**6. Conclusions**

Centered on the aim of improving energy saving, the economy-based torque distribution strategy is proposed in this paper. Upon the building of a complete four-in-wheel motor drive electric vehicle model, featuring a comprehensive vehicle model, a motor model and a battery model, torque distribution methods based on the FLC algorithm and a proposed DP algorithm are investigated through co-simulation studies carried out in AVL Cruise and MATLAB/Simulink software. Additionally, further experimental studies were implemented to verify the simulation results. These were performed considering a straight-line road.

This article produces very interesting results, as shown by the simulation and experimental results. The simulation results show that the torque distribution based on the

DP algorithm is the optimal option for optimized front and rear torque distribution, as it effectively reduces the vehicle's energy consumption by 2.27 kWh, 2.05 kWh and 1.99 kWh for every 100 km of distance travelled in the WLTC, NEDC and custom IM240 driving cycle conditions, respectively, when compared to the torque distribution based on the FLC algorithm. Furthermore, compared to the FLC algorithm, the experimental results show that the energy consumption under the WLTC, NEDC and IM240 drive cycles is reduced by 23.89%, 23.12% and 23.01% with the proposed DP algorithm, respectively. Hence, the proposed DP algorithm produces an optimized front and rear torque distribution that effectively reduces vehicle energy consumption, which leads to an improved energy saving and overall vehicle efficiency in four-in-wheel motor drive electric vehicles. The online global optimization method with the proposed DP algorithm which can be monitored in real-time during simulation and the vehicle experiment studies may assist in optimization and real time control, enabling better simulation results and even experimental results to be obtained with minimal or negligible errors.

It should be noted that DP is an exhaustive search that requires more time and space for its computation. Future work will focus on the algorithm and the reduction of the computation load.

**Author Contributions:** Conceptualization, Y.L. and X.X.; methodology, O.P.A.; software, Q.C. and X.C.; validation, O.P.A., Q.C. and W.Z.; investigation, Y.L. and X.X.; data curation, O.P.A.; writing—original draft preparation, O.P.A.; writing—review and editing, Y.L. and X.C.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is supported by National Natural Science Foundation of China (Grant No. 51705213), China Postdoctoral Science Foundation (Grant No. 2019M660105, Grant No. 2020T130360), Jiangsu Province Postdoctoral Science Foundation (Grant No. 2021K443C), Primary Research & Development Plan of Jiangsu Province (Grant No. BE2019010) and Hunan Innovation Platform Open Fund (Grant No. 20K041).

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
