The control strategies for the regenerative brakes on most current EVs are rudimentary. That’s set to change as more sophisticated techniques are developed. There is much more to regenerative braking in electric vehicles (EVs) than simply ‘hitting the brakes.’ This FAQ begins with a review of the regenerative braking controls in a Tesla Model S that’s representative of current designs, then digs into some development efforts for more sophisticated control strategies.
In a Model S, like most EVs, regenerative braking automatically kicks in when the driver takes their foot off the accelerator, with no need to touch the brake pedal. Controls accessible on the touch screen can be used to change the amount of regenerative braking as follows:
Touch Controls > Pedals & Steering > Regenerative Braking.
Then, choose from two levels:
- Standard: This is the maximum amount of regenerative braking.
- Low: Limits the amount of regenerative braking, and the vehicle takes longer to slow down.
Other factors
While the driver sets the regenerative braking level, the system determines the actual amount of energy sent to the battery. The system assesses the battery condition and adjusts the energy delivery accordingly. For example, the system considers the battery’s state of charge (SoC). If the SoC is high, less energy is sent to the battery, or if the battery is too cold, a limited amount of energy may be returned to the battery, and so on.
The regenerative braking section in the owner’s manual closes with this caution: “In snowy or icy conditions, Model S may experience loss of traction during regenerative braking, particularly when in the Standard setting and/or not using winter tires. Tesla recommends using the Low setting in snowy or icy conditions to help maintain vehicle stability.” A similar caution applies to many current regenerative braking systems, so what to do?
More data for better braking
Regenerative braking controls can be improved with inputs beyond driver actions and basic battery condition information. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications in cars with the appropriate advanced driver assistance system (ADAS) elements can be significant sources of data.
Several factors about the vehicle and its environment, such as the speed, slope of the road, sliding from loss of traction, lateral acceleration, and GPS location information, are expected to contribute to more robust regenerative braking operation (Figure 1). One key is designing control algorithms to use the data.
Figure 1. Data from the vehicle, V2V and V2I communications and driver inputs can be combined to improved regenerative braking. (Image: IEEE Access)
Algorithm choices
Numerous algorithms have been developed or proposed to improve regenerative braking operation. The following is just a sampling. In one case, a fuzzy logic controller considers the needed braking force, vehicle speed, and battery SoC. The output of this controller allocates the amount of regenerative braking relative to the total required braking force.
A so-called cooperative fuzzy logic control algorithm has been proposed that uses pedal stroke, vehicle gearing, motor speed, and the existence of any emergency condition to determine the needed level of regenerative braking based on ‘blending’ priorities for energy capture, anti-lock capability, and safety.
A nonlinear model predictive control (NMPC) cooperative algorithm has been proposed that maximizes vehicle stability to ensure maximum energy recovery. Stability is provided by controlling the distribution of braking force between the front and rear wheels. This NMPC algorithm includes a longitudinal model that considers vehicle speed, front wheel rotation speed, rear wheel rotation speed, desired vehicle speed, and coefficient of friction with the ground.
Control algorithms based on adaptive neural networks (ANNs) have been developed. Model predictive control (MPC) algorithms have been proposed that use a progressive method to consider multiple constraints (Figure 2). MPC algorithms also use predictions of future conditions to develop an optimal control strategy. Like many other approaches, the output of the MPC algorithm attempts to optimize the distribution of braking between the hydraulic and regenerative braking systems.
Figure 2. The MPC block (blue box) can be replaced with other control algorithms like an ANN, NMPC and other algorithms. (Image: International Journal of Energy Research)
Summary
Regenerative braking is an essential function in EVs. It’s widely used, but today’s systems rely on driver inputs with limited data from other sources. There is a wide range of efforts to develop more sophisticated regenerative braking algorithms based on fuzzy logic, ANNs, NMPCs, and different approaches that can consider a broader range of inputs for improved regenerative braking performance and enhanced safety.
References
- Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs, International Journal of Energy Research
- Energy Recovery and Energy Harvesting in Electric and Fuel Cell Vehicles, a Review of Recent Advances, IEEE Access
- Tesla Model S owner’s manual, Tesla
Images
Filed Under: Braking, FAQs