oorja’s drive cycle current app predicts the current in each battery cell, allowing for accurate battery design and enabling accurate calculation of battery degradation and temperature rise

While evaluating battery pack performance during the design phase, information on drive cycles for which the battery is being designed is critical. A drive cycle is basically a vehicle usage pattern representative of how an “average” user uses the vehicle. Whether you are optimizing the pack for thermal runaway or evaluating degradation, it is important to investigate how the pack will behave for any given drive cycle.

Further, regulatory requirements mandate that the vehicle meet requirements for certain well established drive cycles, certified as being representative of standard user behaviour by certification agencies. Some of these standard drive cycles include the US 06, MIDC (modified Indian Drive Cycle) and WLTC (World Harmonized Light Duty Test Cycle) (Fig 1). It is a requirement that users validate their designs for one such drive cycle and submit reports.

Fig 1: Drive Cycle Velocity Profiles for MIDC, US06, WLTC Standards

One challenge that the EV industry faces is that all these standard drive cycles have been designed in the pre-EV era. They produce drive cycles which prescribe the velocity a vehicle should attain as a function of time (see again Fig 1).

Several tools which generate drive cycles also generate them in the same format. However, this information is not adequate while analysing battery packs. The analysis of battery packs requires drive cycles in the format of current vs. time (I vs t).

When traditional drive cycle data is used for EVs, it poses several limitations. For instance, a given velocity can be attained using different acceleration strategies, so having velocity versus time data doesn’t give the full picture. The initial State of Charge (SOC), pack configuration and road gradient can also significantly change the current drawn. Picture this: if I have to attain a velocity of 5m/s and I am going downhill, I can do so with no current drawn, but if I am going up a 20o gradient, I might have to draw a high C rate current. Similarly, if I need to reach the same velocity in 1s and have a SOC of 1 vs say and SOC of 0.3, I may not be able t achieve this velocity in the latter case.

How does one now account for all these variables and convert a traditional drive cycle into one which is EV analysis compliant?

In internal combustion (IC) engines, the primary application of driving cycles was to identify the performance characteristics of a vehicle, such as exhaust emissions and fuel consumption. In electric vehicles, understanding the behaviour of the battery across various drive cycles is extremely valuable since it allows for optimal battery design in terms of battery capacity and power requirements.

‘Drive cycles’ are essentially a series of data points that plot vehicle speed against time under various driving conditions. For instance, drive cycles for urban intra-city commutes vary from those for highway driving.

Understanding drive cycles for a variety of conditions is crucial to get a better understanding of battery behaviour. Driving cycles may be impacted by a number of driving profile characteristics, including the average, maximum, and minimum values as well as the standard deviations of speed, acceleration, and delay. For instance, you might only be able to travel at 35 km/hr in a city, yet you could reach 90 km/hr on a highway.

Computing the drive cycle behaviour for IC vehicles is easier given the availability of huge volumes of open data. However, the data available is mapped to velocity versus time. This data is inaccurate and fairly complex to analyse battery behaviour for drive cycles, because of the non-linear nature of cell behaviour.

Further, traditional methods for calculation that involve copious amounts of Excel-based transactions are not only tedious but also lacking in accuracy.

While it can be difficult to obtain precise data to estimate current, oorja’s drive cycle current generator app provides all the essential details about the vehicle, the type of terrain it will be travelling on, and other factors.

One stop solution to estimate drive cycle current

With oorja’s Drive Cycle Current App, users can add or choose vehicle-related characteristics like terrain type, class of user, conversion efficiency, battery information, cell type, pack configuration.  The power calculation is based on parameters such as frontal area, mass, friction coefficients etc.

Fig 2: Two-Wheeler Velocity and Current Profiles for MIDC with Terrain Type: Flat and Uniform 10o Inclination
Fig 3: Four-Wheeler Current Profile for US06 Standard, Terrain Type: Flat

It aids better design since it can account for phenomena such as regenerative braking. It can also help deduce battery pack configuration. For example, if the battery discharges before the drive cycle is complete, then it is an indication that the battery pack is not adequate for that particular drive cycle.

The output from this can serve as an input for other oorja apps for calculation of capacity fade and thermal management.

Fig 4: Four-Wheeler Current Profile for WLTP Class-2 Standard, Terrain Type: Flat

The ability to accurately predict a vehicle’s driving cycle current is becoming more crucial in today’s intelligent transportation systems, especially for managing energy in hybrid electric vehicles, regulating energy consumption in electric vehicles, and planning the trajectory of autonomous terrestrial vehicles. Oorja’s engineering solutions can help accurately assess the drive cycle current parameters of a battery pack and achieve optimal outcomes. Click here for a demo.