There are effective intergovernmental and public interventions to reduce carbon dioxide (CO₂) emissions, which contribute to climate change ^[1]. Transport is one of the sectors targeted, as the CO₂ emissions in the transport sector make up approximately 30% of the total human-made CO₂ emissions worldwide ^[1].

In the UK, following the Net-Zero Carbon strategy, the sale of petrol and diesel cars is planned to end by 2035, when all cars must be fully zero emission [2,3]^[2][3]. France, the Netherlands, Ireland, and India have all pledged to phase out new petrol and diesel vehicles by 2032 [4,5]^[4][5]. At a local level, cities such as Athens, Paris, Rome, Madrid and Mexico City are introducing city-wide diesel and petrol vehicle restrictions between 2024 and 2030 ^[4], whilst Taiwan, a country whose population favours motorcycle ownership, announced curbs of air pollution through banning the sale of fossil-fuel-burning two-wheeled vehicles by 2030 ^[4].

In response to the increasing government legislation ^[6], automotive manufacturers have started to introduce zero emission vehicles, where the current preferred technology is vehicles with electrified powertrains, with an on-board rechargeable energy storage system in the form of a lithium-ion battery pack.

Despite there being no existing motorcycle regulation to ban the sale of petrol motorcycles (PM) ^[7], several electric motorcycles have been introduced to the market [8^{[8][9][10][11][12]},9,10,11,12], in which the range of motorcycle classes varies between short-range urban usage to high-performance roadsters and high-end motorcycles. According to the projections made in ^[13], it is expected that the EU-28 share of electric motorcycles sales will be around 55% of the annual sales of a projected 1,000,000 motorcycles in total until 2030. In the same study ^[13], a further rise to a total of 1,250,000 annual units until 2050 is expected, of which around 1,100,000 (almost 90%) are expected to be electric. This emerging trend will lead to new development projects with existing brands, and new brands, and will clearly increase the need for development tools that satisfy design challenges specific to electric motorcycles.

In general, the electric vehicle (EV) uptake over conventional internal combustion engine (ICE) vehicles is argued to be influenced by several different financial, technological, and societal factors. The range of electric vehicles or range anxiety is considered to be one of the major issues in EV ^[14] and electric motorcycle (EM) uptake ^[15], among others such as price, long charging times and insufficient numbers of suitable charging points ^[14].

Motorcycles have lower kerb mass than cars and are thus more sensitive to added mass. Also, other vehicle chassis parameters such as the drag coefficient and frontal area change dynamically according to the rider position and posture [20]^[16]. Motorcycle target customer groups also tend to be more sensitive to performance indicators, such as acceleration and braking, and this performance is likely to be retained for an electric powertrain [21,22]^[17][18]. There are different trade-offs in selecting a particular powertrain configuration. Powertrain configuration here is the combination of battery pack, electric traction motor and inverter selection, such as a particular powertrain selection, which might lead to benefits in reducing motorcycle mass while not being able to fulfil 100% of the performance targets. As a result, an early design-phase assessment of the vehicle-level performance indicators and specifications of different configurations is critical. Those performance indicators are, but are not limited to, total vehicle mass, range, acceleration performance, and top speed [23]^[19]. One of the most mainstream methods to make such an assessment is modelling and simulation. There is also another challenge in electric powertrain design specific to lithium-ion batteries. Due to the nature of li-ion batteries [24]^[20], a cell can either be predominantly high-energy density with a lower power density or low energy with high-power density. As discussed previously, the two leading vehicle-level performance requirements are range and acceleration time, which are associated with the energy capacity and power capability of the powertrain, respectively. This introduces a challenge to identify an optimum design to meet both energy and power requirements without oversizing the battery pack for any of the metrics. For example, let 20x be the battery energy capacity required to fulfil a range requirement, while 14x is the battery energy capacity requirement to fulfil peak power requirement of the vehicle. The design question is going to be that as 20x is expensive and heavy, if it is reduced to somewhere between 14x and 19x, is this good enough from the range perspective? In addition to the challenges specific to motorcycles and lithium-ion batteries, there is another trade-off between electric machine torque and power specifications and acceleration performance from stationary and at high speeds. As performance is not likely to be compromised by motorcycle customers [21^[17][18],22], electric machine specifications play a vital part in capturing the optimal design in complete powertrain sizing.

2. Electric Motorcycle Development Approaches

Benchmarking of competitor vehicles and target customer profiling in light of market surveys form the beginning of a typical automotive product development process [25]^[21]. As a result of benchmarking and target customer identification, a set of vehicle-level attributes start to be shaped at the early stages of the vehicle design, and this includes the component sizing and trade-offs between various configurations that can satisfy the high-level vehicle requirements [26]^[22]. Those early-stage decisions might be carried over throughout the entire vehicle programme and impact the success of the NPD; hence, early decision-making capability for component sizing is critical for programme success. There are several different NPD processes that are currently being applied in automotive product development. In such processes, there is idea generation, business case development and project proposal stages coming before the product development cycle starts [27,28]^[23][24]. The ‘pre-development stages’, such as business case development and product proposals, influence the development phase, and some of the decisions made in the pre-development stage might become binding due to long component lead time, short project schedule, costs and other factors. The traditional practice in automotive design can be considered to be requirement-based design, where the vehicle requirements are defined based on benchmark vehicles, market and customer study and surveys [25,29]^[21][25]. Then, the vehicle-level requirements are decomposed into subsystem and component-level requirements. In [30]^[26], a requirement-based design flow is suggested for electric motorcycles, where the component sizing is made to meet the subsystem design specifications. However, there are other possible approaches that have been applied especially in resilient system development in aerospace and defence industries [26,28,31,32,33]^{[22][24][27][28][29]}. The value-driven design example in [31]^[27] is based on the development of a solar race boat. The value function is defined as the probability of winning the race without exceeding a specified total boat cost. Several boat configurations were considered based on different selections of system components, i.e., boat hull, gearbox, propeller, solar panels, etc. As pointed out by the authors, this approach contrasts with requirement-based design, where the performance of the system is decided before completing the design [31]^[27]. The research suggests that requirement-based design or a cost-as-independent-variable approach yields suboptimal designs [31]^[27]. During the development of the first Indonesian electric motorcycle, an agile and lean development was applied as reported in [28]^[24]. The lean start-up methodology and gate-based new product development processes were adopted for electric motorcycle development for the first time. However, the vehicle specification definition and engineering development were carried out in a sequential approach. A model was not developed to aid in the definition of requirements; the model was introduced after the requirements were fully defined [28]^[24]. In [33]^[29], an unmanned aerial vehicle development case study is presented to compare a so-called point-based design against another approach called set-based design enhanced with tradespace exploration. The point-based design is the process where modelling and simulation are used to compare a limited set of alternatives [33]^[29]. Meanwhile, the set-based design explores more design options compared to point-based design. The set-based design is defined as “a group of design alternatives classified by sharing one or more, but not all, specified design choice(s)” [34]^[30]. The set-based design generates a design space. Tradespace exploration (or design space exploration) identifies and evaluates the design space (the set). The goal of the tradespace process is to identify which design option performs better against the set design goals [33]^[29]. In design space exploration, design goals perform as trade-off metrics [35]^[31]. An application of set-based design and design space exploration in electric motorcycles could present better design options by means of performance, cost and future market penetration. It will be able to capture different design configurations to reduce mass while keeping the performance at an acceptable level. Model-based set-based design allows decision-makers to quantify the trade-off and level of compromise in vehicle performance due to the selection of various powertrain configuration options. It is possible to explore a future design space for business development planning as suggested by [26]^[22].

3. Current Early-Phase Modelling Practices for Electric Vehicles

The most common application in electric vehicle early-phase component sizing is to follow the sequential V-model practice. In V-model practice, first, a vehicle requirement list is defined based on the market study, benchmarking activities, etc. [25]^[21]. In the literature, in various examples, disregarding use cases or purposes of the EV being developed, component sizing is performed to match vehicle-level requirements conforming to a V-model sequential development approach [36,37,38]^[32][33][34]. As the V-model approach first freezes the vehicle targets and then performs the component sizing, that requirement-based approach is not the most suitable application for electric motorcycle development, as freezing the vehicle target requirements severely reduces the number of possible configuration options. In an earlier study, a model-based engineering approach was presented, which focused on the simulation of an electric vehicle to make early-stage decisions regarding powertrain configuration [39]^[35]. Here, a model called SysML was built to identify whether a single-motor or double in-wheel motors are better for a particular electric car and the challenges of multidisciplinary (mechanical, electronic, software) simultaneous product development are raised. The suggested approach in [39]^[35] is able to compare two different motor topologies; however, it requires parameterisation and modelling efforts [39]^[35]. There is also a trade-off between the simulation time and level of accuracy, where, in some cases, it might be preferred to produce rapid results in return for an acceptable amount of accuracy [40]^[36]. As use cases for each class of motorcycle are different, the early-phase design activities might vary significantly from one project to another. For example, when the design criterion is so specific as in the case of a race bike [41]^[37], where the only goal is to win the race, and there are design constraints due to race regulations, the requirement-based design might be the best option. However, in the case of L3-class motorcycle development as defined in ^[13], the configuration options might be very broad. Hence, there are several methodologies suggested in the literature for early-stage decision-making for powertrain sizing and component selection. In [42]^[38], equivalent circuit models (ECMs) of powertrain components are developed and parameterised with generic secondary data to compare different powertrain component topologies. The decision-making criterion is set as the efficiency to identify the most efficient powertrain among the configuration options. Even though the most efficient configuration would lead to the least energy consumption and longest range, the other design metrics are completely ignored. The powertrain configuration with the highest efficiency is sought where battery pack voltage and motor-to-wheel gearing ratio are independent variables. The first and second half of the WLTC are considered as urban and highway drive cycles, respectively, in the study. The optimal battery voltage is selected depending on the minimum electrical loss on urban and highway drive cycles. In [42]^[38], the powertrain were first sized, and then some of the variables were attempted to be optimised such as the gear ratio or pack voltage for an already sized battery (fixed total number of cells). As the total number of cells was fixed in [42]^[38], the design space was constrained and the high-level vehicle requirements such as range and acceleration time were not set as a trade-off criteria. A simulation platform for optimization of electric vehicles with modular drivetrain topologies is presented in [43]^[39]. In that study, a relatively high-fidelity model of traction inverter, traction motor, high-voltage battery systems, dc–dc converters and the energy manager were built in MATLAB—Simulink. The goal of the study was to identify the optimum powertrain configuration using modular powertrain components. The model requires extensive parameterisation due to the level of fidelity of the model, where characterization tests for the cells and motor might be required. Due to the characterization and parameterisation challenges, the tool presented is not ideal for the first vehicle development. Moreover, as MATLAB-Simulink is available by licence and requires certain knowledge and training to use, its usage would be limited. Also, high-fidelity outputs are not essential for making informed decisions at the early stage of the development and pre-development. In a study from 2015 [44]^[40], an accessible pre-design tool is introduced for early-stage decision making in development of quadricycle electric vehicles. Automotive regulations and standards are comprehensively covered in the study [44]^[40]. A set of vehicle segment and power and energy constraints are applied to capture an optimal design of a quadricycle electric vehicle. It is a comprehensive study and addresses the lack of pre-design modelling tools, which is easy to build up and use with less parameterisation efforts. However, it does not address challenges specific to ‘battery electric vehicles’, and it is specifically focussed on quadricycle automotive vehicles [44]^[40]. In another study [45]^[41], the authors presented an objective to develop better models at the early-phase design to aid component-level optimisation and system-level global optimisation in EV design. The similar study carried out in [46]^[42] argues that e-component models need to be fully representative of steady-state, dynamic and degradation behaviour. Nevertheless, the full representative models require component characterization and model parameterization efforts to deliver the level of accuracy they are claiming. During an early phase of EM development—especially a first EM of an OEM—there is a lack of such data. As a result, the benefits of having a fully representative model of steady-state, dynamic and degradation behaviour of components cannot be met. In [23]^[19], an innovative method for visualization of battery pack sizing and number of cells in series and parallel (Np/Ns) arrangement was proposed and then applied to a passive hybrid energy storage system design in their following work [47]^[43]. The proposed Ns/Np visualization is the method for tradespace exploration concerning the derived component-level requirements. The component sizing in both [23,47]^[19][43] is for a racing motorcycle with some strict vehicle-level requirements. In the development cases, where the vehicle-level requirements are flexible, constraining the design space by vehicle-level requirements might lead to better design solutions being missed.