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Different Types Of Torque Split Strategies For Hybrid Vehicles

Chetan Shidling
Different Types Of Torque Split Strategies For Hybrid Vehicles

Hello guys, welcome back to our blog. Here in this article, we will discuss the different types of torque split strategies for hybrid vehicles, before that we will discuss why torque split strategy is required and which strategy is best out of many.

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Torque Split Strategies For Hybrid Vehicles

The integration of an internal combustion engine (ICE) with one or more electric motors in hybrid vehicles is a significant development in automotive technology. These vehicles combine the benefits of both internal combustion engine (ICE) and electric propulsion systems to deliver increased performance, less emissions, and improved fuel efficiency. The torque split strategy, which chooses how to divide the overall torque demand between the ICE and the electric motor(s), is an essential component of how they work. This tactic is essential for maximizing the car’s efficiency, performance, and overall driving pleasure.

Hybrid vehicles come in several forms, such as series hybrids, parallel hybrids, and series-parallel hybrids, each having its own configurations and strategies for distributing power. Parallel hybrids enable both the ICE and the electric motor to drive the wheels directly, whereas series hybrids use the ICE to power a generator that either charges the battery or drives the electric motor. Combining the best aspects of each, series-parallel hybrids offer more power distribution flexibility. Based on these combinations, the torque split approach is selected with the goal of striking a compromise between a number of factors, including performance, fuel economy, emissions reduction, and battery management.

Torque split techniques are influenced by various aspects such as driving conditions, driver demands, battery state of charge (SoC), and efficiency characteristics of both the electric motor and internal combustion engine. For example, driving on an urban street may encourage electric-only operation in order to minimize pollutants, whereas driving on a highway may call for a more balanced strategy. Because the ICE’s efficiency fluctuates with speed and load, it must be carefully managed to stay within its ideal operating range. Electric motor efficiency increases with low speed and high torque requirements. Furthermore, sustaining battery charge and overall energy efficiency depends heavily on regenerative braking, which recovers energy during deceleration.

Three types of common torque split strategies exist: heuristic, rule-based, and optimization-based. Rule-based techniques work using pre-established rules that modify the power distribution according to certain driving circumstances and vehicle conditions. Optimization-based solutions strive to achieve optimum fuel efficiency and emissions reduction during the whole drive cycle or in real time by using mathematical models to calculate the appropriate torque split for the current conditions. Heuristic techniques use adaptive algorithms to continuously optimize torque distribution by learning from driving behaviors, such as neural networks and fuzzy logic.

Torque split schemes work well, but they have drawbacks. These include the requirement for real-time computing, the advancement of battery technology, and the incorporation of vehicle-to-infrastructure (V2I) communication for better decision-making. These tactics will be further refined, and the efficiency and performance of hybrid vehicles will be improved, by future advancements in these fields as well as improved predictive models of driver behavior.

Types of Torque Split Strategies

01. Rule-Based Strategies

To regulate the torque split between the internal combustion engine (ICE) and electric motor (s), rule-based techniques for torque split in hybrid vehicles rely on established rules and conditions. These tactics are based on a set of logical conditions that correlate to various driving scenarios, and they are comparatively easy to put into practice. The following are the main features of rule-based tactics:

a. Engine Start/Stop:

When the car is stopped, like at a stop sign or in stop-and-go traffic, the ICE is switched off automatically. The goal is to minimize needless engine running while the car is stopped in order to save fuel and cut pollution. Function: When the driver depresses the accelerator or releases the brake pedal, the engine starts up again right away, guaranteeing a seamless transition with little wait time.

b. Electric-Only Mode:

In some situations, including while driving in an urban area at a slow speed, the car runs exclusively on electricity. Utilize the electric motor to its full potential when it outperforms the internal combustion engine (ICE) in order to minimize pollution and fuel consumption. The electric motor generates all the torque required, and the internal combustion engine stays off. Usually, this mode is activated during light acceleration or at low speeds.

c. Assist Mode:

When there is a strong torque requirement, like when accelerating quickly or ascending a slope, the electric motor helps the internal combustion engine. Increase torque to enhance performance, lower the load on the internal combustion engine (ICE) to improve fuel efficiency, and make it possible to employ a smaller, more efficient ICE. The torque output of the ICE is supplemented by the electric motor, which improves acceleration and overall performance.

d. Regenerative Braking:

In order to transform kinetic energy into electrical energy and store it in the battery, the electric motor functions as a generator when the vehicle is braking. Recover energy during braking that would otherwise be lost as heat, increasing overall energy efficiency and increasing the electric drive’s range. The electric motor’s function is reversed when the driver hits the brakes, producing electricity and slowing down the car. This procedure aids in preserving the state of charge (SoC) of the battery.

Advantages of Rule-Based Strategies

  • Simplicity: Requiring less computer power than more sophisticated solutions, simplicity is easy to grasp and implement.
  • Dependability: Demonstrated and strong, exhibiting distinct actions in particular scenarios.
  • Cost-Effectiveness: Because the rules are simple, there are fewer development and implementation expenses.

Limitations of Rule-Based Strategies

  • Lack of Flexibility: Fixed rules might not adjust to every driving situation in the best way possible, which could result in missed opportunities to increase efficiency even further.
  • Suboptimal Performance: In comparison to more sophisticated, adaptive techniques, it might not be possible to attain the highest levels of fuel efficiency and emissions reduction.
  • Limited Adaptation: Does not adapt in real-time to changing driving conditions and patterns or learn from past data.

Despite these drawbacks, rule-based techniques are frequently employed in hybrid cars because of their dependability and simplicity. They contribute to the overall efficiency and performance of hybrid cars by offering a strong basis for controlling the interaction between the internal combustion engine (ICE) and electric motor or motors.

02. Optimization-Based Strategies

To find the most effective way to divide torque between the internal combustion engine (ICE) and electric motor (or motors), optimization-based solutions for torque split in hybrid vehicles use mathematical models and algorithms. These strategies continuously modify the torque split depending on real-time data and expected driving conditions in an effort to maximize certain goals, such as minimizing fuel consumption, lowering emissions, or improving performance. The following are the main features of techniques based on optimization:

a. Global Optimisation

This method finds the best torque split by taking into account the full drive cycle, which reduces emissions and fuel consumption over a predetermined distance or time. Optimize overall performance by accounting for the entire spectrum of road conditions and vehicle statuses experienced throughout a journey. Computes the ideal control strategy offline using methods like Pontryagin’s Minimum Principle or dynamic programming. The drive cycle is then used to put the solution into practice. For example, pre-trip planning enables the car to choose whether to use the electric motor or internal combustion engine more effectively depending on the predicted driving conditions and the known route.

b. Real-Time Optimization

Based on driver inputs, vehicle states, and current driving conditions, this system dynamically modifies the torque split in real time. Constantly improve performance, emissions, and fuel economy while quickly adjusting to changing circumstances. Uses algorithms to make decisions in real-time, such as Equivalent Consumption Minimization Strategy (ECMS) and Model Predictive Control (MPC). In order to maintain optimal performance, these algorithms forecast future conditions and modify control actions accordingly. An example of this would be an adaptive cruise control system that maintains the required speed and separation from the car in front while optimizing the torque split to minimize emissions and preserve fuel efficiency.

Techniques Used in Optimization-Based Strategies

DP, or dynamic programming: The technique involves breaking down the optimization problem into smaller sub-problems and addressing each one separately in order to find the best answer for the drive cycle as a whole. Ideal for offline optimization when the path and circumstances are known ahead of time.

Pontryagin’s Minimum Principle (PMP): The approach uses optimal control theory to minimize a cost function (fuel consumption, for example) while taking system dynamics and restrictions into account. Suitable for offline and real-time optimization, especially for intricate systems with several goals.

Model Predictive Control (MPC): This is a real-time control technique that predicts future states and optimizes control operations over a shifting time horizon by using a model of the dynamics of the vehicle. Fit for real-time scenarios with quick changes in circumstances, like driving in cities with lots of stops and starts.

Equivalent Consumption Minimization Strategy (ECMS): This is a real-time optimization strategy that balances the utilization of the ICE and electric motor to minimize overall energy expenditure by equating the cost of electric energy to fuel use. Suitable for hybrid cars that switch between electric and internal combustion engine power on a regular basis.

Advantages of Optimization-Based Strategies

  • High Efficiency: By constantly aiming for the ideal torque split, it is possible to greatly increase fuel economy and lower emissions.
  • Adaptive: Able to instantly adapt to a broad variety of driving situations and driver behaviors.
  • Holistic Approach: Takes into account all aspects of system performance, such as vehicle dynamics and battery state of charge (SoC).

Challenges and Limitations

  • Computational Complexity: Needs sophisticated algorithms and a significant amount of processing power, especially for real-time optimization.
  • Implementation Cost: Because of the intricacy of the algorithms and the requirement for sophisticated sensors and controllers, there are higher development and implementation costs.
  • Robustness: It can be difficult to guarantee that the optimization methods are dependable and robust in all potential driving scenarios.

Using cutting-edge mathematical models and real-time data, optimization-based tactics offer a comprehensive approach to torque management in hybrid cars, resulting in increased effectiveness and performance. These tactics are essential to the design of contemporary hybrid vehicles because they can greatly increase the advantages of hybrid technology by continuously adapting to driving circumstances and vehicle states. Optimization-based solutions will become more significant in the development of hybrid vehicles as computer power and algorithmic methodologies progress.

03. Heuristic Strategies

In hybrid automobiles, adaptive and learning-based heuristic techniques for torque split are used to control the torque distribution between the internal combustion engine (ICE) and electric motor (s). Heuristic tactics do not only depend on pre-established rules or intricate mathematical models, in contrast to rule-based or optimization-based techniques. Rather, they use methods akin to human thought processes or learn from past experiences and current inputs to arrive at well-informed conclusions. The following are the main elements of heuristic tactics:

a. Fuzzy Logic Control

Fuzzy logic is a technique that addresses imprecision and ambiguity in decision-making processes. When typical binary logic is insufficient for complicated systems, this technique works well. Offer a more sophisticated and adaptable control approach that can take into account different driving situations and driving styles. In order to calculate the ideal torque split, fuzzy logic controllers transform input variables—such as vehicle speed, battery level, and acceleration demand—into fuzzy sets and then apply a set of fuzzy rules. Optimizing fuel efficiency and performance under different driving situations by adjusting the electric motor and ICE’s contribution based on the fuzzified inputs.

b. Neural Networks

Uses artificial neural networks (ANNs) to simulate intricate interactions between inputs and outputs, gaining knowledge from data to generate forecasts and judgments. Utilising patterns and trends in real-time inputs and past driving data, improve torque split decisions. To determine the ideal torque split under different circumstances, a neural network is trained on a dataset of driving events. The trained network finds the optimal torque distribution by analyzing real-time data while it is in operation. An illustration would be a neural network that predicts the ideal torque split given inputs like road grade, acceleration, current speed, and battery SoC.

Advantages of Heuristic Strategies

  • Adaptability: The ability to learn from fresh data and adapt to a variety of driving situations, resulting in ongoing development.
  • Flexibility: They are appropriate for real-world driving scenarios because they can manage intricate, non-linear interactions between inputs and outputs.
  • Scalability: Doesn’t require a lot of reprogramming to adapt to various hybrid car models and road conditions.

Challenges and Limitations

  • Computational Requirements: Training and real-time decision-making may necessitate a large amount of computing power, particularly for neural networks and reinforcement learning.
  • Data Dependency: The caliber and volume of training data that is available determines how successful heuristic techniques are.
  • Implementation Complexity: Creating and fine-tuning heuristic algorithms can be difficult and time-consuming; it calls for knowledge of control systems and machine learning.

Utilizing cutting-edge methods like fuzzy logic, neural networks, evolutionary algorithms, and reinforcement learning, heuristic solutions provide a potent and adaptable solution for torque management in hybrid cars. These strategies can optimize the torque split more successfully than previous methods, resulting in improved performance, lower emissions, and higher fuel efficiency. They do this by adjusting to real-time situations and learning from data. The advancements in computer power and machine learning techniques will make heuristic strategies more crucial in the creation of intelligent hybrid vehicle control systems.

This was about “Torque Split Strategies For Hybrid Vehicles“. I hope this article may help you all a lot. Thank you for reading.

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