Multi-Mode Hybrid Powertrain Control for Heavy-Truck Emissions
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Solution Overview
Problem
The global heavy-truck industry faces challenges in simultaneously optimizing fuel consumption and pollutant emissions, particularly NOx emissions, to meet stringent regulatory requirements such as the US Federal GHG-II regulations and California's ultra-low NOx emission Omnibus regulations by 2027, while maintaining a high performance-to-cost ratio and ensuring production readiness.
Innovation Solution
An intelligent multi-mode hybrid powertrain (iMMH) with a dual-motor, clutch, transmission, and battery pack, combined with a digital pulse-control engine and predictive supervisory control, allows for dynamic mode switching between series and parallel hybrid operations, optimizing energy use and emissions through intelligent predictive supervisory control (iPSC) and over-the-air updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If traditional diesel engine technology is used to maintain current performance levels, then vehicle power and reliability are preserved, but fuel consumption and emissions exceed regulatory limits by 2027
Solution Approach 1:
The powertrain is segmented into multiple independent components: diesel engine, electric motor, battery pack, and power take-off (PTO) system. This allows each component to operate in its optimal efficiency range and enables flexible configuration of series, parallel, or hybrid modes to minimize fuel consumption and emissions while maintaining reliable vehicle power.
Solution Approach 2:
The patent combines traditional diesel engine technology with electric motor assistance and energy storage systems to create an integrated hybrid powertrain. This merging allows the system to leverage the high torque of diesel engines at low speeds, the efficiency of electric motors at high speeds, and the energy recovery capabilities of regenerative braking, achieving both emission reduction and reliable power delivery.
2Object-generated harmful factors
If hybrid powertrain systems are implemented to reduce fuel consumption, then emissions are reduced, but system complexity and cost increase
Solution Approach 1:
The electric motor in the hybrid system serves multiple functions: it provides torque assistance during acceleration, enables regenerative braking energy recovery, powers auxiliary systems via the PTO, and can operate in series or parallel modes depending on driving conditions. This multi-functionality reduces the need for separate dedicated components, thereby limiting system complexity growth while achieving emission reduction targets.
Solution Approach 2:
The powertrain control system dynamically switches between series mode (engine generates electricity), parallel mode (engine and motor both drive wheels), and electric-only mode based on real-time driving conditions, battery state of charge, and power demands. This dynamic operation optimizes emissions reduction while managing system complexity through intelligent control rather than fixed mechanical configurations.
3Object-generated harmful factors
If advanced emission control technologies are applied to reduce NOx emissions, then regulatory compliance is achieved, but fuel consumption increases
Solution Approach 1:
The patent replaces direct mechanical coupling of the diesel engine to the drivetrain with an electro-mechanical interface through the electric motor and power electronics. This substitution allows the diesel engine to operate at constant optimal efficiency points for emission control while the electric motor handles variable power demands, eliminating the need for complex mechanical emission control mechanisms and reducing overall fuel consumption.
Solution Approach 2:
The system changes the operating parameters of the diesel engine by using the electric motor to supplement power during high-load conditions, allowing the engine to operate in its most efficient and clean-burning range. The power electronics also enable precise control of fuel injection timing and amount, optimizing combustion efficiency and reducing NOx emissions without sacrificing fuel economy.
4Use of energy by moving object
If predictive control algorithms are implemented to optimize energy management, then fuel consumption is minimized, but computational requirements and control complexity increase
Solution Approach 1:
The predictive control algorithm uses GPS and map data to anticipate upcoming terrain features, traffic conditions, and driving patterns. Based on these predictions, the system pre-adjusts battery charge levels, engine operating points, and motor assistance levels to optimize fuel consumption. This preliminary action allows the control system to make proactive rather than reactive decisions, minimizing fuel use without requiring complex real-time computations during critical moments.
Solution Approach 2:
The control system continuously monitors actual fuel consumption, battery state of charge, and driving conditions, comparing these measurements against predicted values. This feedback loop allows the system to learn from actual performance and refine its predictive algorithms over time, improving fuel efficiency while managing control complexity through adaptive rather than purely predetermined control strategies.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The iMMH powertrain achieves a 30% reduction in real-world fuel consumption and ensures long-term stable compliance with emission standards, transforming the challenge into an AI-driven optimization problem, decoupling engine and vehicle performance for efficient energy recovery and emission reduction.
Implementation Method 1
a battery pack, configured as a dual-power battery pack including a high-power battery and an energy recovery battery
Implementation Method 2
PSC on the battery pack
Implementation Method 3
two electric motors
Implementation Method 4
diesel engine
Implementation Method 5
digital pulse-control (DPC) engine
Data Source
Figure 1A~1B
Figure 2~3
Figure 4A~4B
AI summary
An intelligent connected electric (ACE) heavy truck system equipped with an intelligent multi-mode hybrid (iMMH) assembly system, as well as a vehicle supervision and control method, is presented. This system ensures industry-leading power performance and braking effectiveness for ACE heavy trucks while automatically optimizing both energy conservation and emission reduction based on the vehicle's driving data and three-dimensional electronic map information of roads. In this application, the conventional analog electric control (AEC) strategy is replaced by a novel digital pulse control (DPC) strategy. This transformation converts the complex working conditions of the AEC engine in hybrid vehicles into preset fixed linear working conditions for the DPC engine. Consequently, the multi-variable nonlinear technical challenge of simultaneously minimizing actual fuel consumption and pollutant emissions of an ACE heavy truck is significantly simplified, enabling multi-variable orthogonal decoupling. Furthermore, the demands on vehicle-end computing resources for real-time supervision and control of hybrid vehicles are reduced, enhancing the performance, convergence speed, and robustness of the monitoring algorithm. Ultimately, the system achieves an infinitely close approximation to the real-time global optimal solution for energy conservation and emission reduction at high cost-effectiveness.