now regarded as unacceptable. However, simulation models are never correct since they always involve assumptions and approximations they must be proved fit for the intended application. Eliminating potential problems before hardware assembly starts has clear benefits in terms of cost, safety and timely delivery of the final system, as demonstrated in other fields such as aerospace engineering. In engineering applications, simulation methods allow for early considerations of design trade-offs and system integration issues before any prototype system becomes available.
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