• Energy Research

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

Abstract Series-Parallel Hybrid ElectricVehicle (HEV) has well applied in practice due to its significant advantages, this paper aimed at two different kind of Series-Parallel HEV configurations: Power Split and Mode Switching configuration, comparing their control strategies, set model and simulate to verify, obtain features and applications of two configurations. The simulation results show that the power split configuration does better in protecting battery and reducing urban fuel consumption, while mode switching configuration has simpler structure.

This paper aims to answer how to effectively integrate the data-driven method into the traditional predictive energy management algorithm rather than replacing it outright. Given the challenge of selecting an appropriate prediction horizon for predictive energy management, this study seeks to bridge traditional predictive energy management with machine learning approaches, thereby presenting a novel bi-level predictive energy management strategy for fuel cell buses with multi-prediction horizons. In the upper layer, the core parameter, prediction horizon, of the traditional model predictive control energy management framework is optimized using two distinct data-driven methods. The first method employs deep learning to establish a mapping relationship between the vehicle states and the optimal prediction horizon through deep neural networks. The second method utilizes reinforcement learning to obtain the best prediction horizon under varying vehicle states through intelligent agent exploration. In the lower level, predictive energy management is performed on fuel cell buses based on optimization levels. Finally, the proposed strategy is validated using test data from actual fuel cell buses. The results demonstrate that two data-driven methods, based on the optimal ΔSoC approximation and the deep reinforcement learning, can select the appropriate prediction horizon more conducive to energy saving according to the vehicle states. Regarding energy consumption, the multi-horizon predictive energy management based on deep reinforcement learning exhibits a remarkable reduction in energy consumption by 7.62 %, 4.55 %, 4.60 %, and 7.80 %, when compared with the predictive energy management employing fixed prediction horizons of 5 s, 10 s, 15 s, and 20 s, respectively. Furthermore, it outperforms the multi-horizon predictive energy management approach based on the optimal ΔSoC approximation by 3.59 %.

Abstract Based on the design requirements of hybrid electric vehicle(HEV), the matching design of powertrain system of HEV was completed. The parameters of the engine, motor and generator were matched according to the power demand. The characteristic parameters of the planetary gear were determined based on the rotational speed space of the planet. The model of power split HEV was established in the MATLAB/Simulink, and the fuel consumption, the SOC of power battery under the NEDC and the dynamic performance of the vehicle were simulated. The simulation results show that the changes of the SOC of power battery is in line with the requirements of the control strategy, the fuel consumption per 100 km is 4.6L in NEDC which proves that the proposed energy management strategy is effective and the dynamic performance also meet demand matching.

Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multiconstrained least costly formulation is proposed by augmenting the overtemperature penalty and multistress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.

Abstract An energy management strategy, based on double deep Q-learning algorithm, is proposed for a dual-motor driven hybrid electric tracked-vehicle. Typical model framework of tracked-vehicle is established where the lateral dynamic can be taken into consideration. For the propose of optimizing the fuel consumption performance, a double deep Q-learning-based control structure is put forward. Compared to conventional deep Q-learning, the proposed strategy prevents training process falling into the overoptimistic estimate of policy value and highlights its significant advantages in terms of the iterative convergence rate and optimization performance. Unique observation states are selected as input variables of reinforcement learning algorithm in view of revealing tracked-vehicles characteristic. The conventional deep Q-learning and dynamic programming are also employed and compared with the proposed strategy for different driving schedules. Simulation results demonstrate the fuel economy of proposed methodology achieves 7.1% better than that of conventional deep Q learning-based strategy and reaches 93.2% level of Dynamic programing benchmark. Moreover, the designed algorithm has a good performance in battery SOC retention with different initial values.

Abstract Reinforcement learning is a new research hotspot in the artificial intelligence community. Q learning as a famous reinforcement learning algorithm can achieve satisfactory control performance without need to clarify the complex internal factors in controlled objects. However, discretization state is necessary which limits the application of Q learning in energy management for hybrid electric bus (HEB). In this paper the deep Q learning (DQL) is adopted for energy management issue and the strategy is proposed and verified. Firstly, the system modeling of bus configuration are described. Then, the energy management strategy based on deep Q learning is put forward. Deep neural network is employed and well trained to approximate the action value function (Q function). Furthermore, the Q learning strategy based on the same model is mentioned and applied to compare with deep Q learning. Finally, a part of trained decision network is analyzed separately to verify the effectiveness and rationality of the DQL-based strategy. The training results indicate that DQL-based strategy makes a better performance than that of Q learning in training time consuming and convergence rate. Results also demonstrate the fuel economy of proposed strategy under the unknown driving condition achieves 89% of dynamic programming-based method. In addition, the technique can finally learn to the target state of charge under different initial conditions. The main contribution of this study is to explore a novel reinforcement learning methodology into energy management for HEB which solve the curse of state variable dimensionality, and the techniques can be adopted to solve similar problems.

Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This paper proposes a knowledge-based, multi-physics-constrained fast charging strategy for lithium-ion battery (LIB), with a consciousness of the thermal safety and degradation. A universal algorithmic framework combining model-based state observer and a deep reinforcement learning (DRL)-based optimizer is proposed, for the first time, to provide a LIB fast charging solution. Within the DRL framework, a multi-objective optimization problem is formulated by penalizing the over-temperature and degradation. An improved environmental perceptive deep deterministic policy gradient (DDPG) algorithm with priority experience replay is exploited to trade-off smartly the charging rapidity and the compliance of physical constraints. The proposed DDPG-DRL strategy is compared experimentally with the rule-based strategies and the state-of-the-art model predictive controller to validate its superiority in terms of charging rapidity, enforcement of LIB thermal safety and life extension, as well as the computational tractability.