To improve the fuel efficiency of a hybrid electric car with special powertrain features racing in Shell Eco-marathon, a computationally efficient online control system is developed by solving hierarchical optimal control problems. The top-level computes the optimal velocity trajectory based on the given competition track in advance. The lower-level then finds the best instantaneous engine state and torque allocation by the equivalent consumption minimization strategy (ECMS). The special design of the competition car reduces the ECMS into a binary optimization problem. The new controller can run in real-time on low-cost microprocessors and improves the car's fuel efficiency by 50% while maintaining the state of charge of the electrical energy buffer.

The fuel efficiency of the hybrid electric vehicle (HEV) highly relies on the engine efficiency. Thispaper proposes a particular powertrain configuration on a small hybrid electric racing car wherethe engine is only allowed to operate withpeak efficiency. An onlinebinarycontroller, accordingly, is developedbased on dynamic programming (DP)to control the engine on/off status and the torques from the electric motors (EMs) to ensure the HEV can successfully complete the drive mission with minimal fuel consumption.For comparison, the paper also develops anoptimal powertrain controller of the same HEV with the normal usage ofthe engine, i.e., the engine operates at any feasible point of the 2Dfuel efficiency map. The simulation results show that this binary controller can improveroughly13% on fuel efficiency compared with the general engine case.

This paper improves the fuel efficiency of a student-made parallel hybrid electric racing car whose internal combustion engine (ICE) either operates with peak efficiency or is turned off. The control to the ICE thus becomes a binary problem. Owing to the very limited computation resource onboard, the energy management strategy (EMS) for this car must have small time and space complexities. A computationally efficient controller that combines the advantages of dynamic programming (DP) and Pontryagins minimum principle (PMP) is developed to run on a low-cost microprocessor. DP is employed offline to calculate the optimal speed trajectory, which is used as the reference for the online PMP to determine the real-time ICE on/off status and the electric motor (EM) torques. The normal PMP derives the optimal costate trajectory through solving partial differential equations. The proposed quasi-PMP (Q-PMP) method finds the costate from the value function obtained by DP. The fuel efficiency and computational complexity of the proposed controller are compared against several state of art methods through both model-in-the-loop (MIL) and processor-in-the-loop (PIL) simulations. The new method reaches similar fuel efficiency as the explicit DP, but requires less than 1% onboard flash memory. The performance of the Q-PMP controller is compared between binary-controlled and continuously controlled engines. It achieves roughly 12% higher fuel efficiency for the binary engine with only approximately 1/3 CPU utilization.

The fuel economy of a hybrid electric vehicle(HEV) is determined by its energy management strategy (EMS), while the conventional EMS usually suffers from enormous computation loads when solving a nonlinear optimization problem. To resolve this issue, this paper presents a computationally efficient EMS with close-to-optimal performance using very limited computation resources. Relying on the optimal solutions by offline dynamic programming (DP), a constrained model predictive control (MPC) can quickly determine the engine on/off status and then the torque split problem is solved by a value-based Pontryagin’s minimum principle (PMP). Two measures are taken to further reduce the online computation cost: by surface fitting, the tabular value function is replaced by piecewise linear polynomials and thus the memory occupation is greatly reduced; and by model approximation, the nonlinear torque split problem becomes a quadratic programming one that can be more rapidly solved. The testing results from processor-in-the-loop (PIL) simulation indicate that the proposed EMS can generate a fuel efficiency close to the one by DP, but saves 70% onboard memory space and 30% CPU utilization compared with the benchmark EMS without taking the two measures.

Hybrid electric vehicles (HEVs) rely on energy management strategies (EMSs) to achieve optimal fuel economy. However, both model- and learning-based EMSs have their respective limitations which negatively affect their performances in online applications. This paper presents a computationally efficient adaptive dynamic programming (ADP) approach that can not only rapidly calculate optimal control actions but also iteratively update the approximated value function (AVF) according to the actual fuel and electricity consumption with limited computation resources. Exploiting the AVF, the engine on/off switch and torque split problems are solved by one-step lookahead approximation and Pontryagin’s minimum principle (PMP), respectively. To raise the training speed and reduce the memory space, the tabular value function (VF) is approximated by carefully selected piecewise polynomials via parametric approximation. The advantages of the proposed EMS are threefold and verified by processor-in-the-loop (PIL) Monte Carlo simulations. First, the fuel efficiency of the proposed EMS is higher than that of an adaptive PMP and close to the theoretical optimum. Second, the new method can adapt to the changed driving conditions after a small number of learning iterations and then has higher fuel efficiency than a non-adaptive dynamic programming (DP)controller. Third, the computation efficiencies of the proposed AVF and a tabular VF are compared. The AVF data structure enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization.

Energy management strategies (EMSs) are crucial to the fuel economy of hybrid electric vehicles (HEVs). However, due to the lack of efficient solving approaches, most of existing EMSs mainly focus on the optimal torque split between the internal combustion engine (ICE) and the electric motor but neglect improper ICE on/off switches, and thus usually suffer degraded fuel economy and even unacceptable drivability in practice. To tackle this issue, this paper presents a novel EMS that uses an efficient actor-critic (AC) method to regulate ICE switches with limited computation resources. While common AC methods use complex neural networks (NNs) with arbitrary initialization, the proposed AC uses piecewise cubic polynomials whose parameters are initialized based on optimized solutions of dynamic programming (DP). By this means, the AC can quickly converge with high computation efficiency. The testing results from processor-in-the-loop (PIL) simulations showcase that, compared with a rule-based EMS with tabular value functions, the proposed EMS can greatly improve the equivalent fuel economy by eliminating improper ICE switches after only several iterations of adaptive learning and dramatically save onboard memory space owing to the concise AC structure.