Deep reinforcement learning based energy management strategy for hybrid electric vehicle

This research is cited from: Lian R, Peng J, Wu Y, et al. Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle. Energy, 2020: 117297.

Happy to answer any questions you have. Please email us at [email protected] or [email protected].

A rule-interposing deep reinforcement learning (RIDRL) based energy management strategy (EMS) of hybrid electric vehicle (HEV) is investigated. Incorporated with the battery characteristics and the optimal brake specific fuel consumption (BSFC) curve of hybrid electric vehicles (HEVs), we are committed to acclerating the learning process of DRL agents on EMS.

Prius modelling

As shown in Fig. 1, the core power-split component of Prius is a planetary gear (PG) that splits power among the engine, motor, and generator. In this structure, its engine and generator are connected with the planet carrier and sun gear, respectively, and its motor is connected with the ring gear that is linked with the output shaft simultaneously. In addition, Prius is equipped with a small capacity Nickel-metal hydride (Ni-MH) battery, which is used to drive the traction motor and generator. Prius combines the advantages of series and parallel HEVs and consists of three driving modes: pure electric mode, hybrid mode, and charging mode.

In this research, a backward HEV model is built for the training and evaluation of EMS. The vehicle power demand under the given driving cycle is calculated by the longitudinal force balance equation. The engine (Fig. 2), generator, and motor are modeled by their corresponding efficiency maps from bench experiments. The Ni-MH battery is modeled by an equivalent circuit model, wherein the impact of the temperature change and battery aging is not considered. The experiment data of battery, including internal resistance and open-circuit voltage, is shown in Fig. 2.

    Fig. 1. Architecture of Prius powertrain          Fig. 2. Engine map and battery characteristics

DRL-based energy management strategy

DRL agent is encountered with an environment with Markov property. The agent and the environment interact continually, the agent selecting actions and the environment responding rewards to these actions and presenting new states to the agent. In this research, a deep deterministic policy gradient (DDPG) algorithm is incorporated with the expert knowledge of HEV to learn the optimal EMS action. Fig. 3 shows the agent-environment interaction of HEV energy management, i.e., the interaction between the EMS and the vehicle and traffic information. The state and action variables are set as follows, where the continuous action variables are explored from the optimal BSFC curve of the engine. The reward function of DDPG-based EMS consists of two parts: the instantaneous fuel consumption of the engine and the cost of battery charge sustaining. Thus, the multi-objective reward function is defined as:

State = {SoC, velocity, acceleration}

Action = {continuous action: engine power}

$Reward = -{\alpha[fuel(t)]+ \beta[SoC_{ref} - SoC(t)]^{2}}$

where α is the weight of fuel consumption, β the weight of battery charge sustaining, and $SoC_{ref}$ the SoC reference value while maintaining battery charge-sustaining. The SoC_{ref} is determined by the prior knowledge of the battery.

           Fig. 3. Agent-environment interaction for HEV energy management

Simulation results

Extensive comparison experiments are made between RI DDPG and RI deep Q learning (DQL), in which they share the same embedded expert knowledge. Fig. 4 shows the differences of SoC trajectories among dynamic programming (DP), RI DDPG, and RI DQL under the new European driving cycle (NEDC), where their values of terminal SoC are much the same, approximately at 0.6. In Fig. 5 and Fig. 6, it can be found that most of the engine working points of DDPG are distributed in areas with lower equivalent fuel consumption rates, while those of RI DQL is relatively poor. In table 5, The fuel economy of RI DDPG reaches 95.3% of DP’s and get a decrease of 6.5% compared to that of RI DQL. In Fig. 7, RI DQL is hard to guarantee its convergence and fluctuates more frequently compared with RI DDPG that converges to a stable state after the 50th episode. In order to train an EMS for an HEV online, the training process of a controller must be sturdy enough to guarantee the safety of the powertrain. The stability of RI DDPG shows that it is more applicable to real-world applications of DRL-based EMSs.

For further verification, different driving cycles are introduced into these two EMSs. The simulation results in table 1 demonstrate the superiority of the RI DDPG algorithm in performance robustness, where the mean and standard deviation of fuel economy are improved by 8.94% and 2.74%, respectively.:

                Fig. 4. SoC trajectories of the three EMS models

                      Fig. 5. Working points of engine

    Fig. 6. Distributions of fuel consumption rate              Fig. 7. Convergence curves

      Table 1. Comparison between RI DDPG and RI DQL under different driving cycles

        

Dependencies

• tensorflow 1.15.0

• numpy

• matplotlib

• scipy

The code structure

• The Data_Standard Driving Cycles folder contains the driving cycle for training DRL agents.
• The Image folder contains the figures showed in this research.
• Prius_model_new.py is the backward simulation model of the Prius powertrain.
• Prius modelling.ipynb illustrates the modelling process of power-split HEV.
• Mot_eta_quarter.mat and Eng_bsfc_map.mat are the efficiency maps of the motor and engine.
• Priority_Replay.py is the priority replay module for training DRL agents.
• DeepQNetwork_Prius.py performs training for DQN agent.
• DDPG_Prius.py performs training for DDPG agent.

The codes of DDPG and DQN models are developed according to MorvanZhou's DRL course.

Collaborators

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Renzong Lian 💻

Yuankai Wu 💻