Algorithm with foresight
Only when batteries are at the optimum temperature, they can charge at maximum power. The forward-looking thermal management from Porsche Engineering predicts the course of the trip and thereby ensures that the energy storage devices are in the best possible temperature range at the charging station.
Shorter battery charging times and longer ranges: this is the promise of the predictive thermal management system developed by Porsche Engineering last year as a concept study. Thanks to predictive thermal management, however, this phenomenon could soon be a thing of the past: Software in the car will predict the upcoming course of the trip and control all thermal components so that the battery is at the optimum temperature.
In the simplest form currently on the market, it is a control loop that always keeps the engine temperature within a safe corridor. “With them, the temperature can only be regulated very slowly,” explains Björn Pehnert, Lead Engineer Thermal Management at Porsche Engineering.
Simulation of the entire vehicle
In order for the vehicle control system to decide when to intervene for cooling or heating, it must first know how the various components interact. A simulation of the entire vehicle therefore forms the basis of thermal management: everything—from the battery and drive unit to the cooling system and air conditioning system— is reproduced in the simulation using models. For example, if the heating is turned on, the simulation shows exactly how this will affect the battery’s charge state.
In reality, many other factors, which are often not directly measurable, influence the behavior of a vehicle: driving style, payload, road surface, even dirt on the bodywork, or the color of the paint (in black models, the interior heats up more). It compares the actual behavior of the vehicle with the simulation and thus gradually adapts the model to reality.
Compute-intensive method for the control unit
Mathematically, the temperature control system is a model-based predictive system (model predictive control, MPC). The oil industry uses MPC to control refineries, for example. However, the method also has a disadvantage: it’s very compute-intensive. “Traditionally, such optimizations run on extremely powerful computers,” explains Professor Michal Kvasnica from the Czech Technical University in Prague, who, together with his team, developed the core of the prediction code.
This required some tricks, because there is usually less computing power available there than in a smartphone. This saves computing time. “We also had to create a robust system that never fails,” adds Professor Kvasnica.
In brief What was previously only possible on mainframe computers can now be implemented with a control unit: A software that looks into the future while driving and brings the battery in an electric-vehicle to the optimum temperature in time for charging at the charging station.
Info Text: Constantin Gillies Text first published in the Porsche Engineering Magazine, issue 01/2020
Oct 05, 2020 at 11:57