Computationally Efficient Energy Management of Modern Electric Vehicles
Doctoral thesis, 2025
Modern electric vehicles, particularly plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs), often feature over-actuated powertrains with modular architectures that offer high degree of control freedom. Efficient energy management is essential to maximize the operational efficiency (driving range) of these EVs, without compromising performance.
This thesis presents an efficient model-based supervisory energy management framework that co-optimizes torque allocation and discrete decisions online, in over-actuated EVs. Control models capturing powertrain hybrid dynamics are explicitly incorporated into the optimization problem to minimize energy consumption and reduce frequent discrete transitions that degrade performance. Time-scale separation in the supervisory control structure is leveraged to ensure model tractability. To solve the resulting mixed-integer nonlinear problems, customized solution strategies are proposed that exploit their problem structures: relaxation-based methods for PHEVs and bilevel programming approach for BEVs. The framework is implemented using model predictive control and validated with high-fidelity simulations.
The results demonstrate that explicit inclusion of engine dynamics in power-split optimization yields up to 10 % energy savings over a rule-based baseline in PHEVs. At least an additional 3.6 % energy savings is achieved by co-optimizing torque allocation and discrete decisions in both EVs with only a marginal increase in discrete transitions.
Finally, this work also investigates the integration of torque vectoring mechanisms in dual-motor BEVs through a comprehensive torque distribution strategy. This proposed approach enhances energy efficiency, steering performance and dynamic handling, illustrating the potential in advancing the performance envelope of multi-motor EVs.
This thesis presents an efficient model-based supervisory energy management framework that co-optimizes torque allocation and discrete decisions online, in over-actuated EVs. Control models capturing powertrain hybrid dynamics are explicitly incorporated into the optimization problem to minimize energy consumption and reduce frequent discrete transitions that degrade performance. Time-scale separation in the supervisory control structure is leveraged to ensure model tractability. To solve the resulting mixed-integer nonlinear problems, customized solution strategies are proposed that exploit their problem structures: relaxation-based methods for PHEVs and bilevel programming approach for BEVs. The framework is implemented using model predictive control and validated with high-fidelity simulations.
The results demonstrate that explicit inclusion of engine dynamics in power-split optimization yields up to 10 % energy savings over a rule-based baseline in PHEVs. At least an additional 3.6 % energy savings is achieved by co-optimizing torque allocation and discrete decisions in both EVs with only a marginal increase in discrete transitions.
Finally, this work also investigates the integration of torque vectoring mechanisms in dual-motor BEVs through a comprehensive torque distribution strategy. This proposed approach enhances energy efficiency, steering performance and dynamic handling, illustrating the potential in advancing the performance envelope of multi-motor EVs.
torque distribution
dynamic programming
numerical optimization
model predictive control
gear selection
nonlinear optimal control
nonlinear programming
bilevel programming
mixed-integer programming
clutch on-off decision
Lecture hall HC3, Hörsalsvägen 14, Chalmers University of Technology, 412 58 Göteborg, Sweden.
Opponent: Tijs Donkers, Associate Professor, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Author
Anand Ganesan
Chalmers, Electrical Engineering, Systems and control
Mixed-Integer Energy Management for Multi-Motor Electric Vehicles with Clutch On-Off: Finding Global Optimum Efficiently
IEEE Transactions on Vehicular Technology,;Vol. In Press(2025)
Journal article
Numerical Strategies for Mixed-Integer Optimization of Power-Split and Gear Selection in Hybrid Electric Vehicles
IEEE Transactions on Intelligent Transportation Systems,;Vol. 24(2023)p. 3194-3210
Journal article
Effect of engine dynamics on optimal power-split control strategies in hybrid electric vehicles
2020 IEEE Vehicle Power and Propulsion Conference, VPPC 2020 - Proceedings,;(2020)
Paper in proceeding
A. Ganesan, N. Murgovski and D. Yang, "Optimal Torque Vectoring for Performance Enhancement of Multi-Motor Electric Vehicles".
Energy-Optimal Trajectory Planning for Electric Vehicles using Model Predictive Control
2024 European Control Conference, ECC 2024,;(2024)p. 1346-1351
Paper in proceeding
Real-Time Mixed-Integer Energy Management Strategy for Multi-Motor Electric Vehicles
2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023,;(2023)
Paper in proceeding
The electrification of road transport is a cornerstone in the global effort to reduce greenhouse gas emissions and fossil fuel dependence. However, modern electric vehicles (EVs)—particularly plug-in hybrids (PHEVs) and battery electric vehicles (BEVs) with modular and multiactuator powertrains—introduce new control challenges due to their high degree of freedom in power delivery and drivetrain configuration. Effectively managing the energy flow in such systems is essential to maximize their efficiency and performance in real-world driving.
This thesis investigates the development of computationally efficient, model-based supervisory energy management strategies for overactuated EVs. These strategies coordinate both continuous and discrete control decisions—such as power-split between propulsion sources, gear selection, and clutch engagement—while explicitly accounting for the transient and hybrid dynamics of powertrain components. The overarching objective is to enhance energy efficiency without compromising key vehicle performance attributes.
To achieve this, customized control-oriented models of key powertrain components are developed and integrated into mixed-integer model predictive control (MI-MPC) strategies, with specialized solution algorithms proposed to enable online implementation within the computational constraints of embedded automotive systems. The effectiveness of the proposed methods is demonstrated across multiple EV architectures through high-fidelity simulations. Additionally, the thesis explores torque vectoring mechanisms in dual-motor BEVs to improve handling and energy efficiency during dynamic driving maneuvers.
Altogether, this work presents a unified, online-capable energy management framework for overactuated electric vehicles, laying the foundation for intelligent, energy-efficient, and performance-aware vehicle control.
This thesis investigates the development of computationally efficient, model-based supervisory energy management strategies for overactuated EVs. These strategies coordinate both continuous and discrete control decisions—such as power-split between propulsion sources, gear selection, and clutch engagement—while explicitly accounting for the transient and hybrid dynamics of powertrain components. The overarching objective is to enhance energy efficiency without compromising key vehicle performance attributes.
To achieve this, customized control-oriented models of key powertrain components are developed and integrated into mixed-integer model predictive control (MI-MPC) strategies, with specialized solution algorithms proposed to enable online implementation within the computational constraints of embedded automotive systems. The effectiveness of the proposed methods is demonstrated across multiple EV architectures through high-fidelity simulations. Additionally, the thesis explores torque vectoring mechanisms in dual-motor BEVs to improve handling and energy efficiency during dynamic driving maneuvers.
Altogether, this work presents a unified, online-capable energy management framework for overactuated electric vehicles, laying the foundation for intelligent, energy-efficient, and performance-aware vehicle control.
A new generation of algorithms for modern powertrain control
VINNOVA (2017-05506), 2018-09-01 -- 2024-12-31.
Driving Forces
Sustainable development
Areas of Advance
Transport
Energy
Subject Categories (SSIF 2025)
Embedded Systems
Algorithms
Vehicle and Aerospace Engineering
Computer Systems
Control Engineering
ISBN
978-91-8103-235-2
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5693
Publisher
Chalmers
Lecture hall HC3, Hörsalsvägen 14, Chalmers University of Technology, 412 58 Göteborg, Sweden.
Opponent: Tijs Donkers, Associate Professor, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.