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Electrification has emerged as the main trend in new energy vehicles over the past decade. With the increasing number of EVs, reducing their energy consumption aligns with the national strategy for energy conservation and emission reduction, and also improves user's driving experience. The paper first analyzes actual road driving data from an electric SUV. Through dimensional reduction and correlation analysis, the acceleration variance is found as the key factor influencing energy consumption. Furthermore, three typical realroad operating conditions, i.e, constant speed driving, tip in/tip out, and ramp, are chosen for a parametric study by using a 1D model. The tradeoff between energy and time consumption is also discussed. Finally, several principles are established for ecodriving considering the acceleration and speed of vehicles, which include reducing speed during highway cruising, ensuring smooth driving in urban congestion, and utilizing vehicle inertia and minimizing regenerative braking on ramps.

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我国新能源汽车技术路线是以纯电驱动为主,随着纯电动汽车保有量的增加,降低其行驶能耗既符合国家节能减排的战略,又能改善用户的用车体验。基于某纯电动SUV车型的实际道路驾驶数据,通过相关性和降维分析得到加速度的方差是影响能耗的最关键因素;进而采用一维模型仿真,研究匀速、加减速和坡道3种典型工况下能耗的形成机理和优化方向,得到平衡能耗与行驶时长的经济性驾驶原则,如高速巡航时应适当降低车速,市区拥堵时控制加速度变化幅度,上下坡时利用车辆惯性,减少制动能量回收。

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王颖迪(1993-),男,湖北武汉人,博士,主要研究方向为新能源汽车能耗和驾驶性。Tel: 13002053429 E-mail:

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王颖迪(1993-),男,湖北武汉人,博士,主要研究方向为新能源汽车能耗和驾驶性。Tel: 13002053429 E-mail:

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序号 特征参数 参数说明
1 平均车速 ${\mu }_{v}/\left( {\mathrm{{km}}/\mathrm{h}}\right)$ 总里程除以行驶时间
2 车速标准差 ${\delta }_{\mathrm{v}}/\left( {\mathrm{{km}}/\mathrm{h}}\right)$ 车速的标准差
3 平均正加速度 ${\mu }_{a}/\left( {\mathrm{m}/{\mathrm{s}}^{2}}\right)$ 加速度 $> 0$ 时的加速度平均值
4 加速度标准差 ${\delta }_{a}/\left( {\mathrm{m}/{\mathrm{s}}^{2}}\right)$ 加速度的标准差
5 怠速行驶占比 ${\tau }_{\text{idle }}/\%$ 车速 $\left( {\mathrm{{km}}/\mathrm{h}}\right)$ 低于 10 的时间与行驶时间之比
6 低速行驶占比 ${\tau }_{\text{low }}/\%$ 车速 $\left( {\mathrm{{km}}/\mathrm{h}}\right)$$\lbrack {10},{40})$ 区间的时间与行驶时间之比
7 中速行驶占比 ${\tau }_{\mathrm{{med}}}/\%$ 车速 $\left( {\mathrm{{km}}/\mathrm{h}}\right)$$\lbrack {40},{80})$ 区间的时间与行驶时间之比
8 高速行驶占比 ${\tau }_{\text{high }}/\%$ 车速 $\left( {\mathrm{{km}}/\mathrm{h}}\right)$$\lbrack {80},{150})$ 区间的时间与行驶时间之比
9 匀速驾驶占比 ${\varphi }_{\text{const}}/\%$ 加速度绝对值 $\left( {\mathrm{m}/{\mathrm{s}}^{2}}\right)$ 小于 0.15 的时间与行驶时间之比
10 缓和驾驶占比 ${\varphi }_{\mathrm{{mod}}}/\%$ 加速度绝对值 $\left( {\mathrm{m}/{\mathrm{s}}^{2}}\right)$$\lbrack {0.15},{1.5})$ 的时间与行驶时间之比
11 激烈驾驶占比 ${\varphi }_{\mathrm{{vio}}}/\%$ 加速度绝对值 $\left( {\mathrm{m}/{\mathrm{s}}^{2}}\right)$ 大于 1.5 的时间与行驶时间之比
12 制动介入比例 ${\varphi }_{\mathrm{{brk}}}/\%$ 制动踏板踩下的时间与行驶时间之比
13 空调平均功率 ${p}_{\mathrm{{AC}}}/\mathrm{W}$ 空调耗电量除以行驶时间
), ArticleFig(id=1153978808622375749, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1153978734420943625, language=CN, label=表 1, caption=实际道路驾驶的能耗特征参数, figureFileSmall=null, figureFileBig=null, tableContent=
序号 特征参数 参数说明
1 平均车速 ${\mu }_{v}/\left( {\mathrm{{km}}/\mathrm{h}}\right)$ 总里程除以行驶时间
2 车速标准差 ${\delta }_{\mathrm{v}}/\left( {\mathrm{{km}}/\mathrm{h}}\right)$ 车速的标准差
3 平均正加速度 ${\mu }_{a}/\left( {\mathrm{m}/{\mathrm{s}}^{2}}\right)$ 加速度 $> 0$ 时的加速度平均值
4 加速度标准差 ${\delta }_{a}/\left( {\mathrm{m}/{\mathrm{s}}^{2}}\right)$ 加速度的标准差
5 怠速行驶占比 ${\tau }_{\text{idle }}/\%$ 车速 $\left( {\mathrm{{km}}/\mathrm{h}}\right)$ 低于 10 的时间与行驶时间之比
6 低速行驶占比 ${\tau }_{\text{low }}/\%$ 车速 $\left( {\mathrm{{km}}/\mathrm{h}}\right)$$\lbrack {10},{40})$ 区间的时间与行驶时间之比
7 中速行驶占比 ${\tau }_{\mathrm{{med}}}/\%$ 车速 $\left( {\mathrm{{km}}/\mathrm{h}}\right)$$\lbrack {40},{80})$ 区间的时间与行驶时间之比
8 高速行驶占比 ${\tau }_{\text{high }}/\%$ 车速 $\left( {\mathrm{{km}}/\mathrm{h}}\right)$$\lbrack {80},{150})$ 区间的时间与行驶时间之比
9 匀速驾驶占比 ${\varphi }_{\text{const}}/\%$ 加速度绝对值 $\left( {\mathrm{m}/{\mathrm{s}}^{2}}\right)$ 小于 0.15 的时间与行驶时间之比
10 缓和驾驶占比 ${\varphi }_{\mathrm{{mod}}}/\%$ 加速度绝对值 $\left( {\mathrm{m}/{\mathrm{s}}^{2}}\right)$$\lbrack {0.15},{1.5})$ 的时间与行驶时间之比
11 激烈驾驶占比 ${\varphi }_{\mathrm{{vio}}}/\%$ 加速度绝对值 $\left( {\mathrm{m}/{\mathrm{s}}^{2}}\right)$ 大于 1.5 的时间与行驶时间之比
12 制动介入比例 ${\varphi }_{\mathrm{{brk}}}/\%$ 制动踏板踩下的时间与行驶时间之比
13 空调平均功率 ${p}_{\mathrm{{AC}}}/\mathrm{W}$ 空调耗电量除以行驶时间
), ArticleFig(id=1153978808718844745, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1153978734420943625, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
工况 仿真电耗/ 实测电耗/ 相对误 差/% 误差标准 差/%
高速平均 13.8 13.3 -4.4 5.9
市郊平均 12.5 13.0 4.2 2.6
市区平均 13.6 14.2 4.1 6.0
100 km/h 等速 16.9 17.0 0.9
NEDC 12.7 12.9 1.6
CLTC 11.9 12.1 1.7
), ArticleFig(id=1153978808777565004, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1153978734420943625, language=CN, label=表 2, caption=模型仿真电耗与实测对比, figureFileSmall=null, figureFileBig=null, tableContent=
工况 仿真电耗/ 实测电耗/ 相对误 差/% 误差标准 差/%
高速平均 13.8 13.3 -4.4 5.9
市郊平均 12.5 13.0 4.2 2.6
市区平均 13.6 14.2 4.1 6.0
100 km/h 等速 16.9 17.0 0.9
NEDC 12.7 12.9 1.6
CLTC 11.9 12.1 1.7
), ArticleFig(id=1153978808848868176, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1153978734420943625, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
车速/ (km/h) 加速度/ 电池端电耗/ (kWh/100 km) 电耗增幅/ %
20 0 6.97
0.28 9.18 32
0.56 12.14 74
0.83 15.47 122
1.11 19.20 175
1.39 23.48 237
40 0 7.46
0.28 8.90 19
0.56 11.01 48
0.83 13.22 77
1.11 15.62 109
1.39 18.38 146
60 0 9.56
0.28 10.63 11
0.56 12.37 29
0.83 14.11 48
1.11 16.14 69
1.39 18.29 91
80 0 12.90
0.28 13.33 3
0.56 14.81 15
0.83 16.45 28
1.11 18.30 42
1.39 20.21 57
100 0 16.98
0.28 16.70 -2
0.56 18.53 9
0.83 20.12 18
1.11 22.14 30
1.39 24.65 45
), ArticleFig(id=1153978808915977042, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1153978734420943625, language=CN, label=表 3, caption=不同车速和特征加速度下的电池端电耗, figureFileSmall=null, figureFileBig=null, tableContent=
车速/ (km/h) 加速度/ 电池端电耗/ (kWh/100 km) 电耗增幅/ %
20 0 6.97
0.28 9.18 32
0.56 12.14 74
0.83 15.47 122
1.11 19.20 175
1.39 23.48 237
40 0 7.46
0.28 8.90 19
0.56 11.01 48
0.83 13.22 77
1.11 15.62 109
1.39 18.38 146
60 0 9.56
0.28 10.63 11
0.56 12.37 29
0.83 14.11 48
1.11 16.14 69
1.39 18.29 91
80 0 12.90
0.28 13.33 3
0.56 14.81 15
0.83 16.45 28
1.11 18.30 42
1.39 20.21 57
100 0 16.98
0.28 16.70 -2
0.56 18.53 9
0.83 20.12 18
1.11 22.14 30
1.39 24.65 45
), ArticleFig(id=1153978808991474517, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1153978734420943625, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
车速/ (km/h) 电驱转速/ (r/min) 电驱转矩/Nm
匀速 0.28 0.56 0.83 1.11 1.39
20 1800 4.1 20.5 37.0 53.3 69.6 85.9
60 5400 8.0 24.4 40.8 57.2 73.6 89.9
100 9000 15.4 31.7 48.1 64.5 80.8 97.1
), ArticleFig(id=1153978809054389079, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1153978734420943625, language=CN, label=表 4, caption=加减速工况下的电驱工作点, figureFileSmall=null, figureFileBig=null, tableContent=
车速/ (km/h) 电驱转速/ (r/min) 电驱转矩/Nm
匀速 0.28 0.56 0.83 1.11 1.39
20 1800 4.1 20.5 37.0 53.3 69.6 85.9
60 5400 8.0 24.4 40.8 57.2 73.6 89.9
100 9000 15.4 31.7 48.1 64.5 80.8 97.1
), ArticleFig(id=1153978809113109337, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1153978734420943625, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
驾驶方式 初速度/ 坡顶车速/ 末速度/ 电池净耗电量/Wh 总时长
40 km/h 匀速 40.0 40.0 40.0 77.19 72.0
${210}\mathrm{{Nm}}$ 上坡,-101 $\mathrm{{Nm}}$ 下坡 40.0 15.5 40.1 69.34 103.9
${225}\mathrm{{Nm}}$ 上坡,-112 Nm下坡 40.0 21.4 40.2 69.70 93.9
${240}\mathrm{{Nm}}$ 上坡,-124 Nm下坡 40.0 26.0 40.1 70.96 87.4
${260}\mathrm{{Nm}}$ 上坡,-139 Nm下坡 40.0 31.0 40.2 72.98 81.2
${280}\mathrm{{Nm}}$ 上坡,-155 Nm下坡 40.0 35.4 40.1 74.54 76.5
${320}\mathrm{{Nm}}$ 上坡,-186 $\mathrm{{Nm}}$ 下坡 40.0 42.8 40.1 79.01 69.7
340 Nm上坡,-202 Nm下坡 40.0 46.1 40.0 81.49 67.2
${360}\mathrm{{Nm}}$ 上坡,-218 $\mathrm{{Nm}}$ 下坡 40.0 49.1 40.0 83.99 64.8
), ArticleFig(id=1153978809176023900, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1153978734420943625, language=CN, label=表 5, caption=$\;{40}\mathrm{\;{km}}/\mathrm{h}$ 通过坡道的表现, figureFileSmall=null, figureFileBig=null, tableContent=
驾驶方式 初速度/ 坡顶车速/ 末速度/ 电池净耗电量/Wh 总时长
40 km/h 匀速 40.0 40.0 40.0 77.19 72.0
${210}\mathrm{{Nm}}$ 上坡,-101 $\mathrm{{Nm}}$ 下坡 40.0 15.5 40.1 69.34 103.9
${225}\mathrm{{Nm}}$ 上坡,-112 Nm下坡 40.0 21.4 40.2 69.70 93.9
${240}\mathrm{{Nm}}$ 上坡,-124 Nm下坡 40.0 26.0 40.1 70.96 87.4
${260}\mathrm{{Nm}}$ 上坡,-139 Nm下坡 40.0 31.0 40.2 72.98 81.2
${280}\mathrm{{Nm}}$ 上坡,-155 Nm下坡 40.0 35.4 40.1 74.54 76.5
${320}\mathrm{{Nm}}$ 上坡,-186 $\mathrm{{Nm}}$ 下坡 40.0 42.8 40.1 79.01 69.7
340 Nm上坡,-202 Nm下坡 40.0 46.1 40.0 81.49 67.2
${360}\mathrm{{Nm}}$ 上坡,-218 $\mathrm{{Nm}}$ 下坡 40.0 49.1 40.0 83.99 64.8
), ArticleFig(id=1153978809234744158, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1153978734420943625, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
驾驶方式 初速度/ 坡顶车速/(km/h) 末速度/ 电池净耗电量/Wh 总时长
80 km/h 匀速 80.0 80.0 80.0 112.20 36.0
${160}\mathrm{{Nm}}$ 上坡, $- {50}\mathrm{{Nm}}$ 下坡 80.0 58.3 80.2 90.07 41.7
${200}\mathrm{{Nm}}$ 上坡, $- {20}\mathrm{{Nm}}$ 下坡 80.0 63.0 80.4 91.52 40.4
${240}\mathrm{{Nm}}$ 上坡,-15 Nm下坡 80.0 67.5 80.1 93.21 39.2
${280}\mathrm{{Nm}}$ 上坡, $- {40}\mathrm{{Nm}}$ 下坡 80.0 71.6 80.2 100.02 38.1
${320}\mathrm{{Nm}}$ 上坡,-75 Nm下坡 80.0 75.6 80.3 105.80 37.1
${400}\mathrm{{Nm}}$ 上坡,- ${140}\mathrm{{Nm}}$ 下坡 80.0 82.8 80.0 115.74 35.5
440 Nm上坡,-170 Nm下坡 80.0 86.2 80.0 119.74 34.8
), ArticleFig(id=1153978809306047328, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1153978734420943625, language=CN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
驾驶方式 初速度/ 坡顶车速/(km/h) 末速度/ 电池净耗电量/Wh 总时长
80 km/h 匀速 80.0 80.0 80.0 112.20 36.0
${160}\mathrm{{Nm}}$ 上坡, $- {50}\mathrm{{Nm}}$ 下坡 80.0 58.3 80.2 90.07 41.7
${200}\mathrm{{Nm}}$ 上坡, $- {20}\mathrm{{Nm}}$ 下坡 80.0 63.0 80.4 91.52 40.4
${240}\mathrm{{Nm}}$ 上坡,-15 Nm下坡 80.0 67.5 80.1 93.21 39.2
${280}\mathrm{{Nm}}$ 上坡, $- {40}\mathrm{{Nm}}$ 下坡 80.0 71.6 80.2 100.02 38.1
${320}\mathrm{{Nm}}$ 上坡,-75 Nm下坡 80.0 75.6 80.3 105.80 37.1
${400}\mathrm{{Nm}}$ 上坡,- ${140}\mathrm{{Nm}}$ 下坡 80.0 82.8 80.0 115.74 35.5
440 Nm上坡,-170 Nm下坡 80.0 86.2 80.0 119.74 34.8
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纯电动汽车经济性驾驶的影响因素及应用
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王颖迪 1 , 李庆锋 1 , 王师 1 , 刘伟 2 , 王博远 2 , 肖建华 2
汽车工程学报 | 绿色健康技术及其测评 2024,14(3): 519-530
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汽车工程学报 | 绿色健康技术及其测评 2024, 14(3): 519-530
纯电动汽车经济性驾驶的影响因素及应用
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王颖迪1 , 李庆锋1, 王师1, 刘伟2, 王博远2, 肖建华2
作者信息
  • 1 广州汽车集团股份有限公司 汽车工程研究院 广州 511434
  • 2 清华大学 车辆与运载学院 北京 100084
  • 王颖迪(1993-),男,湖北武汉人,博士,主要研究方向为新能源汽车能耗和驾驶性。Tel: 13002053429 E-mail:

Fundamental Factors and Applications of Eco-Driving for Electric Vehicles
Yingdi WANG1 , Qingfeng LI1, Shi WANG1, Wei LIU2, Boyuan WANG2, Jianhua XIAO2
Affiliations
  • 1 GAC Automotive Research & Development Center Guangzhou 511434 China
  • 2 School of Vehicle and Mobility Tsinghua University Beijing 100084 China
doi: 10.3969/j.issn.2095–1469.2024.03.19
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我国新能源汽车技术路线是以纯电驱动为主,随着纯电动汽车保有量的增加,降低其行驶能耗既符合国家节能减排的战略,又能改善用户的用车体验。基于某纯电动SUV车型的实际道路驾驶数据,通过相关性和降维分析得到加速度的方差是影响能耗的最关键因素;进而采用一维模型仿真,研究匀速、加减速和坡道3种典型工况下能耗的形成机理和优化方向,得到平衡能耗与行驶时长的经济性驾驶原则,如高速巡航时应适当降低车速,市区拥堵时控制加速度变化幅度,上下坡时利用车辆惯性,减少制动能量回收。

纯电动汽车  /  经济性驾驶  /  能耗  /  相关性分析  /  降维分析

Electrification has emerged as the main trend in new energy vehicles over the past decade. With the increasing number of EVs, reducing their energy consumption aligns with the national strategy for energy conservation and emission reduction, and also improves user's driving experience. The paper first analyzes actual road driving data from an electric SUV. Through dimensional reduction and correlation analysis, the acceleration variance is found as the key factor influencing energy consumption. Furthermore, three typical realroad operating conditions, i.e, constant speed driving, tip in/tip out, and ramp, are chosen for a parametric study by using a 1D model. The tradeoff between energy and time consumption is also discussed. Finally, several principles are established for ecodriving considering the acceleration and speed of vehicles, which include reducing speed during highway cruising, ensuring smooth driving in urban congestion, and utilizing vehicle inertia and minimizing regenerative braking on ramps.

electric vehicle  /  eco-driving  /  energy consumption  /  correlation analysis  /  dimensional reduction
王颖迪, 李庆锋, 王师, 刘伟, 王博远, 肖建华. 纯电动汽车经济性驾驶的影响因素及应用. 汽车工程学报, 2024 , 14 (3) : 519 -530 . DOI: 10.3969/j.issn.2095–1469.2024.03.19
Yingdi WANG, Qingfeng LI, Shi WANG, Wei LIU, Boyuan WANG, Jianhua XIAO. Fundamental Factors and Applications of Eco-Driving for Electric Vehicles[J]. Chinese Journal of Automotive Engineering, 2024 , 14 (3) : 519 -530 . DOI: 10.3969/j.issn.2095–1469.2024.03.19
近年来, 随着能源供给和环境污染问题的日益突出, 全球各国都在积极推进电动汽车的发展。对于中国而言, 发展电动汽车对汽车产业有着深远意义。2020 年,国家五部委颁布的《乘用车企业平均燃料消耗量与新能源汽车积分并行管理办法 (2021-2023 修订版)》延续了前一阶段的双积分优待政策和财政补贴,以鼓励电动汽车持续发展。随后,发改委颁布的《新能源汽车产业发展规划 (2021-2035年)》对新能源汽车提出了更高的要求和期望, 即 2025 年的纯电动汽车 (BEV) 和插电式混合动力汽车 (PHEV) 销量占汽车总销量的 15%~25%,2035 年销量占比达到 50%~60%,其中, BEV占90%以上。事实上,根据乘联会数据显示, 中国乘用车市场新能源汽车销量占比从 2022 年起已经超过 20%,如 图 1 所示。其中,纯电动汽车约占 80% 的份额,截至 2022 年底,纯电动汽车的保有量已达到 1045 万辆。
虽然电动汽车保有量逐渐提高,但里程焦虑问题并未得到很好的解决, 消费者对表显剩余行驶里程估算不准确的抱怨不降反升。针对这一问题, 一方面, 汽车厂商需要针对续驶里程采取更合适的公告和表显策略;另一方面,消费者也需要具备经济性驾驶意识。
针对经济性驾驶问题, 即如何在实际道路环境中实现更优的能量消耗率 (下文简称能耗), PALACIOS 等 [ 1 ] 在 1999 年提出用车辆特定功率 (Vehicle Specified Power, VSP) 来衡量驾驶风格是否经济,通过瞬时车速、加速度、车重和坡度来计算 VSP 值,即 VSP $= \left( {\mathrm{A}v + \mathrm{B}{v}^{2} + \mathrm{C}{v}^{3}}\right) /m + (a +$ $g\sin \theta )v$ ,结果表明,单次驾驶的 VSP 呈现正态分布的趋势, 分布越集中则表示驾驶越经济。北京交通大学 SONG Guohua 等 [ 2 - 5 ] 基于 VSP 法,也得到了一些经济性驾驶的典型特征, VSP法对能耗的评估十分有效, 但这种方法的本质在于计算车辆的瞬时功率,对于驾驶员自身来说难以准确感知,也无法提供指导。
清华大学的 LI 等 [ 6 ] 针对配备无级变速器的燃油车, 通过伪谱法得到以最优能耗策略为周期的 “加速-滑行”(Pulse-and-Gliding, PnG)策略,在中等车速时收益尤其显著, 这一结论对纯电动汽车的能耗优化也有指导意义。北京航空航天大学的 LIAO Peng 等 [ 7 ] 基于新欧洲驾驶循环(New European Driving Cycle, NEDC) 工况, 通过对电驱、电池和变速器的工作点进行寻优计算, 得到一种更经济的行驶策略, 即更多地进行匀速驾驶, 减少减速段占比,让电驱尽量少地进行动能回收。但实际道路中往往是分段里程下的局部优化问题, 即里程本身为优化的边界条件, 车辆不可避免会有更多的减速需求, 结合实际道路工况的分析更具意义。
ZHANG Yuanjian 等 [ 8 ] 根据电驱效率和动态规划算法, 基于实际道路工况, 对比了 3 种典型驾驶模式 (节能、标准、运动) 对纯电动汽车能耗的影响, 从转矩脉谱图上看, 运动模式在同等油门下的转矩输出最强, 而节能模式最弱, 这意味着运动模式会导致更频繁的加减速情况出现, 节能模式则更平稳。其结果表明, 节能模式相比标准模式和运动模式分别节能 5.3% 和 11.0%,即更平稳的驾驶以及更多地利用滑行, 可实现更低的能耗。但值得注意的是, 能耗的降低也会带来行驶时间的延长, 节能模式耗时与标准模式和运动模式耗时相比分别增加 6% 和 17%。WANG Gang 等 [ 9 ] 针对美国主要城市开展了关于经济性驾驶的问卷调研,这在一定程度上回答了能耗与驾驶时长的矛盾。结果表明,新能源汽车驾驶员相比燃油汽车驾驶员更关注能耗问题, 更倾向于平稳的驾驶及路线规划, 即使牺牲了时长, 仍愿意选择节能模式进行驾驶。
综上所述, 有关经济性驾驶的研究表明, 匀速行驶和滑行占比的增加可以改善能耗, 但往往会牺牲行驶时长。本文针对实际道路驾驶工况, 重点探讨影响纯电动汽车能耗的关键因素, 以及如何在实际驾驶过程中应用经济性驾驶原则来平衡能耗和时长的矛盾关系。
参考以往研究中基于实际道路统计的行驶特征参数的定义 [ 10 - 11 ] ,其特征主要包含车速和加速度的统计量与分布情况。本文选取了类似的特征参数, 将加速度绝对值达到 ${0.15}\mathrm{\;m}/{\mathrm{s}}^{2}$ 作为匀速和缓和加减速行驶的边界, ${1.5}\mathrm{\;m}/{\mathrm{s}}^{2}$ 作为激烈驾驶与缓和驾驶的边界。除车速和加速度的统计量以及分布外, 还增加了空调平均功率来表征环境温度和驾驶员的空调设置, 共计 13 个特征参数, 见 表 1 。由于本文所讨论的电耗为由驾驶行为引起的整车电能消耗率, 所以不考虑空调和座舱中其他额外附件的能耗影响, 即下文讨论的电池端电耗 (下文简称电耗) 已刨除空调电耗。
针对某款 SUV 车型在夏季的实际道路驾驶数据进行分析,统计了其能耗情况与上述驾驶特征。 电耗的相关性分析结果如 图 2 所示, 与电耗呈明显正相关的特征有平均车速 ${\mu }_{\mathrm{v}}$ 、车速标准差 ${\delta }_{\mathrm{v}}$ 、加速度标准差 ${\delta }_{a}$ 、高速行驶占比 ${\tau }_{\text{high }}$ 以及激烈驾驶占比 ${\varphi }_{\text{vio }}$ ,其中速度、加速度波动 ${\delta }_{\mathrm{v}}\text{、}{\delta }_{a}$ 和高速行驶占比 ${\tau }_{\text{high }}$ 受到的影响最大; 呈典型负相关的特征为低速行驶占比 ${\tau }_{\text{low }}$ 和匀速驾驶占比 ${\varphi }_{\text{const }}$ ,上述部分关键特征和电耗的关系如 图 3 ~4 所示。相关性结果与前序研究的结论基本一致, 即高车速行驶和激烈驾驶引起的加速度、车速变化会导致电耗上升, 进而引起续驶里程衰减, 这也符合市场反馈的典型续驶性能劣化的情形。
实际上, 对于同一样本, 表 1 的特征参数中车速和加速度的统计量与其分布具有很强的关联性, 各特征之间不独立。加速度分布基本符合正态分布, 即可用平均值和标准差来描述该驾驶员的驾驶行为,如 图 5 所示。因此,针对特征中 $1 \sim {12}$ 项, 可以使用降维方法找到实际道路驾驶能耗问题的维数以及最关键的影响因素。本文采用切片逆回归 (Sliced Inverse Regression, SIR) 法进行降维分析 [ 12 ] ,计算得到的特征值如 图 6 所示,由特征值大小可知, 维数仍然有 3 维以上, 一定程度反映出实际道路驾驶中能耗问题的复杂性。其中, 最大的 3 个特征值对应的特征向量如 图 7 所示, 特征向量 1 包含的主要分量为平均正加速度 ${\mu }_{a}$ 与标准差 ${\delta }_{a}$ ,特征向量 2 包含匀速驾驶 ${\varphi }_{\text{const }}$ 和缓和驾驶占比 ${\varphi }_{\text{mod }}$ , 特征向量 3 新增了部分车速维度,如平均车速 ${\mu }_{v}$ 和低速行驶占比 ${\tau }_{\mathrm{{low}}}$
综上可知,加速度标准差 ${\delta }_{a}$ 是对纯电动汽车实际道路能耗影响最大的因素, 即驾驶员操作的平稳程度直接影响电耗, 其次是车速维度的影响。
根据 1.2 节和 1.3 节中对实际道路能耗影响因素的分析结果, 结合具体工况定量研究其贡献量以得到能耗的优化方法, 本文采用Matlab/Simulink 搭建一维整车模型, 通过参数化仿真来进行研究。模型主要包括整车控制单元(Vehicle Control Unit, VCU)、电机智能控制单元(Intelligent Power Unit, IPU)、电池管理系统(Battery Management System, BMS)以及整车动力模块,如 图 8 所示。VCU 包括驾驶员模型和整车控制器,发送转矩需求;IPU为电驱模块, 根据电驱效率脉谱图计算机电能量转换的损耗; BMS 为电池模块, 采用安时积分法计算电能和 $\mathrm{{SOC}}$ 变化,进而通过电池包特性得到对应电压和充放电功率, 动力模块则通过单挡传动系将电驱工作点和整车运动联系起来。此外, 制动能量回收为串行制动模式,当减速度小于 $- 2\mathrm{\;m}/{\mathrm{s}}^{2}$ 时均由电驱执行, 超过的部分由液压制动补充。选取实际道路路谱和标准循环工况作为输入, 通过将实际能耗表现与模型仿真结果相对比以验证模型的可靠性, 结果见 表 2
对于通行顺畅的市郊路况, 模型仿真结果与实测数据相对误差的平均值为 4%,标准差为 2.6%, 这意味着并未出现误差很大的组别, 整体表现较稳定。而对于市区路况, 实际道路不确定因素更多, 如仿真中的 PID 前馈滤波, 无法完全复现实际驾驶中加速和制动踏板的激烈变化, 且实际道路存在频繁的立交桥坡道变化情形, 甚至遇到下雨和大风的情况, 使模型无法正确得到转矩需求, 因此, 误差表现即使均值差异不大, 但组间散差很大, 达到 6%,约 15% 的数据组出现了 10% 以上的误差,最高达到 20% 以上。高速工况也易受实际风向和海拔的影响, 实际能耗较仿真结果平均值更低, 数据组虽较少,但散差也非常大。而针对公告的标准工况 NEDC 和 CLTC, 仿真和实测值误差小于 2%。总的来说, 可认为该仿真模型的准确性足够支撑本节纯电动汽车的能耗研究, 但由于缺乏道路环境因素的维度, 无法直接进行实际道路能耗的模拟。
纯电动汽车电耗的构成包括行驶阻力损耗、电驱损耗和传动系统损耗(以下用电驱损耗及其效率来代表整个传动系统的损耗以及车载电器损耗), 车载电器损耗在除去空调能耗后, 低压负载基本为恒定值, 与驾驶行为关系不大, 因此, 在计算中忽略不计。本节拆解出 3 个实际道路的典型工况进行能耗研究, 同时考虑其对行驶时长的影响。
根据市场的主流抱怨, 纯电动汽车在高速续驶工况下往往无法实现公告或表显续驶里程, 从而导致在长途行驶以及低电量的情形下,驾驶员会有明显的里程焦虑。在 1.2 节关于电耗关键影响因素的讨论中, 也得到了平均车速越高, 能耗表现越差的结论, 因此, 本节首先对车速进行研究。
同样, 针对该车型, 各车速匀速行驶的电耗表现,如 图 9 所示,电池端电耗在 ${30}\mathrm{\;{km}}/\mathrm{h}$ 车速左右存在极小值点, 此后随着车速的增加, 电耗单调上升。由于是匀速行驶, 整车电池端电耗如式 (1) 所示。可将能耗一车速曲线分为两部分, 首先是在车速小于 ${40}\mathrm{\;{km}}/\mathrm{h}$ 的低速小转矩工况下行驶时,电驱转矩不超过 $5\mathrm{{Nm}}$ ,电驱效率会随着转矩增加明显提高, 因此, 在低速段会存在驱动力与效率的 trade-off关系。怠速时驱动效率非常低,大部分能量被损耗,电耗也较大,随着车速提高,转矩需求上升, 效率大幅提升, 综合电耗表现存在最优点。
$ \mathrm{{EC}} = \frac{\int {fv}/\eta \mathrm{d}t}{\int v\mathrm{\;d}t}\text{。} $
式中: $f$ 为整车滑行阻力; $v$ 为车速; $\eta$ 为电驱效率。
当车速进一步提升后, 滑行阻力可表示为车速的二次多项式近似, 如式 (2) 所示, 其中, A、 $\mathrm{B}$$\mathrm{C}$ 为常数,与车型体量和配置相关。由于电驱效率 $\eta$ 趋于稳定,所以高速时能耗近似正比于车速的平方。总的来说, 能耗表现随车速呈现鱼钩线变化。尤其是以等速 ${100}\mathrm{\;{km}}/\mathrm{h}$ 行驶时的电耗为 17.0 $\mathrm{{kWh}}/{100}\mathrm{\;{km}},\mathrm{{CLTC}}$ 电耗为 ${11.9}\mathrm{{kWh}}/{100}\mathrm{\;{km}}$ ,所以对应的高速续驶里程表现为公告里程的 70%。
$ f = \mathrm{A} + \mathrm{B} \cdot v + \mathrm{C} \cdot {v}^{2}\text{。} $
在实际驾驶时, 能耗和时间均为驾驶员关注的维度。因此, 将 图 9 进一步转化为对数坐标进行分析, 如 图 10 所示。在对数坐标下, 如果认为时长和能耗的相同变化幅度权重是对等的, 即可通过各车速点在对数坐标中的切线斜率反映二者的敏感度, 蓝色实线所示的斜率为 1 的直线, 即二者变化对等的车速点,约为 ${70} \sim {80}\mathrm{\;{km}}/\mathrm{h}$ 。在电耗极小值点左侧的 I 区域, 即低速甚至怠速情形, 降低车速非但没有时间收益, 还会恶化能耗。当车速在极小值点和切点之间的 II 区域 $\left( {{30} \sim {80}\mathrm{\;{km}}/\mathrm{h}}\right)$ ,斜率小于 1 , 意味着车速提高, 行驶时长相应降低, 但能耗增加幅度小于时长缩短幅度, 即提高车速的收益相较能耗的恶化更大, 行驶时可以适当提速。切点车速右侧的 III 区域, 斜率大于 1 , 意味着更高速行驶 (>80 km/h) 的能耗代价会比时间收益更大,在经济性驾驶中应尽量避免。
实际道路驾驶难以实现匀速行驶, 尤其在市区工况, 会存在频繁的加速和制动切换, 急踩加速踏板(tip-in)和急松加速踏板(tip-out)引起的加速度变化是影响能耗的关键因素, 其影响将在本节讨论。通过锯齿型的目标车速来表征加减速工况, 图 11 所示为 ${40}\mathrm{\;{km}}/\mathrm{h}$ 下的 5 种加减速工况,其特征加速度依次成比例增加, 从而带来车速的周期变化, 同时保证平均车速不变。在不同平均车速下对比这 5 种加减速工况的能耗变化,结果见 表 3
图 12 可知, 随着加减速工况特征加速度的增加, 意味着更频繁和激烈的加减速, 能耗相较匀速行驶时会显著增加。且车速越低,加减速所引起的能耗增幅越大。值得注意的是,在 ${100}\mathrm{\;{km}}/\mathrm{h}$ 车速下出现了加减速较匀速行驶的电耗更低的情形。
考虑到市面上主流纯电动汽车均配置单挡固定齿比的减速器, 因此, 可将车速和电驱转速视为定比例关系,不考虑换挡,且电动汽车大多已采用单踏板模式的串行制动能量回收逻辑, 即在一定的减速度范围内,均由电制动执行,液压制动不介入, 因此, 本节所讨论的全部减速情形, 没有能量的耗散, 均由电驱回收成电能。
从纯电动汽车能耗的形成机理分析, 瞬时转矩的产生是为了克服惯性力和阻力,如式(3)所示。
$ T = \left( {f + {ma}}\right) R\text{。} $
式中: $T$ 为轮端需求转矩; $a$ 为加速度; $m$ 为整车惯量; $R$ 为轮胎滚动半径。
在一个加减速周期 ${t}_{0}$ 内匀速行驶时,电耗 $\mathrm{{EC}}$ 如式(4)所示。
$ \mathrm{{EC}} = \frac{k{\int }_{0}^{{t}_{0}}{fn}/\eta \mathrm{d}t}{{\int }_{0}^{{t}_{0}}v\mathrm{\;d}t}\text{。} $
式中: $n$ 为电驱转速。
在周期 ${t}_{0}$ 内加减速行驶时,电耗如式(5) 所示。
$ \mathrm{{EC}} = \frac{k\left( {{\int }_{0}^{\frac{{t}_{0}}{2}}{T}_{\mathrm{a}}n/{\eta }_{\mathrm{a}}\mathrm{d}t + {\int }_{\frac{{t}_{0}}{2}}^{{t}_{0}}{T}_{\mathrm{d}}n{\eta }_{\mathrm{d}}\mathrm{d}t}\right) }{{\int }_{0}^{{t}_{0}}\mathrm{d}t}。 $
式中: 下标 a 为加速, 电池输出电能转化为机械能; 下标 $\mathrm{d}$ 为减速,机械能经由电驱转化为电能回收到电池。
加减速和匀速行驶之间的区别首先是惯性力的引入导致了轮端需求转矩的变化, 转矩中除滑行阻力外还包括惯性力,这会引起电驱工作点的转移, 进而影响电驱效率。此外, 加速时克服惯性力所积累的动能会在减速时经由电驱回收为电能, 电能转化为机械能再转化为电能, 此二次转化的过程会因电池、电驱的效率而损耗部分能量。从这两个角度将式(3)和式(5)联立得到式(6)。
$ \mathrm{{EC}} = \frac{{k}^{\prime }\left( {{\int }_{0}^{\frac{{t}_{0}}{2}}{fn}/{\eta }_{\mathrm{a}}\mathrm{d}t + {\int }_{\frac{{t}_{0}}{2}}^{{t}_{0}}{fn}{\eta }_{\mathrm{d}}\mathrm{d}t}\right) + {k}^{\prime \prime }{\int }_{0}^{\frac{{t}_{0}}{2}}\left| a\right| n\left( {1/{\eta }_{\mathrm{a}} - {\eta }_{\mathrm{d}}}\right) \mathrm{d}t}{{\int }_{0}^{{t}_{0}}v\mathrm{\;d}t} \\ = \frac{{k}_{1}{\int }_{0}^{{t}_{0}}{fv}\sqrt{\eta }\mathrm{d}t + {k}_{2}{\int }_{0}^{\frac{{t}_{0}}{2}}\left| a\right| v\left( {1 - {\eta }_{\mathrm{r}}{\eta }_{\mathrm{a}}}\right) \mathrm{d}t}{{\int }_{0}^{{t}_{0}}v\mathrm{\;d}t}。 $
式中: ${k}^{\prime }\text{、}{k}^{\prime \prime }\text{、}{k}_{1}\text{、}{k}_{2}$ 为常数。
加和的两项中前者代表行驶阻力损耗部分(以下简称阻力项),在平均车速相同时,可认为 $f$ 基本不变, $\widetilde{\eta }$ 代表驱动和回收效率的综合等效值,由于加速度的存在, 转矩绝对值增加, 使电驱效率有一定的提升 $\widetilde{\eta } > \eta$ ; 后者则表征了加减速行驶时电驱动能回收所引起的机械能和电能二次转化的损耗 (以下简称惯性项),影响因素包含加速度、电驱驱动效率和回收效率。
挑选 图 12 中的 3 个典型工况: 低速 ${20}\mathrm{\;{km}}/\mathrm{h}$ , 模拟城市拥堵跟车工况;中等车速 ${60}\mathrm{\;{km}}/\mathrm{h}$ ,模拟干道和快速路正常通行;高速 ${100}\mathrm{\;{km}}/\mathrm{h}$ 。各工况的电驱工作点见 表 4 ,结合电驱效率脉谱图可以更直观地看到电驱工作点的变化对电耗的影响。
图 13 展示了车速为 ${20}\mathrm{\;{km}}/\mathrm{h}$ 时的情形,其中,符号*代表特征加速度 ${0.28}\mathrm{\;m}/{\mathrm{s}}^{2},\bigtriangleup$ 代表 ${0.56}\mathrm{\;m}/{\mathrm{s}}^{2},\nabla$ 代表 ${0.83}\mathrm{\;m}/{\mathrm{s}}^{2}$ , $○$ 代表 ${1.11}\mathrm{\;m}/{\mathrm{s}}^{2}$ , $\times$ 代表 ${1.39}\mathrm{\;m}/{\mathrm{s}}^{2}$ 。 由于低速时阻力很小, 随着加速度的增加, 惯性力逐渐远大于滑行阻力, 所以阻力项的作用可以忽略。而转矩的变化在低转速时对电驱效率影响较小,因此,电耗的增加速度与 $a$ 几乎成正比。
当车速为 ${60}\mathrm{\;{km}}/\mathrm{h}$ 时,如 图 14 所示,随着加速度的增加, 惯性项依然占据主导, 但电驱效率也有一定的提高, 从 80% 提高到 90%, 使电耗的增幅放缓,但电耗随加速度的变化关系仍接近线性上升。
当车速为 ${100}\mathrm{\;{km}}/\mathrm{h}$ 时,如 图 15 所示,转速范围抵达电驱的高效区附近, 转矩的提升可以明显提升电驱效率。在加速度较小时 $\left( {a < {0.5}\mathrm{\;m}/{\mathrm{s}}^{2}}\right)$ ,由于阻力项较低速情形有数倍提升,已和惯性项数量级相当,加速度的引入使电驱综合效率提升,阻力项产生一定贡献, 与惯性项的额外损耗相当, 所以整车电耗并没有明显增加, 还存在极小值点的可能, 这意味着高速驾驶时, 一定程度的速度波动不会明显影响能耗。
总的来说, 加减速行驶会带来能耗的增加, 其主因为惯性项所导致的能量二次转化的损耗, 因此经济性驾驶要求更平稳的驾驶风格。在车速较低的市区拥堵路况下,加速度的方差影响尤为明显,因此, 跟车时驾驶员应控制加速度变化的大小和频率。而在高车速小加速度的情况下,电驱工作点位于高效区, 阻力项和惯性项相当, 小幅度的加减速对能耗影响很小, 甚至出现加减速行驶更利于能耗的场景, 即高车速时加速度的影响被弱化, 车速的影响被放大。
实际道路驾驶还会遇到较多的坡道情形, 此时驾驶员操作对能耗的影响将在本节进行讨论。根据 CJJ 11-2011《城市桥梁设计规范》规定,大、中桥上的纵坡不宜大于 4%,引桥纵坡不宜大于 5%; 位于市镇附近地段, 桥上及桥头引道纵坡均不得大于3%。
因此,设定坡度为 $4\%$ ,长度为 ${400}\mathrm{\;m}$ 的上下坡工况进行仿真, 以研究不同车速和驾驶方式通过坡道时的能耗表现。考虑到实际驾驶中驾驶员常用恒定车速或恒定油门的方式通过坡道, 而当车速变化范围较小时, 可认为恒定油门表现为恒定输出转矩, 本节主要研究等速和相等转矩的情形。
以车速 ${40}\mathrm{\;{km}}/\mathrm{h}$ 来表征市区工况,相比于等速通过坡道, 在上坡时减速, 下坡时加速的行驶策略可以降低能耗, 使用不同油门通过坡道时的能耗与驾驶时长表现,见 表 5 。结合时间维度予以评价, 如 图 16 所示。与 图 10 类似, 采用对数坐标以评估能耗和时间变化幅度的相对大小。此工况下时长- 能耗曲线表现为凸函数, 且对数坐标下斜率 1 出现在等速点 (蓝色圈) 附近, 意味着虽然可以通过上坡减速下坡加速(平均车速降低)的方式来降低能耗, 但行驶时长增加的幅度更大, 且车速越低时间代价越大, 而上坡加速下坡减速 (平均车速提高) 的方式, 又会导致能耗的代价大于时间收益。综合考虑,等速通过坡道即是最优解。
考虑市郊工况代表车速 ${80}\mathrm{\;{km}}/\mathrm{h}$ ,结果如 表 6图 17 所示。同样的, 上坡减速下坡加速的驾驶方式可以降低能耗, 但和低速时不同的是, 在较大的范围内, 能耗降低与时长增加的幅度呈十分线性的关系, 且斜率小于 1 , 即能耗的收益大于时长的损失。从经济性驾驶的角度,可以适当降低车速来换取能耗的提升。但当车速降低到一定程度时,继续降低能耗的收益会大大降低。
综合以上两种常见工况的结果, 相较于等速通过坡道, 利用惯性, 在上坡时减速下坡时加速可以降低能耗, 其机理依然是减少了惯性项的损失。但能耗降低的同时会伴随行驶时长的牺牲, 在较低车速下,以 ${40}\mathrm{\;{km}}/\mathrm{h}$ 为代表,等速通过即是综合最优的选择。但车速较高时,以 ${80}\mathrm{\;{km}}/\mathrm{h}$ 为代表,对于经济性驾驶来说, 减速通过的时间代价可以换取更显著的能耗降低, 只有当上坡减速过多时, 能耗的降低才会失去收益,同时大大牺牲行驶时长。因此, 通过坡道的经济性驾驶原则是保持一定的油门开度, 利用车辆惯性, 在上坡时以较小加速度减速, 下坡时加速。但随着车速的降低, 能耗收益降低, 行驶时长的代价逐渐提高, 低速时保持车速通过即可。
${1640}\mathrm{\;{km}}/\mathrm{h}$ 通过坡道的时长-能耗(对数坐标)
${1780}\mathrm{\;{km}}/\mathrm{h}$ 通过坡道的时长一能耗(对数坐标)
在 2.2 节中, 通过仿真分析得到了不同情形下的经济性驾驶原则。对于匀速行驶工况, 能耗与车速的关系呈现鱼钩型, 就本文所探讨的车型而言, 最经济的能耗在车速 ${30}\mathrm{\;{km}}/\mathrm{h}$ 左右达到。高速工况下, 能耗与车速成平方关系, 这与该车型的空气阻力直接关联。但考虑到实际路况的匀速行驶大多是在快速路和高速才能实现, 时间和能耗的 trade-off 关系可通过相对增幅来评估损益。随着车速增加, 虽然能耗的增速逐渐加快,但在车速为 ${80}\mathrm{\;{km}}/\mathrm{h}$ 以下时, 适当提高车速获得的时间收益高于能耗增加的代价。例如车速从 ${50}\mathrm{\;{km}}/\mathrm{h}$ 提高至 ${60}\mathrm{\;{km}}/\mathrm{h}$ ,虽然能耗增加了 14%,但行驶时长减少了 17%。因此, 考虑时间维度的经济性驾驶原则,是在 图 10 中的 II 区域 ${30} \sim {80}\mathrm{\;{km}}/\mathrm{h}$ 车速范围内适当提高车速。但当车速高于 ${80}\mathrm{\;{km}}/\mathrm{h}$ 时,车速提高带来的能耗代价会显著高于行驶时长收益,应尽量降低车速行驶,如维持在高速的下限车速。
针对市区的频繁加减速工况, 如跟车和超车场景, 加速度的波动越大, 能耗越高, 但影响程度随车速的提高而降低,当车速为 ${20}\mathrm{\;{km}}/\mathrm{h}$ 且以 ${1.39}\mathrm{\;m}/{\mathrm{s}}^{2}$ 变化时,能耗相比匀速恶化 237%。同样,加速度在 ${60}\mathrm{\;{km}}/\mathrm{h}$ 车速行驶时,能耗增量仅为 ${91}\%$ 。其原因是纯电动汽车的能耗构成为: 克服整车滑行阻力的阻力项、加减速过程中机械能和电能二次转化的惯性项。低速行驶时,阻力项几乎可以忽略,能耗仅受加速度的影响, 因此, 呈现接近线性的关系; 而高速行驶时阻力增大, 电驱转速也增大, 因加减速的存在, 电驱转矩的提升使效率有明显提升, 对阻力项产生显著贡献, 在较小的加速度下, 可与惯性项的损耗相抵消, 不会引起整车能耗的明显增加。这意味着在市区低速跟车时,加速度主导能耗, 经济性驾驶原则是控制加速度的幅值和变化频率,即保持车距平稳驾驶,使车速变化尽量小。而高速行驶时, 能耗由加速度主导向车速主导过渡, 此时适当地超车和变道所带来的加减速情形不会对能耗造成明显影响。
${60}\mathrm{\;{km}}/\mathrm{h}$ 通过坡道的表现
对于通过坡道的情形, 经济性驾驶原则是多利用车辆惯性, 减少能量回收引起的惯性项损耗。实际驾驶时维持油门在上坡段减速, 下坡段加速的方式相较于等速通过更有利于能耗,但车速降低也会引起行驶时长的增加。综合考虑时间和能耗,在市区以 ${40}\mathrm{\;{km}}/\mathrm{h}$ 低速行驶时,上坡降低车速的能耗收益会带来更多的时长牺牲, 保持匀速通过是最优驾驶方式。而在较高车速时, 经济性驾驶原则可以获得明显收益,例如,在 ${80}\mathrm{\;{km}}/\mathrm{h}$ 维持油门以约 ${0.2}\mathrm{\;m}/{\mathrm{s}}^{2}$ 减速度上坡, 再减小油门以同样的加速度下坡, 可实现 17% 的能耗降低,但行驶时长仅延长 9%。不过, 这一策略可运用的车速、加速度范围较窄, 当上坡减速过多时, 时长代价开始大幅增加而能耗也趋于零收益。
本文旨在探讨纯电动汽车在实际道路工况下能耗的关键影响因素, 以及如何应用经济性驾驶原则来指导驾驶行为。
首先, 基于实际道路的驾驶数据, 通过相关性和降维分析, 提取了影响能耗的关键因素是加速度的方差, 频繁、剧烈的加减速会使能耗恶化, 尽可能多地匀速行驶更为经济。同时, 车速维度也有一定影响, 较大的高速行驶比例与较高的平均车速也会使能耗增加。此外, 在绝大多数情况下, 车速降低可以优化能耗, 但行驶时长也会因此而增加。
其次, 结合实际驾驶的需求, 对 3 种典型工况下经济性驾驶的可行方向, 以及如何平衡时间和能耗的关系进行了讨论。在通畅的快速路上行驶时, 最佳巡航车速在 ${80}\mathrm{\;{km}}/\mathrm{h}$ 左右,可实现能耗与时间的综合最优。而在市区低速行驶时, 经济性驾驶要求尽可能平稳驾驶, 减小加速度波动。激烈驾驶带来的能耗增幅与加速度大小近乎线性关系。通过坡道时, 则应利用车辆惯性, 上坡时适当减速, 下坡时减少制动能量回收, 可实现更佳能耗, 但车速变化应控制在较小区间, 否则会带来更多的行驶时长牺牲。
虽然车型参数和电驱类型均会对上述结论产生一定影响(空气阻力更优异的轿车在高速行驶时的能耗增加会明显少于SUV车型,因此,经济性驾驶区间可以倾向于更高的车速; 不具备单踏板模式, 使用并行回收的车型在频繁加减速中能耗恶化更显著, 可视为惯性项损失更大), 但本文总结的经济性驾驶原则依然具有普适性, 其本质是各种工况下阻力项和惯性项的综合影响。总的来说, 加速度和车速两个维度在经济性驾驶中均需考虑, 中低速的市区工况应更多关注加速度变化, 即保持更平稳的驾驶风格, 高速时能耗则和车速强相关, 需避免车速过高。
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2024年第14卷第3期
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doi: 10.3969/j.issn.2095–1469.2024.03.19
  • 接收时间:2023-05-11
  • 首发时间:2025-07-21
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  • 收稿日期:2023-05-11
  • 修回日期:2023-06-13
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    1 广州汽车集团股份有限公司 汽车工程研究院 广州 511434
    2 清华大学 车辆与运载学院 北京 100084
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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