Computationally Efficient and Adaptive Energy Management Strategies for Parallel Hybrid Electric Vehicles
Time: Wed 2023-05-31 13.30
Location: Gladan, Brinellvägen 85, Stockholm
Video link: https://kth-se.zoom.us/j/68364433509
Subject area: Machine Design Optimization and Systems Theory Industrial Information and Control Systems
Doctoral student: PhD Candidate Tong Liu , Mekatronik och inbyggda styrsystem
Opponent: Professor Nikolce Murgovski, Chalmers tekniska högskola
Supervisor: Universitets lektor Lei Feng, KTH-centrum inom inbyggda system, ICES; Universitets lektor Hans Johansson, Mekatronik och inbyggda styrsystem
Hybrid electric vehicles (HEVs) are irreplaceable in attaining sustainable development in contemporary society. Owing to the extra degree of freedom in supplying traction power, HEVs resort to appropriate energy management strategies (EMSs) to present their superiority over conventional internal combustion engine vehicles and pure electric vehicles.
Existing EMSs suffer from heavy computation overheads and excessive mode switches. This thesis proposes several novel methods for developing online EMSs for parallel HEVs that achieve both compelling fuel economy and excellent computation efficiency and adaptivity in online applications with uncertain driving conditions.
First, the solutions of offline dynamic programming (DP) are exploited to develop online EMSs for close-to-optimal control performances. The optimal speed profile serves as the reference in online control and the optimal value function (VF) is utilized to design control methods. To avoid the “curse of dimensionality”, the tabular VF is approximated by piecewise polynomials to substantially decrease the computation and memory overheads in online usage.
Second, to reduce the search space for optimal control actions, two types of special internal combustion engine (ICE) configurations are adopted and analyzed. The first type forces the ICE to strictly operate at the optimal operation line (OOL), whereas the second one allows a narrow band around the OOL. The second one outperforms the first one because it contributes to more robust ICE operations with slightly higher computation complexity.
Third, a hierarchical architecture is proposed for online EMSs so that the transient powertrain mode and torque split scheme are optimized by different methods in sequence. To avoid the exponential complexity of finding the optimal trajectory of the powertrain mode, the optimal VF is leveraged for an optimal decision within one sampling period with the aid of simplified assumptions. Model approximations on the ICE and the electric motor are conducted so as to convert the complex torque split problem into a constrained quadratic programming problem. These methods dramatically facilitate the computation efficiency of online EMSs.
Fourth, learning-based adaptive control is introduced to mitigate the adverse effect caused by the deviations between the model and the reality. For this target, efficient learning algorithms are designed to iteratively update the coefficient matrix of the approximated VF. Moreover, to avoid the pitfall of the “cold start” and prompt a fast convergence, the coefficient matrix is initialized by the optimal VF from offline DP.
Finally, an event-triggered control mechanism is applied to the torque split control and presents its remarkable advantage in eliminating the excessive computation overheads. At each time step, an efficient trigger algorithm decides if the reference ICE torque is still valid or outdated. If it is valid, the EMS directly uses the reference value as the optimal output; otherwise, the optimization algorithm for torque split control is executed to solve a new value and update the reference.
The performances of designed EMSs are tested by processor-in-the-loop simulations so that both the numeric results and the computation efficiency can be obtained for quantitative analysis and comparison. The testing results indicate that the designed EMSs can rapidly adapt to real driving conditions and generate more than 90% fuel economy of the DP optimum, and more importantly, all these EMSs can be implemented on a portable microprocessor with limited onboard computation resources.